cross-cluster flow analysis

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Finance Division

Hospital Authority

Year 2015/16

CROSS-CLUSTER FLOW ANALYSIS FROM THE PERSPECTIVE OF LOCAL COMMUNITIES

Cross-cluster Flow Analysis from the Perspective of Local Communities (Year 2015/16)

Healthcare Financing and Projection, Finance Division, Hospital Authority

November 2017

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Table of Contents

Preface ........................................................................................................................... 7

Executive Summary ........................................................................................................ 9

1. Introduction ............................................................................................................. 13

HA Review and the Refined Population-based Model ........................................... 13

Clustering in HA ...................................................................................................... 15

Hong Kong’s Unique Situation ............................................................................... 17

Previous Study in June 2016 .................................................................................. 18

Analytical Refinements........................................................................................... 20

2. Analytical Framework .............................................................................................. 23

Service Categories .................................................................................................. 23

Carving out Designated Services ............................................................................ 23

Refining the Spatial Unit......................................................................................... 25

Data Source ............................................................................................................ 29

3. Cluster Concordance Analysis .................................................................................. 31

Methodology .......................................................................................................... 31

Results .................................................................................................................... 31

4. Locality-based Travel Time Analysis ........................................................................ 45

Methodology .......................................................................................................... 45

Results .................................................................................................................... 48

5. Potential Accessibility Analysis based on Capacity and Population ........................ 67

Methodology .......................................................................................................... 67

Results .................................................................................................................... 70

6. Discussion ................................................................................................................. 85

Summary of Results ................................................................................................ 85

Limitation and Potential Extensions....................................................................... 86

References ................................................................................................................... 89

Appendix I: Accessibility Statistics by Locality ............................................................. 93

Appendix II: Utilisation within Certain Travel Times by Cluster Catchment.............. 309

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Appendix III: Utilisation within Certain Travel Times by TPU Group ......................... 319

Appendix IV: Utilisation within Threshold Travel Time by TPU Group ...................... 331

Annex I: TPU Maps by Planning Department ............................................................ 339

List of Abbreviations .................................................................................................. 345

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Table of Figures

Figure 1 – Cluster catchment and 209 TPU Groups ..................................................... 28

Figure 2 – Concordance for KCC catchment: before vs. after re-clustering ................ 34

Figure 3 – Concordance for KWC catchment: before vs. after re-clustering............... 34

Figure 4 – Concordance for HA overall: before vs. after re-clustering ........................ 35

Figure 5 – Acute IP concordance by Cluster catchment: with vs. carved out DS ........ 35

Figure 6 – Non-acute IP concordance by Cluster catchment: with vs. carved out DS 36

Figure 7 – SOP concordance by Cluster catchment: with vs. carved out DS ............... 36

Figure 8 – AHOP concordance by Cluster catchment: with vs. carved out DS ............ 37

Figure 9 – HA overall concordance by core service: 3 scenarios ................................. 39

Figure 10 – Concordance by core service: HKEC catchment vs. HA overall ................ 40

Figure 11 – Concordance by core service: HKWC catchment vs. HA overall ............... 40

Figure 12 – Concordance by core service: KCC catchment vs. HA overall ................... 41

Figure 13 – Concordance by core service: KEC catchment vs. HA overall ................... 41

Figure 14 – Concordance by core service: KWC catchment vs. HA overall ................. 42

Figure 15 – Concordance by core service: NTEC catchment vs. HA overall ................ 42

Figure 16 – Concordance by core service: NTWC catchment vs. HA overall ............... 43

Figure 17 – Percentage of utilisation within various travel times by core service ...... 49

Figure 18 – Acute IP: percentage of utilisation sought < 10 mins by TPU Group ........ 52





Figure 23 – Non-acute IP: percentage of utilisation sought < 25 mins by TPU Group 56

Figure 24 – SOP: percentage of utilisation sought < 25 mins by TPU Group .............. 56

Figure 25 – PHC: percentage of utilisation sought < 20 mins by TPU Group .............. 57

Figure 26 – A&E: percentage of utilisation sought < 20 mins by TPU Group .............. 57

Figure 27 – AHOP: percentage of utilisation sought < 25 mins by TPU Group ........... 58

Figure 28 – Acute IP vs. SOP: utilisation sought within threshold time ...................... 59

Figure 29 – Acute IP vs. PHC: utilisation sought within threshold time ...................... 60

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Figure 30 – k-means clustering: classification of TPU Groups into three clusters ...... 61

Figure 31 – Number of acute beds within 20 minutes of travel time ......................... 71

Figure 32 – Average travel time to nearest three facilities by TPU Group .................. 72

Figure 33 – Illustration of 2-step FCA Method ............................................................ 74

Figure 34 – Accessibility score by 2-step FCA Method (20 min) for Acute IP .............. 76

Figure 35 – Accessibility score by 2-step FCA Method (40 min) for Acute IP .............. 76

Figure 36 – Accessibility score by Gravity Model (β = 0.4) for Acute IP ...................... 78



Figure 39 – Accessibility scores by 2-step FCA Method and Gravity Model ............... 80

Appendix II: Figure 1 – HKEC catchment: utilisation < travel time by core service ... 310

Appendix II: Figure 2 – HKWC catchment: utilisation < travel time by core service . 311

Appendix II: Figure 3 – KCC catchment: utilisation < travel time by core service ..... 312

Appendix II: Figure 4 – KEC catchment: utilisation < travel time by core service ..... 313

Appendix II: Figure 5 – KWC catchment: utilisation < travel time by core service.... 314

Appendix II: Figure 6 – NTEC catchment: utilisation < travel time by core service ... 315

Appendix II: Figure 7 – NTWC catchment: utilisation < travel time by core service . 316

Appendix III: Figure 1 – Acute IP: utilisation sought < 10 mins by TPU Group .......... 319

Appendix III: Figure 2 – Acute IP: utilisation sought < 20 mins by TPU Group .......... 319

Appendix III: Figure 3 – Acute IP: utilisation sought < 30 mins by TPU Group ......... 320

Appendix III: Figure 4 – Acute IP: utilisation sought < 40 mins by TPU Group ......... 320

Appendix III: Figure 5 – Non-acute IP: utilisation sought < 10 mins by TPU Group . 321




Appendix III: Figure 9 – SOP: utilisation sought < 10 mins by TPU Group ................ 323

Appendix III: Figure 10 – SOP: utilisation sought < 20 mins by TPU Group .............. 323



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Appendix III: Figure 13 – PHC: utilisation sought < 10 mins by TPU Group .............. 325




Appendix III: Figure 17 – A&E: utilisation sought < 10 mins by TPU Group ............. 327




Appendix III: Figure 21 – AHOP: utilisation sought < 10 mins by TPU Group ........... 329




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Table of Tables

Table 1 – Districts of residence and corresponding Clusters....................................... 16

Table 2 – Initial list of Designated Services .................................................................. 25

Table 5 – Cluster catchment, district and TPU Group mapping in the analysis .......... 27

Table 3 – Concordance for six services by Cluster catchment in 2015/16 .................. 33

Table 4 – Concordance after re-clustering and craving out DS in 2015/16 ................. 38

Table 6 – 90th of travel time by core service for Hong Kong Overall .......................... 50

Table 7 – Threshold travel time set for each core service ........................................... 55

Table 8 – k-means clustering: cluster means ............................................................... 61

Table 9 – TPU Groups with < 50% of utilisation within 25 minutes for Acute IP ........ 63

Table 10 – Population in TPU Groups with < 50% utilisation within threshold time .. 64

Table 11 – Various approaches in quantifying potential spatial accessibility ............. 70

Table 12 – Number of scheduled acute beds by Cluster catchment ........................... 70

Table 13 – TPU Groups with lower Gravity score with larger population size ............ 82

Appendix II: Table 1 – Districts of residence and corresponding Clusters ................ 309

Appendix II: Table 2 – 90th percentile of travel time by core service by Cluster ....... 317

Appendix IV: Table 1 – Percentage of utilisation by TPU Group in HKEC .................. 331

Appendix IV: Table 2 – Percentage of utilisation by TPU Group in HKWC ................ 332

Appendix IV: Table 3 – Percentage of utilisation by TPU Group in KCC .................... 333

Appendix IV: Table 4 – Percentage of utilisation by TPU Group in KEC .................... 334

Appendix IV: Table 5 – Percentage of utilisation by TPU Group in KWC ................... 335

Appendix IV: Table 6 – Percentage of utilisation by TPU Group in NTEC .................. 336

Appendix IV: Table 7 – Percentage of utilisation by TPU Group in NTWC ................ 337

Preface Page | 7

Preface

The Steering Committee (SC) on Review of Hospital Authority (HA) was set up by the

Government in August 2013 to conduct an overall review of the HA to examine its

operation in response to the changes in society such as an expected aging population.

The HA Review covered major areas with impact on HA’s operation and service

provision. Resource management was one of them, where HA was recommended to

develop a Refined Population-based Model to inform resource allocation in the SC’s

report published in July 2015. Cross-cluster movement of patients was one of the

specific considerations that the SC would like HA to take into account in model

development.

Despite being an administrative arrangement and not a registration system, HA’s

Cluster structure draws public’s attention to scrutinise HA from the locality

perspective. As groundwork for the development of the Refined Population-based

Model, an analysis on hospital service utilisation in 2014/15 by patient residence

down to local communities was conducted in 2016, to not only facilitate

understanding on the magnitude of cross-cluster flow, but also to investigate the

patterns of flow by residential location, age and time of day, so as to generate insight

and provide scenarios for assessing how Cluster boundary refinement would impact

the concordance of healthcare utilisation, and corresponding implication on resource

management. The report “Cross-cluster Flow Analysis from the Perspective of Local

Communities – Groundwork for Refined Population-based Resource Allocation

Model Development” was published in June 2016.

The Jockey Club School of Public Health and Primary Care of The Chinese University

of Hong Kong was commissioned as the external consultant to help HA in developing

the Refined Population-based Model, and their Final Report was submitted and

approved by the HA Board in September 2017. While the Refined Model has taken

into account cross-cluster movement of patients, the consultant also recommended

HA to further study the phenomenon with its important influence on Cluster

utilisation. As such, the aforementioned cross-cluster flow analysis was updated

using 2015/16 data and extended with a series of important analytical refinements.

In particular, as proximity of facilities from patients’ places of residence was found to

be among the most influential factors affecting health-seeking behaviour during the

development of the Refined Population-based Model, cross-cluster flow, and more

generally spatial accessibility of HA services, were further analysed with respect to

patients’ travel time in seeking services. In addition, a more refined spatial unit in

Preface Page | 8

Tertiary Planning Unit was employed to enhance the granularity of analysis. This

analysis aims to generate business intelligence and inform planning in short and

longer terms. This report serves to present the methodology, findings, and

limitations of this study, as part of the analytical work under the population-based

paradigm in service planning and resource management.

Executive Summary Page | 9

Executive Summary

Introduction (Chapter 1)

In response to Recommendation 3 of the Hospital Authority (HA) Review, HA has

commissioned the Jockey Club School of Public Health and Primary Care of the

Chinese University of Hong Kong as consultancy and developed a Refined

Population-based Model to inform resource allocation. Extending from the cross-

cluster flow analysis published in June 2016, which was conducted as groundwork for

model development, more in-depth analyses were conducted to provide information

on service accessibility from the perspective of local communities.

Despite being an administrative arrangement only, the Cluster structure draws

public’s attention to scrutinise HA from the perspective of local communities. This

creates some challenges though, as the Cluster structure is not a registration system

to link specific districts and enrollees, and boundaries are largely inferred from

election district boundaries. It is not unreasonable for people living near such

perceived boundaries to consult nearby hospitals that are under the network of a

neighbour Cluster. Classifying such activities as cross-cluster traffic invariably gives a

negative connotation, and will be misleading unless there are ways to reliably

differentiate the nature of cross-cluster patient movement. In addition, while HA

encourages patients to seek medical treatment from hospitals in the Cluster of their

residence to facilitate follow-up of their chronic conditions and the provision of

community support, individual patients may have other considerations when they

choose a medical facility for medical treatment. Notwithstanding the above

limitations, the Cluster concept is well accepted and has been evolving towards

providing services to meet the healthcare needs of the population in its vicinity.

Cross-cluster flow also depends on the level and scope of services, facilities and

expertise available in the hospitals of patient resides. This is because the portfolio of

hospitals was not originally planned on a Cluster basis and not all Clusters started at

the same level. In addition, some services are only provided in designated

institutions (known as Designated Services (DS)) due to (i) the unique skill /

technology / expertise required (e.g. liver transplantation), or (ii) economy of scale

or operational requirements (e.g. Blood Transfusion Service). Thus, these services

are serving beyond their Cluster boundaries and cross-cluster movement of patients

will result. Therefore, a more thorough analysis on cross-cluster flow is warranted to


gain understanding on the variation of accessibility across different localities in Hong

Kong.

Analytical Framework (Chapter 2)

Major analytical refinements over last year’s study include (1) delineation of

Designated Services to assure like-with-like comparison of core hospital service

accessibility across local communities, (2) enhanced granularity of analysis through

evaluation by major service types, namely Acute Inpatient (Acute IP), Non-acute

Inpatient (Non-acute IP), Specialist Outpatient (SOP), Primary Care (PHC) , Accident

and Emergency (A&E), Allied Health Outpatient (AHOP), and (3) adoption of a more

refined spatial unit in Tertiary Planning Unit.

HA service utilisation data for the aforementioned six core services from 1 April 2015

to 31 March 2016 was collected via the Statistics and Workforce Planning (S&WP)

team in the Strategy and Planning (S&P) Division in HA. The utilisation data was

divided into two sets, one for Acute IP and Non-acute IP with the activity measure

being BDO, and the other one for SOP, PHC, A&E and AHOP where the activity

measure is the number of attendance. Population data in 2015 was also utilised in

various parts of this study. The data was collected from the “Projections of

Population Distribution 2015-2024” report, completed in December 2015 by the

Working Group on Population Distribution Projections and published by the

Government’s Planning Department.

Cluster Concordance Analysis (Chapter 3)

As demonstrated in last year’s analysis using 2014/15 data, cross-cluster flow from

the perspective of population could be evaluated by analysing the “Cluster

concordance rate”, which quantifies the level of service being provided in patient’s

own Cluster.

After delineating the effect of Designated Services so as to assure a like-with-like

comparison across localities in Hong Kong, and simulating the current re-clustered

setting, concordance rates are generally higher in Hong Kong and the New Territories,

illustrating that they are relatively confined geographically. This study also shows

how the re-delineation of Cluster boundary increased the concordance of the KCC

and KWC catchments. The concordance rates of the KEC catchment are relatively

lower (e.g. at 74.5% for Acute IP). This can be partly explained by the unavailability of

certain core service (e.g. Oncology services, although planning of the service in KEC is


already underway) where residents in the KEC catchment need to patronise other

Clusters, namely KCC, instead.

Locality-based Travel Time Analysis (Chapter 4)

Extending from the Cluster concordance analysis, revealed spatial accessibility was

analysed one level deeper, i.e. by locality using TPU Groups as a spatial unit. Instead

of studying whether services are provided to residents in the same Cluster,

accessibility was evaluated with respect to patient convenience using travel time as a

proxy. With clustering being an administrative arrangement only, such analysis will

provide more insight on patient convenience in terms of how they were served in

their proximity. Similar to the Cluster concordance analysis, utilisation of Designated

Services was carved out to assure like-with-like comparison across localities. The

analysis revealed that revealed spatial accessibility is generally good in Hong Kong.

For the vast majority (90%) of utilisation, services could be sought within 17 to 20

minutes for PHC and A&E, and within 21 to 24 minutes for Acute IP, Non-acute IP,

SOP, and AHOP. The analysis also helped in identifying regional pockets of higher and

lower accessibility for each core service. For example, urban areas like the majority

of the Kowloon peninsula and HK Island, as well as New Territories town centres like

Tuen Mun, Yuen Long, Sha Tin and Tai Po, have relatively high accessibility in general.

On the other hand, some TPU Groups have relatively lower accessibility, which are

generally found in the parameters of Hong Kong and are observed to have smaller

population sizes (1.6% of total Hong Kong population for Acute IP).

Potential Accessibility Analysis based on Capacity and

Population (Chapter 5)

In addition to analysing retrospective information on revealed accessibility with

respect to travel time, an analysis on potential accessibility based on capacity and

population was conducted for Acute IP. In contrast to revealed accessibility where

the focus on the realised / actual health-seeking behaviour, analysis on potential

accessibility aims at evaluating the spatial distribution of supply (beds in hospitals in

the case for Acute IP) with respect to the geographical distribution of demand

(population), which would help informing longer-term capacity / facility under HA’s

long term objective of providing core services in the vicinities of local populations.

Several approaches in evaluating potential access are presented, with the Gravity

Model being the most suitable one under Hong Kong’s context. Analysis using such

model revealed local pockets of higher / lower potential accessibility. In particular,

TPU Groups that have relatively lower accessibility scores together with significant


population sizes (i.e. higher than the average TPU Group population of 34,916) were

identified and they are mainly found within the catchment of KEC, NTEC, and NTWC.

Such information would inform longer-term service, facility and capacity planning, so

as to facilitate HA to move forward in enhancing local access to care.

Limitation and Potential Extensions (Chapter 6)

The Cluster Concordance Analysis (Chapter 3) evaluates accessibility by analysing to

what extend patients have sought care in their own Cluster. While Cluster catchment

is good reference in general, it has to be stressed that clustering in HA is not a

patient registration system but an administrative networking arrangement that

groups institution together for purposes of continuity of care and economy of scale.

Therefore, patients can choose to go to institutions in any Cluster for most core

services. Under such setting in the Hong Kong healthcare system, analysing service

access merely from a Cluster catchment perspective may not be comprehensive.

Two sets of accessibility analyses were therefore presented (in Chapter 4 and 5),

where TPU Group, rather than Cluster catchment, was used as the spatial unit of

analysis. While it is reasonable to assume the homogeneity of the population in a

TPU Group, analysing on such an aggregated geographical level has one important

limitation in that a centroid needs to be estimated for each unit. Without the

availability of information on the spread of population in each TPU Group, the

geographical centroid was used as a proxy. In addition to adopting a more refined

spatial unit, one can further minimise aggregation error by analysing on an individual

level, i.e. to geocode the location of each individual patient rather than grouping

them into a geographical units. Furthermore, travel time between patients’ places of

residence and facilities was selected as the proxy of patient convenience in the

analysis. As a potential extension of this study, a sensitivity analysis on how different

measures of travel time, e.g. use of public transit, would impact the relative

accessibility among different regions in Hong Kong. In addition, in view of the

extensive expansion of transportation networks in Hong Kong (both road and public

transit like the Mass Transit Railway), it would be also be fruitful to conduct scenario

analysis taking into account various major changes in the transportation

infrastructure anticipated to be completed in the next few years.

1. Introduction Page | 13

1. Introduction

The Hospital Authority (HA) is a statutory body incorporated in Hong Kong on 1

December 1990 under the Hospital Authority Ordinance. The objective of HA is to

manage and develop Hong Kong’s public hospital system to provide quality public

healthcare services to the people of Hong Kong.

HA Review and the Refined Population-based Model

In August 2013, the Government announced the setting up of a Steering Committee

(SC) “to conduct an overall review of the Hospital Authority to examine its operation

in response to the changes in society such as an expected ageing population”. The

SC Report1 was published in July 2015, where a total of ten recommendations were

made, one of which (Recommendation 3) addressed the subject of equity in resource

management within the HA. In particular, HA was recommended to develop a

Refined Population-based Model to inform resource allocation.

1 The report of the Steering Committee can be found on the website of the Food and Health Bureau

at http://www.fhb.gov.hk/en/committees/harsc/report.html

(a) HA should adopt a refined population-based resource allocation model by

reviewing the present approach and taking into consideration the

demographics of the local and territory-wide population. The refined

population-based model should take into account the organisation of the

provision and development of tertiary and quaternary services, and hence

the additional resources required by selected hospitals or clusters, as well as

the demand generated from cross-cluster movement of patients; and

(b) HA should develop the refined population-based resource allocation model

and implement through its service planning and budget allocation process

within a reasonable timeframe. To avoid unintentional and undesirable

impact on the existing baseline services of individual clusters, HA should

consider appropriate ways to address the funding need of clusters identified

with additional resources requirement under the new model, while

maintaining the baseline funding to other clusters.

http://www.fhb.gov.hk/en/committees/harsc/report.html


Unlike some overseas healthcare jurisdictions where funding is tied to defined

territories with explicit boundaries and/or target communities (i.e., directly traceable

and linked to defined populations), the Clustering arrangement in HA is an

administrative one based on the way healthcare facilities are being networked.

There are inferred catchment areas but no strict boundaries between Clusters or

restriction forbidding people to seek care from distance Clusters. Under this context,

the SC has specifically recommended HA to take into account cross-cluster

movement of patients in the development of the Refined Population-based Model.

In response to Recommendation 3, HA committed to developing a Refined

Population-based Model to inform resource allocation, as part of the Action Plan2

formulated by the HA Board and submitted to the Government on 22 October 2015

in response to the HA Review recommendations. As groundwork for model

development, an analysis on hospital service utilisation by patient residence down

to local communities was conducted in 2016, to not only facilitate understanding on

the magnitude of cross-cluster flow, but also to investigate the patterns of flow by

residential location, age and time of day, so as to generate insight and provide

scenarios for assessing how Cluster boundary refinement would impact the

concordance of healthcare utilisation, and corresponding implication on resource

allocation. The report “Cross-cluster Flow Analysis from the Perspective of Local

Communities – Groundwork for Refined Population-based Resource Allocation

Model Development” was published in June 2016.

HA commissioned an external consultancy (the Jockey Club School of Public Health

and Primary Care of the Chinese University of Hong Kong) in April 2016 to help

develop the Refined Population-based model. The Refined Population-based Model

has taken into account 13 Designated Services and cross-cluster flow of patients, so

as to assure like-with-like comparison of Cluster resources. While it is not a direct

funding formula as infrastructure and capacity take time to develop and existing

services must not be disrupted, the Refined Population-based Model is a scientific

tool to analyse and model population healthcare needs objectively, and a starting

point to facilitate understanding through transparent information. The consultant’s

Final Report3 was submitted and approved by the HA Board at its Administrative and

Operational Meeting in September 2017.

2

The Action Plan can be accessed at http://www.ha.org.hk/haho/ho/cc/HA_Review_Action_Plan_Final_en.pdf

3 The Final Report on the development of the Refined Population-based Model is available on the HA

internet website at http://www.ha.org.hk/haho/ho/fdd/report1_en.pdf

http://www.ha.org.hk/haho/ho/cc/HA_Review_Action_‌Plan_Final_en.pdf

http://www.ha.org.hk/haho/ho/cc/HA_Review_Action_‌Plan_Final_en.pdf

http://www.ha.org.hk/haho/ho/fdd/report1_en.pdf


While the Refined Population-based has already taken into account the cross-cluster

flow of patients, the consultant recommended in the Final Report that populations’

health-seeking behaviour and cross-cluster flow should be further studied with their

important influence on Cluster utilisation.

This report discusses the extension of the study published in June 2016, which looks

into the different yet related concepts of cross-cluster flow, proximity to care,

service accessibility etc. to gain more understanding on the current situation and to

further debunk myths surrounding the topics. In addition to updating the study

period from 2014/15 to 2015/16, several other important refinements were made

which will be discussed in the following subsections. Before going over the various

refinements made, it would be worthwhile to further look into the clustering

arrangement in HA.

Clustering in HA

A Cluster is a network of medical facilities and services grouped together to help HA

ensure that patients would receive a continuum of care within the same

geographical setting and throughout their episode of illness – from its acute phase to

convalescence and rehabilitation, and community after-care. This is achieved by

rationalising the operations of the hospitals within each Cluster so that they are

capable of providing a comprehensive and complementary range of services to their

local population. HA Board formally adopted the current seven hospital Clusters in

2001, following evolution of the early clustering formation in 1994 and the piloting

of a new Cluster management approach.

To facilitate capital and service planning, each Cluster has designated catchment

districts demarcated based on the location of the hospitals (primarily the acute

hospitals). Medical facilities and services in the Clusters are planned and reorganised

taking into account the services provided by and the respective roles of the existing

hospitals in the Cluster, the geographical and demographic considerations of the

catchment districts, and service utilisation patterns at that time. Through this

process of clustering which involves service rationalisation and reorganisation,

continuity of care for each episode of illness is provided to the local population in the

Cluster.

In response to the HA Review, a re-delineation of administrative arrangements

between Kowloon West Cluster (KWC) and Kowloon Central Cluster (KCC) was

implemented, which included the re-grouping of Wong Tai Sin district and Mong Kok


area, involving Kwong Wah Hospital (KWH), TWGHs Wong Tai Sin Hospital (WTSH)

and Our Lady of Maryknoll Hospital (OLMH) from KWC to KCC. Such implementation

further enhanced the integration of services as well as reducing cross-cluster

consultations and strengthening the continuity of care for patients. Changes related

to Cluster identity, transfer of functions as well as transfer of accountability were

made on 1 December 2016, while cut-over from KWC to KCC with regard to non-

clinical services (e.g. human resources, business support services, etc.) took place on

1 April 2017.

Districts in Hong Kong and their corresponding Clusters are outlined in Table 1 below.

The various groups of districts are labelled as catchment area for the individual

Clusters for the purpose of the analyses presented in the report.

Patients’ District of Residence Corresponding Cluster

Eastern, Wan Chai, Islands (excl. Lantau) Hong Kong East Cluster (HKEC)

Central & Western, Southern Hong Kong West Cluster (HKWC)

Kowloon City, Mong Kok, Wong Tai Sin, Yau Tsim

Kowloon Central Cluster (KCC)

Kwun Tong, Sai Kung Kowloon East Cluster (KEC)

Sham Shui Po, Kwai Tsing, Tsuen Wan, Lantau Island

Kowloon West Cluster (KWC)

Sha Tin, Tai Po, North New Territories East Cluster (NTEC)

Tuen Mun, Yuen Long New Territories West Cluster (NTWC)

Table 1 – Districts of residence and corresponding Clusters

HA’s longer term objective is that the local population of the respective clusters can

seek public secondary hospital services within the Cluster where they reside. In

implementing the hospital clustering concept, it is recognised that there is

unevenness among the Clusters in terms of population, demographics, demand for

public healthcare, as well as the level and scope of services, facilities and expertise

available. This is because the portfolio of hospitals was not originally planned on a

cluster basis and not all Clusters started at the same level. Such unevenness

inevitably results in certain level of overlap in individual service provision in some

clusters.

Despite being an administrative arrangement only, the Cluster structure draws

public’s attention to scrutinise HA from the perspective of local communities. This

creates some challenges though, as the Cluster structure is not a registration system


to link specific districts and enrollees, and boundaries are largely inferred from

election district boundaries. As such, there are bound to be margins of error in

estimating the size of population served by individual Clusters.

It is not unreasonable for people living near such perceived boundaries to consult

nearby hospitals that are under the network of a neighbour Cluster. Classifying such

activities as cross-cluster traffic invariably gives a negative connotation, and will be

misleading unless there are ways to reliably differentiate the nature of cross-cluster

patient movement. In addition, while HA encourages patients to seek medical

treatment from hospitals in the Cluster of their residence to facilitate follow-up of

their chronic conditions and the provision of community support, individual patients

may have other considerations when they choose a medical facility for medical

treatment. For example, some may choose to stick with the hospital they have been

following up despite moving to a new residence, or prefer a particular hospital due

to relationship with other family members, or as a result of their daytime activities.

Notwithstanding the above limitations, the Cluster concept is well accepted and has

been evolving towards providing services to meet the healthcare needs of the

population in its vicinity.

Hong Kong’s Unique Situation

As discussed above, HA has organised its facilities in Clusters to facilitate synergy and

continuum of care. In addition, for administrative and planning purposes, each

hospital Cluster is associated with catchment districts based on locations of the

hospitals. However, in contrast to some other jurisdictions around the world (e.g. the

National Health Services has divided England into 221 geographical regions named

Clinical Commissioning Groups (CCGs), and their respective population would be the

registered lists of all member General Practitioner practices of the CCGs), clustering

is not a patient registration system in Hong Kong and thus patients are free to visit

the hospitals or clinics that are deemed to be the most desirable/ convenient for

them.

Moreover, unlike other jurisdictions where residents of different regions are

geographically segregated, Hong Kong covers a small land mass (1,104km2) and

residents are highly mobile within the city with its highly developed transportation

network. This convenience in travelling within the territory contributes greatly to the

cross-cluster movement of patients, especially during daytime, where younger /

working population may have travelled outside of the resident district for work,

school, or leisure activities, etc.


In addition, cross-cluster flow also depends on the level and scope of services,

facilities and expertise available in the hospitals of patient resides. This is because

the portfolio of hospitals was not originally planned on a Cluster basis and not all

Clusters started at the same level. Hospitals may not have sufficient capacity for the

demand of patients lived in their catchment area. In addition, some services are only

provided in designated institutions due to (i) the unique skill / technology / expertise

required (e.g. liver transplantation), or (ii) economy of scale or operational

requirements (e.g. Blood Transfusion Service). Thus, these services are serving

beyond their Cluster boundaries and cross-cluster movement of patients will result.

Thus, a more thorough analysis on cross-cluster flow is warranted to gain

understanding on the variation of accessibility across different localities in Hong

Kong.

Previous Study in June 2016

As groundwork for the development of the Refined Population-based Model, a

report was published in June 20164, outlining the data analysis conducted on public

healthcare utilisation of local communities, down to small districts level, so as to

identify unusual patterns of cross-cluster flow, explore possible explanation for the

observation and assessing impact of refining the current Cluster boundaries.

Major Findings

In general, the concept of Cluster catchment was to be a useful reference. In

analysing the concordance rate – as defined by the percentage of activity of a

district’s residents being treated in their own Cluster – of different services in

different Cluster catchments, it was found that Clusters in both Hong Kong Island

(HKEC and HKWC) and New Territories (NTEC and NTWC) are relatively

geographically confined. Taking inpatient activities (in terms of bed days) as an

example, the analysis revealed that the concordance of the residents in the three

Clusters in Kowloon was considerably lower than those in New Territories and Hong

Kong Island. However, scenario analysis envisaged better concordance rates for both

KWC and KCC if Wong Tai Sin and Mong Kok were re-grouped from KWC to KCC. This

laid an objective basis in refining the boundary of KWC and KCC, which was in

progress under HA’s Action Plan in response to Recommendation 1 of the HA Review. 4 The report “Cross-cluster Flow Analysis from the Perspective of Local Communities – Groundwork

for Refined Population-based Resource Allocation Model Development” is accessible at http://www.ha.org.hk/haho/ho/fdd/Report4_en.pdf

http://www.ha.org.hk/haho/ho/fdd/Report4_en.pdf


In addition, daytime mobility in relation to commuter flow was estimated for Hong

Kong using Census 2011 data. The general pattern is that commuters from New

Territories would travel to the central commutable business districts in southern

Kowloon and northern Hong Kong Island. Patient age was found to be a significant

factor in cross-cluster flow, where patients aged 20 to 64 are more likely to seek

healthcare service other than their own Cluster when compared to the other age

groups.

Limitations

As mentioned in last year’s report, the above observations highlight that health-

seeking behaviour is complex. Further analysis is warranted to address the following

limitations of the last round of analysis:

1. Overall service utilisation data in HA was utilised, without dissecting out the

utilisation of highly-specialised services that are only available in certain

designated institutions. Including such utilisation data may skew the analysis

that aimed to evaluate relative accessibility among local communities.

2. The analysis had Acute and Non-acute Inpatient services lumped together.

With major differences in the nature of these two services, the health-

seeking behaviour for these services is expected to differ as well. Moreover,

in addition to inpatient services, only Specialist Outpatient and Accident and

Emergency services were included in last year’s analysis.

3. The spatial unit of the analysis was the 152 districts in Hong Kong as defined

by the HA data repository. They lack compatibility with spatial units utilised in

other government department/agencies.

4. The analysis focused on analysing the Cluster concordance rate, i.e. the

percentage of activity sought within residents’ own Cluster. However, the

notion of cross-cluster flow may not be the sole indicator for accessibility to

care, as clustering is merely an administrative arrangement in Hong Kong and

no rigid boundary exists.

5. Patient’s actual utilisation pattern was analysed to gain understanding on

health-seeking behaviour. Other important factors, such as the geographical


distribution of facilities and population, are also worth building in so as to

enrich the analysis.

Analytical Refinements

In view of the implementation of the re-delineation of Cluster boundary between

KCC and KEC, as well as the need to further analyse the cross-cluster flow

phenomenon, this study attempts to build on top of the last analysis to provide more

in-depth information on service accessibility from the perspective of local

communities. The next chapter (Chapter 2) discusses the overall analytical

framework which was refined over the previous study, namely:

1. To carve out the utilisation of high-specialised services provided in specific

Clusters, known as Designated Services (DS), to assure like-with-like

comparison of core service accessibility across local communities

2. To analyse accessibility by major service types to enhance granularity of

analysis, e.g. to break down service utilisation into six core services including

Acute and Non-acute Inpatient, Specialist Outpatient, Primary Healthcare,

Accident and Emergency, and Allied Health Outpatient services.

3. To adopt a more refined spatial unit in Tertiary Planning Unit (TPU) Groups,

which is demarcated by the Government’s Planning Department and also

utilised by the Census and Statistics Department (C&SD) for statistics

reporting

After the overall analytical framework is outlined in the next chapter, three different

sets of analyses are presented in Chapter 3 to 5:

Cluster Concordance Analysis (Chapter 3)

An update on last year’s analysis that evaluates concordance rate of residents

in the catchment areas of different Clusters using 2015/16 data (with DS

carved out)

Provides retrospective information on whether patients sought care in their

own Cluster


Locality-based Travel Time Analysis (Chapter 4)

Analyses spatial accessibility with respect to travel time between patients’

places of residence and institutions

Provides retrospective information on whether and to what extend patients

sought care in the proximity of their places of residence

Potential Accessibility Analysis based on Capacity and Population (Chapter 5)

Evaluates spatial accessibility by taking into account the geographical

distribution of service capacity and population in Hong Kong

Identifies local pockets of higher / lower accessibility so as to provides

business intelligence to inform longer-term facility and capacity planning

2. Analytical Framework Page | 23

2. Analytical Framework

As this analysis forms part of the development for the Refined Population-based

Model, the analytical components of this study were aligned with those of the

Refined Population-based Model as best as possible. Namely, the choice of service

categories to be analysed, definition of highly-specialised service, etc. are largely

identical to those in the development of the Refined Population-based Model.

Service Categories

There are a variety of healthcare services provided by HA. By considering the

structure of service provision, availability of utilisation data of different healthcare

activities, eight core service categories were identified as the basis of the analysis,

namely Acute Inpatient (Acute IP), Non-acute Inpatient (Non-acute IP), Specialist

Outpatient (SOP), Primary Care (PHC) , Accident and Emergency (A&E), Allied Health

Outpatient (AHOP), Day Hospital, and Community Care. Such granular analysis would

facilitate inter-community comparison down to core services. However, due to the

heterogeneity of service delivery model among Clusters as well as the lack of

comprehensive electronic data for modelling for Day hospital and Community Care

services, these two core services are not included the analysis.

With regard to the measure of utilisation, bed days occupied (BDO), number of

patients, and number of episodes could be used for inpatient services As variations

in terms of BDO could imply significant differences in quantifying related resources,

it is proposed that BDO would be a more relevant measure for the analysis. On the

other hand, the number of attendance will be used for other services, i.e. SOP, PHC,

A&E, and AHOP.

Carving out Designated Services

It is common for public healthcare systems to organise highly specialised services

and operations in designated locations so as to benefit from concentration of

expertise (not only of healthcare professionals but also technology setup and facility

design, etc.) and economies of scale. This is particularly relevant in Hong Kong in

view of the size of its population and territory.


As the subject of the analysis is to assess the relative accessibility to service by local

communities, it would be most relevant to compare using utilisation of core hospital

services that are common across Clusters, i.e. to compare like against like. Therefore,

utilisation of these services (labelled as Designated Services (DS) for the purpose of

this project) are carved out from the analysis such that the remaining core hospital

and clinic services are more comparable in terms of scope, nature, and the target

population intended to serve, which in turn would minimise any potential bias on

different localities’ relative accessibility that may result from the varying provision of

DS in different Clusters.

Through discussing with frontline and deliberating at the Internal Resource

Allocation Model Development Steering Committee (IRAMD SC), the following

definition for DS was formulated.

Designated Services mainly follow corporate direction when setting out

corresponding service delivery models, and are:

(a) highly complex in nature with respect to skill, technology and/or expertise

that are only adequately available in specific Cluster(s) to serve populations

beyond its Cluster boundary, or

(b) being centralised at specific Cluster(s) to serve populations beyond its Cluster

boundary on operational reasons or economy of scale.

An identification and vetting mechanism based on materiality and data availability

was set up by the IRAMD SC. Two rounds of DS proposals were called from Clusters

and an initial list of 13 DS was shortlisted. On very rough estimation, these DS

roughly constitute to around 4% of HA’s total operating expenditure.

For more information on the detailed definition of DS, the vetting and prioritisation

mechanism, as well as the costing methodologies and results, please refer to the

“Report on Costing of Designated Services Year 2012/13 to 2015/16”5.

5 The “Report on Costing of Designated Services Year 2012/13 to 2015/16” is available on the HA

internet website at http://www.ha.org.hk/haho/ho/fdd/Report3_en.pdf

http://www.ha.org.hk/haho/ho/fdd/Report3_en.pdf


Designated Services Cluster(s)

AIDS Service Kowloon Central Cluster Kowloon West Cluster

Blood Transfusion Service Kowloon Central Cluster

Bone Marrow Transplantation (Allogeneic) Hong Kong West Cluster

Cardiothoracic Surgery Hong Kong West Cluster Kowloon Central Cluster New Territories East Cluster#

Heart Transplantation Hong Kong West Cluster

Lung Transplantation Hong Kong West Cluster

Developmental Disabilities Unit Kowloon West Cluster

Forensic Psychiatry New Territories West Cluster

Infectious Disease Centre Kowloon West Cluster

Liver Transplantation Hong Kong West Cluster

Severe Mentally Handicapped Services New Territories West Cluster

Toxicology Kowloon East Cluster Kowloon West Cluster New Territories East Cluster

Teaching and Research Component in Teaching Hospitals

Hong Kong West Cluster New Territories East Cluster

# With a Thoracic Surgery satellite site in New Territories West Cluster Table 2 – Initial list of Designated Services

Refining the Spatial Unit

To conduct a more granular analysis from a geographical perspective, a spatial unit

more refined than the administrative Cluster boundaries needs to be utilised. In the

previous study published in 2014/15, a spatial unit of “district”, defined in the HA

data repository which split Hong Kong into 162 parts, was used. While being quite

granular, its limitation lies in the fact that its definition was not aligned with the

spatial units used in common statistics (e.g. population size) published by the

Government.

The size of Hong Kong population is generally reported at different spatial units for

basic description of geographical differences or other purposes. Common spatial

units being used in Hong Kong Population Census 2011 included area (3; being Hong

Kong Island, Kowloon and the New Territories), District Council District (18), Tertiary

Planning Unit (TPU) (289), and District Council Constituency Area (DCCA) (412).

Generally speaking, a more granular spatial unit would be preferably in this type of

study that targets in evaluating regional variation in accessibility. Other important

considerations in deciding the spatial unit include alignment with the boundaries of


HA Cluster catchment areas, as well as consistency of the boundaries of the spatial

unit over time. For example, while a simple split of Hong Kong into Hong Kong Island,

Kowloon, and New Territories would be consistent over time, this spatial unit lacks

the necessary granularity in evaluating geographical accessibility. On the other hand,

DCCA offers finer granularity, it is defined for election purposes and its boundaries

are subject to amendment according to the election schedule. In addition, their

boundaries do not quite match with the boundaries of HA Cluster catchment areas.

For example, in the New Territories, the Islands District is split into the catchment

areas HKEC and KWC. As such, the recommended choice goes to TPU, where it does

not only offer the necessary spatial granularity, but it also matches quite well with

the boundaries of Cluster catchment areas in HA. More information on the TPU

system is presented below.

Tertiary Planning Unit

TPU is a geographic reference system demarcated by the Planning Department for

town planning purposes. The whole territory of Hong Kong is divided into 289 TPUs,

which are aggregated under 52 Secondary Planning Units and in turn under nine

Primary Planning Units (as of 2011). The TPU boundaries are mainly delineated by

the nature of the geographic features of the area such as roads, railway lines, zoning

boundaries of town plans, etc. They also provide a common geographic system for

the compilation of statistical data. In particular, statistical data from the population

census/by-census are produced using this basic system.

Each of the TPU is identified by a unique three-digit number. The first digit of a TPU

number identifies the Primary Planning Unit code while the first and second digits

together correspond to the Secondary Planning Unit code. While the Planning

Department does not name the TPU (only the three digit code would be used),

names that identify the TPUs approximate location would be given in this report just

to facilitate reader’s understanding when references are made to specific TPUs.

According to the C&SD, having regard to data precision and protection of data

privacy of individual respondents, a TPU with less than 1,000 persons is merged with

adjacent TPU(s) for the release of statistics. The resultant 209 TPU Groups are

employed in the analysis. These TPU Groups and how they are mapped to Cluster

catchments (for presentation purposes only – according to the current re-clustered

setting) and districts (as defined internally by HA6) are presented in Table 3 below.

6 There are 20 districts defined in HA. The definition is the same as the 18 District Council Districts in

Hong Kong, with exemptions being (1) the split of Yau Tsim Mong district into Yau Tsim and Mong Kok; and (2) the split of the Islands district into Lantau Island and Islands (excluding Lantau Island).


Cluster Catchment

District Tertiary Planning Unit Groups

HKEC Eastern 148; 151; 152; 153; 154; 155; 157; 161; 162; 163; 166; 167; 156 & 158; 164 & 165

Islands (excl. Lantau)

920; 963; 976; 911 - 913

Wan Chai 131; 132; 133; 134; 135; 140; 144; 145; 149; 146 & 147; 183 & 184

HKWC Central & Western

111; 112; 113; 114; 115; 116; 141; 142; 143; 121 & 122; 123 & 124; 181 & 182

Southern 171; 172; 173; 174; 191; 196; 197; 175 & 176; 190, 192 & 194; 193, 195 & 198

KCC# Kowloon City 231; 232; 233; 234; 235; 236; 237; 241; 242; 243; 244; 245; 246; 247; 271; 272; 285; 213 & 215 - 216

Mong Kok 220; 221; 222; 227; 228; 229; 253; 254

Wong Tai Sin 281; 282; 283; 284; 287; 288 & 289

Yau Tsim 211; 212; 214; 217; 225; 226; 251, 252 & 256

KEC Kwun Tong 290; 291; 292; 297; 298; 280 & 286; 293 & 296; 294 & 295

Sai Kung 821; 822; 823; 825; 827; 829; 831; 833; 835; 836; 837; 838; 839; 811 - 815; 820 & 824; 826 & 828; 832 & 834

KWC# Kwai Tsing 326; 327; 328; 350; 351

Lantau Island 944; 931 & 933; 932 & 934; 941-943; 950 & 951; 961 & 962; 971 - 974

Sham Shui Po 260; 261; 262; 263; 264; 265; 266; 267; 268; 255 & 269

Tsuen Wan 310; 321; 322; 323; 325; 335; 975; 320, 324 & 329; 331 - 334, 336 & 340

NTEC North 623; 625; 626; 627; 628; 634; 641; 642; 545 & 546; 610 & 621; 620 & 622; 624 & 629; 651 - 653

Sha Tin 755; 757; 758; 759; 731, 733 & 754; 732, 751 & 753; 756 & 761 – 762

Tai Po 720; 723; 724; 725; 726; 729; 631 - 633; 711 - 712, 721 & 728; 722 & 727; 741 - 744

NTWC Tuen Mun 424; 425; 426; 441; 442; 411 - 416 & 427; 421 & 422; 423 & 428; 431 - 434

Yuen Long 510; 511; 512; 513; 514; 516; 518; 519; 521; 522; 523; 524; 527; 528; 529; 531; 532; 533; 541; 542; 543; 544; 515 & 517; 525 & 526

Note: A small number of TPU Groups span across two Cluster catchments. They include TPU

Group 190, 192 & 194 (HKEC / HKWC), 280 & 286 (KCC / KEC), 610 & 621 (NTEC / NTWC) and

731, 733 & 754 (NTEC / NTWC). These TPU Groups were assigned to the Cluster catchment

with a bigger share of their population size.

# After re-clustering

Table 3 – Cluster catchment, district and TPU Group mapping in the analysis


A high-level map showing the spatial distribution of the TPU Groups is illustrated in

Figure 1. The official map prepared by Government’s Planning Department can be

found in Annex I.

Figure 1 – Cluster catchment and 209 TPU Groups

Utilisation Data by Tertiary Planning Unit – Technical Adjustments

With TPU Groups selected as the spatial unit, utilisation data for the six core services

(i.e. Acute IP, Non-acute IP, SOP, PHC, A&E, and AHOP), with patients’ residence

information broken down to TPU Groups, was collected from the S&WP team in HA.

As the TPU Group information was retrospectively and programmatically mapped

according to the patients’ self-reported residential addresses, the TPU Group data

element would be left blank if such conversion cannot be conducted. For example, if

patients’ residential addresses are unstructured, the detailed geocoding process

could not be undertaken successfully and therefore no TPU Group information

would be available (although district-level information would still be available). For

this exercise, the amount of data with blanked TPU Group information was not

significant (e.g. 10% of BDO for Acute IP). Considering the potential bias if all such


data was removed from the analysis (i.e. it is safe to assume that patients’ residing in

rural area would be more likely to self-report an unstructured addresses), it was

decided that keeping these records and imputing the missing values would be more

appropriate. In the imputation process, the distribution of utilisation in each

individual district would be computed and such proportion would be applied to the

total amount of utilisation that had no TPU Group information in each district. Such

mechanism assumes that the proportion of utilisation among TPU Groups in each

district is the same for those with and without TPU Group information.

Data Source

HA service utilisation data for the aforementioned six core services from 1 April 2015

to 31 March 2016 was collected via the Statistics and Workforce Planning (S&WP)

team in the Strategy and Planning (S&P) Division in HA. The utilisation data was

divided into two sets, one for Acute IP and Non-acute IP with the activity measure

being BDO, and the other one for SOP, PHC, A&E and AHOP where the activity

measure is the number of attendance.

In each set of data, utilisation figures were broken down by various key fields of

information, with the most important ones being the core service type, institution,

and patients’ residence (in both TPU Group and District Council Districts).

Population data in 2015 was also utilised in various parts of this study. Population

refers to the Hong Kong resident population, covering both usual residents and

mobile residents, in each TPU Group. The data was collected from the “Projections

of Population Distribution 2015-2024” report, completed in December 2015 by the

Working Group on Population Distribution Projections and published by the

Government’s Planning Department.

3. Cluster Concordance Analysis Page | 31

3. Cluster Concordance Analysis

As demonstrated in last year’s analysis using 2014/15 data, cross-cluster flow from

the perspective of population could be evaluated by analysing the “Cluster

concordance rate”, which quantifies the level of service being provided in patient’s

own Cluster.

Methodology

The notion of concordance, or self-sufficiency as often described in the literature,

measures how well a local community is providing service to its residents, and is

often used for evaluating population’s access to public services such as public

healthcare services (Messina et al., 2012), education (McClelland & Gandy, 2012),

and correctional services (Gurrieri, 2008).

Mathematically, the concordance rates could be derived by letting R be the activity

of residents in region r being treated by their local Cluster institutions, E be the

level of activity treated by other Clusters, and the concordance rate for region r (Cr)

is given by:

𝐶𝑟 = 𝑅

𝑅 + 𝐸× 100

The care type-specific concordance rates are defined as:

Acute IP and Non-acute IP: percentage of BDO of a region’s residents being

treated in their own Cluster

SOP, PHC, A&E and AHOP : percentage of attendances of a region’s residents

being treated in their own Cluster

Results

The concordance rates for each Cluster catchment for the six core services in

2015/16 are presented in Table 4 below. For inpatient, SOP and A&E where


concordance rates for 2014/15 were worked out in last year’s analysis, the results

this year using 2015/16 are found to be comparable7.

Similar to the observation last year, concordances are found to be generally higher in

the New Territories, moderate in Hong Kong Island, and lowest in the Kowloon Area.

In general, the concordance rates are:

highest in the New Territories (NTEC and NTWC): > 80%

moderate in Hong Kong Island (HKEC and HKWC): ~70 - 80%

lowest in Kowloon (KCC, KEC, and KWC): ~60 - 80%

As one may expect, the concordance for PHC and A&E are generally higher than

those for the other four core services. Namely, their overall concordance rates in

2015/16 were found to be 87.8% and 84.5% respectively, compared to the other

four (Acute IP, Non-Acute IP, SOP and AHOP) which are hovering around the 70% -

80% range.

As discussed in Chapter 1, a re-delineation of administrative arrangements between

KWC and KCC was implemented per recommendation of the HA Review, which

included the re-grouping of Wong Tai Sin district and Mong Kok area, involving KWH,

WTSH and OLMH from KWC to KCC. As the study period was 2015/16, which is prior

to the actual implementation of the new Cluster boundary in April 2017, the above

calculation was made using the old Cluster arrangement.

7 The definition of core services is aligned with the one used for building the Refined Population-

based Model. For inpatient services, both inpatient and daypatient BDOs are included for 2015/16, while only inpatient BDO was used in 2014/15. A&E total attendances, which included 1

st and

follow-up attendance, were used in this exercise, while only 1st

attendance was used in 2014/15.

Cluster concordance rates are generally higher in the New Territories,

moderate in Hong Kong Island, and lowest in the Kowloon Area.


2015/16 Concordance Rate

Cluster Catchment

Districts * Acute

IP Non-

Acute IP SOP PHC A&E AHOP

HKEC Eastern, Islands (excl. Lantau), Wan Chai

82.0% 83.2% 79.8% 93.4% 89.3% 82.6%

HKWC Central & Western, Southern

89.2% 52.9% 89.0% 87.7% 79.1% 73.8%

KCC Kowloon City, Yau Tsim

73.9% 70.3% 74.0% 82.5% 68.9% 72.9%

KEC Kwun Tong, Sai Kung

73.0% 45.3% 66.9% 85.8% 82.4% 72.8%

KWC

Kwai Tsing, Lantau Island, Mong Kok, Sham Shui Po, Tsuen Wan, Wong Tai Sin

70.2% 65.4% 69.6% 84.2% 79.5% 66.2%

NTEC North, Sha Tin, Tai Po

89.5% 84.0% 84.8% 90.3% 89.8% 81.5%

NTWC Tuen Mun, Yuen Long

85.7% 90.5% 84.9% 93.0% 88.5% 87.7%

HA Overall

79.1% 71.2% 76.8% 87.8% 83.5% 75.6%

* Before re-clustering as the study period was 2015/16

Table 4 – Concordance for six services by Cluster catchment in 2015/16

However, as noted in the analysis conducted in 2016, better concordance rates could

be achieved for both KWC and KCC if Wong Tai Sin and Mong Kok were indeed re-

grouped from KWC to KCC. A similar simulation was conducted using 2015/16 data

and the concordance rate before and after re-clustering for KCC, KWC and HA

Overall for the six core services are presented in Figure 2 – Figure 4 below.

The concordance rates for all six core services increased in the case where the

Cluster boundary is refined. For example, the Acute IP concordance rate for KCC and

KWC catchments improved from 73.9% to 84.8% and from 70.2% to 78.1%

respectively (as shown in Figure 2 and Figure 3). While re-clustering would not affect

other Cluster catchments, it would also make an impact on the overall concordance

rate for Hong Kong as a whole. For example, the Acute IP concordance rate for Hong

Kong would change from 79.1% to 82.8% in the re-clustered scenario (as shown in

Figure 4).


Figure 2 – Concordance for KCC catchment: before vs. after re-clustering

Figure 3 – Concordance for KWC catchment: before vs. after re-clustering


Figure 4 – Concordance for HA overall: before vs. after re-clustering

As an important refinement to last year’s analysis, the utilisation of Designated

Services was carved out to assure like-with-like comparison across local communities.

The service-specific Cluster concordance rate after carving out the utilisation of DS is

presented in Figure 5 – Figure 8 below. Figures for PHC and A&E are omitted as there

is no DS activity in these two core services.

As one would reasonably expect, the exclusion of DS utilisation increases the

concordance rates in general. For example, the HA overall concordance rate for

Acute IP and Non-Acute increased from 82.8% to 83.9% and from 72.7% to 75.5%

respectively (as illustrated in Figure 5 and Figure 6). This observation generally holds

true on the Cluster catchment level as well.

Figure 5 – Acute IP concordance by Cluster catchment: with vs. carved out DS


Figure 6 – Non-acute IP concordance by Cluster catchment: with vs. carved out DS

Figure 7 – SOP concordance by Cluster catchment: with vs. carved out DS


Figure 8 – AHOP concordance by Cluster catchment: with vs. carved out DS

The Cluster concordance rates by core service after re-clustering and with DS carved

out are presented in Table 5 below. One can observe that the catchment of HKWC,

KCC, KEC and KWC all had concordance rates below the HA average for selected

services. In particular, KEC encountered lower than HA average concordance for all

six services. On the other hand, catchment of HKEC, NTEC and NTWC had a higher-

than average concordance rates for all six core services.


2015/16 Concordance Rate

(After Re-clustering and Craved out DS)

Cluster Catchment

District Acute

IP Non-

Acute IP SOP PHC A&E AHOP

HKEC Eastern, Islands (excl. Lantau), Wan Chai

84.1% 87.6% 81.4% 93.4% 89.3% 82.8%

HKWC Central & Western, Southern

89.0% 55.3% 89.3% 87.7% 79.1% 73.8%

KCC Kowloon City, Yau Tsim

85.9% 75.1% 77.0% 90.2% 79.6% 82.6%

KEC Kwun Tong, Sai Kung

74.5% 48.1% 67.8% 85.8% 82.4% 72.8%

KWC

Kwai Tsing, Lantau Island, Mong Kok, Sham Shui Po, Tsuen Wan, Wong Tai Sin

78.9% 75.2% 76.7% 87.2% 87.2% 71.5%

NTEC North, Sha Tin, Tai Po 90.3% 88.6% 85.3% 90.3% 89.8% 81.4%

NTWC Tuen Mun, Yuen Long 87.2% 89.4% 85.8% 93.0% 88.5% 87.8%

HA Overall

83.9% 75.5% 79.6% 89.5% 85.8% 79.1%

Table 5 – Concordance after re-clustering and craving out DS in 2015/16

In summary, three scenarios, namely (1) with no adjustments, (2) with KCC/KWC

boundary refined, and (3) DS carved out with re-clustering, were illustrated above in

calculating concordance rates. The HA overall concordance rates for these three

scenarios are shown in Figure 9 below. To ensure a better reflection of the current

situation as well as to assure like-with-like comparison across localities, the third

scenario, which simulated the re-clustered setting and had DS carved out, should

offer the most unbiased evaluation on Cluster concordance among the three.

Simulating the re-clustered setting and carving out DS offer the most useful

and unbiased evaluation on whether residents have sought core hospital

service in their own Clusters.


Figure 9 – HA overall concordance by core service: 3 scenarios

To better illustrate the Cluster concordance rates of each of the core service for the

residents of the seven Cluster catchments, the following set of spider charts (Figure

10 – Figure 16) provide a visual summary on how the Cluster concordance of a

particular core service in a particular Cluster catchment relate to the HA average.

Using HKEC catchment as an example, one can make the following observations from

the chart:

Acute IP and SOP: similar to HA average

PHC, A&E, and AHOP: slightly higher than HA average

Non-acute IP: significantly higher than HA average


Figure 10 – Concordance by core service: HKEC catchment vs. HA overall

Figure 11 – Concordance by core service: HKWC catchment vs. HA overall


Figure 12 – Concordance by core service: KCC catchment vs. HA overall

Figure 13 – Concordance by core service: KEC catchment vs. HA overall


Figure 14 – Concordance by core service: KWC catchment vs. HA overall

Figure 15 – Concordance by core service: NTEC catchment vs. HA overall


Figure 16 – Concordance by core service: NTWC catchment vs. HA overall

To facilitate in-depth understanding on the patronage pattern by TPU Group under

each Cluster catchment, the percentage of utilisation by Cluster for each core service

for each TPU Group is presented in Appendix I. The percentage of utilisation in the

TPU Group’s own Cluster is bolded to highlight the Cluster concordance rate for each

core service for that TPU Group.

Similar to the observations made in last year’s study, the concept of Cluster

catchment is a useful reference. Concordance rates are generally higher in Hong

Kong and the New Territories, illustrating that they are relatively confined

geographically. This study also shows how the re-delineation of Cluster boundary

increased the concordance of the KCC and KWC catchments. The concordance rates

of the KEC catchment are relatively lower (e.g. at 74.5% for Acute IP). This can be

partly explained by the unavailability of certain core service (e.g. Oncology services)

where residents in the KEC catchment need to patronise other Clusters, namely KCC,

instead.

Spider charts offer an effective visual tool in comparing each Cluster’s

concordance rates with the HA overall average.


While the Cluster concordance analysis generated valuable insights as to how

services are provided to residents on a Cluster level, the underlying principle of

analysing whether residents are served within their own Cluster catchment have

limitations of its own. Namely, with clustering being an administrative rather than a

registration arrangement, analysing health-seeking behaviour from a Cluster

catchment perspective may not be comprehensive.

Therefore, building on top of the Cluster concordance analysis, accessibility was

analysed with respect to travel time from the TPUs’ perspective. The methodology

and results are presented in the next chapter.

4. Locality-based Travel Time Analysis Page | 45

4. Locality-based Travel Time Analysis

The previous chapter outlines an analysis that evaluates spatial accessibility on the

Cluster catchment level. Via the computation of a Cluster concordance rate, defined

as the percentage of utilisation sought within patient’s own Cluster, it generated

high-level information on whether patients have sought care in their own Cluster.

However, while such methodology is simple and intuitive, analysing service access

merely from a Cluster catchment perspective may not be comprehensive, as

evaluating whether the patients were served within a set of arbitrary Cluster

boundaries would neglect one important factor affecting health-seeking behaviour:

patient convenience. This is particularly relevant in the Hong Kong setting, with its

relatively small land mass and efficient transportation network. Rather than focusing

on whether patients are seeking healthcare service in facilities within their own

catchment, a more important metric in analysing accessibility would be to evaluate

whether they could be served in the proximity of their places of residence.

In fact, the development of the Refined Population-based Model, where 16 factors

that would influence health-seeking behaviour were identified and modelled,

revealed that (1) distance between a patient’s residence to facilities as well as (2) the

amount of capacity in each facility were found to be the most influential factors

among those analysed8. Therefore, service accessibility was analysed with respect to

whether patients sought care near their places of residence, rather than whether

they were served within / outside an administrative boundary. Such analysis using

retrospective utilisation data in analysing accessibility (like the Cluster concordance

analysis presented in the previous chapter) is often referred as “revealed

accessibility analysis” in the literature.

Methodology

Scope of Analysis

Extending from the Cluster concordance analysis presented in the previous chapter,

a more granular spatial accessibility analysis based on proximity to care was

conducted. Similar to the Cluster concordance analysis, utilisation of Designated

Services was carved out to assure like-with-like comparison across localities, and the

8 The Final Report on the development of the Refined Population-based Model is available on the HA

internet website at http://www.ha.org.hk/haho/ho/fdd/report1_en.pdf



six core services were separately analysed so as to give a more detailed picture to

the potentially varying accessibility in different settings.

Travel Time as Measure of Patient Convenience / Proximity

In the literature, different measures of “proximity” are used in evaluating spatial

accessibility of healthcare services. Euclidean or straight-line distance is often used

(Haynes et al., 1999, Hewko et al., 2002), partly due to it being the least intensive

computationally. However, it does not capture the varying distances travelled

between the differential road network structures often seen in urban vs. suburban

areas. According to Apparicio et al. (2008) and Bliss et al. (2012), while the use of

Euclidean distance does not introduce significant error in urban areas, the use of

such in suburban areas may lead to substantial error.

Travel distance (or technically, network distance) is another popular proxy used to

evaluate the proximity of facilities from residential locations (Fotheringham &

Brunsdon, 2000; Witten et al., 2003). While it is more sophisticated than simple

straight-line distance, it is limited with its lack of handling of the varying availability

high-speed roadways in different parts of a region (Apparicio et al., 2008).

Travel time, on the hand, captures the time spent on travelling from patients’ places

of residence to facilities. One use of travel time is illustrated by Luo and Wang (2003),

where travel time was used to measure the spatial barrier between residents and

physicians. Such measurement takes into account local variation in distances, which

are most notably observed in suburban areas, and therefore was selected as the

measure for proximity for this analysis. Use of travel time has an additional strength,

in that the general public can easily gauge proximity using travel time, but not using

travel distance (e.g. most people has a sense of proximity / convenience when asked

about travelling for 5, 10, or 15 minutes, but not for 5, 10 or 20km). With the help of

modern Geographic Information System (GIS) technologies, measuring travel time

between a large set of origins and destinations is no longer an impossible task.

Use of Geographic Information System

GIS has been used extensively in this area of research. In fact, GIS has been used in

the health sector for more than three decades (Higgs & Gould, 2001), ranging from

analysing spatial patterns of disease to examining spatial patterns of healthcare

capacity to inform planning of new facilities (Gatrell & Senior, 1999). These studies

would make use of standard functionality of GIS, such as buffering (e.g. generating


catchments at travel distance/ time away from supply points), overlay analysis (e.g.

examining location of patients in relation to such areas), and network analysis (e.g.

using travel time to assess how long it takes patients to access a facility) (Higgs,

2005).

Measuring Travel Time

As the spatial unit of this analysis was set at the TPU level, pairwise travel time

between each of the centroid of the 209 TPU Groups and each of the 120 institutions

were obtained using the Google Maps Distance Matrix API. Travel time was

estimated based on a recommended route with the shortest travel time. Travel time

based on the use of the existing road network via a private vehicle was chosen over

the use of public transport, so as to assure a like-with-like comparison on

accessibility across different regions (especially urban vs. rural) in Hong Kong. Travel

time was estimated using historical traffic pattern. For island TPUs, which are

inaccessible by the road network, travel time to various institutions was estimated

based on the summation of (1) average waiting time for ferry, (2) the ferry travel

time, and (3) the travel time between the location of disembarkation and the

facilities.

With the lack of population distribution information inside each TPU Group, the

geographical centroid was used in the travel time estimation. However, to better

reflect reality, for those TPUs having their geographical centroids fall at an

inaccessible location by road, their centroids would be moved to their nearest

accessible point (e.g. the geographical centroid of TPU Groups 141 – Mid-level West

(latitude 22.27967o and longitude 114.13697o) was inaccessible by road in the

middle of the Lung Fu Shan Country Park, and thus its centroid was moved slightly to

the nearby Kotewall Road (latitude 22.28109o and longitude 114.13946o).

In total, 25,080 data points on travel time between the centroids of the 209 TPU

Groups and 120 facility locations were collected and fed into the analysis.

Quantifying Spatial Accessibility

Extending from the Cluster concordance concept as illustrated in the previous

chapter, the percentages of activity sought within different threshold travel times

were computed on the TPU Group level (expressed mathematically below). Such

analysis enables one to evaluate accessibility from the perspective of patient

convenience, rather than using the rigid yet arbitrary Cluster boundaries.


For each locality (TPU Group) 𝑖, compute the proportion of utilisation U that are

sought in the set of facilities 𝑗 which are within a threshold travel time 𝑑0 from

locality 𝑖

𝐴𝑖 =∑ 𝑈𝑖𝑘𝑘∈{𝑑𝑖𝑘≤𝑑0}

∑ 𝑈𝑖𝑗𝑗

The numerator sums up the utilisation of residents in locality 𝑖 at the set of facilities

𝑘 that fall within the threshold travel time (𝑑𝑖𝑘 ≤ 𝑑0). The denominator, on the

other hand, sums up the total utilisation for residents in locality 𝑖.

When applicable, the results of the analysis would be plotted on a map to facilitate

readers’ understanding on the geographical variation in accessibility. Spatial

visualisation was achieved through the use of the open-source desktop geographic

information system application QGIS (version 2.16.2).

Results

The above methodology was applied to analyse the revealed spatial accessibility of

the six core services in 2015/16, with the results presented in this section.

Analysis for Hong Kong as a Whole

Before stepping down to the TPU Group level, the percentage of utilisation sought

within a threshold travel time of 10 to 30 minutes (in 10 minute increments) for

Hong Kong as whole was analysed and shown in Figure 17 below. Similar to the

analysis in the previous chapter, the six core services were separately shown to

enhance granularity of analysis. Using Non-acute IP as an example, 30%, 67% and

85% of total BDO in Hong Kong were sought within 10, 20 and 30 minutes

respectively. On the other hand, accessibility for PHC is considerably higher, with

68%, 88%, and 94% of attendance sought care within 10, 20 and 30 minutes

respectively. In general, accessibility for PHC and A&E are the highest, which is

similar to the observation made in the previous chapter when Cluster concordance

was analysed. Acute IP, SOP, and AHOP have similar levels of accessibility, which is

sensible given their inter-related nature operationally (SOP / AHOP appointments of

the same Cluster would usually be given after an Acute IP episode in the Cluster).

Partly due to its subacute nature, Non-acute IP was found to have the lowest

accessibility among the six core services.


Interesting insight can be obtained by observing the varying rates of change of the

line graphs in Figure 17. All six lines were of similar shape, which have a big increase

from 10 to 15 minutes, with the slope slowly decreasing as the threshold travel time

goes up. While the percentages of utilisation were quite low with a threshold time of

10 minutes (<50% for all six services except PHC), the percentages quickly increased

and by a threshold time of 20 minutes, the percentage of utilisation vastly increased

to over 80% for all six services. Such a big jump could be attributed to Hong Kong’s

small land mass and its efficient road network. In fact, all six services achieved a

>90% service level with a threshold time of 25 minutes.

Hong Kong Overall Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 39% 74% 89% 94% 96%

Non-acute IP 29% 66% 85% 92% 95%

SOP 36% 74% 87% 93% 96%

PHC 68% 87% 93% 97% 98%

A&E 48% 81% 91% 95% 97%

AHOP 40% 74% 88% 94% 97%

Figure 17 – Percentage of utilisation within various travel times by core service

Arriving at a meaningful threshold for each of the six core services would be

beneficial in further evaluating spatial accessibility on the TPU Group level. Making

reference to Figure 17 above, the 90th percentile of travel time for each core services

90%


was extrapolated as shown in Table 6. The same set of information, broken down by

Cluster catchment, can be found in Appendix II.

Core Service 90th Percentile of Travel Time

Acute IP 21.2

Non-acute IP 23.5

SOP 22.3

PHC 17.2

A&E 19.4

AHOP 21.4

Table 6 – 90th of travel time by core service for Hong Kong Overall

Analysis on TPU Group Level – Illustration

The previous section illustrated the spatial accessibility for the six core services for

Hong Kong as a whole. Valuable insight could be obtained if one extends the analysis

to the locality level, i.e. to analyse accessibility by TPU groups. By studying how far /

close patients residing in each TPU group sought services, regional pockets of lower /

higher spatial accessibility could be identified.

Taking Acute IP as an example, the percentage of utilisation (BDO) that sought care

within 10, 20, 30 and 40 minutes are presented in Figure 18, Figure 19, Figure 20 and

Figure 21 respectively. To facilitate visualisation on a map, the percentages are

shown using five shades of blue, with the darkest shade having the highest values.

The classification to five shades was made using the “equal interval” method, where

a set of values is arranged into groups that contain an equal range of values. While

this method has limitations on classifying groups that are not close in size, “equal

interval” is selected for its simplicity and how it enables cross-sectional analysis (in

this case, comparison across maps for different core services and different threshold

times), as compared to other classification methods such as using quantile and Jenks

Natural Breaks. The red dots, on the other hand, provide the audience a reference

Through studying travel time, spatial accessibility is found to be generally

good in Hong Kong. For the majority (90%) of utilisation, services could be

sought within 17 to 20 minutes for PHC and A&E, and within 21 to 24 minutes

for Acute IP, Non-acute IP, SOP, and AHOP.


on the geographical distribution of the service providers (i.e. institutions). The sizes

of the dots are scaled with their relative level of utilisation.

For instance, Figure 18 shows the percentage of utilisation within 10 minutes of

travel time, where the 209 TPU Groups were classified into five equal intervals in

term of BDO, with each increment at 18% as the minimum value and maximum value

were found to be 0% and 90% respectively. The five groups were coloured by five

different shades of blue, with white for 0 to 18%, progressing with darker and darker

shades and ending with the darkest blue for a percentage of 72% to 90%. The

hospitals providing Acute IP service were plotted with varying sizes of red dots based

on their relative service utilisation in terms of BDO in the same study period

(2015/16).

One can observe from this figure that with the threshold travel time set at 10

minutes, only those TPU Groups in close proximity with a large-scale facility had

spatial accessibility at the highest group at over 72% (e.g. TPU Groups very close to

large hospitals like Queen Elizabeth Hospital (QEH), Queen Mary Hospital (QMH),

Prince of Wales Hospital (PWH), Tuen Mun Hospital (TMH), etc.). On the other hand,

a handful of TPU groups (119 out of 209) had a percentage at the lowest group

(<18%). This finding is intuitive, as it would be only be possible for residents to seek

care within 10 minutes of travel time, if they are actually residing in a locality that

are in extreme proximity of a large institution.

The map with threshold travel time set at 20, 30, and 40 minutes were prepared

using the same logic (Figure 19, Figure 20, Figure 21). One can observe how more

and more TPU Groups were coloured in darker and darker shades of blue, denoting

higher and higher percentages of activity being sought within the threshold travel

time. Figure 21 shows that for a threshold of 40 minutes, almost the entire Hong

Kong (195 out of the 209 TPU Groups) was covered with dark blue, showing that

residents in the vast majority of regions in Hong Kong are able to seek care with 40

minutes of travel time.


Figure 18 – Acute IP: percentage of utilisation sought < 10 mins by TPU Group



One can identify local pockets of higher / lower accessibility by referring to these

maps. Taking 30 minutes as an example threshold (Figure 20), the TPU Groups in Sha

Tau Kok , Mai Po, Lau Fau Shan, Sai Kung East, Sai Kung West, Kau Sai Chau, Tiu

Chung Chau, the Islands (including Po Toi Island and Lamma Island) and Lantau Island

(except Tung Chung and Chek Lap Kok) are of lower accessibility while the entire

Kowloon, Hong Kong Island, Kwai Tsing, Tsuen Wan, Shai Tin, Tai Po, Tuen Mun and

Yuen Long in New Territories are of higher accessibility for Acute IP.

Interested readers can refer to Appendix III for similar maps (illustrating the

percentage of utilisation sought within 10 to 40 minutes) replicated for the other five

core services.

Analysis by Core Service

To facilitate the visualisation of regional accessibility for each core service, a

threshold travel time was set for each, making reference to the results shown in

Table 6 above (the 90th percentile of travel time for each core service) while

rounding them up to their nearest 5-minute interval. This ensures that the service

level plotted would be strictly over 90%. The following figures show the percentage

of utilisation sought within 20 minutes for PHC (Figure 25) and A&E (Figure 26), and

within 25 minutes for Acute IP (Figure 22), Non-acute IP (Figure 23), SOP (Figure 24),

and AHOP (Figure 27).

Spatial accessibility by locality can easily be visualised on a map by shading

TPU Groups using various colours, representing different percentages of

utilisation sought within a specific travel time.


Core Service 90th Percentile of Travel Time

(Minutes) (HK Overall)

Threshold Travel Time (Minutes) (90th percentile rounded up

to nearest 5 minutes)

Acute IP 21.2 25

Non-acute IP 23.5 25

SOP 22.3 25

PHC 17.2 20

A&E 19.4 20

AHOP 21.4 25

Table 7 – Threshold travel time set for each core service

To facilitate comparison across services, the classification to five shades of blue for

the TPU Groups was consistently conducted using the “equal interval” method.

Similar to those maps in the above section, the red dots denotes the facilities

providing service, with the size of the dots scaled with their relative level of

utilisation (in terms of number of BDO for Acute IP and Non-acute IP, and number of

attendances for the other four services). Interested readers can refer to Appendix IV

for the detailed data underlying the various maps.



Figure 23 – Non-acute IP: percentage of utilisation sought < 25 mins by TPU Group

Figure 24 – SOP: percentage of utilisation sought < 25 mins by TPU Group


Figure 25 – PHC: percentage of utilisation sought < 20 mins by TPU Group

Figure 26 – A&E: percentage of utilisation sought < 20 mins by TPU Group


Figure 27 – AHOP: percentage of utilisation sought < 25 mins by TPU Group

Cross-sectional Analysis

In addition to making high-level observations for each core service using the above

figures, one can also analyse these findings in a cross-sectional manner. First, one

can observe that most TPU Groups in the urban area have a relatively high

accessibility across all six core services. A total of 147 TPU Groups had over 80% of

utilisation sought within the threshold travel time and they cover the entire Kowloon,

the majority of Hong Kong Island, Kwai Tsing, Tsuen Wan, Shai Tin, Tai Po, Tuen Mun

and Yuen Long in New Territories.

On the other hand, there are five TPU Groups that have relatively lower accessibility

(<20% utilisation) across all six core services. They include: TPU Groups 620 & 622 –

Ho Sheung Heung, 741 - 744 – Sai Kung West Country Park, 911 - 913 – Lamma & Po

Toi, 931 & 933 – Pui O and 963 – Hei Ling Chau.

In addition, it can be observed that relative spatial accessibility on the TPU Group

level is largely consistent among the six core services, i.e. TPU Groups with darker

and lighter shades of blue are generally the same across the various figures. In

particular, such relationship is apparent between services that are linked

operationally, like Acute IP and SOP. A scatterplot of the percentage of utilisation


within a threshold of 25 minutes for Acute IP and SOP is presented in Figure 28

below, which revealed a statistically significant positive correlation (p<0.05) between

the two. Similar observations can be made for the relationship between Acute IP and

A&E, Acute IP and AHOP.

Figure 28 – Acute IP vs. SOP: utilisation sought within threshold time

While the TPU Groups’ relative spatial accessibilities are largely correlated between

each service, one could also identify a small number of TPU Groups that have

relatively higher accessibility in one service than in the other. Figure 29 shows the

relationship between the spatial accessibility between Acute IP and PHC.

While one can observe a positive correlation pattern, there are a few outliers worth

mentioning. On one hand, there are a few TPU Groups on the upper left-hand

quadrant of the figure (circled in blue), meaning that they have significantly higher

accessibility for PHC than for Acute IP. On the other hand, there are also a few TPU

Groups on the lower right-hand quadrant of the figure, which have a relatively lower

spatial accessibility for PHC than for Acute IP, which may indicate a potential need

for PHC capacity expansion for residents in these areas. While such observations are

made on a very high level and are by no means definitive, they could provide a

direction for further analyses in understanding the potential reasons behind the

differential accessibility among core services.


Figure 29 – Acute IP vs. PHC: utilisation sought within threshold time

Overall Classification

The above provides a granular look at the regional spatial accessibility for the six

core services. Next, statistical clustering was conducted to summarise the above

findings on an aggregate level – to classify TPU Groups into high-level sets with

differential spatial accessibility. Namely, the underlying TPU-specific data for Figure

22 – Figure 27 (percentage of utilisation sought within the threshold travel time by

core service) was partitioned into three groups using k-means clustering, an

unsupervised machine learning algorithm that partition observations with

dimensions into Clusters so that the within-cluster sum of squares is minimised. In

this case, the 209 TPU Groups, each with six dimensions (a percentage of utilisation

within the threshold travel time for each of the six core services), are classified into

three groups: relatively high accessibility, moderate accessibility, and relatively lower

accessibility.

The results is shown in Figure 30 below, where the three clusters of TPU Groups are

shaded in dark blue (relatively high accessibility), light blue (moderate accessibility),

Spatial accessibility is largely correlated across core services, especially

between those that are highly related operationally (e.g. among Acute IP, SOP

and A&E). Reference information for capacity planning could also be made by

identifying and conducting further studies on specific outliers.


and white (relatively low accessibility). The cluster mean of each cluster is also

presented in Table 8 below. In summary, there are 164 TPUs with relatively high

accessibility, 32 moderate, and 13 with relatively low accessibility. The mean for

each cluster is presented in Table 8 below as well.

Figure 30 – k-means clustering: classification of TPU Groups into three clusters

Mean Percentage of Utilisation within Threshold Travel Time

for Each Core Service by Cluster via k-means Clustering

Core Service Cluster 1 Cluster 2 Cluster 3

Acute IP 95% 72% 2%

Non-acute IP 93% 53% 3%

SOP 93% 59% 1%

PHC 94% 67% 4%

A&E 92% 66% 6%

AH OP 95% 70% 6%

No. of TPU Groups 164 32 13

Accessibility Relatively Higher Moderate Relatively Lower

Table 8 – k-means clustering: cluster means

Under this method, utilisations of the six services are weighted equally. While a

weight could be derived using the relative share of expenditure among the services,

such weighting neglects the fact that less expensive services (e.g. PHC and A&E)

actually are the ones that require frequent and/or urgent trips taken by patients.


With the lack of objective data in evaluating the relative importance of spatial

accessibility among the six services, no weighting is applied in this analysis at the

moment.

Nevertheless, the objective of classifying TPU Groups into high-level sets with

differential spatial accessibility is achieved. The results are intuitive as one can

observe that most urban area, e.g. the entire Kowloon peninsula, most parts in Hong

Kong Island, the Tuen Mun and Yuen Long areas, as well as the Sha Tin, Tai Po,

Sheung Sui areas are all in dark blue, meaning that they have relatively good spatial

access in general. On the other hand, those TPU Groups in white have relatively

lower spatial access, e.g. namely, Lok Ma Chau, Sha Tau Kok, Sai Kung Country Park,

Kau Sai Chau, Tiu Chung Chau, the Islands (including Po Toi and Lamma island) and

Lantau Island (except Tung Chung and Chek Lap Kok).

Accessibility and Population Size

While there are a few TPU Groups that have lower spatial accessibility, it should be

viewed in context of their population sizes. Population size by TPU Group was

collected from the Government’s Planning Department (population size by TPU

Group can be found in Appendix IV).

Taking Acute IP as an example, all TPU Groups having less than 50% of utilisation

sought within 25 minutes of travel time has a population of <25,000, which in total

they represent a mere 1.6% of the total Hong Kong population (as shown in Table 9

below).

Generally speaking, TPU Groups with higher spatial accessibility across all six

services cover most urban area in Hong Kong (e.g. Kowloon, most parts in HK

Island, town centres in the New Territories such as Tuen Mun, Yuen Long, Sha

Tin, Tai Po, etc.). On the other hand, TPUs Groups with lower spatial

accessibility were generally found in the parameter of Hong Kong.


TPU Group % of

Utilisation < 25 minutes

Population Size (2015)

Share of Total HK

Population

181 & 182 – Peak 44% 4,600 0.1%

183 & 184 – Stubbs Road 44% 4,100 0.1%

532 – Pat Heung 46% 10,700 0.1%

543 – Shek Wu Wai San Tsuen 35% 3,100 0.0%

544 – San Wai Tsuen 45% 4,200 0.1%

620 & 622 – Ho Sheung Heung 0% 3,000 0.0%

651 - 653 – Sha Tau Kok, North-eastern islands, e.g. Kat O / Crooked Island

0% 8,000 0.1%

741 - 744 – Sai Kung West Country Park 0% 8,300 0.1%

811 - 815 – Sai Kung East Country Park & Kau Sai Chau

0% 2,400 0.0%

911 - 913 – Lamma & Po Toi 0% 6,300 0.1%

920 – Cheung Chau 23% 22,500 0.3%

931 & 933 – Pui O 0% 4,100 0.1%

932 & 934 – Lantau Island Shui Hau Tsuen 15% 2,200 0.0%

941 - 943 – Ngong Ping 0% 1,100 0.0%

944 – Tai O 0% 2,900 0.0%

961 & 962 – Mui Wo 0% 6,000 0.1%

963 – Hei Ling Chau 0% 1,000 0.0%

971 - 974 – Discovery Bay 6% 13,100 0.2%

976 – Peng Chau 0% 6,500 0.1%

Total 114,100 1.6%

Table 9 – TPU Groups with < 50% of utilisation within 25 minutes for Acute IP

The same observation holds true for all three other core services, namely SOP, A&E

and AHOP, where no TPU Group with < 50% of utilisation sought within the

threshold travel time have a population over 25,000. For Acute IP and PHC, only one

outlier TPU Group was found for each9. Table 10 illustrates that the share of total

Hong Kong population for those TPU Groups with < 50% of utilisation sought within

the threshold time ranges from 1.1% to 3.1% depending on the core service under

study.

9 TPU Group 950 - 951 – Tung Chung and Chek Lap Kok with population of 83,100 (Non-acute IP) and

TPU Group 833 – Tiu Keng Leng with population 67,000 (PHC)


Core Service Total Population of TPU Groups with <50% of utilisation sought within the threshold travel time

Share of Total HK

Population

Acute IP 114,100 1.6%

Non-acute 229,100 3.1%

SOP 121,300 1.7%

PHC 159,500 2.2%

A&E 92,700 1.3%

AHOP 83,300 1.1%

Table 10 – Population in TPU Groups with < 50% utilisation within threshold time

Overall Findings

In general, spatial accessibility in terms of travel time is good in Hong Kong. For the

majority (90%) of patients, services could be sought within 17 to 20 minutes for PHC

and A&E, and within 21 to 24 minutes for Acute IP, Non-acute IP, SOP, and AHOP.

While the Cluster concordance analysis revealed a lower concordance in the

Kowloon area, this analysis shows that as far as proximity to services is concerned,

spatial accessibility for all six services is high for this area. In other words, while some

of the residents in Kowloon did not patronise service within their “own” Cluster, they

only needed to travel for a reasonable amount of distance/time for service. In fact,

the entire Kowloon area was observed to have high spatial accessibility for all six

services in terms of travel time.

The analysis also helped in identifying regional pockets of higher and lower

accessibility for each core service. For example, urban areas like the majority of the

Kowloon peninsula and HK Island, as well as town centres in New Territories such as

Tuen Mun, Yuen Long, Sha Tin, and Tai Po have relatively high accessibility in general.

On the other hand, TPU Groups with relatively lower accessibility are observed to be

located in the parameters of Hong Kong and have lower population sizes.

Spatial accessibility by locality needs to be evaluated in the context of the

localities’ population size. True for all six core services, TPU Groups found to

have lower spatial accessibility have relatively small population sizes

(accounting for 1.1 to 3.1% of the Hong Kong population depending on the

core service).


While this section summarises the results of the travel time-based accessibility

analysis on a high-level, detailed results broken down by TPU Groups, namely the

patronage pattern of their residents as well as the percentage of utilisation within

various travel time thresholds for each core service, are presented in Appendix I.

5. Potential Accessibility Analysis Page | 67

5. Potential Accessibility Analysis based on

Capacity and Population

The above analysis provides retrospective information on where patients actually

patronised services in 2015/16. In the literature, this is called “revealed access”,

where actual and retrospective utilisation by locality and institution would be

analysed to gain insight on the utilisation of services given then barriers and

facilitators of both the service system and the potential users (Khan, 1992). On the

other hand, “potential access” is the other useful perspective in analysing

geographical access of healthcare services, where it assesses the nature and pattern

over space of physical access to service facilities (Joseph & Phillips, 1984). The term

“potential” is used here as no actual interaction between supply and demand is

analysed; instead, it takes into account the distribution of both population and

capacity in evaluating accessibility.

While analysing revealed access would enrich one’s understanding on the current

state, analysis on potential access provides insight on the relative distribution of

capacity with respect to locality populations, and would therefore inform longer

term capacity and facility planning with respect to population development. An

illustration using Acute IP, which accounts for the largest share of resources in HA

among the six, is presented in this chapter.

Methodology

Access to health care may be classified according to two dichotomous dimensions

(potential versus revealed, and spatial versus aspatial) into four categories, such as

potential spatial access, potential aspatial access, revealed spatial access, and

revealed aspatial access (Khan, 1992). Spatial access emphasises the importance of

the spatial/distance variable (as a barrier or a facilitator), whereas the aspatial access

stresses nongeographic barriers or facilitators, such as social class, income, ethnicity,

age, sex, etc. (Joseph & Phillips, 1984; Khan, 1992).

Revealed vs. Potential Spatial Accessibility

This report focuses on spatial accessibility. In particular, the previous sections

illustrated two sets of analysis on revealed accessibility (in Chapter 3 and 4

respectively), where focus was made on actual use of health care services. On the


other hand, potential accessibility signifies the probable entry into the health care

system, but does not ensure the automatic utilisation of the offered services (Khan,

1992).

In analysing potential spatial accessibility (which is the focus of this chapter), each

facility is assumed to be providing service that is only limited by its capacity, and that

every member of the population is a potential user of the service. As such, the

pattern of accessibility will only depend on the relative location of the population

and service facilities (Joseph & Bantock, 1982). Potential accessibility is often

measured by travel time, road or map distance (Higgs, 2005).

Use of Geographic Information System

Similar to analysing revealed access to service, GIS has been widely used in

evaluating potential accessibility. For example, many studies have used spatially

disaggregated data sets to calculate accessibility based on patients’ address / postal

codes to examine travelling distance / time to their nearby healthcare facilities

(Charlton et al., 2001; Brabyn & Skelly, 2002; Slack et al., 2002; Brabyn & Gower,

2003; Rosero-Bixby, 2004).

Proxy for Capacity

Unlike analysing revealed accessibility, service capacity information would need to

be taken into account when evaluating potential accessibility. However, unlike

measures of utilisation which are relatively straight-forward (e.g. BDO for Acute IP),

the measurement of service capacity could be multi-dimensional and a suitable

supply factor should be chosen for each service category to be the proxy of capacity.

As the most straight-forward and internationally-recognised proxy for capacity of

inpatient services in hospitals, the number of acute beds was chosen as the

surrogate for capacity for Acute IP. In particular, the number of “scheduled beds”,

defined as beds with manpower and other necessary resources allocated to them for

service provision, was chosen over the “bed complement” (HA’s official planned bed

capacity as presented on the Controlling Officer’s Report) as the number of

“scheduled beds” would better reflect the true capacity of hospitals. With the study

period being 2015/16, the number of scheduled acute beds as of 31 March 2016,

broken down by institution, was collected from the S&WP team in the S&P Division.


Similar to the analysis presented in Chapter 3 and 4, the effect of the varying

provision of DS in different institutions would be addressed to assure like-with-like

comparison among localities. To do so, in addition to carving out the utilisation of DS

as discussed in the previous analysis, capacity in each facility that is related to the

provision of DS would need to be carved out as well, so that only utilisation and

capacity for core services is included in the analysis. With no readily available data

(as it can be difficult to concretely define a bed to be serving for core service or DS

with the mixed use of beds operationally), the proportion of DS beds for each

individual facility was estimated using the proportion of DS utilisation at the same

site. In other words, the number of core service scheduled acute beds at facility j,

𝐵𝑒𝑑𝑗𝑐𝑜𝑟𝑒, is estimated by taking the total number of scheduled acute beds at facility j,

and multiplying by one minus the proportion of DS BDO over total.

𝐵𝑒𝑑𝑗𝑐𝑜𝑟𝑒 = 𝐵𝑒𝑑𝑗 × (1 −

𝐵𝐷𝑂𝑗𝐷𝑆

𝐵𝐷𝑂𝑗

)

This choice on the proxy for capacity, as well as the handling of DS, is consistent with

those adjustments made in developing the Refined Population-based Model10.

Quantifying Potential Accessibility in Healthcare

Several different measures exist in the literature for quantifying potential healthcare

access, ranging from simple count of facilities available within a certain travel

distance / time, to more sophisticated measures taking into both the distribution of

capacity and population at the same time. According to Higgs (2005), accessibility

measures can be grouped under the following categories of approaches.

10

The Final Report on the development of the Refined Population-based Model is available on the HA internet website at http://www.ha.org.hk/haho/ho/fdd/report1_en.pdf

Using various approaches, accessibility scores could be calculated for each

locality to facilitate evaluation on their relative spatial accessibility, taking

into account the distribution of both capacity (supply points) and population

(demand points) in the jurisdiction.



Approach Definition

Container Capacity within a given spatial unit (e.g. an administrative district)

Coverage Capacity within a given distance / travel time from a point of origin

Minimum distance Distance / travel time between a point of origin and the nearest facility

Travel cost Average distance / travel time between a point of origin and a set of nearby facilities

Floating Catchment Area / Gravity

Score / index in which the sum of capacity in all facilities is divided by the “frictional effect” of distance / travel time

Table 11 – Various approaches in quantifying potential spatial accessibility

Results

Employing data for Acute IP in 2015/16 (with DS carved out), the potential

accessibility measures as discussed above were computed with results presented in

this section.

Container Approach

The container approach, which counts the number of facilities / amount of capacity

in each administrative region, is the most basic form of an accessibility measure. For

example, the number of scheduled acute beds (after craving out DS) within each

Cluster catchment is shown in Table 12 below.

Cluster Catchment Number of Scheduled Acute Beds (Designated Services Carved Out)

HKEC 1,656

HKWC 1,992

KCC 3,259

KEC 1,835

KWC 2,511

NTEC 2,693

NTWC 1,914

Table 12 – Number of scheduled acute beds by Cluster catchment

While it is simple to compute, the resulting accessibility measure is limited and

biased with its underlying assumption of zero cross-boundary flow. Such assumption

is rather unrealistic in most regional healthcare jurisdictions, and the sensitivity of


such measures to the spatial resolution of the spatial unit under consideration has

long been recognised (Makuc et al., 1991).

Coverage Approach

The coverage approach counts the number of facilities within a given distance /

travel time from a point of origin. It offers a better measure as compared the one

given by the container approach above, as the assumption of a rigid barrier between

administrative boundaries is removed, and the idea of proximity in terms of travel

distance / time is incorporated.

For example, the number of acute beds within travel time of 20 minutes for each

TPU Group is shown in Figure 31 below. Each TPU Group is coloured with a shade of

orange; the darker the colour, the larger the number of acute beds within 20

minutes. The green dots represent the facility locations, with their sizes scaled with

the number of acute beds at each site.

Figure 31 – Number of acute beds within 20 minutes of travel time


Minimum Distance / Travel Cost Approach

The minimum distance / travel cost approach is similar to the coverage approach

above, but just looking at the information from an opposite perspective. Rather than

counting the amount of capacity within a fixed distance / travel time from a point of

origin, these approaches look at the average distance / travel time from a point of

origin to its closest facilities. To get a better picture of the accessibility in terms of

the set of nearby facilities for each locality, proximity to the nearest three to five

facilities would be considered in the travel cost approach. The minimum distance

approach, on the other hand, is a special case of travel cost, where only the closest

facility is included in the calculation.

The average travel time to a TPU Group’s nearest three facilities (irrespective of their

capacity) for Acute IP are illustrated in the Figure 32 below. One can observe that

other than the islands, almost the entire territory of Hong Kong is shaded in dark

brown (average travel time to the nearest three facilities being less than 1502

seconds or 25 minutes). Such finding is rather intuitive with Hong Kong’s small land

mass and efficient transportation network.

Figure 32 – Average travel time to nearest three facilities by TPU Group

While approaches such as coverage, minimum distance and travel cost disregard the

boundary concept and uses distance / travel time from a point of origin to its nearby


facilities in computing an accessibility measure, they lack an important consideration

in the geographical distribution of population in the study area. For example, it is

intuitive that regions in close proximity to facility locations would have better

coverage scores, and vice versa. However, these resultant relative accessibility score

may be misleading as the availability of facilities / supply was not analysed with

respect to the distribution of population among each locality.

2-Step Floating Catchment Area Method

While simple area-based capacity-to-population ratios could be used to address the

above lack of consideration on regional population size, the concept of rigid

boundaries would need to be introduced again, which is severely limited with the

assumption of zero cross-boundary flow. The Floating Catchment Area (FCA)

method was developed to overcome such limitation. According to Luo and Wang

(2003), the FCA Method was first used in assessing job accessibility, and resembles

kernel estimation where a “window” defined by distance / travel time is moved

across a study area, and the density of events within the window is used to

represent the density at the centre of the window. The underlying assumption is that

services fall within the catchment area will be fully available to residents within that

catchment area, which may not hold true in some cases. To address this limitation

for measuring access to social services, Radke and Mu (2000) refined the FCA

Method such that the supplier to resident ratio for each location centred at

individual suppliers would be calculated, and as a next step the ratios for residents

living in areas where different providers’ service overlap would be summed up. Such

spatial decomposition method is named the 2-step FCA Method, and it is a major

enhancement to the FCA Method in the sense that it accounts for the interactions

between patients and service capacity by evaluating accessibility as the ratio

between supply and demand, both of which determined by distance / travel time

catchments.

An illustration of the 2-step FCA Method inspired by Luo and Wang (2003) is given

below. Figure 33 shows a jurisdiction with 12 localities (denoted by numbers 1 – 12)

and two hospitals (A and B). For simplicity, assume that each locality has 100 people

residing at its centroid (marked by dots within each locality), and 100 beds are

available in each hospital. First looking from the facilities’ perspective, the dotted

line represents the travel time catchment of the two hospitals. The catchment area

for Hospital A has 100 beds and 500 residents, translating to a beds-to-population

ratio of 1/5. On the other hand, the beds-to-population ratio for Hospital B is 1/6.

Locality 2, 4, and 5 have access to Hospital A only and their accessibility score is thus


the beds-to-population ratio of Hospital A at 1/5. Similarly, for Locality 9, 10, 11, and

12, their accessibility score is 1/6 as they only have access to Hospital B. However,

Locality 7 and 8 are in an area overlapped by the travel time catchment area of

Hospital A and B. Residents in these two localities enjoy better accessibility (as they

have access to both hospitals) and their accessibility score are both 1/5 + 1/6 = 11 /

30.

Figure 33 – Illustration of 2-step FCA Method

As illustrated, this method has two steps, where the first step goes through each

facility and calculates the beds-to-population ratio based on the facility’s travel time

catchment area. At the second step, the catchment is then drawn for each locality,

and the beds-to-population ratio for each facility that it can reach within the

threshold travel time is summed. The two steps can be written mathematically as

follows (Luo & Wang, 2003):

Step 1: For each facility location 𝑗, search all localities 𝑘 that are within a threshold

travel time (𝑑0) from facility 𝑗, and compute the beds-to-population ratio 𝑅𝑗:

𝑅𝑗 =𝑆𝑗

∑ 𝑃𝑘𝑘∈{𝑑𝑘𝑗≤𝑑0}


where the denominator sums up the population of the set of localities 𝑘 that fall

within the threshold travel time (𝑑𝑘𝑗 ≤ 𝑑0), and the numerator 𝑆𝑗 denotes the

number of beds at facility location j.

Step 2: For each population location 𝑖, search all facility locations 𝑗 that are within

the threshold travel time from location 𝑖, and sum up the beds-to-population ratios

𝑅𝑗 at these locations:

𝐴𝑖𝐹 = ∑ 𝑅𝑗

𝑗∈{𝑑𝑖𝑗≤𝑑0}

= ∑𝑆𝑗

∑ 𝑃𝑘𝑘∈{𝑑𝑘𝑗≤𝑑0}𝑗∈{𝑑𝑖𝑗≤𝑑0}

Here 𝐴𝑖𝐹 represents the accessibility score at resident location 𝑖, where a higher

value indicates a better accessibility. In essence, it evaluates the ratio of capacity to

demand, with selected facilities and resident locations based on the threshold travel

time (irrespective of any administrative borders).

The 2-step FCA Method was utilised to analyse the potential accessibility for Acute IP.

Similar to the above approaches, the number of acute beds at each facility was used

as a proxy for capacity and each TPU Group represents one resident location.

Maps showing the accessibility score 𝐴𝑖𝐹 for each TPU using threshold travel time of

20 and 40 minutes are shown in Figure 34 and Figure 35. One can observe that as the

threshold increases, so is the spatial smoothing effect. It is indeed intuitive to see

that a larger threshold travel time would reduce variability of accessibility across

TPUs, as residents are less and less discouraged by travelling.


Figure 34 – Accessibility score by 2-step FCA Method (20 min) for Acute IP

Figure 35 – Accessibility score by 2-step FCA Method (40 min) for Acute IP

One major drawback of the 2-step FCA Method is that the threshold travel time

draws an artificial line between an accessible and an inaccessible facility. Facilities in


these two groups are treated equally regardless of the actual travel time. Using a

threshold travel time of 30 minutes as an example, a facility one minute away from a

point of origin is equally accessible when compared to a facility 29 minutes away.

Similarly, a facility 31 minutes away is as inaccessible as a facility 300 minutes away.

Gravity Model

To avoid treating travel time decisions as a simple dichotomous one, and to take into

account the frictional effect of travel time (to weight a facility as more accessible

than a more remote one), a gravity-based accessibility measure was developed and

applied in various health care accessibility studies (Cromley & McLafferty, 2002;

Rosero-Bixby, 2004; Zhang et al., 2017). The gravity-based accessibility 𝐴𝑖𝐺 measure

at location i can be written as the following (Luo & Wang, 2003):

𝐴𝑖𝐺 = ∑

𝑆𝑗𝑑𝑖𝑗−𝛽

𝑉𝑗

𝑛𝑗=1

where

𝑉𝑗 = ∑ 𝑃𝑘𝑑𝑘𝑗−𝛽

𝑚

𝑘=1

and 𝑛 and 𝑚 are the total numbers of facility and population locations respectively

(other variables are the same in the 2-step FCA Method).

A larger 𝐴𝑖𝐺 score implies better accessibility. Just like the 2-step FCA Method, it may

be interpreted as a ratio of supply vs. demand, but this time the exponential fictional

effect of travel time is incorporated via the travel fiction coefficient β. The bigger the

coefficient, the larger the fictional effect of travel time on discouraging residents to

patronise a facility farther away. Various ranges of β values have been used in the

literature. For example, Rosero-Bixby (2004) used 1.56 in evaluating spatial

healthcare access in Costa Rica. Zhang et al. (2016) set β at 0.8 in a location-

allocation study for Hong Kong (2016). Wang and Tang (2013) tested a range of β

values between 0.6 and 1.8 and settled for 0.6 at the end for developing a location

allocation optimisation. On the other hand, Luo and Wang (2003) used a range of 1.0

to 2.2 in measuring accessibility in the Chicago region.


With the travel friction coefficient β being a scaling constant without any straight-

forward physical meaning (in contrast with the threshold travel time used in the 2-

step FCA Method), a sensitivity analysis using β values ranging from 0.4 to 2.2 was be

conducted for Acute IP in HA. To illustrate, the accessibility score 𝐴𝑖𝐺 for each TPU

under the Gravity Model with β values of 0.4, 0.8, and 1.2 are shown in Figure 36,

Figure 37, and Figure 38 below.

Figure 36 – Accessibility score by Gravity Model (β = 0.4) for Acute IP




One can observe that lower the β value, the higher the smoothing effect it has on

accessibility. In other words, higher β value would translate to a more uneven


accessibility score, as a higher travel fiction coefficient assumes that people are more

discouraged by travelling. Comparing the results of the accessibility measures

generated by the 2-step FCA Method and the Gravity model, it is intuitive to see that

a larger threshold travel time for the 2-step FCA Method corresponds to a smaller

travel fiction coefficient for the Gravity model, in that both assume residents would

be less discouraged to travel longer to patronise service.

Taking a closer look at the individual TPU’s accessibility score computed by both

methods provide interesting insight. For example, the TPUs’ accessibility score

computed using the 2-step FCA Method (threshold travel time of 30 min) was

plotted against those computed using the Gravity model (β =0.8) in Figure 39 below:

Figure 39 – Accessibility scores by 2-step FCA Method and Gravity Model

While the two sets of accessibility scores are largely positively correlated, two main

groups of outliers can be identified. The first group (circled in red), with a relatively

higher score computed by the Gravity Model when compared to the one by the 2-

step FCA Method, comprises TPU Group 220 – Southeast Mong Kok and TPU Group

226 – King’s Park. Their extreme Gravity-based scores are due to their close

proximity with two large-scale facilities (i.e. KWH and QEH respectively), where their

scores would get significantly magnified due to the exponential nature of the travel

time decay function embedded in the Gravity Model. On the other hand, the second

group of outliers (circled in blue) have a relatively higher 2-Step FCA score when

compared to their Gravity-based one. These TPU Groups are all at the western part

of Kowloon (i.e. Tai Kok Tsui, Sham Shui Po, Ma Wan, Tsing Yi, Tsuen Wan), where

residents in these areas can reach multiple facilities within the threshold time of 30

minutes, including Castle Peak Hospital (CPH), Northern District Hospital (NDH),

North Lantau Hospital (NLTH), and Tuen Mun Hospital (TMH). In contrast,


surprisingly, the aforementioned two TPU Groups with the highest Gravity-based

score cannot reach any of these sites within 30 minutes.

This highlights the key difference between the two methods for evaluating

accessibility. The 2-step FCA Method assumes a discrete and dichotomous decision,

while the Gravity Model assumes a continuous decay function with respect to the

effect of travel time on patients’ health-seeking behaviour. There are diverging views

on whether one method is superior to the other. On one hand, the Gravity Model

can be viewed as a refined version of the 2-step FCA Method, in that it addresses the

latter’s major limitation in its dichotomous approach that defines a facility inside a

catchment as accessible and one outside as inaccessible (Wang, 2012). On the other

hand, Luo & Wang (2003) prefers the 2-step FCA Method, as the Gravity Model is not

only more computation intensive and harder to explain (especially on travel fiction

coefficient β), it could also conceal particular TPUs with relatively poorer accessibility,

as all facilities, no matter how far away, are theoretically accessible under the

Gravity Model.

After reviewing both sets of results for Acute IP, the Gravity Model seems to offer a

better reflection under the Hong Kong context, where basically the entirety of Hong

Kong is accessible within a reasonable amount of travel time. This special feature of

Hong Kong, being a small city with efficient transportation network, calls for a

continuous definition for “accessibility”, rather than a dichotomous one. For

example, with TPU Group 226 – King’s Park being in the centre of Hong Kong, the

fact that it is 30.1 and 35.9 minutes away (just over the threshold of 30 minutes)

from NDH and TMH respectively should not make them totally inaccessible as the 2-

step FCA Method would assume.

Accessibility score computed by the Gravity Model (β=0.8) is shown above in Figure

39. High accessibility is observed for the most parts of Hong Kong Island and

Kowloon, and selected New Territories town centres like Sha Tin, which is largely

similar to the observations made from the revealed accessibility analyses as

presented in Chapter 3 and 4.

There are various methods in evaluating potential spatial accessibility taking

into account both capacity and population. Under Hong Kong’s context, the

Gravity Model is useful in generating insights on how well locality populations

are served in their proximity.


The Gravity-based accessibility score was also analysed against population size of

each TPU Group. Similar to the case of revealed accessibility as shown in the

previous chapter, the accessibility score calculated using the Gravity Model is largely

positively correlated with population size (in general, the larger the population, the

higher the accessibility index). Those TPU Groups with a lower-than-average Gravity-

based score (i.e. < 2.04), but with a population greater than the average TPU Group

population size (i.e. > 34,916), are tabulated below (Table 13):

TPU Group with Gravity-based Score < 2.04 and Population Size > 34,916

Cluster Catchment

Gravity-based (β=0.8) Score

Population Size (2015)

167 – Siu Sai Wan HKEC 1.77 58,300

174 – Ap Lei Chau HKWC 1.82 86,100

293 & 296 – Sau Mau Ping KEC 2.01 85,400

421 & 422 – Butterfly Beach NTWC 1.76 89,600

423 & 428 – Siu Hong & Shan King NTWC 2.01 181,000

424 – On Ting NTWC 1.74 107,600

425 – Lingnan University NTWC 1.87 73,500

510 – Tin Shui Wai NTWC 1.71 291,400

527 – Long Ping NTWC 1.87 67,500

624 & 629 – Sheung Shui Wai NTEC 1.83 56,600

625 – Luen Wo Hui NTEC 1.80 57,200

628 – Fanling South NTEC 1.86 88,200

722 & 727 – Fu Heng NTEC 1.86 50,200

723 – Tai Po Market NTEC 1.87 77,300

726 – Tai Po City Centre NTEC 1.94 105,500

833 – Tiu Keng Leng KEC 1.70 67,000

836 – Po Lam KEC 2.03 90,400

950 & 951 – Tung Chung KWC 1.58 83,100

Table 13 – TPU Groups with lower Gravity score with larger population size

As seen from the above table, the vast majority of TPU Groups (15 out of 18) that

may warrant for a higher accessibility lie in the catchment areas of KEC, NTEC, and

NTWC. However, a series of development projects are in progress or in the pipeline,

e.g. expansion of United Christian Hospital, redevelopment of PWH, and extension of

Operation Theatre Block of TMH.

In particular, one notable case is TPU 510 – Tin Sui Wai in the catchment of NTWC.

Being the largest TPU in Hong Kong in terms of population size, it poses a lower-

than-average accessibility index at 1.71Such observation would call for capacity

expansion to better serve the Tin Sui Wai area, which in fact is already underway


with the establishment and incremental upscaling of service in the Tin Sui Wai

Hospital (with A&E service commenced in March 2017).

Potential accessibility is found to be generally good in Hong Kong. On the

regional level, relatively high accessibility is observed for most of the areas in

Kowloon and Hong Kong Island.

TPU Groups with relatively lower accessibility and significant population sizes

(>34,916) are mainly within the catchment of KEC, NTEC, and NTWC, which

may indicate these Clusters’ need for capacity development / facility

expansion in the longer-term so as to enhance proximity to care. A series of

development projects in these areas are already underway.

6. Discussion Page | 85

6. Discussion

Summary of Results

A quantitative evaluation on spatial accessibility of healthcare services was

conducted to understand the magnitude of cross-cluster flow as well as patterns of

flow by residential location, so as to generate insight on health-seeking behaviour,

and in turn provide reference information for longer-term facility and capacity

planning in HA.

First, through analysing the Cluster concordance rates, defined as the proportion of

activity of a region’s residents being treated in their own Cluster, the concept of

Cluster catchment proves to be a useful reference. After delineating the effect of

Designated Services so as to assure a like-with-like comparison across localities in

Hong Kong, and simulating the current re-clustered setting to reflect the current

state of operation, concordance rates are generally higher in Hong Kong and the

New Territories, illustrating that they are relatively confined geographically. This

study also shows how the re-delineation of Cluster boundary increased the

concordance of the KCC and KWC catchments. The concordance rates of the KEC

catchment are relatively lower (e.g. at 74.5% for Acute IP). This can be partly

explained by the unavailability of certain core service (e.g. Oncology services) where

residents in the KEC catchment need to patronise other Clusters, namely KCC,

instead.

Extending from the Cluster concordance analysis, revealed spatial accessibility was

analysed one level deeper, i.e. by locality using TPU Groups as a spatial unit. Instead

of studying whether services are provided to residents in the same Cluster,

accessibility was evaluated with respect to patient convenience using travel time as a

proxy. With clustering being an administrative arrangement only, such analysis will

provide more insight on patient convenience in terms of how they were served in

their proximity. Similar to the Cluster concordance analysis, utilisation of Designated

Services was carved out to assure like-with-like comparison across localities. The

analysis revealed that revealed spatial accessibility is generally good in Hong Kong.

For the vast majority (90%) of utilisation, services could be sought within 17 to 20

minutes for PHC and A&E, and within 21 to 24 minutes for Acute IP, Non-acute IP,

SOP, and AHOP. The analysis also helped in identifying regional pockets of higher and

lower accessibility for each core service. For example, urban areas like the majority

of the Kowloon peninsula and HK Island, as well as New Territories town centres like


Tuen Mun, Yuen Long, Sha Tin and Tai Po, have relatively high accessibility in general.

On the other hand, some TPU Groups have relatively lower accessibility, which are

generally found in the parameters of Hong Kong and are observed to have smaller

population sizes (1.6% of total Hong Kong population for Acute IP).

In addition to analysing retrospective information on revealed accessibility with

respect to travel time, an analysis on potential accessibility based on capacity and

population was conducted for Acute IP. In contrast to revealed accessibility where

the focus on the realised / actual health-seeking behaviour, analysis on potential

accessibility aims at evaluating the spatial distribution of supply (beds in hospitals in

the case for Acute IP) with respect to the geographical distribution of demand

(population), which would help informing longer-term capacity / facility under HA’s

long term objective of providing core services in the vicinities of local populations.

Several approaches in evaluating potential access are presented, with the Gravity

Model being the most suitable one under Hong Kong’s context. Analysis using such

model revealed local pockets of higher / lower potential accessibility. In particular,

TPU Groups that have relatively lower accessibility scores together with significant

population sizes (i.e. higher than the average TPU Group population of 34,916) were

identified and they are mainly found within the catchment of KEC, NTEC, and NTWC.

Such information would inform longer-term service, facility and capacity planning, so

as to facilitate HA to move forward in enhancing local access to care.

Limitation and Potential Extensions

As in any scientific study, the observations and findings need to be viewed in the

context of the study’s limitations.

Cluster Catchment as a Spatial Unit

First and foremost, the Cluster Concordance Analysis (shown in Chapter 3) evaluates

accessibility by analysing to what extend patients have sought care in their own

Cluster. While Cluster catchment is good reference in general, it has to be stressed

that clustering in HA is not a patient registration system but an administrative

networking arrangement that groups institution together for purposes of continuity

of care and economy of scale. Therefore, patients can choose to go to institutions in

any Cluster for most core services. Imagine a patient residing near a Cluster

boundary; it may be more convenient for him / her to visit facilities of the adjacent

Cluster. Under such setting in the Hong Kong healthcare system, analysing service

access merely from a Cluster catchment perspective may not be comprehensive.


Instead, one can convincingly argue that patient’s health-seeking behaviour might be

driven other factors, such as daytime mobility, preference / prior experience with

particular institutions, and patient convenience in terms of travelling, rather than the

administrative Cluster boundaries.

Granularity of TPU Group

Two sets of accessibility analyses were therefore presented (in Chapter 4 and 5),

where TPU Group, rather than Cluster catchment, was used as the spatial unit of

analysis. As noted in Chapter 2 where the rationale of choosing TPU Groups as the

geographical unit is presented, TPU Groups were selected over Cluster Catchment

and DCCA with their granularity, and the availability of population data on the TPU

Group level. It has to be noted that other more granular spatial unit is available in

Hong Kong. Examples include “Street Block and Village Cluster”11 and “Planning Data

Zones”. Extension of this analysis using these spatial units could be conducted when

utilisation and population data broken down to such granular level is available.

Use of Geographical Centroids

While it is reasonable to assume the homogeneity of the population in a TPU Group,

analysing on such an aggregated geographical level has one important limitation in

that a centroid needs to be estimated for each unit. Without the availability of

information on the spread of population in each TPU Group, the geographical

centroid was used as a proxy. As mentioned above, one way of mitigating this

limitation is the use of a more refined spatial unit. To further minimise aggregation

error arising from the distribution of population around the centroid of spatial units,

one possible extension of this study is to analyse on an individual level, i.e. to

geocode the location of each individual patient rather than grouping them into a

geographical units. This, however, requires much more granular utilisation and

population data, and would be significantly more intensive computationally.

Travel Time based on Drive Time via Road Network

Travel time between patients’ places of residence and facilities was selected as the

proxy of patient convenience in the analysis. To facilitate a like-with-like comparison

on accessibility among different parts of Hong Kong and to arrive at more

11

Each TPU is sub-divided into a number of Street Blocks for urban areas and Village Clusters for rural areas


generalisable results, travel time estimated by means of private car using the existing

road network was utilised. As a potential extension of this study, a sensitivity

analysis on how different measures of travel time, e.g. use of public transit, would

impact the relative accessibility among different regions in Hong Kong. In addition, in

view of the extensive expansion of transportation networks in Hong Kong (both road

and public transit like the Mass Transit Railway), it would be also be fruitful to

conduct scenario analysis taking into account various major changes in the

transportation infrastructure anticipated to be completed in the next few years.

References Page | 89

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Appendix I: Accessibility Statistics by Locality Page | 93

Appendix I: Accessibility Statistics by Locality

Appendix I presents various accessibility statistics by TPU Group for the six core

services, namely Acute Inpatient (Acute IP), Non-acute Inpatient (Non-acute IP),

Specialist Outpatient (SOP), Primary Care (PHC), Accident and Emergency (A&E),

Allied Health Outpatient (AHOP).

Cluster Catchment

District Tertiary Planning Unit Groups Page no.

HKEC Eastern 148; 151; 152; 153; 154; 155; 157; 161; 162; 163; 166; 167; 156 & 158; 164 & 165

99-112

Islands (excl. Lantau)

920; 963; 976; 911-913 113-116

Wan Chai 131; 132; 133; 134; 135; 140; 144; 145; 149; 146 & 147; 183 & 184

117-127

HKWC Central & Western

111; 112; 113; 114; 115; 116; 141; 142; 143; 121 & 122; 123 & 124; 181 & 182

128-139

Southern 171; 172; 173; 174; 191; 196; 197; 175 & 176; 190, 192 & 194; 193, 195 & 198

140-149

KCC Kowloon City 231; 232; 233; 234; 235; 236; 237; 241; 242; 243; 244; 245; 246; 247; 271; 272; 285; 213 & 215-216

150-167

Mong Kok 220; 221; 222; 227; 228; 229; 253; 254 168-175

Wong Tai Sin 281; 282; 283; 284; 287; 288 & 289 176-181

Yau Tsim 211; 212; 214; 217; 225; 226; 251, 252 & 256 182-188

KEC Kwun Tong 290; 291; 292; 297; 298; 280 & 286; 293 & 296; 294 & 295

189-196

Sai Kung 821; 822; 823; 825; 827; 829; 831; 833; 835; 836; 837; 838; 839; 811-815; 820 & 824; 826 & 828; 832 & 834

197-213

KWC Kwai Tsing 326; 327; 328; 350; 351 214-218

Lantau Island 944; 931 & 933; 932 & 934; 941-943; 950 & 951; 961 & 962; 971-974

219-225

Sham Shui Po

260; 261; 262; 263; 264; 265; 266; 267; 268; 255 & 269

226-235

Tsuen Wan 310; 321; 322; 323; 325; 335; 975; 320, 324 & 329; 331-334, 336 & 340

236-244

NTEC North 623; 625; 626; 627; 628; 634; 641; 642; 545 & 546; 610 & 621; 620 & 622; 624 & 629; 651-653

245-257

Sha Tin 755; 757; 758; 759; 731, 733 & 754; 732, 751 & 753; 756 & 761-762

258-264

Tai Po 720; 723; 724; 725; 726; 729; 631-633; 711-712, 721 & 728; 722 & 727; 741-744

265-274

NTWC Tuen Mun 424; 425; 426; 441; 442; 411-416 & 427; 421 & 422; 423 & 428; 431-434

275-283

Yuen Long 510; 511; 512; 513; 514; 516; 518; 519; 521; 522; 523; 524; 527; 528; 529; 531; 532; 533; 541; 542; 543; 544; 515 & 517; 525 & 526

284-307


TPU Groups by Cluster Catchment


HKEC Catchment – Page 99 - 127

HKWC Catchment – Page 128-149


KCC Catchment – Page 150-188

KEC Catchment – Page 189-213


KWC Catchment – Page 214-244

NTEC Catchment – Page 245-274


NTWC Catchment – Page 275-307


Cluster Concordance Analysis

The patronage pattern in 2015/16 for residents in TPU Group 148 is presented below. The Cluster

concordance rates (bolded in green) range from 73% for Acute IP to 91% for PHC.

Core Service Percentage of Utilisation* sought in Cluster# in 2015/16 Total

Utilisation HKEC HKWC KCC KEC KWC NTEC NTWC Total %

Acute IP 73% 23% - 1% 1% 1% - 100% 9,719

Non-acute IP 90% 8% - - - 1% - 100% 5,227

SOP 82% 15% 1% - 1% - - 100% 15,868

PHC 91% 6% 1% 1% 1% - - 100% 10,003

A&E 90% 5% 1% 1% 1% 1% - 100% 3,458

AHOP 87% 7% 2% 2% 2% - - 100% 4,687

Travel Time Analysis

The following table illustrates the percentages of utilisation sought within various threshold travel

times for TPU Group 148 in 2015/16. For example, the percentages of utilisation sought within 20

minutes of travel time range from 92% for Non-acute IP to 99% for PHC (bolded in green).

Core Service Percentage of Utilisation* sought within Certain Travel Time in 2015/16 Total

Utilisation <10 min < 20 min < 30 min <40 min

Acute IP 46% 95% 100% 100% 9,719

Non-acute IP 55% 92% 100% 100% 5,227

SOP 53% 98% 100% 100% 15,868

PHC 27% 99% 100% 100% 10,003

A&E 79% 97% 98% 99% 3,458

AHOP 43% 95% 100% 100% 4,687

* In terms of BDO for Acute IP and Non-acute IP, and in terms of attendances for SOP, PHC, A&E and AHOP #

Assuming the re-delineated Cluster boundary between KCC and KWC

TPU Group: 148

District: Eastern

Corresponding Cluster: Hong Kong East Cluster

Population in 2015: 9,500

TPU Group 148 (highlighted on the map) is located in

the Eastern district, with its corresponding Cluster

being the Hong Kong East Cluster.







Acute IP 77% 19% 1% 1% 1% 1% - 100% 22,734

Non-acute IP 88% 9% 1% 1% 1% - - 100% 11,883

SOP 78% 16% 3% 1% 1% 1% - 100% 42,996

PHC 90% 5% 2% 1% 1% - - 100% 24,201

A&E 87% 6% 2% 2% 2% 1% 1% 100% 7,794

AHOP 77% 11% 8% 1% 1% - - 100% 13,905







Acute IP 35% 97% 99% 100% 22,734

Non-acute IP 57% 89% 100% 100% 11,883

SOP 37% 96% 100% 100% 42,996

PHC 41% 98% 99% 100% 24,201

A&E 56% 95% 98% 100% 7,794

AHOP 25% 93% 100% 100% 13,905



TPU Group: 151

District: Eastern









concordance rates (bolded in green) range from 70% for SOP to 88% for PHC.



Acute IP 73% 21% 3% - 2% 1% - 100% 4,159

Non-acute IP 81% 19% - - 1% - - 100% 2,953

SOP 70% 21% 4% 2% 1% 1% 1% 100% 10,149

PHC 88% 5% 4% 1% 1% 1% - 100% 4,314

A&E 86% 6% 3% 1% 2% 2% 1% 100% 1,726

AHOP 78% 13% 7% 2% - - - 100% 3,494




minutes of travel time range from 73% for SOP to 94% for PHC (bolded in green).



Acute IP - 77% 99% 100% 4,159

Non-acute IP - 79% 99% 100% 2,953

SOP - 73% 99% 99% 10,149

PHC 30% 94% 99% 100% 4,314

A&E - 86% 98% 99% 1,726

AHOP - 83% 100% 100% 3,494



TPU Group: 152

District: Eastern









concordance rates (bolded in green) range from 84% for AHOP to 94% for PHC.



Acute IP 87% 9% 2% 1% 1% 1% - 100% 50,622

Non-acute IP 89% 9% - 1% - 1% - 100% 28,932

SOP 84% 11% 2% 1% 1% 1% - 100% 69,069

PHC 94% 3% 1% 1% - - - 100% 48,952

A&E 90% 4% 2% 2% 1% 1% 1% 100% 15,587

AHOP 84% 9% 4% 1% 1% 1% - 100% 20,095







Acute IP 14% 97% 99% 100% 50,622

Non-acute IP 20% 87% 99% 100% 28,932

SOP 11% 97% 100% 100% 69,069

PHC 54% 98% 99% 100% 48,952

A&E 18% 95% 98% 100% 15,587

AHOP 7% 95% 100% 100% 20,095



TPU Group: 153

District: Eastern









concordance rates (bolded in green) range from 83% for AHOP to 97% for Non-acute IP.



Acute IP 90% 8% 1% - 1% - - 100% 8,620

Non-acute IP 97% 2% - - - 1% - 100% 4,389

SOP 84% 12% 2% 1% 1% - - 100% 14,461

PHC 95% 3% 1% 1% 1% - - 100% 11,802

A&E 91% 4% 2% 2% 1% 1% - 100% 3,271

AHOP 83% 11% 2% 1% 2% 1% - 100% 4,228







Acute IP 3% 92% 100% 100% 8,620

Non-acute IP 29% 81% 100% 100% 4,389

SOP 13% 88% 100% 100% 14,461

PHC 79% 99% 100% 100% 11,802

A&E - 93% 99% 100% 3,271

AHOP 14% 93% 100% 100% 4,228



TPU Group: 154

District: Eastern









concordance rates (bolded in green) range from 80% for Non-acute IP to 95% for PHC.



Acute IP 90% 8% 1% - - - - 100% 14,039

Non-acute IP 80% 18% - - - - 3% 100% 6,259

SOP 86% 10% 1% 1% 1% 1% - 100% 21,335

PHC 95% 2% 1% 1% 1% - - 100% 15,067

A&E 92% 3% 1% 2% 1% 1% - 100% 4,903

AHOP 85% 6% 5% 2% 1% 1% - 100% 5,816







Acute IP - 91% 100% 100% 14,039

Non-acute IP - 89% 97% 97% 6,259

SOP - 88% 100% 100% 21,335

PHC 61% 98% 99% 100% 15,067

A&E - 93% 99% 100% 4,903

AHOP - 90% 99% 100% 5,816



TPU Group: 155

District: Eastern












Acute IP 82% 14% 2% 1% 1% 1% - 100% 23,279

Non-acute IP 72% 14% 1% - 13% - - 100% 12,025

SOP 80% 13% 4% 1% 1% 1% - 100% 46,778

PHC 93% 3% 2% 1% 1% 1% - 100% 29,359

A&E 88% 5% 2% 2% 2% 1% - 100% 8,026

AHOP 80% 9% 8% 1% 1% 1% - 100% 14,973







Acute IP 75% 87% 100% 100% 23,279

Non-acute IP 27% 71% 100% 100% 12,025

SOP 65% 85% 99% 100% 46,778

PHC 68% 97% 99% 100% 29,359

A&E 81% 91% 99% 100% 8,026

AHOP 57% 89% 100% 100% 14,973



TPU Group: 157

District: Eastern












Acute IP 92% 6% 1% 1% 1% - - 100% 85,599

Non-acute IP 88% 9% 1% - 1% - 1% 100% 46,458

SOP 87% 9% 2% 1% 1% 1% - 100% 109,138

PHC 94% 3% 1% 1% 1% - - 100% 80,293

A&E 92% 3% 1% 1% 1% 1% - 100% 26,768

AHOP 86% 7% 4% 1% 1% 1% 1% 100% 31,435







Acute IP 79% 93% 99% 100% 85,599

Non-acute IP 25% 69% 99% 99% 46,458

SOP 75% 89% 99% 100% 109,138

PHC 6% 96% 99% 100% 80,293

A&E 88% 94% 98% 99% 26,768

AHOP 66% 87% 99% 99% 31,435



TPU Group: 161

District: Eastern









concordance rates (bolded in green) range from 80% for SOP to 89% for Non-acute IP.



Acute IP 86% 10% 2% 1% 1% 1% - 100% 8,244

Non-acute IP 89% 9% 1% - - - - 100% 3,712

SOP 80% 12% 4% 1% 1% 1% - 100% 18,707

PHC 88% 5% 3% 1% 2% 1% - 100% 9,877

A&E 89% 4% 2% 2% 2% 1% 1% 100% 3,121

AHOP 81% 7% 8% 1% 2% 2% - 100% 6,115







Acute IP 80% 88% 100% 100% 8,244

Non-acute IP 35% 82% 100% 100% 3,712

SOP 68% 82% 99% 100% 18,707

PHC 49% 91% 99% 99% 9,877

A&E 82% 91% 98% 99% 3,121

AHOP 61% 82% 99% 100% 6,115



TPU Group: 162

District: Eastern












Acute IP 93% 4% 1% 1% - 1% - 100% 35,748

Non-acute IP 94% 1% - - 3% 2% - 100% 18,768

SOP 89% 8% 1% 1% 1% 1% - 100% 57,669

PHC 94% 3% 1% 1% 1% 1% - 100% 40,970

A&E 93% 2% 2% 1% 1% 1% 1% 100% 14,138

AHOP 89% 6% 4% 1% 1% - - 100% 17,506







Acute IP 86% 94% 99% 100% 35,748

Non-acute IP 31% 68% 100% 100% 18,768

SOP 80% 90% 100% 100% 57,669

PHC 83% 96% 99% 100% 40,970

A&E 89% 94% 98% 99% 14,138

AHOP 75% 89% 99% 99% 17,506



TPU Group: 163

District: Eastern












Acute IP 88% 7% 1% 1% 1% 2% - 100% 35,722

Non-acute IP 91% 3% 1% 1% 3% 1% - 100% 13,021

SOP 87% 9% 2% 1% 1% 1% - 100% 56,418

PHC 94% 3% 1% 1% - - - 100% 43,926

A&E 91% 3% 2% 2% 2% 1% 1% 100% 14,104

AHOP 86% 6% 5% 1% 1% 1% 1% 100% 17,467







Acute IP - 90% 99% 100% 35,722

Non-acute IP - 88% 99% 100% 13,021

SOP - 89% 100% 100% 56,418

PHC 40% 97% 99% 99% 43,926

A&E - 92% 98% 99% 14,104

AHOP - 88% 99% 100% 17,467



TPU Group: 166

District: Eastern









concordance rates (bolded in green) range from 87% for Acute IP to 94% for Non-acute IP.



Acute IP 87% 9% 1% - 1% 1% - 100% 45,581

Non-acute IP 94% 2% 1% - 1% 3% - 100% 19,497

SOP 87% 9% 2% 1% 1% 1% - 100% 78,073

PHC 93% 4% 2% 1% 1% 1% - 100% 51,284

A&E 92% 2% 2% 1% 1% 1% 1% 100% 17,581

AHOP 88% 6% 3% 1% 1% 1% - 100% 23,686




minutes of travel time range from 41% for Non-acute IP to 87% for A&E (bolded in green).



Acute IP - 81% 90% 99% 45,581

Non-acute IP - 41% 98% 100% 19,497

SOP - 77% 91% 100% 78,073

PHC 61% 85% 98% 99% 51,284

A&E - 87% 94% 99% 17,581

AHOP - 70% 93% 99% 23,686



TPU Group: 167

District: Eastern








The patronage pattern in 2015/16 for residents in TPU Group 156 & 158 is presented below. The

Cluster concordance rates (bolded in green) range from 83% for SOP to 93% for PHC.



Acute IP 88% 9% 2% 1% 1% 1% - 100% 28,037

Non-acute IP 88% 8% - - - 2% 1% 100% 9,345

SOP 83% 11% 3% 1% 1% 1% - 100% 52,092

PHC 93% 3% 2% 1% 1% 1% - 100% 34,893

A&E 90% 3% 2% 2% 1% 1% 1% 100% 9,449

AHOP 85% 7% 5% 2% 1% 1% - 100% 16,124



times for TPU Group 156 & 158 in 2015/16. For example, the percentages of utilisation sought within

20 minutes of travel time range from 83% for Non-acute IP to 97% for PHC (bolded in green).



Acute IP - 91% 100% 100% 28,037

Non-acute IP - 83% 99% 99% 9,345

SOP - 87% 100% 100% 52,092

PHC 55% 97% 99% 100% 34,893

A&E - 93% 98% 100% 9,449

AHOP - 92% 100% 100% 16,124



TPU Group: 156 & 158

District: Eastern



TPU Group 156 & 158 (highlighted on the map) is

located in the Eastern district, with its corresponding

Cluster being the Hong Kong East Cluster.




Cluster concordance rates (bolded in green) range from 87% for AHOP to 95% for PHC.



Acute IP 92% 5% 1% 1% - 1% 1% 100% 69,742

Non-acute IP 92% 4% 1% - - 1% 1% 100% 37,490

SOP 90% 7% 1% 1% 1% 1% - 100% 87,242

PHC 95% 2% 1% 1% - - - 100% 66,948

A&E 94% 2% 1% 1% 1% 1% - 100% 27,202

AHOP 87% 7% 3% 2% 1% - - 100% 24,575







Acute IP 82% 93% 99% 99% 69,742

Non-acute IP 42% 78% 98% 99% 37,490

SOP 81% 91% 100% 100% 87,242

PHC 59% 97% 99% 100% 66,948

A&E 91% 95% 99% 99% 27,202

AHOP 75% 89% 99% 100% 24,575




District: Eastern




located in the Eastern district, with its corresponding








Acute IP 55% 42% 1% - 1% - - 100% 22,717

Non-acute IP 61% 28% 5% - 6% - - 100% 8,959

SOP 38% 57% 2% 1% 1% 1% - 100% 23,522

PHC 96% 3% - - 1% - - 100% 33,145

A&E 84% 12% 1% - 1% 1% 1% 100% 11,757

AHOP 71% 24% 2% 1% 1% - - 100% 9,572







Acute IP 24% 24% 24% 24% 22,717

Non-acute IP 12% 12% 12% 12% 8,959

SOP - - - - 23,522

PHC 92% 92% 92% 92% 33,145

A&E 73% 73% 73% 73% 11,757

AHOP 58% 58% 58% 58% 9,572



TPU Group: 920

District: Islands (excl. Lantau)




the Islands (excl. Lantau) district, with its corresponding





concordance rates (bolded in green) are 0% for each of the six services.



Acute IP - - - - - - - - -

Non-acute IP - - - - - - - - -

SOP - - - - - - - - -

PHC - - - - - - - - -

A&E - - - 100% - - - 100% 3

AHOP - - - - - - - - -




minutes of travel time are 0% for each of the six services (bolded in green).



Acute IP - - - - -

Non-acute IP - - - - -

SOP - - - - -

PHC - - - - -

A&E - - - - 3

AHOP - - - - -



TPU Group: 963













Acute IP 27% 68% 1% - 2% 1% - 100% 4,646

Non-acute IP 26% 48% - - 26% - - 100% 1,446

SOP 28% 64% 3% 1% 3% 1% - 100% 6,526

PHC 92% 5% 1% - 1% 1% - 100% 10,090

A&E 28% 55% 3% - 12% 2% - 100% 1,466

AHOP 49% 42% 4% - 5% 1% - 100% 1,427




minutes of travel time are 90% for PHC and 0% for other services (bolded in green).



Acute IP - - - - 4,646

Non-acute IP - - - - 1,446

SOP - - - - 6,526

PHC 90% 90% 90% 90% 10,090

A&E - - - - 1,466

AHOP - - - - 1,427



TPU Group: 976









The patronage pattern in 2015/16 for residents in TPU Group 911-913 is presented below. The Cluster




Acute IP 14% 80% 5% - - 1% - 100% 2,356

Non-acute IP 46% 54% - - - - - 100% 685

SOP 20% 75% 1% - 1% 1% 1% 100% 4,473

PHC 97% 2% - - - - - 100% 11,230

A&E 37% 56% 2% 2% 2% 2% - 100% 1,044

AHOP 51% 39% 5% 4% - 1% - 100% 848



times for TPU Group 911-913 in 2015/16. For example, the percentages of utilisation sought within 20




Acute IP - - - - 2,356

Non-acute IP - - - - 685

SOP - - - - 4,473

PHC 6% 6% 6% 6% 11,230

A&E - - - - 1,044

AHOP - - - - 848



TPU Group: 911-913




TPU Group 911-913 (highlighted on the map) is located

in the Islands (excl. Lantau) district, with its

corresponding Cluster being the Hong Kong East

Cluster.







Acute IP 75% 21% 1% - 1% 1% 1% 100% 19,047

Non-acute IP 83% 10% 1% - 1% 4% 1% 100% 11,666

SOP 73% 22% 2% 1% 1% 1% - 100% 30,114

PHC 92% 4% 2% 1% 1% 1% 1% 100% 18,168

A&E 86% 7% 2% 1% 2% 1% 1% 100% 9,043

AHOP 75% 15% 7% 1% 1% 1% - 100% 10,782




minutes of travel time range from 95% for Non-acute IP to 98% for SOP (bolded in green).



Acute IP 51% 97% 98% 100% 19,047

Non-acute IP 56% 95% 96% 100% 11,666

SOP 42% 98% 99% 100% 30,114

PHC 86% 97% 98% 100% 18,168

A&E 82% 96% 98% 100% 9,043

AHOP 58% 98% 99% 100% 10,782



TPU Group: 131

District: Wan Chai




the Wan Chai district, with its corresponding Cluster








Acute IP 79% 16% 1% 1% 1% 2% - 100% 13,362

Non-acute IP 85% 12% - - - 2% - 100% 9,951

SOP 75% 20% 2% 1% 1% 1% - 100% 19,733

PHC 92% 4% 2% 1% 1% 1% - 100% 12,934

A&E 88% 7% 2% 1% 1% 1% 1% 100% 5,947

AHOP 83% 12% 3% 1% - - - 100% 7,106




minutes of travel time range from 94% for AHOP to 97% for PHC (bolded in green).



Acute IP 56% 95% 99% 100% 13,362

Non-acute IP 57% 96% 98% 100% 9,951

SOP 51% 96% 99% 100% 19,733

PHC 88% 97% 99% 99% 12,934

A&E 84% 96% 98% 100% 5,947

AHOP 72% 94% 100% 100% 7,106



TPU Group: 132

District: Wan Chai












Acute IP 76% 18% 2% - 1% 3% 1% 100% 7,587

Non-acute IP 89% 4% 2% - 1% 3% 1% 100% 3,704

SOP 76% 20% 2% - 1% 1% - 100% 12,353

PHC 93% 5% 1% - - - - 100% 7,731

A&E 87% 6% 2% 1% 2% 1% 1% 100% 3,590

AHOP 80% 12% 6% - 1% 1% 1% 100% 3,921







Acute IP 47% 96% 98% 100% 7,587

Non-acute IP 61% 94% 99% 100% 3,704

SOP 54% 98% 99% 100% 12,353

PHC 91% 99% 99% 100% 7,731

A&E 82% 96% 98% 100% 3,590

AHOP 69% 97% 99% 100% 3,921



TPU Group: 133

District: Wan Chai









concordance rates (bolded in green) range from 0% for Non-acute IP to 100% for AHOP.



Acute IP 32% 68% - - - - - 100% 67

Non-acute IP - - - - - - 100% 100% 33

SOP 69% 14% 7% - - 10% - 100% 59

PHC 88% 11% 2% - - - - 100% 76

A&E 85% 12% 2% 2% - - - 100% 86

AHOP 100% - - - - - - 100% 58




minutes of travel time range from 0% for Non-acute IP to 100% for Acute IP, PHC, A&E and AHOP

(bolded in green).



Acute IP 17% 100% 100% 100% 67

Non-acute IP - - - 100% 33

SOP 45% 90% 94% 100% 59

PHC 98% 100% 100% 100% 76

A&E 83% 100% 100% 100% 86

AHOP 96% 100% 100% 100% 58



TPU Group: 134

District: Wan Chai












Acute IP 42% 49% - - 7% 1% 1% 100% 490

Non-acute IP 48% 29% - - - - 23% 100% 144

SOP 59% 33% - 1% 5% 1% - 100% 672

PHC 90% 4% 1% 2% 3% - - 100% 370

A&E 88% 8% 2% - 1% 1% - 100% 288

AHOP 82% 16% 1% 1% - - 1% 100% 228




minutes of travel time range from 77% for Non-acute IP to 99% for AHOP (bolded in green).



Acute IP 34% 98% 98% 100% 490

Non-acute IP 48% 77% 77% 100% 144

SOP 48% 98% 99% 100% 672

PHC 90% 96% 100% 100% 370

A&E 87% 98% 99% 100% 288

AHOP 69% 99% 99% 100% 228



TPU Group: 135

District: Wan Chai












Acute IP 87% 11% 1% - 1% 1% - 100% 4,737

Non-acute IP 98% 1% - - 1% - 1% 100% 3,871

SOP 64% 30% 4% 1% 1% 1% - 100% 4,435

PHC 88% 8% 3% 1% - 1% - 100% 1,846

A&E 89% 7% 1% - 1% 1% - 100% 1,449

AHOP 84% 11% 1% 2% 1% 1% - 100% 1,728




minutes of travel time range from 91% for Non-acute IP to 98% for Acute IP (bolded in green).



Acute IP 77% 98% 99% 100% 4,737

Non-acute IP 70% 91% 99% 99% 3,871

SOP 44% 96% 100% 100% 4,435

PHC 83% 97% 100% 100% 1,846

A&E 84% 97% 99% 100% 1,449

AHOP 78% 93% 100% 100% 1,728



TPU Group: 140

District: Wan Chai












Acute IP 59% 30% 4% 3% 1% 2% 1% 100% 11,234

Non-acute IP 93% 2% 3% - 2% - 1% 100% 6,275

SOP 66% 25% 4% 1% 2% 2% 1% 100% 19,850

PHC 89% 5% 3% 1% 1% 1% - 100% 8,414

A&E 82% 7% 6% 2% 2% 1% 1% 100% 4,724

AHOP 73% 12% 8% 1% 1% 4% 1% 100% 7,015







Acute IP 37% 92% 98% 100% 11,234

Non-acute IP 53% 93% 100% 100% 6,275

SOP 44% 94% 99% 100% 19,850

PHC 8% 96% 98% 100% 8,414

A&E 77% 95% 98% 100% 4,724

AHOP 24% 89% 99% 100% 7,015



TPU Group: 144

District: Wan Chai












Acute IP 76% 14% 3% - 4% 2% - 100% 2,706

Non-acute IP 90% 8% - - - - 2% 100% 1,848

SOP 67% 23% 5% 1% 2% 2% 1% 100% 6,424

PHC 90% 5% 3% 1% 1% - - 100% 3,030

A&E 84% 9% 2% 1% 2% 2% 1% 100% 1,444

AHOP 71% 14% 11% 1% - 2% 1% 100% 2,367







Acute IP 40% 93% 97% 99% 2,706

Non-acute IP 54% 61% 98% 98% 1,848

SOP 42% 92% 99% 99% 6,424

PHC 19% 96% 100% 100% 3,030

A&E 68% 95% 97% 99% 1,444

AHOP 26% 88% 98% 98% 2,367



TPU Group: 145

District: Wan Chai












Acute IP 86% 10% 2% 1% - 1% - 100% 3,685

Non-acute IP 93% 3% 2% 1% - - 2% 100% 2,044

SOP 65% 24% 5% 2% 2% 2% 1% 100% 3,649

PHC 86% 8% 3% 1% 1% 1% - 100% 1,450

A&E 88% 7% 2% 1% 1% 1% - 100% 1,230

AHOP 70% 12% 13% 2% 2% 1% - 100% 1,323







Acute IP - 76% 99% 100% 3,685

Non-acute IP 5% 91% 98% 98% 2,044

SOP 11% 46% 97% 99% 3,649

PHC 8% 91% 98% 99% 1,450

A&E - 83% 99% 100% 1,230

AHOP 11% 65% 97% 100% 1,323



TPU Group: 149

District: Wan Chai









Cluster concordance rates (bolded in green) range from 76% for Acute IP to 93% for Non-acute IP.



Acute IP 76% 20% 1% - 2% - - 100% 12,905

Non-acute IP 93% 5% - - - - 2% 100% 9,574

SOP 79% 17% 2% 1% 1% 1% 1% 100% 19,981

PHC 89% 7% 1% 1% 1% 1% 1% 100% 10,523

A&E 87% 7% 2% 1% 1% 1% 1% 100% 4,902

AHOP 81% 10% 6% 1% 1% - - 100% 6,370




20 minutes of travel time range from 95% for AHOP to 98% for SOP (bolded in green).



Acute IP 50% 97% 99% 100% 12,905

Non-acute IP 45% 97% 98% 100% 9,574

SOP 52% 98% 99% 100% 19,981

PHC 24% 97% 99% 100% 10,523

A&E 79% 97% 98% 100% 4,902

AHOP 37% 95% 99% 100% 6,370




District: Wan Chai




located in the Wan Chai district, with its corresponding








Acute IP 76% 21% - - 1% 1% - 100% 539

Non-acute IP 83% 6% - - - - 11% 100% 313

SOP 65% 29% 3% 1% 2% - - 100% 1,138

PHC 91% 9% - - - - - 100% 735

A&E 90% 8% 1% - - - - 100% 388

AHOP 80% 13% 4% 1% 1% - - 100% 378




20 minutes of travel time range from 42% for Acute IP to 94% for PHC (bolded in green).



Acute IP - 42% 99% 100% 539

Non-acute IP - 62% 89% 89% 313

SOP - 44% 98% 100% 1,138

PHC - 94% 100% 100% 735

A&E - 88% 100% 100% 388

AHOP - 76% 99% 100% 378




District: Wan Chai




located in the Wan Chai district, with its corresponding





concordance rates (bolded in green) range from 65% for Non-acute IP to 93% for Acute IP.



Acute IP 3% 93% 1% 1% 1% 1% 1% 100% 49,466

Non-acute IP 27% 65% 2% 1% 1% 1% 4% 100% 27,742

SOP 4% 92% 2% 1% 1% 1% - 100% 74,547

PHC 5% 92% 1% 1% 1% 1% - 100% 50,315

A&E 7% 87% 2% 1% 2% 1% 1% 100% 15,244

AHOP 8% 84% 5% 1% 1% 1% 1% 100% 23,659







Acute IP 1% 96% 99% 100% 49,466

Non-acute IP 32% 79% 92% 100% 27,742

SOP 4% 96% 100% 100% 74,547

PHC 82% 97% 99% 100% 50,315

A&E - 95% 98% 100% 15,244

AHOP 11% 95% 99% 100% 23,659



TPU Group: 111

District: Central & Western

Corresponding Cluster: Hong Kong West Cluster



the Central & Western district, with its corresponding

Cluster being the Hong Kong West Cluster.







Acute IP 3% 94% 1% 1% - - - 100% 45,629

Non-acute IP 29% 69% 1% - 1% - - 100% 25,306

SOP 4% 92% 1% - 1% 1% 1% 100% 59,435

PHC 4% 93% 1% 1% 1% - - 100% 44,964

A&E 8% 86% 2% 1% 2% 1% 1% 100% 13,176

AHOP 6% 89% 3% - - - - 100% 18,973







Acute IP - 97% 100% 100% 45,629

Non-acute IP - 82% 100% 100% 25,306

SOP 10% 97% 99% 100% 59,435

PHC 80% 98% 99% 100% 44,964

A&E - 95% 98% 100% 13,176

AHOP 54% 97% 99% 100% 18,973



TPU Group: 112













Acute IP 4% 92% 1% 1% 2% 1% - 100% 23,837

Non-acute IP 24% 63% 2% - 10% 1% - 100% 15,101

SOP 5% 91% 2% 1% 1% 1% - 100% 25,331

PHC 3% 95% 1% - 1% - - 100% 22,348

A&E 10% 83% 2% 1% 2% 1% 1% 100% 6,329

AHOP 14% 79% 4% 2% 2% - 1% 100% 8,217







Acute IP 25% 96% 99% 100% 23,837

Non-acute IP 15% 82% 99% 100% 15,101

SOP 22% 96% 100% 100% 25,331

PHC 93% 98% 99% 100% 22,348

A&E - 95% 98% 100% 6,329

AHOP 50% 92% 99% 100% 8,217



TPU Group: 113










concordance rates (bolded in green) range from 70% for A&E to 93% for Acute IP.



Acute IP 3% 93% 1% 1% - 1% 2% 100% 3,715

Non-acute IP 3% 86% - 2% - - 8% 100% 899

SOP 6% 88% 2% 1% 1% 2% - 100% 6,927

PHC 5% 92% 1% - 1% - - 100% 5,866

A&E 18% 70% 4% 1% 3% 2% 1% 100% 1,400

AHOP 9% 82% 6% 1% 1% 1% 1% 100% 2,513







Acute IP 13% 95% 98% 100% 3,715

Non-acute IP 3% 87% 92% 100% 899

SOP 19% 95% 99% 100% 6,927

PHC 91% 98% 99% 100% 5,866

A&E - 91% 95% 100% 1,400

AHOP 57% 94% 99% 100% 2,513



TPU Group: 114










concordance rates (bolded in green) range from 76% for A&E to 93% for PHC.



Acute IP 4% 90% 2% - - 3% 2% 100% 1,340

Non-acute IP 7% 83% - - - 10% - 100% 337

SOP 5% 91% 1% - - 1% 2% 100% 2,259

PHC 4% 93% 1% 1% 1% - 1% 100% 2,354

A&E 17% 76% 3% - 2% 1% 1% 100% 618

AHOP 4% 89% 1% - - 5% - 100% 674







Acute IP 14% 95% 96% 100% 1,340

Non-acute IP 6% 90% 90% 100% 337

SOP 27% 97% 99% 100% 2,259

PHC 93% 99% 99% 100% 2,354

A&E 15% 97% 98% 100% 618

AHOP 53% 95% 100% 100% 674



TPU Group: 115













Acute IP 4% 93% 1% 1% - - 1% 100% 14,399

Non-acute IP 15% 84% - - - 1% - 100% 5,685

SOP 4% 92% 1% 1% 1% - - 100% 17,881

PHC 4% 94% 1% - 1% 1% - 100% 12,301

A&E 9% 86% 2% 1% 1% 1% 1% 100% 4,215

AHOP 10% 84% 3% 2% - 1% - 100% 4,773







Acute IP - 96% 99% 100% 14,399

Non-acute IP - 90% 99% 100% 5,685

SOP 3% 97% 100% 100% 17,881

PHC 80% 98% 99% 100% 12,301

A&E - 95% 99% 100% 4,215

AHOP 2% 95% 99% 100% 4,773



TPU Group: 116













Acute IP 5% 89% 2% - 3% 1% - 100% 7,565

Non-acute IP 2% 74% - - 24% - - 100% 1,601

SOP 10% 83% 3% 1% 1% 1% 1% 100% 13,660

PHC 11% 85% 1% 1% 1% 1% - 100% 7,317

A&E 16% 76% 4% 2% 2% 1% 1% 100% 2,098

AHOP 10% 82% 6% 1% 1% 1% - 100% 4,604







Acute IP - 94% 99% 100% 7,565

Non-acute IP - 76% 100% 100% 1,601

SOP 4% 90% 99% 99% 13,660

PHC 71% 96% 98% 99% 7,317

A&E - 90% 98% 100% 2,098

AHOP 3% 90% 99% 100% 4,604



TPU Group: 141










concordance rates (bolded in green) range from 65% for A&E to 86% for Acute IP.



Acute IP 6% 86% 6% 1% 1% 1% 1% 100% 10,288

Non-acute IP 16% 74% 8% - 2% - - 100% 3,653

SOP 9% 83% 4% 1% 2% 1% - 100% 20,112

PHC 19% 76% 2% 1% 1% 1% 1% 100% 10,268

A&E 27% 65% 3% 1% 2% 1% 1% 100% 3,674

AHOP 22% 68% 7% 1% 1% 1% 1% 100% 7,051




minutes of travel time range from 96% for A&E to 100% for Non-acute IP (bolded in green).



Acute IP - 98% 99% 100% 10,288

Non-acute IP - 100% 100% 100% 3,653

SOP 4% 97% 99% 100% 20,112

PHC 67% 96% 99% 99% 10,268

A&E - 96% 98% 99% 3,674

AHOP 2% 98% 99% 100% 7,051



TPU Group: 142










concordance rates (bolded in green) range from 35% for PHC to 68% for Acute IP.



Acute IP 27% 68% 3% 2% 1% - - 100% 1,632

Non-acute IP 43% 58% - - - - - 100% 699

SOP 19% 66% 9% 3% 2% 1% 1% 100% 2,712

PHC 59% 35% 2% 2% 1% 1% 1% 100% 1,435

A&E 57% 35% 2% 2% 3% 1% - 100% 960

AHOP 35% 50% 11% 3% 1% 1% - 100% 1,021







Acute IP 23% 40% 100% 100% 1,632

Non-acute IP 43% 73% 100% 100% 699

SOP 14% 30% 99% 99% 2,712

PHC 56% 92% 96% 97% 1,435

A&E 55% 55% 98% 100% 960

AHOP 31% 65% 99% 99% 1,021



TPU Group: 143










Cluster concordance rates (bolded in green) range from 65% for A&E to 91% for Acute IP.



Acute IP 6% 91% 1% 1% - 1% - 100% 2,829

Non-acute IP 15% 86% - - - - - 100% 1,250

SOP 7% 88% 3% 1% 1% 1% - 100% 5,468

PHC 10% 86% 2% 1% 1% - 1% 100% 4,237

A&E 27% 65% 3% 1% 1% 2% 1% 100% 1,058

AHOP 14% 76% 10% - 1% - - 100% 1,867




20 minutes of travel time range from 93% for AHOP to 97% for Non-acute IP (bolded in green).



Acute IP 26% 96% 100% 100% 2,829

Non-acute IP 6% 97% 100% 100% 1,250

SOP 18% 95% 100% 100% 5,468

PHC 84% 97% 99% 100% 4,237

A&E - 94% 97% 100% 1,058

AHOP 53% 93% 100% 100% 1,867








located in the Central & Western district, with its

corresponding Cluster being the Hong Kong West

Cluster.




Cluster concordance rates (bolded in green) range from 17% for AHOP to 61% for SOP.



Acute IP 21% 61% 8% - - 11% - 100% 107

Non-acute IP 41% 59% - - - - - 100% 98

SOP 22% 61% 10% 4% - 4% 1% 100% 249

PHC 42% 33% 3% - - 22% - 100% 81

A&E 58% 31% 4% 1% 5% 1% - 100% 111

AHOP 22% 17% 57% 4% - - - 100% 64




20 minutes of travel time range from 78% for PHC to 100% for Non-acute IP (bolded in green).



Acute IP 23% 89% 98% 100% 107

Non-acute IP 41% 100% 100% 100% 98

SOP 24% 92% 99% 100% 249

PHC 66% 78% 84% 100% 81

A&E 55% 94% 100% 100% 111

AHOP 31% 96% 100% 100% 64










Cluster.




Cluster concordance rates (bolded in green) range from 20% for PHC to 100% for Non-acute IP.



Acute IP 53% 38% 8% 1% - - - 100% 240

Non-acute IP - 100% - - - - - 100% 23

SOP 23% 65% 10% 2% - 1% - 100% 699

PHC 78% 20% 2% - 1% - - 100% 361

A&E 76% 20% 3% 1% - - - 100% 199

AHOP 40% 52% 8% - 1% - - 100% 402




20 minutes of travel time range from 19% for SOP to 100% for Non-acute IP (bolded in green).



Acute IP - 29% 99% 100% 240

Non-acute IP - 100% 100% 100% 23

SOP - 19% 97% 100% 699

PHC - 94% 100% 100% 361

A&E - 74% 99% 100% 199

AHOP - 39% 99% 100% 402










Cluster.







Acute IP 6% 93% 1% 1% 1% - - 100% 10,157

Non-acute IP 39% 56% 1% 1% 3% - - 100% 7,990

SOP 9% 85% 3% 1% 1% 1% 1% 100% 7,193

PHC 14% 82% 2% 1% 1% 1% 1% 100% 2,572

A&E 8% 89% 1% 1% 1% - - 100% 2,358

AHOP 14% 82% 2% 1% 1% - - 100% 2,209







Acute IP 71% 95% 100% 100% 10,157

Non-acute IP 47% 92% 99% 100% 7,990

SOP 74% 93% 99% 100% 7,193

PHC 36% 93% 99% 100% 2,572

A&E 89% 95% 100% 100% 2,358

AHOP 43% 95% 100% 100% 2,209



TPU Group: 171

District: Southern




the Southern district, with its corresponding Cluster

being the Hong Kong West Cluster.







Acute IP 3% 94% 2% - 1% 1% - 100% 61,535

Non-acute IP 34% 59% - - 3% 4% - 100% 29,989

SOP 4% 93% 1% - 1% - - 100% 95,485

PHC 7% 91% 1% - 1% - - 100% 66,705

A&E 7% 88% 2% 1% 1% 1% 1% 100% 21,455

AHOP 14% 80% 3% 1% 1% 1% 1% 100% 29,425




minutes of travel time range from 66% for Non-acute IP to 97% for SOP (bolded in green).



Acute IP 79% 95% 99% 100% 61,535

Non-acute IP 60% 66% 96% 100% 29,989

SOP 80% 97% 100% 100% 95,485

PHC 62% 95% 99% 99% 66,705

A&E 88% 94% 98% 99% 21,455

AHOP 35% 93% 99% 100% 29,425



TPU Group: 172

District: Southern












Acute IP 3% 95% 1% - - 1% - 100% 73,311

Non-acute IP 47% 51% 1% - - 1% - 100% 48,333

SOP 4% 93% 1% - 1% 1% - 100% 97,843

PHC 5% 93% 1% 1% - - - 100% 67,671

A&E 10% 85% 2% 1% 1% 1% 1% 100% 22,568

AHOP 23% 73% 3% - 1% - - 100% 29,275







Acute IP 76% 97% 100% 100% 73,311

Non-acute IP 29% 66% 99% 100% 48,333

SOP 77% 96% 100% 100% 97,843

PHC 83% 96% 99% 99% 67,671

A&E 85% 94% 98% 99% 22,568

AHOP 28% 96% 100% 100% 29,275



TPU Group: 173

District: Southern












Acute IP 4% 94% 2% - 1% 1% - 100% 77,484

Non-acute IP 37% 58% 2% - 1% - 2% 100% 41,059

SOP 5% 91% 2% - 1% 1% - 100% 113,619

PHC 9% 88% 1% 1% 1% - - 100% 82,000

A&E 13% 81% 2% 1% 1% 1% 1% 100% 23,916

AHOP 29% 63% 5% 1% 1% 1% - 100% 36,932




minutes of travel time range from 38% for AHOP to 91% for A&E (bolded in green).



Acute IP 13% 80% 99% 100% 77,484

Non-acute IP 9% 61% 97% 98% 41,059

SOP 5% 81% 98% 100% 113,619

PHC 78% 78% 98% 99% 82,000

A&E - 91% 96% 98% 23,916

AHOP 1% 38% 98% 99% 36,932



TPU Group: 174

District: Southern









concordance rates (bolded in green) range from 0% for Non-acute IP to 66% for SOP.



Acute IP 52% 41% 7% - - - - 100% 206

Non-acute IP 14% - 86% - - - - 100% 34

SOP 23% 66% 5% 1% 1% 3% 1% 100% 352

PHC 58% 39% 2% - 1% - - 100% 245

A&E 60% 36% 2% 1% 1% - 1% 100% 155

AHOP 20% 62% 3% 15% - - - 100% 72







Acute IP 10% 97% 100% 100% 206

Non-acute IP - 14% 100% 100% 34

SOP 6% 92% 98% 98% 352

PHC 18% 99% 100% 100% 245

A&E - 96% 99% 100% 155

AHOP - 85% 100% 100% 72



TPU Group: 191

District: Southern












Acute IP 93% 6% 1% - - - - 100% 331

Non-acute IP - - - - - - - - -

SOP 67% 21% 6% 2% 1% 1% 3% 100% 504

PHC 93% 3% 2% - - 2% - 100% 468

A&E 86% 9% 2% 1% 1% - - 100% 299

AHOP 52% 28% 11% - 3% - 6% 100% 126







Acute IP - 88% 93% 100% 331

Non-acute IP - - - - -

SOP - 51% 76% 97% 504

PHC - 67% 96% 100% 468

A&E - 71% 89% 99% 299

AHOP - 33% 55% 94% 126



TPU Group: 196

District: Southern









concordance rates (bolded in green) range from 0% for Non-acute IP, A&E and AHOP to 5% for SOP.



Acute IP 99% 1% - - - - - 100% 900

Non-acute IP 100% - - - - - - 100% 150

SOP 95% 5% 1% - - - - 100% 380

PHC 100% - - - - - - 100% 270

A&E 99% - 1% - - - - 100% 86

AHOP 100% - - - - - - 100% 67







Acute IP - 90% 99% 100% 900

Non-acute IP - 12% 100% 100% 150

SOP - 79% 96% 100% 380

PHC - 77% 100% 100% 270

A&E - 97% 99% 100% 86

AHOP - 86% 100% 100% 67



TPU Group: 197

District: Southern









Cluster concordance rates (bolded in green) range from 21% for Non-acute IP to 81% for PHC.



Acute IP 55% 39% 1% 1% 3% - 1% 100% 18,420

Non-acute IP 71% 21% 2% 1% - 2% 3% 100% 17,511

SOP 17% 77% 2% 1% 1% 1% 1% 100% 17,386

PHC 14% 81% 2% 1% 1% 1% 1% 100% 11,205

A&E 63% 32% 1% 1% 1% 1% 1% 100% 4,569

AHOP 39% 56% 4% 1% 1% 1% - 100% 5,328







Acute IP 4% 88% 99% 99% 18,420

Non-acute IP 21% 79% 95% 98% 17,511

SOP 7% 90% 99% 99% 17,386

PHC 69% 95% 98% 99% 11,205

A&E - 93% 98% 99% 4,569

AHOP 3% 94% 99% 100% 5,328




District: Southern




located in the Southern district, with its corresponding




The patronage pattern in 2015/16 for residents in TPU Group 190, 192 & 194 is presented below. The

Cluster concordance rates (bolded in green) range from 11% for PHC to 61% for Acute IP.



Acute IP 35% 61% 4% - - - - 100% 866

Non-acute IP 81% 12% 7% - - - - 100% 151

SOP 43% 45% 7% 1% 2% 2% - 100% 1,186

PHC 87% 11% 1% 1% - - - 100% 738

A&E 74% 19% 5% - 1% - - 100% 557

AHOP 64% 27% 2% - 4% 3% - 100% 282



times for TPU Group 190, 192 & 194 in 2015/16. For example, the percentages of utilisation sought

within 20 minutes of travel time range from 59% for AHOP to 94% for PHC (bolded in green).



Acute IP - 77% 100% 100% 866

Non-acute IP - 60% 100% 100% 151

SOP 2% 67% 97% 99% 1,186

PHC - 94% 100% 100% 738

A&E - 83% 98% 99% 557

AHOP 1% 59% 97% 100% 282



TPU Group: 190, 192 & 194

District: Southern



TPU Group 190, 192 & 194 (highlighted on the map) is






Cluster concordance rates (bolded in green) range from 3% for Non-acute IP to 30% for SOP.



Acute IP 76% 18% 2% 1% 1% 1% 1% 100% 7,729

Non-acute IP 86% 3% 1% - 11% - - 100% 8,219

SOP 65% 30% 2% 1% 1% 1% 1% 100% 12,849

PHC 87% 10% 1% 1% - 1% 1% 100% 10,273

A&E 79% 13% 2% 1% 2% 1% 1% 100% 3,283

AHOP 75% 14% 7% 1% 1% 1% 2% 100% 4,568




within 20 minutes of travel time range from 0% for A&E to 64% for Non-acute IP (bolded in green).



Acute IP - 1% 93% 99% 7,729

Non-acute IP - 64% 86% 91% 8,219

SOP - 1% 91% 99% 12,849

PHC 53% 53% 80% 98% 10,273

A&E - - 93% 97% 3,283

AHOP - - 56% 97% 4,568



TPU Group: 193, 195 & 198

District: Southern









concordance rates (bolded in green) range from 75% for SOP to 89% for Acute IP.



Acute IP - 4% 89% 5% 1% 1% - 100% 5,549

Non-acute IP - - 89% 5% 4% 3% - 100% 5,971

SOP 2% 3% 75% 6% 8% 5% 1% 100% 4,271

PHC 1% 1% 87% 3% 5% 2% 1% 100% 2,307

A&E 1% - 86% 7% 3% 1% 1% 100% 1,291

AHOP - 2% 83% 4% 6% 4% 1% 100% 2,169




minutes of travel time range from 94% for SOP to 100% for Non-acute IP (bolded in green).



Acute IP 2% 96% 100% 100% 5,549

Non-acute IP 63% 100% 100% 100% 5,971

SOP 23% 94% 99% 100% 4,271

PHC 71% 97% 99% 100% 2,307

A&E - 97% 99% 100% 1,291

AHOP 46% 98% 100% 100% 2,169



TPU Group: 231

District: Kowloon City

Corresponding Cluster: Kowloon Central Cluster



the Kowloon City district, with its corresponding Cluster

being the Kowloon Central Cluster.







Acute IP 1% 6% 89% 1% - 1% 3% 100% 803

Non-acute IP - - 98% - 1% - - 100% 316

SOP 3% 7% 79% 3% 5% 3% 1% 100% 1,919

PHC 3% 2% 90% 3% 2% 1% - 100% 1,174

A&E 3% 3% 86% 1% 4% 2% 2% 100% 329

AHOP 2% 1% 90% 1% 4% 2% - 100% 1,114




minutes of travel time range from 90% for Acute IP to 100% for Non-acute IP (bolded in green).



Acute IP 63% 90% 98% 100% 803

Non-acute IP 45% 100% 100% 100% 316

SOP 41% 91% 99% 100% 1,919

PHC 81% 98% 99% 100% 1,174

A&E 56% 93% 98% 100% 329

AHOP 67% 97% 100% 100% 1,114



TPU Group: 232










concordance rates (bolded in green) range from 79% for A&E to 100% for Non-acute IP.



Acute IP 1% 3% 83% 1% 9% 2% 1% 100% 1,141

Non-acute IP - - 100% - - - - 100% 371

SOP 2% 6% 80% 1% 8% 3% - 100% 2,613

PHC 1% - 91% 4% 2% 1% - 100% 2,051

A&E 4% 4% 79% 2% 7% 3% 1% 100% 372

AHOP 1% 1% 91% - 5% 3% - 100% 1,553







Acute IP 1% 90% 99% 100% 1,141

Non-acute IP 84% 100% 100% 100% 371

SOP 25% 92% 99% 100% 2,613

PHC 78% 97% 99% 100% 2,051

A&E - 92% 98% 99% 372

AHOP 60% 96% 100% 100% 1,553



TPU Group: 233










concordance rates (bolded in green) range from 80% for SOP to 92% for AHOP.



Acute IP 1% 4% 90% 2% 2% 1% - 100% 14,619

Non-acute IP 4% - 90% 1% 3% 2% - 100% 11,290

SOP 2% 6% 80% 3% 5% 4% 1% 100% 14,117

PHC 2% 1% 92% 3% 2% 1% - 100% 9,589

A&E 2% 1% 90% 2% 3% 2% 1% 100% 3,532

AHOP 1% 2% 92% 2% 3% 1% - 100% 8,963







Acute IP 89% 96% 100% 100% 14,619

Non-acute IP 77% 98% 100% 100% 11,290

SOP 76% 92% 99% 100% 14,117

PHC 81% 98% 100% 100% 9,589

A&E 90% 97% 99% 100% 3,532

AHOP 88% 98% 100% 100% 8,963



TPU Group: 234













Acute IP 1% 4% 82% 7% 1% 4% 1% 100% 14,127

Non-acute IP 1% - 77% 3% 5% 5% 9% 100% 12,349

SOP 3% 5% 81% 2% 5% 4% 1% 100% 15,575

PHC 2% 1% 88% 3% 3% 2% 1% 100% 9,088

A&E 2% 1% 86% 3% 4% 3% 2% 100% 3,719

AHOP 1% 1% 90% 2% 4% 2% - 100% 7,564







Acute IP 82% 95% 98% 100% 14,127

Non-acute IP 32% 86% 91% 100% 12,349

SOP 81% 92% 99% 100% 15,575

PHC 82% 97% 98% 100% 9,088

A&E 86% 94% 97% 100% 3,719

AHOP 91% 98% 99% 100% 7,564



TPU Group: 235













Acute IP 1% 3% 90% 1% 2% 2% 1% 100% 23,200

Non-acute IP 2% 4% 84% - 3% 2% 5% 100% 15,270

SOP 2% 3% 88% 1% 5% 2% - 100% 40,956

PHC 1% 1% 91% 3% 2% 1% - 100% 33,329

A&E 3% 1% 86% 2% 4% 2% 2% 100% 9,701

AHOP 2% 2% 91% 1% 4% 1% 1% 100% 16,753







Acute IP 70% 96% 99% 100% 23,200

Non-acute IP - 90% 95% 100% 15,270

SOP 44% 96% 99% 100% 40,956

PHC 47% 98% 99% 100% 33,329

A&E 65% 94% 97% 100% 9,701

AHOP 40% 98% 99% 100% 16,753



TPU Group: 236













Acute IP 1% 2% 92% 1% 3% 2% 1% 100% 19,512

Non-acute IP 1% 1% 93% - 4% 1% - 100% 11,501

SOP 1% 2% 88% 1% 5% 2% 1% 100% 26,754

PHC 1% 1% 92% 3% 2% 1% - 100% 24,781

A&E 2% 1% 87% 2% 4% 3% 1% 100% 7,157

AHOP 2% - 92% 2% 3% 1% 1% 100% 11,769







Acute IP 71% 96% 99% 100% 19,512

Non-acute IP 61% 98% 100% 100% 11,501

SOP 68% 96% 99% 100% 26,754

PHC 56% 98% 99% 100% 24,781

A&E 56% 94% 98% 100% 7,157

AHOP 57% 97% 99% 100% 11,769



TPU Group: 237













Acute IP 1% 2% 90% 3% 2% 2% - 100% 38,008

Non-acute IP 1% - 89% 3% 2% 2% 4% 100% 25,218

SOP 2% 3% 87% 3% 3% 2% 1% 100% 47,488

PHC 1% 1% 93% 3% 1% 1% 1% 100% 44,150

A&E 3% 1% 86% 3% 3% 2% 1% 100% 11,839

AHOP 2% 1% 88% 4% 3% 1% 1% 100% 20,768







Acute IP 1% 96% 100% 100% 38,008

Non-acute IP - 93% 96% 96% 25,218

SOP 14% 94% 99% 100% 47,488

PHC 19% 97% 99% 100% 44,150

A&E - 93% 98% 99% 11,839

AHOP 3% 96% 99% 100% 20,768



TPU Group: 241













Acute IP 1% 2% 90% 4% 2% 1% 1% 100% 39,892

Non-acute IP - - 91% 3% 5% 1% - 100% 29,339

SOP 1% 3% 87% 3% 3% 2% 1% 100% 48,676

PHC 1% 1% 93% 4% 1% 1% - 100% 48,383

A&E 2% 1% 86% 4% 4% 2% 1% 100% 15,240

AHOP 1% 2% 86% 5% 4% 1% - 100% 16,804







Acute IP 1% 96% 99% 100% 39,892

Non-acute IP - 99% 100% 100% 29,339

SOP 14% 95% 99% 100% 48,676

PHC 78% 97% 100% 100% 48,383

A&E - 96% 98% 100% 15,240

AHOP 4% 96% 99% 100% 16,804



TPU Group: 242













Acute IP 1% 2% 91% 3% 1% 2% 1% 100% 28,223

Non-acute IP 1% - 95% 1% 1% 1% - 100% 16,669

SOP 2% 3% 86% 2% 4% 2% 1% 100% 35,065

PHC 2% 1% 92% 3% 1% 1% - 100% 32,180

A&E 2% 1% 87% 3% 4% 2% 1% 100% 9,740

AHOP 1% 3% 88% 2% 4% 1% 1% 100% 12,068




minutes of travel time range from 93% for A&E to 98% for PHC (bolded in green).



Acute IP 82% 96% 99% 100% 28,223

Non-acute IP - 98% 99% 100% 16,669

SOP 54% 94% 99% 100% 35,065

PHC 79% 98% 99% 100% 32,180

A&E 80% 93% 97% 100% 9,740

AHOP 55% 95% 98% 100% 12,068



TPU Group: 243













Acute IP 1% 3% 91% 1% 1% 2% 1% 100% 11,452

Non-acute IP - - 74% 3% 3% 19% 1% 100% 6,785

SOP 2% 4% 86% 2% 3% 2% 1% 100% 20,877

PHC 2% 1% 90% 4% 2% 1% - 100% 17,226

A&E 3% 2% 86% 3% 4% 3% 1% 100% 5,260

AHOP 2% 1% 87% 3% 4% 1% 1% 100% 7,853







Acute IP 78% 93% 99% 100% 11,452

Non-acute IP - 78% 100% 100% 6,785

SOP 53% 90% 99% 100% 20,877

PHC 60% 97% 99% 100% 17,226

A&E 77% 91% 97% 100% 5,260

AHOP 46% 93% 99% 100% 7,853



TPU Group: 244













Acute IP 2% 5% 88% 1% 3% 2% - 100% 25,161

Non-acute IP 1% 4% 87% 1% 2% 5% - 100% 13,741

SOP 2% 4% 86% 2% 4% 3% 1% 100% 44,762

PHC 2% 1% 92% 3% 2% 1% - 100% 34,759

A&E 3% 1% 86% 2% 4% 3% 1% 100% 8,571

AHOP 2% 2% 89% 2% 4% 2% - 100% 18,016







Acute IP 81% 93% 99% 100% 25,161

Non-acute IP - 94% 100% 100% 13,741

SOP 58% 92% 99% 100% 44,762

PHC 84% 96% 99% 100% 34,759

A&E 80% 92% 98% 100% 8,571

AHOP 56% 95% 100% 100% 18,016



TPU Group: 245













Acute IP 1% 3% 87% 4% 3% 3% - 100% 14,265

Non-acute IP 3% - 93% 1% 3% 1% - 100% 10,323

SOP 1% 3% 85% 3% 5% 3% 1% 100% 20,969

PHC 1% 1% 92% 4% 1% 1% 1% 100% 18,130

A&E 2% 1% 84% 4% 5% 2% 1% 100% 4,401

AHOP 2% 1% 90% 3% 3% 1% - 100% 10,137







Acute IP 3% 96% 100% 100% 14,265

Non-acute IP 71% 97% 98% 100% 10,323

SOP 30% 96% 99% 100% 20,969

PHC 81% 98% 99% 100% 18,130

A&E - 96% 99% 100% 4,401

AHOP 54% 98% 100% 100% 10,137



TPU Group: 246













Acute IP 2% 3% 91% 1% 1% 2% - 100% 2,197

Non-acute IP - - 100% - - - - 100% 573

SOP 4% 3% 84% 3% 3% 3% - 100% 4,095

PHC 3% 1% 86% 7% 1% 2% 1% 100% 3,318

A&E 4% 1% 82% 3% 6% 2% 1% 100% 1,145

AHOP 2% 1% 81% 4% 10% 2% - 100% 1,362




minutes of travel time range from 93% for PHC to 100% for Non-acute IP (bolded in green).



Acute IP 1% 95% 100% 100% 2,197

Non-acute IP - 100% 100% 100% 573

SOP 11% 94% 99% 100% 4,095

PHC 51% 93% 99% 100% 3,318

A&E - 94% 98% 100% 1,145

AHOP 5% 97% 100% 100% 1,362



TPU Group: 247













Acute IP 1% 7% 81% 1% 5% 4% - 100% 7,085

Non-acute IP 1% - 85% 2% 6% 7% 1% 100% 2,673

SOP 4% 8% 70% 4% 8% 5% 1% 100% 9,668

PHC 4% 2% 81% 5% 5% 4% 1% 100% 4,957

A&E 5% 3% 73% 5% 9% 5% 1% 100% 1,804

AHOP 2% 5% 81% 1% 5% 5% - 100% 5,985







Acute IP 4% 91% 100% 100% 7,085

Non-acute IP 85% 97% 100% 100% 2,673

SOP 25% 88% 99% 100% 9,668

PHC 72% 96% 99% 100% 4,957

A&E 5% 90% 100% 100% 1,804

AHOP 46% 91% 100% 100% 5,985



TPU Group: 271













Acute IP 1% 3% 87% 1% 3% 4% 1% 100% 5,947

Non-acute IP 1% - 99% - - - - 100% 3,758

SOP 2% 5% 75% 3% 7% 7% 1% 100% 6,982

PHC 2% 2% 86% 4% 3% 2% 1% 100% 4,012

A&E 3% 2% 83% 2% 5% 4% 1% 100% 1,548

AHOP 2% 1% 87% 2% 4% 3% 1% 100% 3,458







Acute IP 5% 95% 100% 100% 5,947

Non-acute IP 99% 100% 100% 100% 3,758

SOP 26% 92% 99% 100% 6,982

PHC 78% 97% 99% 100% 4,012

A&E 2% 94% 99% 100% 1,548

AHOP 48% 97% 100% 100% 3,458



TPU Group: 272













Acute IP - 2% 90% 2% 2% 3% 1% 100% 9,895

Non-acute IP - - 87% 1% 9% 1% 1% 100% 8,344

SOP 1% 2% 84% 4% 5% 3% 1% 100% 12,494

PHC 1% - 92% 5% 1% 1% - 100% 13,433

A&E 2% 1% 81% 8% 5% 3% 1% 100% 3,133

AHOP 1% 1% 87% 5% 4% 2% 1% 100% 5,429







Acute IP 2% 96% 100% 100% 9,895

Non-acute IP 78% 97% 99% 100% 8,344

SOP 31% 95% 99% 100% 12,494

PHC 87% 98% 100% 100% 13,433

A&E - 95% 99% 100% 3,133

AHOP 50% 97% 99% 100% 5,429



TPU Group: 285









The patronage pattern in 2015/16 for residents in TPU Group 213 & 215 - 216 is presented below. The

Cluster concordance rates (bolded in green) range from 63% for Non-acute IP to 93% for Acute IP.



Acute IP 1% 2% 93% - 1% 2% - 100% 1,581

Non-acute IP - - 63% - 37% - - 100% 755

SOP 5% 4% 79% 2% 4% 6% 1% 100% 2,432

PHC 4% 2% 81% 5% 3% 3% 2% 100% 1,563

A&E 4% 2% 81% 1% 4% 4% 5% 100% 763

AHOP 4% 4% 83% 3% 2% 1% 2% 100% 870



times for TPU Group 213 & 215 - 216 in 2015/16. For example, the percentages of utilisation sought

within 20 minutes of travel time range from 92% for A&E to 100% for Non-acute IP (bolded in green).



Acute IP 90% 99% 100% 100% 1,581

Non-acute IP 63% 100% 100% 100% 755

SOP 76% 94% 98% 100% 2,432

PHC 77% 97% 98% 100% 1,563

A&E 75% 92% 96% 100% 763

AHOP 79% 97% 98% 100% 870



TPU Group: 213 & 215 - 216




TPU Group 213 & 215 - 216 (highlighted on the map) is

located in the Kowloon City district, with its

corresponding Cluster being the Kowloon Central

Cluster.







Acute IP - 2% 89% - 4% 1% 5% 100% 947

Non-acute IP - - 35% - 49% - 16% 100% 497

SOP - 5% 82% 2% 7% 4% - 100% 1,532

PHC 1% 3% 91% 2% 2% 1% 1% 100% 1,225

A&E 2% - 90% 1% 3% 3% 1% 100% 396

AHOP - 1% 92% 1% 6% 2% - 100% 533







Acute IP 87% 93% 95% 100% 947

Non-acute IP 13% 84% 84% 100% 497

SOP 83% 94% 99% 100% 1,532

PHC 90% 94% 98% 100% 1,225

A&E 90% 95% 97% 100% 396

AHOP 93% 99% 100% 100% 533



TPU Group: 220

District: Mong Kok

Corresponding Cluster: Kowloon Central Cluster #



the Mong Kok district, with its corresponding Cluster

being the Kowloon Central Cluster#.







Acute IP 1% 3% 86% 2% 6% 2% - 100% 74,855

Non-acute IP 3% 1% 66% 3% 21% 3% 2% 100% 51,945

SOP 1% 3% 71% 2% 21% 2% 1% 100% 77,086

PHC 1% 1% 87% 1% 8% 1% 1% 100% 62,141

A&E 2% 1% 79% 2% 13% 2% 1% 100% 26,720

AHOP 1% 2% 83% 2% 10% 1% 1% 100% 30,094







Acute IP 69% 96% 99% 100% 74,855

Non-acute IP - 89% 96% 100% 51,945

SOP 46% 95% 99% 100% 77,086

PHC 79% 97% 99% 100% 62,141

A&E 68% 94% 99% 100% 26,720

AHOP 57% 95% 99% 100% 30,094



TPU Group: 221

District: Mong Kok












Acute IP 1% 4% 89% 1% 4% 2% - 100% 13,425

Non-acute IP 4% - 72% 1% 19% 4% - 100% 6,957

SOP 2% 3% 77% 1% 14% 3% 1% 100% 19,222

PHC 2% 1% 88% 1% 6% 2% - 100% 14,151

A&E 3% 1% 82% 2% 8% 3% 1% 100% 5,256

AHOP 2% 1% 89% 1% 5% 1% 1% 100% 7,855







Acute IP 65% 94% 99% 100% 13,425

Non-acute IP 18% 92% 100% 100% 6,957

SOP 59% 95% 99% 100% 19,222

PHC 73% 97% 99% 100% 14,151

A&E 66% 93% 97% 100% 5,256

AHOP 74% 96% 99% 100% 7,855



TPU Group: 222

District: Mong Kok












Acute IP - 3% 89% 1% 2% 3% 2% 100% 14,011

Non-acute IP 5% - 68% - 27% - - 100% 8,077

SOP 1% 3% 82% 1% 10% 2% 1% 100% 20,122

PHC 1% 1% 92% 2% 3% 1% 1% 100% 15,912

A&E 2% 1% 89% 2% 4% 3% 1% 100% 5,914

AHOP 1% 2% 90% 1% 4% 1% 1% 100% 7,253




minutes of travel time range from 94% for Acute IP to 98% for PHC (bolded in green).



Acute IP 88% 94% 98% 100% 14,011

Non-acute IP 17% 95% 96% 100% 8,077

SOP 82% 95% 99% 100% 20,122

PHC 91% 98% 99% 100% 15,912

A&E 89% 95% 98% 100% 5,914

AHOP 90% 95% 99% 100% 7,253



TPU Group: 227

District: Mong Kok












Acute IP 1% 3% 87% 2% 3% 4% 1% 100% 18,156

Non-acute IP 1% 3% 77% 3% 13% 3% 1% 100% 9,278

SOP 1% 3% 82% 1% 11% 2% 1% 100% 16,124

PHC 1% - 96% 1% 2% 1% - 100% 15,165

A&E 1% 1% 87% 1% 5% 3% 1% 100% 6,972

AHOP 1% 1% 92% 1% 3% 2% - 100% 6,680







Acute IP 85% 93% 99% 100% 18,156

Non-acute IP - 92% 99% 100% 9,278

SOP 68% 96% 99% 100% 16,124

PHC 95% 98% 99% 100% 15,165

A&E 87% 94% 99% 100% 6,972

AHOP 77% 96% 100% 100% 6,680



TPU Group: 228

District: Mong Kok












Acute IP 2% 5% 87% 1% 3% 2% 1% 100% 13,291

Non-acute IP - 1% 61% 3% 28% 2% 6% 100% 7,754

SOP 1% 3% 78% 1% 14% 2% 1% 100% 17,214

PHC 1% 1% 93% 1% 3% 1% 1% 100% 14,407

A&E 2% 1% 88% 1% 6% 2% 1% 100% 6,184

AHOP 2% 2% 89% 2% 4% 1% 1% 100% 7,173







Acute IP 85% 95% 99% 100% 13,291

Non-acute IP - 92% 95% 100% 7,754

SOP 68% 97% 99% 100% 17,214

PHC 92% 98% 99% 100% 14,407

A&E 89% 96% 99% 100% 6,184

AHOP 84% 98% 100% 100% 7,173



TPU Group: 229

District: Mong Kok












Acute IP 1% 6% 85% 1% 4% 2% 1% 100% 25,731

Non-acute IP 2% 1% 69% 1% 21% 5% 1% 100% 13,689

SOP 2% 5% 73% 1% 15% 2% 1% 100% 39,272

PHC 2% 2% 88% 1% 5% 1% 1% 100% 31,206

A&E 2% 2% 84% 2% 7% 2% 1% 100% 9,382

AHOP 2% 4% 83% 2% 8% 1% 1% 100% 16,323







Acute IP 61% 97% 99% 100% 25,731

Non-acute IP - 93% 99% 100% 13,689

SOP 48% 96% 99% 100% 39,272

PHC 81% 97% 99% 100% 31,206

A&E 67% 95% 98% 100% 9,382

AHOP 53% 96% 100% 100% 16,323



TPU Group: 253

District: Mong Kok












Acute IP 2% 6% 77% 1% 10% 3% 1% 100% 5,106

Non-acute IP 1% - 74% - 25% 1% - 100% 1,494

SOP 4% 9% 67% 2% 14% 3% 2% 100% 9,802

PHC 5% 4% 65% 2% 17% 5% 2% 100% 4,765

A&E 5% 3% 69% 2% 16% 3% 2% 100% 2,175

AHOP 2% 5% 76% 1% 12% 3% 1% 100% 4,001







Acute IP 8% 97% 100% 100% 5,106

Non-acute IP 1% 100% 100% 100% 1,494

SOP 7% 93% 100% 100% 9,802

PHC 24% 90% 99% 100% 4,765

A&E 4% 90% 100% 100% 2,175

AHOP 9% 94% 100% 100% 4,001



TPU Group: 254

District: Mong Kok












Acute IP 1% 2% 88% 3% 3% 3% 1% 100% 176,214

Non-acute IP 2% 1% 70% 1% 19% 2% 5% 100% 112,931

SOP 1% 3% 73% 4% 16% 3% 1% 100% 237,010

PHC 1% 1% 92% 3% 2% 1% - 100% 252,199

A&E 2% 1% 77% 8% 7% 4% 1% 100% 53,725

AHOP 1% 2% 80% 5% 9% 2% 1% 100% 88,736







Acute IP 8% 95% 99% 100% 176,214

Non-acute IP 38% 92% 94% 100% 112,931

SOP 25% 95% 99% 100% 237,010

PHC 79% 98% 100% 100% 252,199

A&E - 93% 99% 100% 53,725

AHOP 29% 96% 99% 100% 88,736



TPU Group: 281

District: Wong Tai Sin




the Wong Tai Sin district, with its corresponding Cluster








Acute IP 1% 2% 88% 2% 5% 2% 1% 100% 47,213

Non-acute IP 3% - 66% 1% 25% 4% 2% 100% 29,525

SOP 1% 3% 76% 2% 15% 3% 1% 100% 74,637

PHC 1% 1% 94% 2% 2% 1% - 100% 82,555

A&E 2% 1% 80% 5% 7% 4% 1% 100% 14,971

AHOP 1% 1% 83% 3% 9% 2% 1% 100% 31,534







Acute IP 13% 96% 99% 100% 47,213

Non-acute IP 67% 96% 100% 100% 29,525

SOP 40% 96% 100% 100% 74,637

PHC 88% 98% 100% 100% 82,555

A&E 7% 94% 99% 100% 14,971

AHOP 48% 97% 100% 100% 31,534



TPU Group: 282













Acute IP 1% 3% 89% 2% 2% 3% 1% 100% 54,739

Non-acute IP - 2% 66% 1% 29% 2% - 100% 34,062

SOP 1% 2% 76% 3% 15% 2% 1% 100% 82,513

PHC 1% - 93% 3% 2% 1% - 100% 84,843

A&E 2% 1% 80% 6% 6% 3% 1% 100% 18,695

AHOP 1% 1% 83% 4% 8% 2% 1% 100% 31,770







Acute IP 8% 95% 100% 100% 54,739

Non-acute IP 32% 96% 100% 100% 34,062

SOP 36% 96% 99% 100% 82,513

PHC 87% 98% 100% 100% 84,843

A&E 4% 93% 99% 100% 18,695

AHOP 26% 96% 100% 100% 31,770



TPU Group: 283













Acute IP 1% 2% 90% 4% 1% 2% - 100% 15,921

Non-acute IP 3% - 84% 4% 6% 1% 4% 100% 13,310

SOP 1% 3% 73% 5% 15% 3% 1% 100% 13,293

PHC 1% - 93% 3% 1% 1% - 100% 12,277

A&E 2% 1% 86% 7% 3% 2% 1% 100% 4,251

AHOP 1% 3% 74% 9% 11% 2% - 100% 5,438







Acute IP 4% 97% 99% 100% 15,921

Non-acute IP 38% 96% 100% 100% 13,310

SOP 29% 95% 97% 100% 13,293

PHC 22% 98% 100% 100% 12,277

A&E - 96% 99% 100% 4,251

AHOP 19% 96% 98% 100% 5,438



TPU Group: 284













Acute IP 1% 5% 78% 10% 3% 3% 1% 100% 19,661

Non-acute IP 1% 4% 64% 4% 23% 5% - 100% 11,691

SOP 1% 2% 67% 9% 17% 3% 1% 100% 26,622

PHC 1% 1% 81% 14% 2% 1% - 100% 28,507

A&E 2% 1% 64% 22% 6% 4% 1% 100% 6,845

AHOP 1% 2% 68% 15% 11% 3% - 100% 10,030







Acute IP 4% 93% 99% 100% 19,661

Non-acute IP 16% 95% 100% 100% 11,691

SOP 22% 96% 100% 100% 26,622

PHC 65% 97% 99% 100% 28,507

A&E - 96% 99% 100% 6,845

AHOP 13% 96% 100% 100% 10,030



TPU Group: 287










Cluster concordance rates (bolded in green) range from 34% for A&E to 74% for PHC.



Acute IP 2% 5% 49% 39% 3% 3% 1% 100% 40,456

Non-acute IP 1% 1% 47% 22% 25% - 4% 100% 21,906

SOP 2% 3% 55% 23% 14% 3% 1% 100% 72,368

PHC 1% 1% 74% 21% 2% 1% - 100% 76,231

A&E 2% 1% 34% 52% 6% 3% 1% 100% 16,326

AHOP 2% 2% 57% 29% 8% 2% 1% 100% 29,091




20 minutes of travel time range from 71% for Non-acute IP to 92% for A&E (bolded in green).



Acute IP - 92% 96% 100% 40,456

Non-acute IP - 71% 95% 96% 21,906

SOP - 90% 97% 100% 72,368

PHC 5% 92% 99% 100% 76,231

A&E - 92% 96% 99% 16,326

AHOP - 92% 98% 100% 29,091








located in the Wong Tai Sin district, with its

corresponding Cluster being the Kowloon Central

Cluster#.







Acute IP 1% 9% 87% - 2% 1% - 100% 7,909

Non-acute IP - - 88% - 6% 6% - 100% 3,262

SOP 1% 4% 87% 1% 5% 2% 1% 100% 11,082

PHC 1% 1% 95% 1% 1% 1% - 100% 10,362

A&E 2% 2% 89% 1% 3% 2% 1% 100% 3,987

AHOP 1% 2% 92% 1% 3% 1% 1% 100% 3,965







Acute IP 72% 95% 99% 100% 7,909

Non-acute IP - 94% 94% 100% 3,262

SOP 57% 95% 99% 100% 11,082

PHC 5% 98% 99% 100% 10,362

A&E 80% 95% 97% 100% 3,987

AHOP 61% 97% 99% 100% 3,965



TPU Group: 211

District: Yau Tsim




the Yau Tsim district, with its corresponding Cluster








Acute IP 1% 3% 91% 1% 2% 2% - 100% 7,530

Non-acute IP 1% - 92% 1% 2% 4% 1% 100% 2,862

SOP 2% 4% 86% 1% 4% 2% - 100% 11,399

PHC 2% 1% 92% 2% 1% 2% - 100% 8,215

A&E 3% 1% 89% 1% 3% 3% 1% 100% 3,867

AHOP 2% 3% 88% 2% 2% 2% - 100% 4,094







Acute IP - 93% 99% 100% 7,530

Non-acute IP - 93% 99% 100% 2,862

SOP 49% 88% 99% 100% 11,399

PHC 2% 95% 98% 100% 8,215

A&E - 92% 97% 100% 3,867

AHOP 1% 90% 98% 100% 4,094



TPU Group: 212

District: Yau Tsim












Acute IP 1% 2% 92% - 1% 3% - 100% 2,981

Non-acute IP 4% - 95% - 1% - - 100% 945

SOP 2% 2% 89% 1% 3% 3% 1% 100% 3,132

PHC 2% - 94% 1% 1% 2% 1% 100% 3,810

A&E 3% 1% 87% 1% 3% 3% 2% 100% 2,231

AHOP 1% 3% 90% - 2% 2% 2% 100% 1,371







Acute IP 80% 95% 98% 100% 2,981

Non-acute IP - 96% 100% 100% 945

SOP 55% 96% 98% 100% 3,132

PHC 5% 97% 97% 100% 3,810

A&E 77% 93% 95% 100% 2,231

AHOP 41% 95% 98% 100% 1,371



TPU Group: 214

District: Yau Tsim









concordance rates (bolded in green) range from 44% for AHOP to 79% for A&E.



Acute IP 9% 15% 71% 1% 4% - 1% 100% 555

Non-acute IP 3% 14% 60% - - 24% - 100% 161

SOP 6% 10% 71% 1% 4% 6% 3% 100% 635

PHC 11% 5% 70% 2% 4% 6% 2% 100% 262

A&E 4% 5% 79% 2% 3% 4% 3% 100% 192

AHOP 5% 5% 44% 14% 1% 21% 10% 100% 113




minutes of travel time range from 86% for AHOP to 97% for A&E (bolded in green).



Acute IP 71% 92% 100% 100% 555

Non-acute IP 60% 86% 100% 100% 161

SOP 70% 92% 95% 100% 635

PHC 66% 93% 95% 97% 262

A&E 79% 97% 100% 100% 192

AHOP 44% 86% 98% 100% 113



TPU Group: 217

District: Yau Tsim












Acute IP 1% 4% 91% 1% 2% 2% 1% 100% 29,168

Non-acute IP 7% 1% 79% - 11% 1% 1% 100% 16,065

SOP 2% 3% 87% 1% 5% 2% 1% 100% 37,358

PHC 1% 1% 96% 1% 1% 1% - 100% 47,483

A&E 2% 1% 90% 1% 3% 1% 1% 100% 14,459

AHOP 1% 6% 86% 1% 4% 1% 1% 100% 15,588







Acute IP 88% 98% 99% 100% 29,168

Non-acute IP - 91% 97% 100% 16,065

SOP 68% 97% 99% 100% 37,358

PHC 94% 99% 100% 100% 47,483

A&E 90% 95% 99% 100% 14,459

AHOP 74% 97% 99% 100% 15,588



TPU Group: 225

District: Yau Tsim












Acute IP 2% 3% 84% 3% 6% 3% 1% 100% 6,365

Non-acute IP 1% - 90% 2% 8% - - 100% 4,967

SOP 3% 5% 79% 3% 6% 3% 1% 100% 7,165

PHC 2% 1% 92% 1% 2% 2% 1% 100% 5,078

A&E 3% 1% 88% 2% 3% 2% 2% 100% 2,725

AHOP 2% 3% 87% 2% 5% 1% 1% 100% 3,323







Acute IP 82% 96% 99% 100% 6,365

Non-acute IP 77% 100% 100% 100% 4,967

SOP 80% 94% 99% 100% 7,165

PHC 89% 98% 99% 100% 5,078

A&E 88% 95% 98% 100% 2,725

AHOP 87% 96% 99% 100% 3,323



TPU Group: 226

District: Yau Tsim












Acute IP 2% 26% 59% 3% 7% 3% 1% 100% 3,607

Non-acute IP 6% 6% 81% 5% - 3% - 100% 1,022

SOP 3% 13% 67% 3% 8% 4% 3% 100% 5,439

PHC 7% 4% 76% 3% 5% 4% 2% 100% 2,369

A&E 8% 5% 76% 2% 5% 3% 1% 100% 1,606

AHOP 1% 9% 76% 1% 7% 5% 1% 100% 2,683




within 20 minutes of travel time range from 92% for PHC to 98% for AHOP (bolded in green).



Acute IP 54% 95% 99% 100% 3,607

Non-acute IP - 93% 100% 100% 1,022

SOP 49% 93% 98% 100% 5,439

PHC 3% 92% 99% 100% 2,369

A&E 65% 94% 99% 100% 1,606

AHOP 50% 98% 99% 100% 2,683



TPU Group: 251, 252 & 256

District: Yau Tsim




located in the Yau Tsim district, with its corresponding

Cluster being the Kowloon Central Cluster.







Acute IP 5% 6% 19% 62% 4% 4% 1% 100% 20,615

Non-acute IP 7% 5% 35% 41% 7% 2% 3% 100% 9,884

SOP 6% 6% 23% 58% 4% 4% 1% 100% 40,400

PHC 5% 2% 12% 78% 3% 1% - 100% 36,186

A&E 6% 2% 10% 76% 4% 2% 1% 100% 10,187

AHOP 4% 2% 23% 67% 2% 2% - 100% 19,041







Acute IP - 90% 99% 99% 20,615

Non-acute IP - 79% 90% 97% 9,884

SOP 6% 91% 99% 99% 40,400

PHC 32% 93% 99% 100% 36,186

A&E - 94% 98% 100% 10,187

AHOP 33% 95% 99% 100% 19,041



TPU Group: 290

District: Kwun Tong

Corresponding Cluster: Kowlloon East Cluster



the Kwun Tong district, with its corresponding Cluster

being the Kowlloon East Cluster.




concordance rates (bolded in green) range from 38% for Non-acute IP to 70% for A&E.



Acute IP 2% 2% 23% 65% 4% 3% 1% 100% 53,239

Non-acute IP 2% 2% 52% 38% 5% 1% - 100% 32,329

SOP 3% 3% 31% 53% 6% 3% 1% 100% 85,502

PHC 2% 1% 25% 66% 4% 1% 1% 100% 86,917

A&E 3% 1% 17% 70% 6% 3% 1% 100% 24,927

AHOP 2% 2% 33% 57% 3% 2% 1% 100% 33,027




minutes of travel time range from 95% for SOP to 96% for Acute IP (bolded in green).



Acute IP 63% 96% 99% 100% 53,239

Non-acute IP 57% 96% 99% 100% 32,329

SOP 54% 95% 99% 100% 85,502

PHC 80% 96% 99% 100% 86,917

A&E 65% 96% 98% 100% 24,927

AHOP 62% 95% 99% 100% 33,027



TPU Group: 291

District: Kwun Tong












Acute IP 1% 2% 14% 78% 2% 2% 1% 100% 40,462

Non-acute IP 1% 2% 48% 45% 1% 1% 3% 100% 29,746

SOP 1% 2% 21% 71% 2% 2% - 100% 62,281

PHC 1% 1% 8% 88% 1% 1% - 100% 75,328

A&E 2% 1% 9% 83% 3% 2% 1% 100% 19,280

AHOP 1% 1% 20% 74% 2% 1% - 100% 24,110







Acute IP 75% 96% 97% 100% 40,462

Non-acute IP 15% 93% 97% 98% 29,746

SOP 62% 94% 97% 100% 62,281

PHC 81% 95% 99% 100% 75,328

A&E 80% 95% 98% 100% 19,280

AHOP 68% 95% 99% 100% 24,110



TPU Group: 292

District: Kwun Tong












Acute IP 2% 3% 15% 74% 2% 3% - 100% 68,597

Non-acute IP 2% 3% 45% 46% 2% 1% 1% 100% 43,325

SOP 3% 4% 17% 71% 2% 2% 1% 100% 113,071

PHC 2% 1% 6% 90% 1% 1% - 100% 121,424

A&E 3% 1% 8% 81% 3% 2% 1% 100% 29,418

AHOP 3% 1% 17% 76% 2% 2% 1% 100% 45,878







Acute IP - 94% 99% 100% 68,597

Non-acute IP - 92% 99% 99% 43,325

SOP 6% 94% 99% 100% 113,071

PHC 65% 97% 100% 100% 121,424

A&E - 95% 98% 99% 29,418

AHOP 26% 96% 99% 100% 45,878



TPU Group: 297

District: Kwun Tong












Acute IP 3% 4% 14% 75% 2% 2% 1% 100% 30,493

Non-acute IP 6% 3% 38% 45% 1% 5% 2% 100% 20,516

SOP 5% 5% 18% 67% 3% 3% 1% 100% 50,860

PHC 3% 1% 10% 82% 2% 1% - 100% 50,931

A&E 4% 2% 9% 79% 3% 2% 1% 100% 15,053

AHOP 5% 2% 17% 72% 2% 1% 1% 100% 20,785







Acute IP - 90% 99% 100% 30,493

Non-acute IP - 83% 98% 98% 20,516

SOP - 88% 99% 100% 50,860

PHC 21% 90% 99% 99% 50,931

A&E - 90% 98% 99% 15,053

AHOP - 88% 99% 100% 20,785



TPU Group: 298

District: Kwun Tong












Acute IP 3% 4% 42% 45% 4% 3% 1% 100% 38,880

Non-acute IP 2% 2% 57% 31% 7% 1% 1% 100% 20,629

SOP 3% 4% 41% 42% 6% 3% 1% 100% 60,924

PHC 2% 1% 16% 77% 2% 1% 1% 100% 69,023

A&E 4% 1% 36% 49% 7% 3% 1% 100% 15,964

AHOP 3% 2% 45% 42% 3% 3% 1% 100% 25,753







Acute IP - 95% 99% 100% 38,880

Non-acute IP - 97% 99% 100% 20,629

SOP 4% 94% 99% 100% 60,924

PHC 74% 97% 99% 100% 69,023

A&E - 95% 98% 100% 15,964

AHOP 14% 95% 99% 100% 25,753




District: Kwun Tong




located in the Kwun Tong district, with its

corresponding Cluster being the Kowlloon East Cluster.







Acute IP 2% 3% 13% 78% 2% 3% - 100% 55,087

Non-acute IP 1% 2% 46% 49% 1% 1% - 100% 33,141

SOP 2% 3% 16% 75% 3% 2% 1% 100% 92,666

PHC 2% 1% 9% 86% 2% 1% - 100% 95,775

A&E 2% 1% 7% 85% 3% 2% 1% 100% 30,701

AHOP 2% 1% 16% 78% 2% 1% - 100% 35,793







Acute IP - 89% 96% 100% 55,087

Non-acute IP 30% 93% 99% 100% 33,141

SOP 7% 88% 97% 100% 92,666

PHC 7% 91% 99% 100% 95,775

A&E - 89% 97% 99% 30,701

AHOP 17% 89% 98% 100% 35,793




District: Kwun Tong












Acute IP 1% 3% 13% 78% 1% 3% 1% 100% 130,013

Non-acute IP 3% 1% 42% 43% 2% 3% 7% 100% 104,796

SOP 2% 3% 17% 74% 2% 2% 1% 100% 184,310

PHC 1% 1% 6% 90% 1% 1% - 100% 208,953

A&E 2% 1% 7% 85% 3% 2% 1% 100% 57,982

AHOP 2% 1% 15% 79% 1% 1% - 100% 70,867







Acute IP 75% 95% 99% 100% 130,013

Non-acute IP 13% 89% 93% 94% 104,796

SOP 64% 95% 99% 100% 184,310

PHC 84% 98% 99% 100% 208,953

A&E 81% 96% 98% 100% 57,982

AHOP 73% 97% 99% 100% 70,867




District: Kwun Tong












Acute IP 3% 2% - 76% - 19% - 100% 160

Non-acute IP - - 46% - 54% - - 100% 218

SOP 4% 3% 21% 46% 2% 24% - 100% 312

PHC 2% - 6% 91% - 1% - 100% 377

A&E - - 7% 85% - 8% - 100% 70

AHOP 41% - 11% 46% - 3% - 100% 120







Acute IP - 70% 96% 99% 160

Non-acute IP - - 46% 100% 218

SOP - 41% 83% 97% 312

PHC 87% 89% 97% 100% 377

A&E - 84% 88% 100% 70

AHOP - 33% 57% 100% 120



TPU Group: 821

District: Sai Kung




the Sai Kung district, with its corresponding Cluster








Acute IP - 1% 3% 79% - 17% - 100% 778

Non-acute IP - - 37% 55% - 8% - 100% 257

SOP 2% 3% 23% 56% 3% 14% - 100% 966

PHC - 1% 2% 91% - 6% - 100% 2,093

A&E 2% 1% 7% 77% 1% 13% 1% 100% 336

AHOP 1% 2% 19% 69% 5% 4% - 100% 584







Acute IP - - 95% 99% 778

Non-acute IP - - 63% 100% 257

SOP - - 72% 96% 966

PHC 88% 92% 97% 99% 2,093

A&E - - 89% 98% 336

AHOP - - 73% 97% 584



TPU Group: 822

District: Sai Kung












Acute IP 3% 4% 6% 81% 2% - 4% 100% 421

Non-acute IP - - 37% 63% - - - 100% 116

SOP 5% 6% 27% 46% 8% 5% 3% 100% 687

PHC 2% 1% 2% 86% 6% - 3% 100% 938

A&E 2% - 8% 77% 6% 4% 3% 100% 290

AHOP 6% 3% 22% 60% 9% - 1% 100% 279







Acute IP - 81% 90% 92% 421

Non-acute IP - 63% 100% 100% 116

SOP - 46% 87% 91% 687

PHC - 86% 93% 97% 938

A&E - 77% 93% 96% 290

AHOP - 60% 91% 96% 279



TPU Group: 823

District: Sai Kung












Acute IP - 1% 12% 76% - 8% 2% 100% 732

Non-acute IP - - - 100% - - - 100% 256

SOP 3% 1% 29% 50% 4% 12% 1% 100% 1,252

PHC - - 2% 96% - 2% - 100% 2,349

A&E - 1% 3% 85% 1% 10% 1% 100% 374

AHOP - - 17% 77% 2% 4% - 100% 420







Acute IP - 72% 87% 97% 732

Non-acute IP - 23% 100% 100% 256

SOP - 44% 78% 98% 1,252

PHC 91% 96% 99% 100% 2,349

A&E - 83% 94% 99% 374

AHOP - 67% 92% 100% 420



TPU Group: 825

District: Sai Kung












Acute IP 8% - 11% 74% - 6% - 100% 1,815

Non-acute IP 2% - 80% 18% - - - 100% 698

SOP 3% 2% 24% 60% 2% 9% - 100% 2,423

PHC - - 1% 97% - 2% - 100% 5,437

A&E 9% - 5% 81% 2% 3% 1% 100% 728

AHOP 1% - 12% 77% 4% 6% - 100% 579







Acute IP - 56% 80% 100% 1,815

Non-acute IP - 2% 98% 100% 698

SOP - 51% 85% 99% 2,423

PHC 92% 97% 98% 100% 5,437

A&E - 78% 83% 99% 728

AHOP - 68% 89% 100% 579



TPU Group: 827

District: Sai Kung












Acute IP 1% 1% 4% 83% - 11% - 100% 1,108

Non-acute IP - - 5% 86% 8% - - 100% 362

SOP 2% 2% 30% 54% 4% 7% 2% 100% 773

PHC 1% - 2% 96% - - - 100% 1,216

A&E 1% - 6% 91% 1% 1% - 100% 309

AHOP - - 20% 71% 2% 8% - 100% 256







Acute IP - 81% 84% 100% 1,108

Non-acute IP - 10% 92% 100% 362

SOP - 46% 76% 97% 773

PHC 90% 95% 98% 100% 1,216

A&E - 89% 91% 99% 309

AHOP - 42% 82% 100% 256



TPU Group: 829

District: Sai Kung












Acute IP 3% 2% 18% 71% 1% 5% - 100% 3,791

Non-acute IP 14% - 31% 20% - 36% - 100% 2,929

SOP 3% 3% 28% 53% 5% 7% 1% 100% 5,555

PHC 2% 1% 13% 81% 1% 1% 1% 100% 5,632

A&E 1% 1% 6% 86% 3% 3% 1% 100% 2,294

AHOP 2% 1% 26% 60% 5% 5% 1% 100% 2,256







Acute IP 45% 72% 97% 100% 3,791

Non-acute IP 18% 20% 87% 100% 2,929

SOP 40% 65% 96% 99% 5,555

PHC 24% 92% 99% 99% 5,632

A&E 70% 86% 97% 98% 2,294

AHOP 54% 64% 95% 99% 2,256



TPU Group: 831

District: Sai Kung












Acute IP 4% 5% 12% 74% 1% 4% 1% 100% 32,428

Non-acute IP 3% 4% 34% 48% 3% 1% 6% 100% 12,139

SOP 5% 5% 16% 67% 3% 3% 1% 100% 57,284

PHC 5% 2% 8% 81% 2% 2% - 100% 43,298

A&E 3% 1% 5% 87% 2% 2% 1% 100% 22,655

AHOP 5% 3% 15% 73% 2% 3% 1% 100% 21,344







Acute IP - 59% 94% 99% 32,428

Non-acute IP - 7% 89% 94% 12,139

SOP - 55% 93% 99% 57,284

PHC - 17% 90% 99% 43,298

A&E - 83% 96% 99% 22,655

AHOP - 61% 96% 99% 21,344



TPU Group: 833

District: Sai Kung












Acute IP 1% 1% 8% 86% 1% 2% 1% 100% 23,516

Non-acute IP 1% - 23% 64% 3% 2% 7% 100% 11,514

SOP 2% 2% 13% 78% 1% 3% 1% 100% 29,758

PHC 1% 1% 5% 92% 1% 1% - 100% 29,198

A&E 2% 1% 5% 88% 2% 2% 1% 100% 11,160

AHOP 2% 3% 11% 82% 1% 2% - 100% 11,521







Acute IP - 94% 98% 100% 23,516

Non-acute IP 50% 86% 92% 93% 11,514

SOP 6% 90% 97% 99% 29,758

PHC 48% 89% 99% 100% 29,198

A&E - 92% 97% 99% 11,160

AHOP 5% 91% 99% 100% 11,521



TPU Group: 835

District: Sai Kung












Acute IP 2% 3% 11% 80% 1% 3% 1% 100% 67,752

Non-acute IP 2% 2% 22% 69% 1% 4% 1% 100% 27,154

SOP 4% 4% 14% 73% 2% 3% - 100% 94,942

PHC 2% 1% 4% 91% 1% 1% - 100% 82,880

A&E 3% 1% 4% 87% 2% 2% 1% 100% 29,980

AHOP 3% 2% 12% 80% 1% 2% - 100% 36,678







Acute IP 66% 80% 97% 99% 67,752

Non-acute IP 63% 69% 96% 99% 27,154

SOP 61% 83% 96% 100% 94,942

PHC 83% 89% 99% 100% 82,880

A&E 83% 87% 96% 99% 29,980

AHOP 72% 81% 98% 100% 36,678



TPU Group: 836

District: Sai Kung












Acute IP 2% 4% 10% 81% 1% 2% - 100% 57,381

Non-acute IP 6% 3% 21% 68% 1% 1% - 100% 21,965

SOP 3% 5% 13% 74% 2% 3% - 100% 86,867

PHC 2% 1% 3% 93% 1% 1% - 100% 82,543

A&E 2% 1% 3% 91% 2% 2% 1% 100% 30,265

AHOP 3% 2% 11% 81% 1% 1% - 100% 34,476







Acute IP 73% 82% 95% 100% 57,381

Non-acute IP 65% 68% 99% 100% 21,965

SOP 65% 84% 95% 100% 86,867

PHC 85% 89% 99% 100% 82,543

A&E 88% 91% 97% 99% 30,265

AHOP 75% 82% 98% 100% 34,476



TPU Group: 837

District: Sai Kung












Acute IP 3% 4% 12% 76% 1% 3% 1% 100% 58,732

Non-acute IP 4% 4% 28% 57% 2% 1% 3% 100% 25,169

SOP 4% 5% 16% 69% 2% 3% 1% 100% 91,958

PHC 3% 1% 7% 85% 2% 2% - 100% 72,021

A&E 3% 1% 4% 88% 2% 2% 1% 100% 29,832

AHOP 4% 2% 15% 75% 1% 2% - 100% 33,843







Acute IP 63% 91% 95% 99% 58,732

Non-acute IP 52% 85% 92% 97% 25,169

SOP 58% 88% 95% 100% 91,958

PHC 70% 85% 99% 99% 72,021

A&E 83% 93% 97% 99% 29,832

AHOP 68% 91% 98% 100% 33,843



TPU Group: 838

District: Sai Kung









concordance rates (bolded in green) range from 56% for SOP to 85% for A&E.



Acute IP 6% 3% 11% 73% 3% 4% - 100% 9,035

Non-acute IP 5% 2% 9% 82% 2% - - 100% 3,474

SOP 11% 8% 17% 56% 3% 4% 1% 100% 9,652

PHC 13% 4% 9% 70% 2% 2% 1% 100% 5,480

A&E 6% 1% 5% 85% 1% 2% - 100% 4,100

AHOP 9% 2% 13% 71% 2% 3% - 100% 3,545




minutes of travel time range from 58% for SOP to 85% for A&E (bolded in green).



Acute IP 65% 73% 98% 100% 9,035

Non-acute IP 72% 82% 99% 100% 3,474

SOP 47% 58% 91% 99% 9,652

PHC 45% 73% 98% 99% 5,480

A&E 81% 85% 97% 99% 4,100

AHOP 63% 72% 98% 100% 3,545



TPU Group: 839

District: Sai Kung












Acute IP - 1% 61% 37% - - 1% 100% 443

Non-acute IP - - 48% 52% - - - 100% 137

SOP 3% 4% 36% 42% 2% 10% 4% 100% 250

PHC 1% 1% - 95% - - 2% 100% 414

A&E 2% 1% 8% 79% 1% 4% 5% 100% 137

AHOP 2% - 4% 92% - - 2% 100% 53







Acute IP - - - 37% 443

Non-acute IP - - - 52% 137

SOP - - - 57% 250

PHC - 93% 93% 95% 414

A&E - - - 84% 137

AHOP - - - 92% 53



TPU Group: 811-815

District: Sai Kung




in the Sai Kung district, with its corresponding Cluster








Acute IP - 1% 16% 79% 1% 3% - 100% 1,253

Non-acute IP - - 52% 48% - - - 100% 1,220

SOP 1% 2% 31% 55% 5% 6% 1% 100% 1,597

PHC 1% - 5% 91% - 1% 1% 100% 2,719

A&E 2% 1% 6% 85% 2% 3% 2% 100% 653

AHOP 1% 2% 28% 66% 1% 1% 2% 100% 884







Acute IP - 79% 82% 99% 1,253

Non-acute IP - 48% 100% 100% 1,220

SOP - 55% 87% 98% 1,597

PHC - 89% 96% 98% 2,719

A&E - 85% 88% 97% 653

AHOP - 65% 79% 98% 884




District: Sai Kung




located in the Sai Kung district, with its corresponding

Cluster being the Kowlloon East Cluster.







Acute IP 1% 2% 11% 78% 1% 8% - 100% 6,367

Non-acute IP 1% - 35% 53% - 11% - 100% 2,397

SOP 1% 3% 25% 56% 2% 12% - 100% 7,189

PHC - - 1% 98% - 1% - 100% 17,254

A&E 1% - 7% 85% 2% 4% - 100% 2,089

AHOP 1% 1% 18% 69% 2% 8% 1% 100% 2,453







Acute IP - 68% 86% 99% 6,367

Non-acute IP - 12% 99% 100% 2,397

SOP - 49% 82% 97% 7,189

PHC 93% 97% 99% 100% 17,254

A&E - 81% 89% 99% 2,089

AHOP - 63% 87% 98% 2,453




District: Sai Kung









Cluster concordance rates (bolded in green) range from 62% for SOP to 96% for Non-acute IP.



Acute IP 4% - 4% 89% - 3% 1% 100% 1,714

Non-acute IP - - - 96% - - 4% 100% 1,170

SOP 4% 2% 23% 62% 3% 6% - 100% 921

PHC 1% - 12% 84% - 3% 1% 100% 579

A&E 2% - - 94% 1% 2% - 100% 553

AHOP 3% 1% 5% 89% 1% 1% 1% 100% 369







Acute IP 85% 85% 99% 99% 1,714

Non-acute IP 11% 96% 96% 96% 1,170

SOP 48% 50% 96% 100% 921

PHC 51% 58% 99% 99% 579

A&E 92% 92% 99% 99% 553

AHOP 54% 72% 98% 99% 369




District: Sai Kung












Acute IP 1% 2% 5% 1% 88% 3% 1% 100% 142,432

Non-acute IP 1% 2% 11% 1% 80% 4% 2% 100% 70,178

SOP 1% 2% 6% 1% 87% 2% 1% 100% 184,570

PHC 1% 1% 4% 1% 92% 1% 1% 100% 152,026

A&E 1% 1% 3% 1% 91% 2% 1% 100% 65,042

AHOP 1% 2% 10% 1% 81% 2% 2% 100% 52,617







Acute IP 29% 93% 99% 100% 142,432

Non-acute IP 11% 88% 100% 100% 70,178

SOP 23% 92% 99% 100% 184,570

PHC 87% 96% 99% 100% 152,026

A&E 45% 94% 99% 100% 65,042

AHOP 32% 90% 99% 100% 52,617



TPU Group: 326

District: Kwai Tsing

Corresponding Cluster: Kowloon West Cluster



the Kwai Tsing district, with its corresponding Cluster

being the Kowloon West Cluster.







Acute IP 1% 2% 6% 1% 87% 2% 1% 100% 102,001

Non-acute IP 3% 2% 13% - 74% 4% 4% 100% 66,116

SOP 1% 2% 7% 1% 86% 3% 1% 100% 123,670

PHC 1% 1% 4% 1% 93% 1% 1% 100% 106,186

A&E 1% 1% 4% 1% 91% 2% 1% 100% 41,117

AHOP 1% 2% 12% 1% 82% 2% 1% 100% 33,543







Acute IP 11% 91% 98% 100% 102,001

Non-acute IP 3% 82% 95% 100% 66,116

SOP 18% 91% 99% 100% 123,670

PHC 78% 96% 99% 100% 106,186

A&E 31% 92% 98% 100% 41,117

AHOP 24% 86% 99% 100% 33,543



TPU Group: 327













Acute IP 1% 3% 7% 1% 87% 2% 1% 100% 43,043

Non-acute IP 1% 4% 13% - 76% 5% 1% 100% 20,804

SOP 1% 3% 9% 1% 84% 2% 1% 100% 66,509

PHC 1% 1% 8% 1% 88% 1% 1% 100% 59,905

A&E 1% 1% 4% 1% 91% 1% 1% 100% 20,613

AHOP 2% 2% 15% 2% 76% 2% 2% 100% 19,609







Acute IP 86% 95% 100% 100% 43,043

Non-acute IP 75% 91% 100% 100% 20,804

SOP 84% 94% 100% 100% 66,509

PHC 80% 96% 99% 100% 59,905

A&E 90% 95% 100% 100% 20,613

AHOP 77% 93% 100% 100% 19,609



TPU Group: 328













Acute IP 1% 3% 8% 1% 83% 3% 1% 100% 62,895

Non-acute IP 2% 1% 10% - 79% 5% 2% 100% 29,083

SOP 1% 3% 9% 1% 82% 2% 1% 100% 108,477

PHC 1% 1% 3% - 94% 1% 1% 100% 115,625

A&E 1% 1% 5% 1% 88% 2% 2% 100% 29,008

AHOP 1% 2% 15% 1% 77% 3% 1% 100% 33,292







Acute IP - 93% 99% 100% 62,895

Non-acute IP - 86% 100% 100% 29,083

SOP 1% 92% 100% 100% 108,477

PHC 86% 97% 99% 100% 115,625

A&E - 91% 99% 100% 29,008

AHOP - 90% 100% 100% 33,292



TPU Group: 350













Acute IP 1% 4% 6% - 84% 2% 2% 100% 49,501

Non-acute IP 2% 1% 7% - 85% 4% 1% 100% 23,671

SOP 1% 3% 8% 1% 85% 2% 1% 100% 83,489

PHC 1% 1% 5% 1% 92% 1% 1% 100% 79,748

A&E 1% 1% 5% 1% 89% 2% 2% 100% 26,500

AHOP 1% 3% 14% 1% 78% 2% 1% 100% 25,672







Acute IP 13% 90% 99% 100% 49,501

Non-acute IP 1% 89% 98% 100% 23,671

SOP 19% 91% 99% 100% 83,489

PHC 87% 96% 99% 100% 79,748

A&E 33% 91% 99% 100% 26,500

AHOP 26% 88% 99% 100% 25,672



TPU Group: 351













Acute IP 6% 17% 2% - 75% - - 100% 1,738

Non-acute IP 2% - 20% - 78% - - 100% 483

SOP 4% 26% 7% 1% 58% 1% 3% 100% 1,779

PHC - - 1% - 98% - - 100% 9,408

A&E 4% 6% 3% 1% 85% 1% 1% 100% 595

AHOP 2% 7% 3% 1% 80% - 8% 100% 442







Acute IP - - - 14% 1,738

Non-acute IP - - - 46% 483

SOP - - - 10% 1,779

PHC 95% 95% 95% 97% 9,408

A&E - - - 75% 595

AHOP - - - 68% 442



TPU Group: 944

District: Lantau Island




the Lantau Island district, with its corresponding Cluster








Acute IP 1% 32% - - 67% - - 100% 853

Non-acute IP 7% 88% - - 6% - - 100% 872

SOP 6% 61% 2% 1% 28% 2% 1% 100% 1,456

PHC 1% 1% 1% - 97% - - 100% 3,463

A&E 4% 4% 1% - 91% - - 100% 815

AHOP 4% 23% 2% 1% 71% - - 100% 220




20 minutes of travel time are 0% for each of the six services (bolded in green).



Acute IP - - - 18% 853

Non-acute IP - - - 6% 872

SOP - - - 5% 1,456

PHC - - - 94% 3,463

A&E - - - 89% 815

AHOP - - - 62% 220








located in the Lantau Island district, with its

corresponding Cluster being the Kowloon West Cluster.







Acute IP - 8% 4% - 86% - 1% 100% 443

Non-acute IP 14% - 2% - 84% - - 100% 537

SOP 5% 37% 4% - 45% 1% 8% 100% 584

PHC 1% 1% 1% - 95% 1% 1% 100% 931

A&E 1% 3% 1% - 94% - 1% 100% 371

AHOP 5% 29% 21% - 43% - 2% 100% 235




20 minutes of travel time are 10% for PHC and 0% for other services (bolded in green).



Acute IP - - 15% 15% 443

Non-acute IP - - 5% 5% 537

SOP - - 10% 10% 584

PHC - 10% 95% 95% 931

A&E - - 92% 92% 371

AHOP - - 35% 35% 235













concordance rates (bolded in green) range from 71% for PHC to 100% for Acute IP and Non-acute IP.



Acute IP - - - - 100% - - 100% 32

Non-acute IP - - - - 100% - - 100% 17

SOP 4% - 3% - 93% - - 100% 62

PHC - - 2% - 71% 22% 5% 100% 128

A&E - - 4% - 96% - - 100% 34

AHOP 5% - - - 95% - - 100% 23







Acute IP - - - 54% 32

Non-acute IP - - - 92% 17

SOP - - - 10% 62

PHC 47% 47% 47% 71% 128

A&E - - - 77% 34

AHOP - - - 76% 23



TPU Group: 941-943





in the Lantau Island district, with its corresponding

Cluster being the Kowloon West Cluster.




Cluster concordance rates (bolded in green) range from 74% for SOP to 96% for A&E.



Acute IP 1% 8% 6% 1% 81% 2% 1% 100% 46,639

Non-acute IP 5% 2% 10% - 79% 3% 2% 100% 18,039

SOP 3% 9% 8% 1% 74% 3% 2% 100% 70,270

PHC 2% 1% 3% 1% 92% 1% 1% 100% 67,396

A&E 1% 1% 1% - 96% 1% 1% 100% 75,852

AHOP 2% 3% 6% - 86% 2% 1% 100% 35,445




20 minutes of travel time range from 11% for SOP to 93% for A&E (bolded in green).



Acute IP 12% 12% 87% 99% 46,639

Non-acute IP 19% 19% 87% 94% 18,039

SOP 11% 11% 81% 98% 70,270

PHC 85% 85% 94% 99% 67,396

A&E 93% 93% 98% 100% 75,852

AHOP 68% 68% 91% 99% 35,445
















Acute IP 3% 37% - - 59% 1% - 100% 3,322

Non-acute IP 37% 19% 5% - 37% 2% - 100% 1,416

SOP 7% 73% 2% 1% 16% 1% - 100% 3,917

PHC 2% 1% - - 96% - - 100% 11,479

A&E 7% 12% 1% 1% 77% 2% - 100% 1,257

AHOP 19% 38% 1% 1% 40% 1% - 100% 1,262




20 minutes of travel time are 94% for PHC and 0% for other services (bolded in green).



Acute IP - - - 14% 3,322

Non-acute IP - - - 27% 1,416

SOP - - - 3% 3,917

PHC - 94% 94% 95% 11,479

A&E - - - 73% 1,257

AHOP - - - 33% 1,262
















Acute IP 4% 44% 2% 2% 45% 3% - 100% 3,357

Non-acute IP 17% 1% 7% - 75% - - 100% 462

SOP 9% 63% 5% 1% 21% 2% 1% 100% 6,110

PHC 28% 14% 3% 1% 52% 1% 1% 100% 2,956

A&E 4% 11% 1% - 83% 1% 1% 100% 2,810

AHOP 13% 29% 3% 1% 53% 1% - 100% 2,344







Acute IP - 6% 44% 92% 3,357

Non-acute IP - 34% 37% 82% 462

SOP - 3% 18% 88% 6,110

PHC - 49% 50% 68% 2,956

A&E - 80% 82% 96% 2,810

AHOP - 44% 53% 84% 2,344



TPU Group: 971-974





in the Lantau Island district, with its corresponding








Acute IP 1% 5% 15% 1% 74% 4% 1% 100% 23,580

Non-acute IP 7% - 11% - 76% 6% - 100% 9,938

SOP 2% 5% 19% 1% 70% 3% 1% 100% 44,458

PHC 2% 2% 19% 2% 73% 2% 1% 100% 22,988

A&E 2% 1% 8% 1% 85% 2% 2% 100% 8,734

AHOP 2% 3% 30% 1% 62% 1% 2% 100% 13,460







Acute IP 67% 92% 99% 100% 23,580

Non-acute IP 46% 86% 100% 100% 9,938

SOP 43% 88% 99% 100% 44,458

PHC - 93% 99% 100% 22,988

A&E 70% 92% 98% 100% 8,734

AHOP 40% 91% 98% 100% 13,460



TPU Group: 260

District: Sham Shui Po




the Sham Shui Po district, with its corresponding








Acute IP 1% 4% 20% - 73% 1% - 100% 8,389

Non-acute IP 8% - 21% - 64% 5% 2% 100% 4,908

SOP 1% 3% 17% 1% 75% 2% 1% 100% 8,687

PHC 1% 1% 9% 1% 86% 1% 1% 100% 5,880

A&E 1% 1% 6% 1% 90% 1% 1% 100% 4,565

AHOP 2% 5% 20% 1% 71% 1% 1% 100% 2,837







Acute IP 79% 95% 100% 100% 8,389

Non-acute IP 55% 85% 98% 100% 4,908

SOP 64% 94% 99% 100% 8,687

PHC 85% 97% 100% 100% 5,880

A&E 88% 96% 99% 100% 4,565

AHOP 63% 92% 100% 100% 2,837



TPU Group: 261













Acute IP - 4% 22% - 71% 2% - 100% 17,047

Non-acute IP - - 16% - 84% - - 100% 8,046

SOP 1% 3% 18% 1% 76% 2% 1% 100% 28,187

PHC 1% - 7% 1% 90% 1% - 100% 21,256

A&E 1% 1% 9% 1% 86% 2% 1% 100% 7,510

AHOP 1% 3% 24% 1% 69% 2% 1% 100% 7,872







Acute IP 71% 96% 100% 100% 17,047

Non-acute IP 84% 100% 100% 100% 8,046

SOP 76% 95% 100% 100% 28,187

PHC 14% 98% 100% 100% 21,256

A&E 85% 95% 100% 100% 7,510

AHOP 70% 96% 100% 100% 7,872



TPU Group: 262













Acute IP 1% 2% 31% 1% 62% 3% 1% 100% 54,665

Non-acute IP 2% - 15% 1% 71% 4% 6% 100% 33,547

SOP 1% 3% 29% 1% 63% 2% 1% 100% 82,169

PHC 1% 1% 19% 2% 75% 1% 1% 100% 64,284

A&E 2% 1% 21% 2% 72% 3% 1% 100% 27,134

AHOP 2% 2% 42% 2% 48% 2% 1% 100% 24,222







Acute IP 6% 96% 99% 100% 54,665

Non-acute IP 11% 89% 94% 100% 33,547

SOP 2% 95% 99% 100% 82,169

PHC 3% 97% 99% 100% 64,284

A&E 1% 95% 99% 100% 27,134

AHOP 3% 94% 99% 100% 24,222



TPU Group: 263













Acute IP 1% 2% 20% 1% 74% 2% 1% 100% 68,039

Non-acute IP 2% - 11% 1% 84% 1% 1% 100% 30,223

SOP 1% 2% 18% 1% 75% 2% 1% 100% 83,296

PHC 1% 1% 10% 1% 86% 1% 1% 100% 57,047

A&E 1% 1% 9% 1% 86% 1% 1% 100% 29,528

AHOP 1% 2% 26% 1% 68% 2% 1% 100% 23,693







Acute IP 73% 95% 100% 100% 68,039

Non-acute IP 84% 96% 100% 100% 30,223

SOP 74% 95% 100% 100% 83,296

PHC 84% 97% 100% 100% 57,047

A&E 85% 95% 100% 100% 29,528

AHOP 66% 95% 100% 100% 23,693



TPU Group: 264













Acute IP - 3% 24% 1% 71% 2% - 100% 30,517

Non-acute IP 6% 2% 13% 1% 67% 5% 7% 100% 15,768

SOP 1% 3% 20% 1% 73% 2% 1% 100% 33,708

PHC 1% 1% 12% 1% 84% 1% 1% 100% 24,990

A&E 1% 1% 11% 1% 84% 2% 1% 100% 11,405

AHOP 1% 2% 31% 1% 63% 1% 1% 100% 10,224







Acute IP 70% 98% 100% 100% 30,517

Non-acute IP 49% 87% 100% 100% 15,768

SOP 64% 97% 100% 100% 33,708

PHC 69% 98% 100% 100% 24,990

A&E 83% 97% 99% 100% 11,405

AHOP 59% 98% 100% 100% 10,224



TPU Group: 265













Acute IP 1% 2% 33% 2% 60% 2% 1% 100% 35,728

Non-acute IP 3% - 17% 2% 74% 2% 2% 100% 19,430

SOP 1% 2% 31% 1% 62% 2% 1% 100% 42,353

PHC 1% 1% 22% 1% 73% 1% 1% 100% 33,876

A&E 1% 1% 21% 1% 73% 2% 1% 100% 18,697

AHOP 2% 1% 39% 1% 55% 1% 1% 100% 12,937







Acute IP - 94% 99% 100% 35,728

Non-acute IP - 91% 98% 100% 19,430

SOP - 94% 99% 100% 42,353

PHC 56% 96% 99% 100% 33,876

A&E - 94% 99% 100% 18,697

AHOP - 94% 99% 100% 12,937



TPU Group: 266













Acute IP 1% 2% 33% 2% 59% 2% 1% 100% 27,931

Non-acute IP 1% 3% 25% 2% 61% 7% 2% 100% 14,317

SOP 2% 3% 40% 1% 52% 2% 1% 100% 28,715

PHC 1% 1% 37% 1% 58% 1% 1% 100% 21,835

A&E 2% 1% 30% 2% 63% 2% 1% 100% 11,215

AHOP 1% 5% 49% 2% 40% 2% 1% 100% 10,137







Acute IP 50% 95% 99% 100% 27,931

Non-acute IP 37% 90% 98% 100% 14,317

SOP 41% 95% 99% 100% 28,715

PHC 76% 97% 99% 100% 21,835

A&E 58% 95% 99% 100% 11,215

AHOP 38% 94% 98% 100% 10,137



TPU Group: 267













Acute IP 1% 3% 33% - 59% 3% - 100% 18,977

Non-acute IP 2% - 17% - 78% 2% - 100% 9,718

SOP 1% 4% 39% 2% 51% 3% 1% 100% 27,452

PHC 1% 1% 21% 2% 75% 1% 1% 100% 21,493

A&E 1% 1% 28% 2% 65% 3% 1% 100% 8,052

AHOP 2% 3% 57% 3% 32% 3% - 100% 8,645







Acute IP 1% 94% 99% 100% 18,977

Non-acute IP 8% 95% 100% 100% 9,718

SOP 6% 94% 99% 100% 27,452

PHC 63% 97% 99% 100% 21,493

A&E - 96% 98% 100% 8,052

AHOP 18% 93% 100% 100% 8,645



TPU Group: 268










Cluster concordance rates (bolded in green) range from 56% for AHOP to 78% for A&E.



Acute IP 1% 6% 28% 1% 59% 4% 1% 100% 36,481

Non-acute IP 8% 4% 18% 1% 64% 1% 6% 100% 15,072

SOP 1% 4% 26% 1% 64% 2% 1% 100% 65,695

PHC 2% 1% 24% 2% 69% 1% 1% 100% 48,966

A&E 2% 1% 15% 1% 78% 2% 1% 100% 22,560

AHOP 1% 3% 36% 2% 56% 2% 2% 100% 22,452




20 minutes of travel time range from 86% for Non-acute IP to 96% for SOP (bolded in green).



Acute IP 14% 96% 100% 100% 36,481

Non-acute IP 34% 86% 96% 100% 15,072

SOP 23% 96% 100% 100% 65,695

PHC 43% 96% 100% 100% 48,966

A&E 10% 96% 100% 100% 22,560

AHOP 16% 96% 100% 100% 22,452








located in the Sham Shui Po district, with its








Acute IP - - - - 100% - - 100% 64

Non-acute IP - - - - - - 100% 100% -

SOP - - 14% - 86% 1% - 100% 128

PHC 1% - - 1% 95% - 3% 100% 112

A&E - - - - 93% 7% - 100% 35

AHOP - - - - 94% 6% - 100% 36







Acute IP - 100% 100% 100% 64

Non-acute IP - - - 100% -

SOP - 62% 93% 100% 128

PHC - 94% 94% 100% 112

A&E - 93% 93% 100% 35

AHOP - 88% 94% 100% 36



TPU Group: 310

District: Tsuen Wan




the Tsuen Wan district, with its corresponding Cluster








Acute IP 1% 1% 4% - 93% 1% 1% 100% 5,052

Non-acute IP - - 35% 1% 47% 16% 1% 100% 2,072

SOP 1% 2% 8% 2% 82% 2% 2% 100% 3,772

PHC - 1% 7% 2% 87% 2% 1% 100% 2,563

A&E - - 3% 1% 94% 1% 1% 100% 1,628

AHOP 1% 2% 12% 3% 81% 1% 1% 100% 1,039







Acute IP 79% 94% 100% 100% 5,052

Non-acute IP 29% 56% 100% 100% 2,072

SOP 37% 84% 99% 100% 3,772

PHC 76% 90% 99% 99% 2,563

A&E 86% 94% 100% 100% 1,628

AHOP 62% 83% 100% 100% 1,039



TPU Group: 321

District: Tsuen Wan












Acute IP 1% 6% 3% - 89% 1% - 100% 1,426

Non-acute IP 23% - 7% - 60% 11% - 100% 777

SOP 2% 8% 11% 1% 71% 5% 3% 100% 1,643

PHC 2% 2% 9% 1% 82% 3% 3% 100% 816

A&E 1% 2% 4% 1% 89% 3% 2% 100% 499

AHOP 1% 3% 8% - 76% 10% 2% 100% 418







Acute IP - 72% 93% 99% 1,426

Non-acute IP - 28% 77% 77% 777

SOP - 36% 83% 99% 1,643

PHC - 78% 92% 100% 816

A&E - 75% 93% 100% 499

AHOP - 38% 92% 100% 418



TPU Group: 322

District: Tsuen Wan












Acute IP 1% 3% 6% 1% 85% 3% 1% 100% 51,458

Non-acute IP 1% 2% 11% 1% 72% 11% 3% 100% 16,286

SOP 2% 4% 11% 1% 77% 4% 2% 100% 69,938

PHC 2% 1% 6% 1% 87% 2% 2% 100% 44,811

A&E 2% 1% 4% 1% 89% 2% 1% 100% 17,756

AHOP 2% 2% 17% 1% 73% 2% 3% 100% 22,893







Acute IP - 87% 99% 100% 51,458

Non-acute IP - 73% 99% 100% 16,286

SOP - 85% 99% 100% 69,938

PHC 49% 92% 99% 100% 44,811

A&E - 90% 99% 100% 17,756

AHOP - 79% 99% 100% 22,893



TPU Group: 323

District: Tsuen Wan












Acute IP 1% 3% 4% 1% 88% 2% 1% 100% 50,110

Non-acute IP 4% - 10% - 82% 3% 1% 100% 22,720

SOP 1% 2% 5% 1% 87% 3% 1% 100% 69,467

PHC - 1% 3% 1% 94% 1% 1% 100% 65,295

A&E 1% 1% 3% 1% 91% 2% 1% 100% 25,449

AHOP 1% 1% 9% 1% 83% 2% 2% 100% 18,311







Acute IP 56% 92% 99% 100% 50,110

Non-acute IP 32% 87% 100% 100% 22,720

SOP 36% 91% 100% 100% 69,467

PHC 91% 97% 100% 100% 65,295

A&E 69% 93% 100% 100% 25,449

AHOP 48% 90% 99% 100% 18,311



TPU Group: 325

District: Tsuen Wan












Acute IP 1% 11% 11% 1% 67% 6% 2% 100% 9,007

Non-acute IP 9% - 16% - 69% - 6% 100% 2,306

SOP 2% 8% 14% 1% 65% 4% 5% 100% 15,235

PHC 3% 2% 8% 2% 77% 2% 5% 100% 8,402

A&E 2% 3% 7% 1% 81% 3% 4% 100% 3,515

AHOP 3% 5% 19% 1% 66% 3% 4% 100% 5,273




minutes of travel time range from 80% for Acute IP to 92% for A&E (bolded in green).



Acute IP - 80% 99% 100% 9,007

Non-acute IP - 89% 100% 100% 2,306

SOP 1% 82% 99% 100% 15,235

PHC 41% 90% 99% 100% 8,402

A&E - 92% 99% 100% 3,515

AHOP - 81% 99% 100% 5,273



TPU Group: 335

District: Tsuen Wan












Acute IP 3% 9% 9% 1% 73% 4% 1% 100% 3,517

Non-acute IP 7% 4% 18% - 58% 14% - 100% 731

SOP 6% 14% 13% 2% 60% 5% 1% 100% 7,076

PHC 5% 6% 8% 3% 69% 7% 2% 100% 3,681

A&E 5% 3% 6% 1% 80% 3% 2% 100% 1,685

AHOP 6% 9% 16% 2% 61% 6% 1% 100% 2,460







Acute IP - 73% 96% 100% 3,517

Non-acute IP - 52% 80% 100% 731

SOP - 61% 95% 100% 7,076

PHC - 69% 91% 100% 3,681

A&E - 78% 98% 100% 1,685

AHOP - 61% 98% 100% 2,460



TPU Group: 975

District: Tsuen Wan









Cluster concordance rates (bolded in green) range from 70% for Non-acute IP to 92% for A&E.



Acute IP 1% 3% 5% - 86% 3% 2% 100% 72,202

Non-acute IP 1% 2% 8% - 70% 4% 14% 100% 27,341

SOP 1% 3% 9% 1% 81% 3% 2% 100% 89,656

PHC 1% 1% 4% 1% 91% 1% 2% 100% 64,433

A&E 1% 1% 3% 1% 92% 2% 2% 100% 33,651

AHOP 1% 2% 13% 1% 79% 2% 2% 100% 27,662




within 20 minutes of travel time range from 83% for Non-acute IP to 96% for Acute IP (bolded in

green).



Acute IP 86% 96% 100% 100% 72,202

Non-acute IP 69% 83% 100% 100% 27,341

SOP 82% 96% 100% 100% 89,656

PHC 90% 96% 100% 100% 64,433

A&E 91% 96% 100% 100% 33,651

AHOP 79% 96% 100% 100% 27,662



TPU Group: 320, 324 & 329

District: Tsuen Wan




located in the Tsuen Wan district, with its




The patronage pattern in 2015/16 for residents in TPU Group 331-334, 336 & 340 is presented below.

The Cluster concordance rates (bolded in green) range from 57% for Non-acute IP to 79% for PHC.



Acute IP 1% 7% 5% 1% 64% 3% 19% 100% 2,516

Non-acute IP - - 6% - 57% 2% 35% 100% 787

SOP 3% 7% 14% 1% 63% 4% 9% 100% 5,850

PHC 2% 3% 7% 1% 79% 1% 8% 100% 3,324

A&E 2% 3% 6% 1% 71% 3% 14% 100% 1,360

AHOP 1% 3% 23% 2% 59% 2% 10% 100% 1,930



times for TPU Group 331-334, 336 & 340 in 2015/16. For example, the percentages of utilisation

sought within 20 minutes of travel time range from 0% for Acute IP, Non-acute IP and A&E to 45% for

PHC (bolded in green).



Acute IP - - 88% 99% 2,516

Non-acute IP - - 98% 100% 787

SOP - 1% 82% 98% 5,850

PHC - 45% 91% 98% 3,324

A&E - - 88% 99% 1,360

AHOP - 1% 77% 98% 1,930



TPU Group: 331-334, 336 & 340

District: Tsuen Wan



TPU Group 331-334, 336 & 340 (highlighted on the

map) is located in the Tsuen Wan district, with its





concordance rates (bolded in green) range from 59% for PHC to 100% for Acute IP, Non-acute IP, A&E

and AHOP.



Acute IP - - - - - 100% - 100% 44

Non-acute IP - - - - - 100% - 100% 509

SOP 4% - 7% - - 82% 8% 100% 37

PHC 9% - 20% - - 59% 11% 100% 67

A&E - - - - - 100% - 100% 21

AHOP - - - - - 100% - 100% 4







Acute IP 77% 94% 100% 100% 44

Non-acute IP - 100% 100% 100% 509

SOP 37% 61% 89% 96% 37

PHC 54% 58% 71% 91% 67

A&E 69% 100% 100% 100% 21

AHOP 34% 67% 100% 100% 4



TPU Group: 623

District: North

Corresponding Cluster: New Territories East Cluster



the North district, with its corresponding Cluster being

the New Territories East Cluster.







Acute IP 1% 2% 3% 1% 1% 91% 1% 100% 38,263

Non-acute IP - - 5% 1% 4% 88% 2% 100% 13,760

SOP 1% 2% 5% 1% 2% 87% 2% 100% 53,133

PHC 1% - 3% 1% 1% 94% 1% 100% 51,117

A&E 1% - 3% 1% 2% 91% 2% 100% 18,323

AHOP 1% 1% 9% 1% 1% 86% 2% 100% 18,107







Acute IP 53% 65% 93% 98% 38,263

Non-acute IP - 88% 99% 100% 13,760

SOP 43% 64% 92% 98% 53,133

PHC 86% 92% 96% 100% 51,117

A&E 72% 87% 95% 100% 18,323

AHOP 56% 79% 93% 99% 18,107



TPU Group: 625

District: North












Acute IP - 1% 3% 1% 1% 93% 2% 100% 22,033

Non-acute IP - 2% 1% 1% - 97% - 100% 11,157

SOP 1% 1% 4% 1% 2% 89% 2% 100% 30,848

PHC 1% - 2% 1% 1% 95% 2% 100% 30,522

A&E 1% - 2% 1% 2% 93% 2% 100% 12,104

AHOP - 1% 8% - 1% 87% 3% 100% 8,972







Acute IP 66% 76% 96% 99% 22,033

Non-acute IP - 97% 98% 98% 11,157

SOP 50% 71% 94% 99% 30,848

PHC 91% 94% 97% 100% 30,522

A&E 81% 91% 96% 100% 12,104

AHOP 62% 82% 91% 99% 8,972



TPU Group: 626

District: North












Acute IP - 2% 3% 1% 1% 91% 2% 100% 28,420

Non-acute IP 2% - 4% 2% 1% 88% 3% 100% 12,672

SOP 1% 2% 4% 1% 1% 89% 3% 100% 35,963

PHC 1% - 2% 1% 1% 94% 2% 100% 31,852

A&E 1% - 1% 1% 1% 95% 1% 100% 19,100

AHOP 1% 1% 12% - 1% 83% 4% 100% 11,368







Acute IP 60% 71% 94% 98% 28,420

Non-acute IP - 79% 91% 98% 12,672

SOP 47% 70% 92% 98% 35,963

PHC 87% 94% 97% 100% 31,852

A&E 83% 93% 96% 99% 19,100

AHOP 58% 77% 86% 99% 11,368



TPU Group: 627

District: North












Acute IP 1% 2% 3% 1% 1% 91% 2% 100% 64,999

Non-acute IP 2% 2% 1% 1% - 87% 8% 100% 33,792

SOP 1% 2% 6% 1% 2% 87% 2% 100% 92,041

PHC 1% 1% 3% 1% 1% 92% 1% 100% 86,470

A&E 1% 1% 3% 1% 2% 91% 2% 100% 33,770

AHOP 1% 1% 9% 1% 2% 85% 2% 100% 32,008







Acute IP 55% 66% 94% 98% 64,999

Non-acute IP - 87% 96% 100% 33,792

SOP 44% 65% 92% 98% 92,041

PHC 84% 90% 95% 99% 86,470

A&E 70% 87% 94% 99% 33,770

AHOP 56% 77% 90% 99% 32,008



TPU Group: 628

District: North












Acute IP - 1% 4% 2% 2% 92% - 100% 2,384

Non-acute IP - - 6% 1% - 93% - 100% 861

SOP 5% 3% 7% 3% 3% 79% 1% 100% 2,381

PHC 1% 1% 6% 2% 1% 88% 2% 100% 2,212

A&E 1% 1% 3% 1% 2% 91% 1% 100% 1,032

AHOP 3% - 6% 1% 1% 88% 2% 100% 786







Acute IP - 56% 92% 95% 2,384

Non-acute IP - - 93% 99% 861

SOP - 35% 79% 86% 2,381

PHC 3% 83% 89% 94% 2,212

A&E - 65% 92% 94% 1,032

AHOP - 53% 89% 92% 786



TPU Group: 634

District: North












Acute IP - - 1% - - 99% - 100% 234

Non-acute IP - - - - - 100% - 100% 124

SOP - 2% 4% - 2% 88% 4% 100% 228

PHC - 2% 2% 2% 1% 89% 4% 100% 295

A&E - - 1% 1% - 98% - 100% 102

AHOP - 2% 2% - - 97% - 100% 75







Acute IP - 70% 99% 99% 234

Non-acute IP - - 100% 100% 124

SOP - 47% 89% 94% 228

PHC 22% 87% 93% 96% 295

A&E - 84% 98% 99% 102

AHOP - 90% 97% 97% 75



TPU Group: 641

District: North












Acute IP - - 3% 1% - 95% 1% 100% 2,496

Non-acute IP 4% - 5% 1% 1% 86% 4% 100% 2,350

SOP 1% - 5% 1% - 91% 2% 100% 741

PHC 1% - 1% - - 97% 1% 100% 1,189

A&E 1% - 1% 1% - 96% 1% 100% 547

AHOP - - 4% 1% - 94% 1% 100% 139







Acute IP - - 92% 95% 2,496

Non-acute IP - - - 86% 2,350

SOP - - 65% 91% 741

PHC 62% 62% 97% 98% 1,189

A&E - - 92% 96% 547

AHOP - - 71% 95% 139



TPU Group: 642

District: North












Acute IP - - 1% 1% - 97% 1% 100% 13,104

Non-acute IP 3% - 4% 1% - 88% 5% 100% 12,573

SOP - - 1% 1% 1% 94% 4% 100% 3,512

PHC - - 1% - - 98% 1% 100% 2,278

A&E - - 1% - - 95% 3% 100% 2,468

AHOP - - 1% 1% - 96% 2% 100% 808







Acute IP 90% 94% 98% 100% 13,104

Non-acute IP - 80% 94% 97% 12,573

SOP 69% 84% 98% 100% 3,512

PHC - 98% 99% 100% 2,278

A&E 92% 94% 99% 100% 2,468

AHOP 78% 89% 99% 99% 808




District: North




located in the North district, with its corresponding

Cluster being the New Territories East Cluster.




Cluster concordance rates (bolded in green) range from 66% for AHOP to 99% for Non-acute IP.



Acute IP 1% 1% 3% - 3% 87% 5% 100% 882

Non-acute IP - - 1% - - 99% - 100% 202

SOP 3% 4% 9% 2% 4% 73% 5% 100% 962

PHC 7% 1% 5% 1% 4% 75% 8% 100% 675

A&E 2% 1% 7% 1% 4% 78% 8% 100% 517

AHOP 5% 2% 15% - 3% 66% 9% 100% 251




20 minutes of travel time range from 53% for AHOP to 82% for A&E (bolded in green).



Acute IP 41% 59% 92% 98% 882

Non-acute IP - 67% 99% 100% 202

SOP 40% 55% 78% 93% 962

PHC 2% 74% 84% 97% 675

A&E 69% 82% 86% 98% 517

AHOP 33% 53% 76% 94% 251




District: North












Acute IP - - 1% 2% 5% 90% 1% 100% 799

Non-acute IP - - 1% - 21% 40% 37% 100% 4,717

SOP 1% 1% 7% 2% 4% 81% 5% 100% 717

PHC - - 3% - 2% 92% 2% 100% 598

A&E 1% 1% 2% 5% 4% 87% 1% 100% 249

AHOP - - 9% 1% - 90% - 100% 138




20 minutes of travel time are 0% for each of the six services (bolded in green).



Acute IP - - 81% 86% 799

Non-acute IP - - - 39% 4,717

SOP - - 64% 73% 717

PHC - - 87% 89% 598

A&E - - 78% 84% 249

AHOP - - 83% 87% 138




District: North












Acute IP - 1% 3% - 1% 93% 2% 100% 46,467

Non-acute IP 3% 1% 4% 2% 3% 86% 1% 100% 18,550

SOP 1% 2% 5% 1% 2% 88% 3% 100% 52,011

PHC 1% - 2% 1% 1% 94% 2% 100% 52,951

A&E 1% 1% 2% 1% 2% 92% 2% 100% 20,496

AHOP 1% 1% 7% 1% 1% 87% 3% 100% 16,775




20 minutes of travel time range from 68% for SOP to 94% for PHC (bolded in green).



Acute IP 66% 75% 95% 99% 46,467

Non-acute IP - 80% 90% 96% 18,550

SOP 49% 68% 93% 98% 52,011

PHC 91% 94% 97% 100% 52,951

A&E 80% 90% 96% 99% 20,496

AHOP 63% 81% 91% 99% 16,775




District: North












Acute IP - - - 1% - 99% - 100% 4,625

Non-acute IP - - - - - 100% - 100% 2,067

SOP - - 2% 1% 1% 95% 1% 100% 3,980

PHC 1% - 1% - - 98% - 100% 5,295

A&E - - 1% 1% 1% 96% 1% 100% 1,787

AHOP - - 4% 1% 1% 94% - 100% 977







Acute IP - - 80% 99% 4,625

Non-acute IP - - 100% 100% 2,067

SOP - - 67% 95% 3,980

PHC 66% 66% 96% 98% 5,295

A&E - - 92% 97% 1,787

AHOP - - 84% 94% 977



TPU Group: 651-653

District: North




in the North district, with its corresponding Cluster

being the New Territories East Cluster.




concordance rates (bolded in green) range from 80% for AHOP to 90% for Acute IP.



Acute IP 1% 2% 5% 1% 2% 90% 1% 100% 53,826

Non-acute IP - - 2% 1% 4% 90% 3% 100% 48,440

SOP 1% 2% 6% 1% 3% 86% 1% 100% 74,293

PHC 1% 1% 6% 2% 2% 88% 1% 100% 69,863

A&E 1% 1% 4% 2% 5% 86% 1% 100% 20,216

AHOP 1% 2% 12% 1% 3% 80% 1% 100% 24,841




minutes of travel time range from 89% for AHOP to 96% for Non-acute IP (bolded in green).



Acute IP 87% 92% 99% 100% 53,826

Non-acute IP 75% 96% 97% 100% 48,440

SOP 82% 91% 99% 100% 74,293

PHC 82% 94% 99% 100% 69,863

A&E 82% 90% 98% 100% 20,216

AHOP 76% 89% 99% 100% 24,841



TPU Group: 755

District: Sha Tin




the Sha Tin district, with its corresponding Cluster








Acute IP 1% 3% 5% 1% 2% 88% 1% 100% 106,805

Non-acute IP - 1% 5% 1% 3% 83% 7% 100% 64,321

SOP 1% 3% 8% 2% 3% 82% 1% 100% 178,215

PHC 1% 1% 5% 2% 2% 89% 1% 100% 177,463

A&E 2% 1% 5% 2% 4% 84% 1% 100% 40,161

AHOP 2% 1% 14% 3% 3% 77% 1% 100% 65,149







Acute IP - 88% 97% 100% 106,805

Non-acute IP 64% 88% 92% 100% 64,321

SOP - 85% 97% 100% 178,215

PHC 74% 92% 99% 100% 177,463

A&E - 85% 98% 100% 40,161

AHOP - 82% 98% 100% 65,149



TPU Group: 757

District: Sha Tin












Acute IP 1% 2% 4% 1% 2% 90% 1% 100% 34,545

Non-acute IP - - 3% 1% 6% 89% 1% 100% 22,927

SOP 1% 2% 6% 1% 4% 86% 1% 100% 47,997

PHC 1% - 3% 1% 2% 93% - 100% 55,380

A&E 1% 1% 4% 2% 5% 86% 1% 100% 12,958

AHOP 1% 2% 11% 2% 5% 78% 1% 100% 16,871







Acute IP 87% 94% 99% 100% 34,545

Non-acute IP 67% 98% 100% 100% 22,927

SOP 82% 95% 99% 100% 47,997

PHC 90% 98% 100% 100% 55,380

A&E 81% 92% 99% 100% 12,958

AHOP 73% 91% 99% 100% 16,871



TPU Group: 758

District: Sha Tin












Acute IP 1% 2% 6% 1% 2% 88% 1% 100% 86,149

Non-acute IP 1% 1% 3% 1% 1% 93% 1% 100% 61,856

SOP 1% 2% 9% 2% 4% 82% 1% 100% 122,076

PHC 1% 1% 10% 2% 3% 83% 1% 100% 110,276

A&E 2% 1% 7% 2% 5% 83% 1% 100% 30,597

AHOP 1% 2% 16% 2% 4% 74% 1% 100% 41,972







Acute IP 85% 95% 100% 100% 86,149

Non-acute IP 3% 97% 99% 100% 61,856

SOP 79% 94% 99% 100% 122,076

PHC 37% 96% 99% 100% 110,276

A&E 77% 93% 99% 100% 30,597

AHOP 70% 94% 100% 100% 41,972



TPU Group: 759

District: Sha Tin












Acute IP 1% 3% 5% 2% 3% 86% 1% 100% 39,772

Non-acute IP - - 2% 3% 3% 89% 3% 100% 27,265

SOP 2% 3% 9% 2% 5% 80% 1% 100% 56,991

PHC 1% 1% 9% 2% 4% 83% 1% 100% 47,535

A&E 2% 1% 7% 2% 6% 81% 1% 100% 14,598

AHOP 2% 1% 18% 2% 5% 72% 1% 100% 19,754




within 20 minutes of travel time range from 75% for Non-acute IP to 90% for PHC (bolded in green).



Acute IP - 88% 99% 100% 39,772

Non-acute IP - 75% 99% 100% 27,265

SOP - 86% 98% 100% 56,991

PHC - 90% 99% 100% 47,535

A&E - 84% 98% 100% 14,598

AHOP - 82% 98% 100% 19,754



TPU Group: 731, 733 & 754

District: Sha Tin




located in the Sha Tin district, with its corresponding





Cluster concordance rates (bolded in green) range from 69% for AHOP to 85% for Acute IP.



Acute IP 1% 4% 7% 1% 1% 85% 1% 100% 15,188

Non-acute IP 6% 3% 2% - 4% 84% - 100% 10,673

SOP 2% 4% 10% 2% 4% 77% 1% 100% 25,760

PHC 2% 1% 8% 2% 2% 84% 1% 100% 18,438

A&E 3% 1% 6% 2% 4% 83% 1% 100% 5,302

AHOP 2% 1% 23% 2% 2% 69% 1% 100% 10,485




within 20 minutes of travel time range from 84% for AHOP to 90% for Non-acute IP (bolded in green).



Acute IP 82% 86% 99% 100% 15,188

Non-acute IP - 90% 96% 100% 10,673

SOP 72% 84% 99% 100% 25,760

PHC 75% 90% 99% 100% 18,438

A&E 75% 86% 98% 100% 5,302

AHOP 63% 84% 100% 100% 10,485



TPU Group: 732, 751 & 753

District: Sha Tin








The patronage pattern in 2015/16 for residents in TPU Group 756 & 761-762 is presented below. The




Acute IP 1% 3% 4% 1% 2% 90% 1% 100% 60,647

Non-acute IP 1% 1% 4% 1% 1% 93% - 100% 41,560

SOP 1% 3% 8% 2% 3% 83% 1% 100% 89,157

PHC 1% 1% 6% 2% 2% 88% - 100% 76,893

A&E 1% 1% 4% 2% 3% 88% 1% 100% 21,970

AHOP 2% 1% 15% 2% 2% 77% 1% 100% 32,919



times for TPU Group 756 & 761-762 in 2015/16. For example, the percentages of utilisation sought

within 20 minutes of travel time range from 93% for SOP to 98% for Non-acute IP (bolded in green).



Acute IP 87% 94% 97% 100% 60,647

Non-acute IP 73% 98% 99% 100% 41,560

SOP 80% 93% 97% 100% 89,157

PHC 55% 95% 99% 100% 76,893

A&E 84% 94% 98% 100% 21,970

AHOP 74% 94% 99% 100% 32,919



TPU Group: 756 & 761-762

District: Sha Tin



TPU Group 756 & 761-762 (highlighted on the map) is









Acute IP 2% 3% 7% 2% 2% 83% 3% 100% 1,784

Non-acute IP - - 7% - - 85% 8% 100% 787

SOP 1% 4% 11% 4% 4% 75% 2% 100% 3,172

PHC 2% 1% 4% 3% 1% 88% 1% 100% 1,942

A&E 1% 1% 4% 3% 2% 88% 1% 100% 998

AHOP 2% - 12% 1% 1% 83% 2% 100% 1,241







Acute IP - 55% 85% 100% 1,784

Non-acute IP - 85% 92% 100% 787

SOP - 49% 86% 100% 3,172

PHC - 87% 93% 98% 1,942

A&E - 81% 92% 100% 998

AHOP - 75% 91% 99% 1,241



TPU Group: 720

District: Tai Po




the Tai Po district, with its corresponding Cluster being








Acute IP - 1% 3% 1% 1% 94% 1% 100% 74,404

Non-acute IP 2% 1% 2% 1% 2% 88% 4% 100% 49,152

SOP 1% 2% 5% 1% 2% 90% 1% 100% 83,026

PHC 1% 1% 3% 1% 1% 94% 1% 100% 76,086

A&E 1% - 2% 1% 2% 94% 1% 100% 38,983

AHOP 1% 1% 5% 1% 2% 89% 1% 100% 33,375







Acute IP 59% 94% 95% 100% 74,404

Non-acute IP 73% 88% 93% 99% 49,152

SOP 62% 90% 94% 100% 83,026

PHC 90% 94% 97% 100% 76,086

A&E 87% 94% 96% 100% 38,983

AHOP 80% 89% 94% 100% 33,375



TPU Group: 723

District: Tai Po












Acute IP - - 15% 2% 2% 81% - 100% 808

Non-acute IP - - - 2% - 98% - 100% 274

SOP - 2% 12% 3% 3% 79% 1% 100% 1,492

PHC - 1% 5% 1% 2% 88% 2% 100% 1,298

A&E 1% 1% 3% 1% 1% 91% 2% 100% 674

AHOP - 1% 7% 1% 1% 89% 1% 100% 546







Acute IP - 48% 81% 100% 808

Non-acute IP - 98% 98% 100% 274

SOP - 52% 79% 96% 1,492

PHC - 79% 89% 99% 1,298

A&E - 80% 91% 98% 674

AHOP - 80% 89% 99% 546



TPU Group: 724

District: Tai Po












Acute IP - 3% 2% 3% 2% 89% - 100% 712

Non-acute IP - - - - - 100% - 100% 157

SOP 2% 5% 13% 3% 3% 73% 2% 100% 1,206

PHC 3% 2% 4% 4% 5% 81% - 100% 736

A&E 1% 1% 4% 3% 5% 86% - 100% 511

AHOP 2% - 13% 2% 3% 80% - 100% 350







Acute IP - 89% 97% 100% 712

Non-acute IP - 100% 100% 100% 157

SOP - 73% 91% 100% 1,206

PHC - 81% 97% 100% 736

A&E - 86% 96% 98% 511

AHOP - 80% 98% 100% 350



TPU Group: 725

District: Tai Po












Acute IP 1% 2% 2% 1% 1% 92% 1% 100% 91,379

Non-acute IP 1% 1% 2% 1% 2% 91% 2% 100% 49,221

SOP 1% 2% 4% 1% 1% 91% 1% 100% 125,933

PHC - - 2% 1% 1% 95% 1% 100% 121,537

A&E 1% - 2% 1% 1% 94% 1% 100% 57,083

AHOP 1% 1% 5% 1% 1% 91% 1% 100% 51,156







Acute IP 56% 92% 97% 100% 91,379

Non-acute IP 82% 91% 97% 99% 49,221

SOP 62% 91% 97% 100% 125,933

PHC 91% 95% 99% 100% 121,537

A&E 88% 94% 98% 100% 57,083

AHOP 83% 91% 98% 100% 51,156



TPU Group: 726

District: Tai Po












Acute IP 1% 4% 5% 2% 1% 87% - 100% 2,987

Non-acute IP - - 3% - - 97% - 100% 897

SOP 3% 5% 12% 4% 4% 71% 1% 100% 4,452

PHC 5% 2% 9% 3% 3% 79% 1% 100% 2,565

A&E 2% 1% 3% 2% 3% 88% 1% 100% 1,753

AHOP 4% 2% 17% 2% 5% 69% 1% 100% 2,091







Acute IP - 88% 96% 100% 2,987

Non-acute IP - 99% 100% 100% 897

SOP - 75% 91% 100% 4,452

PHC 46% 83% 96% 100% 2,565

A&E - 90% 96% 100% 1,753

AHOP - 73% 94% 100% 2,091



TPU Group: 729

District: Tai Po












Acute IP 1% 2% 1% 1% 2% 94% - 100% 1,283

Non-acute IP - - 5% - 16% 79% - 100% 1,129

SOP 2% 3% 10% 2% 3% 81% 1% 100% 1,812

PHC 1% - 5% 2% 1% 91% 1% 100% 1,393

A&E 1% 1% 4% 1% 2% 90% 1% 100% 731

AHOP - 1% 4% - 1% 93% 1% 100% 807







Acute IP - 94% 96% 100% 1,283

Non-acute IP - 79% 100% 100% 1,129

SOP - 81% 87% 100% 1,812

PHC - 91% 95% 99% 1,393

A&E - 90% 93% 100% 731

AHOP - 93% 95% 100% 807



TPU Group: 631-633

District: Tai Po




in the Tai Po district, with its corresponding Cluster




The patronage pattern in 2015/16 for residents in TPU Group 711-712, 721 & 728 is presented below.

The Cluster concordance rates (bolded in green) range from 85% for AHOP to 98% for Non-acute IP.



Acute IP - - 3% - - 96% - 100% 9,062

Non-acute IP 1% - 1% - - 98% - 100% 6,575

SOP 1% 2% 6% 1% 2% 87% 1% 100% 4,220

PHC 1% 1% 4% 1% 1% 92% - 100% 3,391

A&E 1% - 2% 1% 1% 95% 1% 100% 2,630

AHOP 1% 1% 10% 2% 1% 85% 1% 100% 1,257



times for TPU Group 711-712, 721 & 728 in 2015/16. For example, the percentages of utilisation

sought within 20 minutes of travel time range from 0% for PHC to 89% for A&E (bolded in green).



Acute IP - 77% 89% 96% 9,062

Non-acute IP - 85% 98% 99% 6,575

SOP - 60% 81% 92% 4,220

PHC - - 91% 96% 3,391

A&E - 89% 94% 97% 2,630

AHOP - 76% 82% 93% 1,257



TPU Group: 711-712, 721 & 728

District: Tai Po



TPU Group 711-712, 721 & 728 (highlighted on the

map) is located in the Tai Po district, with its

corresponding Cluster being the New Territories East

Cluster.




Cluster concordance rates (bolded in green) range from 88% for SOP to 94% for A&E.



Acute IP 1% 2% 4% 1% 2% 89% 1% 100% 25,468

Non-acute IP 1% - 1% 1% 4% 91% 2% 100% 13,649

SOP 1% 2% 5% 1% 2% 88% 1% 100% 40,091

PHC 1% - 3% 1% 1% 94% 1% 100% 35,322

A&E 1% - 2% 1% 1% 94% 1% 100% 21,868

AHOP 1% 1% 6% 1% 2% 90% 1% 100% 15,555




20 minutes of travel time range from 88% for SOP to 94% for A&E (bolded in green).



Acute IP 53% 89% 94% 100% 25,468

Non-acute IP 78% 91% 97% 100% 13,649

SOP 60% 88% 96% 100% 40,091

PHC 28% 93% 99% 100% 35,322

A&E 89% 94% 97% 100% 21,868

AHOP 81% 90% 96% 100% 15,555




District: Tai Po




located in the Tai Po district, with its corresponding








Acute IP - 14% 4% 6% - 75% - 100% 1,494

Non-acute IP - - 32% 7% - 61% - 100% 460

SOP 3% 4% 11% 18% 4% 60% - 100% 2,471

PHC 1% - 11% 43% 1% 43% - 100% 2,666

A&E 3% 1% 8% 13% 4% 72% 1% 100% 574

AHOP 2% - 27% 22% 3% 46% - 100% 1,178




minutes of travel time are 0% for each of the six services (bolded in green).



Acute IP - - - 68% 1,494

Non-acute IP - - - 61% 460

SOP - - - 57% 2,471

PHC - - 39% 75% 2,666

A&E - - - 63% 574

AHOP - - - 40% 1,178



TPU Group: 741-744

District: Tai Po




in the Tai Po district, with its corresponding Cluster








Acute IP - 2% 3% - 2% 2% 90% 100% 81,008

Non-acute IP 1% 3% 4% - 1% 4% 88% 100% 57,542

SOP 1% 2% 3% 1% 3% 2% 89% 100% 116,785

PHC 1% 1% 2% - 2% 1% 94% 100% 98,434

A&E 1% 1% 2% 1% 5% 2% 90% 100% 35,216

AHOP 1% 1% 5% - 3% 1% 89% 100% 37,194







Acute IP - 90% 93% 99% 81,008

Non-acute IP - 88% 88% 99% 57,542

SOP - 89% 92% 99% 116,785

PHC 85% 93% 96% 99% 98,434

A&E - 90% 94% 100% 35,216

AHOP - 89% 92% 100% 37,194



TPU Group: 424

District: Tuen Mun

Corresponding Cluster: New Territories West Cluster



the Tuen Mun district, with its corresponding Cluster

being the New Territories West Cluster.







Acute IP 1% 2% 3% - 2% 2% 90% 100% 56,578

Non-acute IP 2% - 1% - 3% 3% 92% 100% 42,798

SOP 1% 2% 4% 1% 3% 2% 87% 100% 72,148

PHC 1% 1% 2% - 3% 1% 92% 100% 54,657

A&E 1% 1% 2% 1% 4% 2% 90% 100% 26,204

AHOP 1% 1% 4% 1% 3% 2% 88% 100% 25,905







Acute IP 86% 90% 93% 98% 56,578

Non-acute IP 82% 92% 94% 98% 42,798

SOP 82% 87% 89% 99% 72,148

PHC 77% 92% 94% 99% 54,657

A&E 86% 90% 94% 99% 26,204

AHOP 84% 88% 91% 98% 25,905



TPU Group: 425

District: Tuen Mun












Acute IP 3% 3% 12% 1% 10% 4% 68% 100% 3,898

Non-acute IP 3% 2% 12% - - 3% 79% 100% 1,509

SOP 3% 7% 9% 1% 11% 4% 66% 100% 7,403

PHC 3% 2% 7% 1% 10% 2% 75% 100% 4,224

A&E 2% 2% 5% 1% 14% 3% 71% 100% 1,885

AHOP 3% 2% 9% 1% 10% 1% 74% 100% 2,387







Acute IP - 64% 83% 98% 3,898

Non-acute IP - 77% 80% 100% 1,509

SOP - 62% 84% 98% 7,403

PHC - 72% 90% 98% 4,224

A&E - 68% 88% 99% 1,885

AHOP - 71% 86% 99% 2,387



TPU Group: 426

District: Tuen Mun












Acute IP 1% 2% 6% - 2% 1% 88% 100% 6,827

Non-acute IP 1% - 5% - 3% 1% 90% 100% 3,466

SOP 1% 2% 7% 1% 4% 2% 82% 100% 8,309

PHC 1% 1% 4% 1% 5% 1% 88% 100% 5,386

A&E 1% 1% 3% 1% 5% 2% 88% 100% 2,595

AHOP 1% 1% 5% - 6% 1% 86% 100% 3,274







Acute IP - 88% 89% 97% 6,827

Non-acute IP - 90% 91% 96% 3,466

SOP 11% 82% 84% 95% 8,309

PHC - 88% 91% 98% 5,386

A&E - 88% 90% 98% 2,595

AHOP 3% 86% 89% 98% 3,274



TPU Group: 441

District: Tuen Mun












Acute IP - 2% 3% 1% 4% 2% 89% 100% 1,902

Non-acute IP - - 5% - 1% 2% 93% 100% 1,367

SOP 1% 2% 7% 1% 6% 3% 80% 100% 1,737

PHC 1% 1% 6% 1% 2% 2% 88% 100% 1,127

A&E 1% 1% 3% 1% 6% 2% 86% 100% 674

AHOP 1% - 7% - 3% 2% 87% 100% 700







Acute IP - 89% 92% 97% 1,902

Non-acute IP - 93% 94% 95% 1,367

SOP 11% 80% 81% 95% 1,737

PHC - 82% 90% 98% 1,127

A&E - 86% 88% 96% 674

AHOP 2% 87% 89% 97% 700



TPU Group: 442

District: Tuen Mun








The patronage pattern in 2015/16 for residents in TPU Group 411-416 & 427 is presented below. The




Acute IP 1% 2% 7% - 37% 2% 52% 100% 909

Non-acute IP - - 11% - - - 89% 100% 410

SOP 2% 7% 8% 1% 20% 4% 59% 100% 1,758

PHC 1% 3% 5% 1% 17% 2% 72% 100% 1,089

A&E 2% 2% 4% 1% 20% 2% 69% 100% 415

AHOP 3% 7% 11% 1% 19% 2% 56% 100% 537



times for TPU Group 411-416 & 427 in 2015/16. For example, the percentages of utilisation sought

within 20 minutes of travel time range from 60% for Acute IP to 89% for Non-acute IP (bolded in

green).



Acute IP - 60% 97% 100% 909

Non-acute IP - 89% 89% 100% 410

SOP - 65% 88% 100% 1,758

PHC - 83% 97% 100% 1,089

A&E - 82% 95% 100% 415

AHOP - 63% 93% 100% 537



TPU Group: 411-416 & 427

District: Tuen Mun



TPU Group 411-416 & 427 (highlighted on the map) is

located in the Tuen Mun district, with its corresponding

Cluster being the New Territories West Cluster.




Cluster concordance rates (bolded in green) range from 86% for SOP to 94% for Non-acute IP.



Acute IP 1% 4% 4% - 3% 2% 87% 100% 53,067

Non-acute IP 1% 1% 2% - 1% 1% 94% 100% 27,741

SOP 1% 3% 4% - 4% 2% 86% 100% 92,189

PHC 1% 1% 2% - 3% 1% 93% 100% 82,344

A&E 1% 1% 3% 1% 6% 2% 87% 100% 24,692

AHOP 1% 1% 5% 1% 3% 2% 87% 100% 31,976







Acute IP - 87% 90% 99% 53,067

Non-acute IP - 94% 95% 99% 27,741

SOP - 86% 91% 99% 92,189

PHC 84% 92% 96% 99% 82,344

A&E - 87% 92% 99% 24,692

AHOP - 87% 91% 100% 31,976




District: Tuen Mun












Acute IP 1% 3% 2% - 2% 2% 89% 100% 125,732

Non-acute IP 1% - 2% - 3% 4% 91% 100% 93,005

SOP 1% 2% 3% 1% 3% 2% 89% 100% 203,292

PHC 1% 1% 2% 1% 2% 1% 93% 100% 161,810

A&E 1% 1% 2% 1% 4% 2% 89% 100% 67,459

AHOP 1% 1% 4% 1% 2% 1% 90% 100% 69,687




20 minutes of travel time range from 89% for SOP to 93% for PHC (bolded in green).



Acute IP 83% 89% 92% 99% 125,732

Non-acute IP 85% 91% 93% 99% 93,005

SOP 85% 89% 92% 99% 203,292

PHC 75% 93% 95% 99% 161,810

A&E 85% 89% 94% 100% 67,459

AHOP 85% 90% 93% 99% 69,687




District: Tuen Mun












Acute IP - - 1% 4% - 1% 94% 100% 646

Non-acute IP - - - - - - 100% 100% 79

SOP - 3% 5% 5% 6% - 81% 100% 598

PHC 1% - - 1% 4% - 95% 100% 444

A&E - 1% 3% 3% 5% 3% 86% 100% 169

AHOP - - 1% 3% - - 96% 100% 332







Acute IP - - 94% 95% 646

Non-acute IP - - 100% 100% 79

SOP - - 81% 88% 598

PHC - 86% 93% 95% 444

A&E - - 86% 92% 169

AHOP - - 96% 96% 332



TPU Group: 431-434

District: Tuen Mun




in the Tuen Mun district, with its corresponding Cluster








Acute IP 1% 3% 4% 1% 3% 4% 85% 100% 198,813

Non-acute IP 1% - 1% - 2% 3% 91% 100% 104,668

SOP 1% 2% 4% 1% 3% 4% 85% 100% 330,709

PHC 1% 1% 2% - 2% 2% 93% 100% 303,144

A&E 1% 1% 2% 1% 4% 4% 87% 100% 120,252

AHOP 1% 1% 5% 1% 3% 3% 87% 100% 117,389







Acute IP - 85% 89% 96% 198,813

Non-acute IP - 91% 92% 97% 104,668

SOP - 85% 89% 96% 330,709

PHC 79% 93% 96% 99% 303,144

A&E - 87% 92% 98% 120,252

AHOP - 87% 90% 97% 117,389



TPU Group: 510

District: Yuen Long




the Yuen Long district, with its corresponding Cluster





concordance rates (bolded in green) range from 76% for SOP to 94% for AHOP.



Acute IP 1% 8% 2% - 3% 7% 80% 100% 1,328

Non-acute IP - - - - - 7% 93% 100% 847

SOP 2% 7% 4% 3% 3% 5% 76% 100% 1,541

PHC 1% 1% 3% 1% 1% 1% 93% 100% 1,402

A&E 1% 1% 2% 1% 5% 4% 87% 100% 564

AHOP - 1% - 1% 2% 3% 94% 100% 693




minutes of travel time range from 76% for SOP to 94% for AHOP (bolded in green).



Acute IP - 80% 82% 91% 1,328

Non-acute IP - 93% 93% 100% 847

SOP - 76% 78% 87% 1,541

PHC - 93% 95% 99% 1,402

A&E - 87% 92% 98% 564

AHOP - 94% 95% 98% 693



TPU Group: 511

District: Yuen Long












Acute IP 1% 3% 1% - 1% 1% 92% 100% 5,992

Non-acute IP - - 1% - - 25% 74% 100% 4,345

SOP - 4% 4% 1% 3% 4% 84% 100% 3,830

PHC 1% - 2% 1% 1% 5% 90% 100% 2,653

A&E 1% - 2% 1% 2% 2% 93% 100% 1,745

AHOP - - 1% - 2% 1% 95% 100% 1,759







Acute IP - 92% 93% 95% 5,992

Non-acute IP - 74% 74% 100% 4,345

SOP - 84% 85% 92% 3,830

PHC - 89% 91% 96% 2,653

A&E - 93% 94% 97% 1,745

AHOP - 95% 95% 99% 1,759



TPU Group: 512

District: Yuen Long












Acute IP - 1% 29% - 1% - 70% 100% 237

Non-acute IP - - - - - - 100% 100% 82

SOP - 3% 35% - 6% 4% 52% 100% 326

PHC 3% 1% 46% 1% - 1% 49% 100% 330

A&E 2% - 22% - 2% 1% 74% 100% 153

AHOP - 4% 10% - 62% - 24% 100% 175




minutes of travel time range from 11% for AHOP to 65% for Acute IP (bolded in green).



Acute IP - 65% 70% 70% 237

Non-acute IP - 56% 100% 100% 82

SOP - 41% 52% 58% 326

PHC - 23% 49% 50% 330

A&E - 36% 74% 74% 153

AHOP - 11% 24% 86% 175



TPU Group: 513

District: Yuen Long









concordance rates (bolded in green) range from 78% for A&E to 92% for AHOP.



Acute IP 8% 3% 3% 1% 3% 1% 82% 100% 649

Non-acute IP 9% - - - - - 91% 100% 217

SOP 2% 2% 7% 1% 3% 2% 84% 100% 975

PHC - - 4% - - 5% 90% 100% 795

A&E 2% - 7% 1% 6% 5% 78% 100% 364

AHOP - - 7% - 1% - 92% 100% 249




minutes of travel time range from 78% for A&E to 92% for AHOP (bolded in green).



Acute IP - 82% 83% 89% 649

Non-acute IP - 91% 91% 91% 217

SOP - 84% 85% 93% 975

PHC 13% 90% 93% 99% 795

A&E - 78% 85% 96% 364

AHOP - 92% 93% 100% 249



TPU Group: 514

District: Yuen Long












Acute IP 5% 1% 2% - 1% 27% 65% 100% 567

Non-acute IP - - - - - - 100% 100% 117

SOP 2% 2% 6% - 2% 3% 85% 100% 686

PHC - - 1% 2% - 5% 91% 100% 666

A&E - 1% 1% 2% 3% 6% 88% 100% 299

AHOP - 1% 8% 5% - 2% 84% 100% 144







Acute IP - 26% 75% 76% 567

Non-acute IP - 14% 100% 100% 117

SOP - 19% 86% 88% 686

PHC - 53% 95% 96% 666

A&E - 76% 93% 95% 299

AHOP - 45% 85% 85% 144



TPU Group: 516

District: Yuen Long












Acute IP - 2% - - - 1% 97% 100% 775

Non-acute IP - - - - - - 100% 100% 1,128

SOP 1% 4% 1% - 2% 5% 88% 100% 832

PHC 2% - 5% - 2% 1% 90% 100% 1,213

A&E 1% - 2% - 2% 3% 92% 100% 363

AHOP - - 5% - 3% 2% 90% 100% 255







Acute IP 33% 97% 97% 99% 775

Non-acute IP 11% 100% 100% 100% 1,128

SOP 22% 88% 89% 96% 832

PHC 22% 90% 92% 98% 1,213

A&E 72% 92% 95% 99% 363

AHOP 34% 90% 93% 100% 255



TPU Group: 518

District: Yuen Long












Acute IP 1% 3% 7% - 2% 4% 84% 100% 3,745

Non-acute IP - - - - - 26% 75% 100% 2,645

SOP 2% 5% 8% 1% 4% 4% 77% 100% 7,120

PHC 2% - 4% 1% 3% 2% 89% 100% 6,185

A&E 1% 1% 3% 1% 5% 3% 87% 100% 2,435

AHOP 1% 2% 10% 1% 2% 9% 76% 100% 2,552







Acute IP - 84% 86% 96% 3,745

Non-acute IP - 75% 75% 100% 2,645

SOP - 77% 81% 92% 7,120

PHC 42% 89% 92% 98% 6,185

A&E - 87% 91% 97% 2,435

AHOP - 76% 78% 94% 2,552



TPU Group: 519

District: Yuen Long












Acute IP 1% 1% 1% - 5% 1% 92% 100% 5,953

Non-acute IP 19% - - - - 7% 73% 100% 3,722

SOP 1% 4% 4% 1% 4% 6% 82% 100% 3,093

PHC 1% - 4% 1% 2% 6% 86% 100% 2,827

A&E - - 2% 1% 2% 3% 92% 100% 1,928

AHOP 1% 1% 6% 1% 3% 3% 86% 100% 1,066







Acute IP 68% 68% 97% 100% 5,953

Non-acute IP 53% 53% 73% 81% 3,722

SOP 21% 31% 89% 100% 3,093

PHC 30% 85% 93% 99% 2,827

A&E 85% 85% 95% 100% 1,928

AHOP 51% 54% 88% 100% 1,066



TPU Group: 521

District: Yuen Long












Acute IP - 2% 6% 1% 2% 2% 87% 100% 6,339

Non-acute IP 15% - - - 2% 1% 81% 100% 4,245

SOP 1% 2% 7% - 3% 5% 82% 100% 7,128

PHC 1% - 2% 1% 2% 2% 93% 100% 6,865

A&E - - 2% 1% 3% 3% 90% 100% 2,659

AHOP 1% 1% 4% - 4% 4% 86% 100% 2,869







Acute IP - 39% 87% 96% 6,339

Non-acute IP - 29% 81% 84% 4,245

SOP - 21% 83% 92% 7,128

PHC - 90% 94% 98% 6,865

A&E - 78% 92% 98% 2,659

AHOP - 48% 88% 95% 2,869



TPU Group: 522

District: Yuen Long












Acute IP - - 1% - - 1% 97% 100% 2,779

Non-acute IP - - 1% - - - 99% 100% 1,854

SOP 1% 1% 5% - 4% 2% 87% 100% 2,571

PHC - - 1% - 1% 4% 94% 100% 2,694

A&E - - 1% - 2% 3% 94% 100% 1,177

AHOP - - 5% - - 14% 81% 100% 878







Acute IP - 97% 98% 99% 2,779

Non-acute IP - 99% 99% 100% 1,854

SOP - 87% 88% 97% 2,571

PHC - 93% 95% 100% 2,694

A&E - 94% 96% 99% 1,177

AHOP - 81% 81% 98% 878



TPU Group: 523

District: Yuen Long












Acute IP - 1% 3% - 1% 4% 91% 100% 67,392

Non-acute IP - - 2% 1% 2% 5% 91% 100% 37,880

SOP 1% 2% 5% 1% 3% 4% 84% 100% 57,460

PHC 1% - 2% 1% 2% 2% 94% 100% 52,625

A&E 1% - 2% - 2% 2% 93% 100% 30,405

AHOP 1% - 4% - 3% 2% 89% 100% 22,758







Acute IP 50% 92% 96% 100% 67,392

Non-acute IP 46% 91% 98% 100% 37,880

SOP 23% 85% 92% 99% 57,460

PHC 35% 95% 98% 100% 52,625

A&E 82% 94% 98% 100% 30,405

AHOP 51% 90% 97% 100% 22,758



TPU Group: 524

District: Yuen Long












Acute IP 1% 3% 3% 1% 1% 3% 89% 100% 53,148

Non-acute IP - 2% 2% - 1% 1% 95% 100% 26,861

SOP 1% 2% 3% 1% 2% 3% 88% 100% 69,490

PHC - - 1% - 1% 1% 96% 100% 76,886

A&E 1% - 2% 1% 2% 3% 92% 100% 32,054

AHOP - 1% 3% - 1% 2% 91% 100% 27,437







Acute IP 41% 90% 92% 99% 53,148

Non-acute IP 39% 95% 96% 99% 26,861

SOP 22% 89% 92% 99% 69,490

PHC 67% 97% 98% 100% 76,886

A&E 76% 93% 96% 100% 32,054

AHOP 46% 92% 94% 100% 27,437



TPU Group: 527

District: Yuen Long












Acute IP - 1% 3% - 1% 4% 91% 100% 956

Non-acute IP - - - - - - 100% 100% 229

SOP 1% 2% 5% - 2% 4% 86% 100% 1,814

PHC - - 2% 1% 3% 2% 92% 100% 1,828

A&E 1% - 3% 1% 3% 4% 89% 100% 730

AHOP - 1% 2% - 2% 2% 93% 100% 681







Acute IP 35% 91% 92% 98% 956

Non-acute IP 28% 100% 100% 100% 229

SOP 22% 86% 89% 96% 1,814

PHC 59% 92% 94% 99% 1,828

A&E 70% 89% 92% 99% 730

AHOP 44% 93% 95% 99% 681



TPU Group: 528

District: Yuen Long












Acute IP - 2% 8% - 3% 10% 78% 100% 6,605

Non-acute IP - 1% 3% - - 3% 93% 100% 1,865

SOP 2% 3% 9% 1% 5% 6% 74% 100% 10,697

PHC 1% 1% 2% 1% 2% 2% 92% 100% 10,613

A&E 2% 1% 3% 1% 3% 4% 87% 100% 4,187

AHOP 1% 1% 6% 1% 1% 3% 88% 100% 4,966







Acute IP 35% 83% 86% 100% 6,605

Non-acute IP 42% 93% 96% 100% 1,865

SOP 21% 75% 81% 99% 10,697

PHC 70% 93% 95% 99% 10,613

A&E 74% 89% 93% 99% 4,187

AHOP 56% 88% 90% 99% 4,966



TPU Group: 529

District: Yuen Long












Acute IP 1% 3% 3% - 5% 5% 83% 100% 2,816

Non-acute IP 2% - - - - 12% 86% 100% 989

SOP 2% 6% 8% 1% 6% 12% 64% 100% 4,486

PHC 1% 1% 5% 1% 3% 7% 83% 100% 4,168

A&E 1% - 4% 1% 5% 7% 83% 100% 1,896

AHOP 2% 3% 14% 1% 7% 8% 67% 100% 1,773







Acute IP - 46% 94% 100% 2,816

Non-acute IP - 50% 98% 98% 989

SOP - 19% 83% 98% 4,486

PHC - 44% 96% 99% 4,168

A&E - 76% 94% 99% 1,896

AHOP - 42% 90% 99% 1,773



TPU Group: 531

District: Yuen Long












Acute IP - 1% 1% - 10% 17% 70% 100% 2,657

Non-acute IP - - - - - 14% 86% 100% 1,529

SOP 1% 3% 6% - 4% 16% 69% 100% 2,950

PHC 1% 1% 2% - 1% 9% 86% 100% 3,120

A&E 1% 1% 2% 1% 3% 20% 72% 100% 1,269

AHOP - 1% 7% 3% 3% 9% 77% 100% 1,016







Acute IP - 46% 84% 98% 2,657

Non-acute IP - 21% 93% 100% 1,529

SOP - 28% 80% 95% 2,950

PHC 29% 61% 95% 99% 3,120

A&E - 83% 92% 98% 1,269

AHOP - 53% 85% 96% 1,016



TPU Group: 532

District: Yuen Long









concordance rates (bolded in green) range from 79% for SOP to 96% for Acute IP.



Acute IP - - - 1% - 3% 96% 100% 4,885

Non-acute IP - - - 2% - 5% 93% 100% 4,164

SOP 2% 1% 4% 2% 3% 10% 79% 100% 1,861

PHC 1% - 3% 1% 2% 5% 88% 100% 1,426

A&E - - 1% 1% 1% 5% 93% 100% 1,274

AHOP - - 9% 1% - 4% 86% 100% 679







Acute IP - 67% 99% 99% 4,885

Non-acute IP - 52% 98% 98% 4,164

SOP - 22% 91% 97% 1,861

PHC - 31% 94% 99% 1,426

A&E - 88% 98% 99% 1,274

AHOP - 57% 90% 99% 679



TPU Group: 533

District: Yuen Long












Acute IP - 2% 4% 1% 12% 10% 71% 100% 9,900

Non-acute IP 4% - 10% 4% 4% 19% 59% 100% 8,844

SOP 2% 4% 9% 1% 6% 13% 65% 100% 9,159

PHC 1% 1% 3% 1% 3% 7% 85% 100% 6,423

A&E - 1% 2% 1% 10% 9% 78% 100% 3,143

AHOP 1% 1% 10% - 4% 5% 79% 100% 3,891







Acute IP - 46% 79% 97% 9,900

Non-acute IP - 19% 64% 92% 8,844

SOP - 34% 75% 93% 9,159

PHC - 87% 91% 98% 6,423

A&E - 78% 86% 98% 3,143

AHOP - 57% 84% 99% 3,891



TPU Group: 541

District: Yuen Long












Acute IP 1% 2% 2% - 7% 47% 41% 100% 3,215

Non-acute IP - - 11% - 1% 55% 34% 100% 1,183

SOP 3% 4% 7% 1% 4% 27% 54% 100% 3,326

PHC 2% 2% 3% 1% 2% 32% 59% 100% 2,462

A&E 1% 1% 2% 1% 3% 37% 56% 100% 1,623

AHOP 1% - 5% - 3% 20% 72% 100% 1,395







Acute IP - 46% 77% 97% 3,215

Non-acute IP - 22% 89% 90% 1,183

SOP - 31% 73% 91% 3,326

PHC - 55% 90% 97% 2,462

A&E - 83% 94% 98% 1,623

AHOP - 56% 88% 99% 1,395



TPU Group: 542

District: Yuen Long












Acute IP - 20% 1% - 7% 26% 47% 100% 438

Non-acute IP - - - - - 83% 17% 100% 128

SOP 2% 10% 8% 2% 2% 22% 54% 100% 643

PHC - 3% 2% 1% - 7% 88% 100% 455

A&E 1% 2% 4% 1% 2% 14% 76% 100% 233

AHOP 1% 1% 2% - 2% 23% 71% 100% 219




minutes of travel time range from 4% for PHC to 68% for A&E (bolded in green).



Acute IP - 24% 58% 79% 438

Non-acute IP - 17% 17% 100% 128

SOP - 16% 63% 78% 643

PHC - 4% 92% 94% 455

A&E - 68% 89% 92% 233

AHOP - 61% 91% 96% 219



TPU Group: 543

District: Yuen Long












Acute IP - - 3% - 3% 51% 43% 100% 1,775

Non-acute IP - - 23% - - 56% 22% 100% 1,658

SOP - - 3% 2% 2% 15% 78% 100% 956

PHC - - 1% - 1% 9% 89% 100% 656

A&E - - - - 3% 31% 65% 100% 415

AHOP - - - 1% - 17% 82% 100% 283







Acute IP - 25% 48% 100% 1,775

Non-acute IP - 12% 24% 100% 1,658

SOP - 15% 82% 98% 956

PHC - 89% 92% 100% 656

A&E - 64% 70% 99% 415

AHOP - 59% 83% 100% 283



TPU Group: 544

District: Yuen Long












Acute IP - 2% 3% - 2% 2% 91% 100% 6,505

Non-acute IP - - 1% 3% 5% 17% 75% 100% 4,128

SOP 1% 2% 5% 1% 5% 4% 82% 100% 5,426

PHC 1% 1% 2% 1% 2% 1% 92% 100% 4,373

A&E 1% 1% 2% 1% 2% 2% 92% 100% 2,298

AHOP 1% 1% 7% 1% 2% 2% 86% 100% 2,054







Acute IP - 91% 93% 97% 6,505

Non-acute IP - 75% 79% 97% 4,128

SOP - 82% 86% 96% 5,426

PHC 56% 92% 94% 98% 4,373

A&E - 92% 94% 98% 2,298

AHOP - 86% 89% 97% 2,054




District: Yuen Long




located in the Yuen Long district, with its corresponding








Acute IP 2% - 1% - 1% 1% 96% 100% 3,756

Non-acute IP 10% - - 1% - - 89% 100% 1,623

SOP - 1% 4% 1% 2% 2% 90% 100% 2,084

PHC 1% - - - 2% 2% 96% 100% 1,243

A&E - - 1% - 1% 2% 95% 100% 1,367

AHOP - - 9% 1% 5% 1% 84% 100% 807




20 minutes of travel time range from 85% for AHOP to 97% for Acute IP (bolded in green).



Acute IP 71% 97% 98% 98% 3,756

Non-acute IP 62% 89% 89% 90% 1,623

SOP 22% 91% 96% 100% 2,084

PHC 50% 97% 98% 100% 1,243

A&E 93% 96% 99% 100% 1,367

AHOP 59% 85% 99% 100% 807




District: Yuen Long




located in the Yuen Long district, with its corresponding


Appendix II: Utilisation within Certain Travel Times by Cluster Catchment Page | 309

Appendix II: Utilisation within Certain Travel

Times by Cluster Catchment

This appendix shows the percentage of utilisation with travel time of 10 to 30

minutes by core service for each Cluster catchment.

Districts in Hong Kong and their corresponding Clusters are outlined below. The

various groups of districts are labelled as catchment area for the individual Clusters

for the purpose of the analyses presented in the report.

Patients’ District of Residence Corresponding Cluster

Eastern, Wan Chai, Islands (excl. Lantau) Hong Kong East Cluster (HKEC)

Central & Western, Southern Hong Kong West Cluster (HKWC)

Kowloon City, Mong Kok, Wong Tai Sin, Yau Tsim

Kowloon Central Cluster (KCC)

Kwun Tong, Sai Kung Kowloon East Cluster (KEC)

Sham Shui Po, Kwai Tsing, Tsuen Wan, Lantau Island

Kowloon West Cluster (KWC)

Sha Tin, Tai Po, North New Territories East Cluster (NTEC)

Tuen Mun, Yuen Long New Territories West Cluster (NTWC)

Appendix II: Table 1 – Districts of residence and corresponding Clusters


HKEC Cluster Catchment

HKEC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 44% 75% 88% 91% 94%

Non-acute IP 29% 52% 76% 91% 95%

SOP 40% 72% 86% 91% 95%

PHC 52% 85% 94% 96% 97%

A&E 56% 85% 91% 94% 95%

AH OP 38% 71% 86% 93% 96%

Appendix II: Figure 1 – HKEC catchment: utilisation < travel time by core service


HKWC Catchment

HKWC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 32% 77% 91% 98% 99%

Non-acute IP 23% 51% 73% 95% 97%

SOP 32% 81% 90% 97% 99%

PHC 76% 84% 92% 97% 98%

A&E 32% 84% 91% 95% 98%

AH OP 22% 59% 81% 96% 98%

Appendix II: Figure 2 – HKWC catchment: utilisation < travel time by core service


KCC Catchment

KCC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 35% 58% 95% 97% 99%

Non-acute IP 25% 71% 92% 96% 97%

SOP 38% 71% 94% 98% 99%

PHC 69% 91% 97% 99% 99%

A&E 35% 60% 94% 96% 98%

AH OP 40% 73% 96% 98% 99%

Appendix II: Figure 3 – KCC catchment: utilisation < travel time by core service


KEC Catchment

KEC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 42% 72% 89% 95% 97%

Non-acute IP 25% 51% 83% 93% 96%

SOP 37% 70% 88% 94% 97%

PHC 64% 84% 90% 97% 99%

A&E 47% 78% 92% 96% 97%

AH OP 46% 73% 89% 96% 98%

Appendix II: Figure 4 – KEC catchment: utilisation < travel time by core service


KWC Catchment

KWC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 33% 81% 88% 95% 98%

Non-acute IP 25% 76% 84% 90% 97%

SOP 30% 78% 87% 93% 97%

PHC 70% 88% 95% 97% 98%

A&E 50% 88% 93% 96% 98%

AH OP 35% 77% 88% 94% 98%

Appendix II: Figure 5 – KWC catchment: utilisation < travel time by core service


NTEC Catchment

NTEC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 33% 81% 88% 95% 98%

Non-acute IP 25% 76% 84% 90% 97%

SOP 30% 78% 87% 93% 97%

PHC 70% 88% 95% 97% 98%

A&E 50% 88% 93% 96% 98%

AH OP 35% 77% 88% 94% 98%

Appendix II: Figure 6 – NTEC catchment: utilisation < travel time by core service


NTWC Catchment

NTWC Catchment Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service <10 < 15 < 20 < 25 < 30

Acute IP 30% 83% 86% 89% 91%

Non-acute IP 33% 82% 87% 89% 91%

SOP 26% 81% 84% 87% 90%

PHC 71% 88% 92% 95% 96%

A&E 37% 86% 89% 91% 93%

AH OP 30% 84% 87% 89% 91%

Appendix II: Figure 7 – NTWC catchment: utilisation < travel time by core service

90%


Summary by Cluster Catchment

Percentage of Utilisation Sought within Travel Time of 10 to 30 Minutes

Core Service HKEC HKWC KCC KEC KWC NTEC NTWC Overall

Acute IP 23.0 19.8 19.3 21.1 21.4 22.9 28.0 21.2

Non-acute IP 24.7 23.8 19.4 23.4 25.1 22.4 27.1 23.5

SOP 24.2 19.9 19.1 21.7 22.6 23.7 30.1 22.3

PHC 17.8 18.9 14.8 19.7 16.4 16.9 17.2 17.2

A&E 19.2 19.4 19.5 19.4 17.3 20.1 23.0 19.4

AHOP 22.6 23.0 18.8 20.5 21.8 23.1 26.7 21.4

Appendix II: Table 2 – 90th percentile of travel time by core service by Cluster

Appendix III: Utilisation within Certain Travel Times by TPU Group Page | 319

Appendix III: Utilisation within Certain Travel

Times by TPU Group Acute Inpatient (Acute IP)

Appendix III: Figure 1 – Acute IP: utilisation sought < 10 mins by TPU Group



Non-acute Inpatient (Non-acute IP)

Appendix III: Figure 5 – Non-acute IP: utilisation sought < 10 mins by TPU Group



Specialist Outpatient (SOP)

Appendix III: Figure 9 – SOP: utilisation sought < 10 mins by TPU Group



Primary Care (PHC)

Appendix III: Figure 13 – PHC: utilisation sought < 10 mins by TPU Group



Accident and Emergency (A&E)

Appendix III: Figure 17 – A&E: utilisation sought < 10 mins by TPU Group



Allied Health Outpatient (AHOP)

Appendix III: Figure 21 – AHOP: utilisation sought < 10 mins by TPU Group


Appendix IV: Utilisation within Threshold Travel Time by TPU Group Page | 331

Appendix IV: Utilisation within Threshold Travel

Time by TPU Group

Tables below illustrate the TPU Group-specific percentage of utilisation within the

threshold travel time for each core service as defined in the Chapter 4 of this report,

namely 25 minutes for Acute Inpatient (Acute IP), Non-Acute Inpatient (Non-acute

IP), Specialist Outpatient (SOP), Allied Health Outpatient (AHOP) and 20 minutes for

Primary Care (PHC) and Accident and Emergency (A&E). For presentation purpose,

TPU Groups are grouped by District and in turn Cluster catchment (after re-

clustering). Formatting of the table cells is based on the key below:

HKEC Catchment

Appendix IV: Table 1 – Percentage of utilisation by TPU Group in HKEC

Key: 0% - 20%

20% - 40%

40% - 60%

60% - 80%

80% - 100%

Percentage of Utilisation within Threshold Travel Time by Core Service by TPU Group

Cluster

CatchmentDistrict TPU Group

2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

HKEC Eastern 148 9,500 99% 99% 99% 99% 97% 99%

151 46,800 99% 99% 99% 98% 95% 99%

152 18,800 95% 83% 96% 94% 86% 95%

153 71,000 99% 99% 99% 98% 95% 99%

154 11,200 99% 84% 99% 99% 93% 99%

155 21,800 99% 92% 99% 98% 93% 98%

156 & 158 50,600 100% 90% 99% 97% 93% 99%

157 52,800 99% 81% 99% 97% 91% 100%

161 82,800 94% 94% 91% 96% 93% 92%

162 19,400 91% 95% 86% 91% 91% 90%

163 40,900 96% 96% 92% 96% 94% 94%

164 & 165 58,500 95% 96% 93% 97% 95% 94%

166 39,600 92% 89% 91% 97% 92% 94%

167 58,300 88% 91% 89% 85% 87% 89%

911-913 6,300 0% 0% 0% 5% 0% 0%

920 22,500 23% 12% 0% 92% 73% 58%

963 1,000 0% 0% 0% 0% 0% 0%

976 6,500 0% 0% 0% 90% 0% 0%

Wan Chai 131 29,700 98% 95% 99% 97% 96% 99%

132 19,000 99% 98% 99% 97% 96% 100%

133 11,400 97% 96% 99% 99% 96% 98%

134 1,300 100% 0% 94% 100% 100% 100%

135 1,100 98% 77% 99% 96% 98% 99%

140 8,000 99% 99% 100% 97% 97% 100%

144 33,700 98% 99% 99% 96% 94% 99%

145 12,300 97% 98% 97% 96% 95% 98%

146 & 147 19,200 99% 98% 99% 97% 97% 99%

149 8,500 98% 97% 94% 91% 83% 96%

183 & 184 4,100 44% 61% 46% 94% 88% 84%

Islands (excl.

Lantau)


HKWC Catchment

Appendix IV: Table 2 – Percentage of utilisation by TPU Group in HKWC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

HKWC 111 61,800 98% 90% 99% 97% 95% 98%

112 53,100 99% 99% 99% 98% 95% 99%

113 26,400 99% 96% 99% 98% 95% 97%

114 10,100 97% 89% 98% 98% 91% 98%

115 2,600 95% 90% 98% 99% 97% 100%

116 13,900 99% 99% 99% 98% 95% 97%

121 & 122 6,600 98% 100% 99% 97% 94% 100%

123 & 124 1,100 98% 100% 99% 78% 94% 100%

141 20,800 99% 100% 97% 96% 90% 98%

142 37,400 99% 100% 98% 96% 96% 99%

143 11,000 97% 100% 95% 92% 55% 96%

181 & 182 4,600 44% 100% 36% 94% 74% 78%

Southern 171 20,600 99% 95% 98% 93% 95% 99%

172 58,100 99% 96% 99% 95% 94% 98%

173 58,600 99% 99% 98% 96% 94% 98%

174 86,100 98% 96% 97% 78% 91% 96%

175 & 176 13,200 97% 92% 97% 95% 93% 98%

190, 192 & 194 12,800 99% 93% 95% 94% 83% 93%

191 3,900 100% 14% 94% 99% 96% 85%

193, 195 & 198 15,400 69% 76% 53% 53% 0% 42%

196 5,000 93% 0% 69% 67% 71% 54%

197 3,000 90% 12% 79% 77% 97% 86%

Central & Western


KCC Catchment

Appendix IV: Table 3 – Percentage of utilisation by TPU Group in KCC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

KCC Kowloon City 213 & 215-216 12,400 100% 100% 97% 97% 92% 97%

231 7,200 98% 100% 99% 97% 97% 98%

232 4,300 97% 100% 99% 97% 93% 99%

233 4,000 97% 100% 95% 97% 92% 99%

234 14,800 99% 99% 99% 98% 96% 100%

235 16,300 98% 87% 99% 97% 94% 99%

236 29,700 99% 95% 99% 98% 94% 99%

237 16,200 99% 100% 99% 98% 94% 99%

241 46,200 98% 95% 97% 97% 93% 98%

242 49,300 97% 99% 97% 97% 96% 98%

243 33,800 99% 99% 99% 98% 93% 98%

244 29,000 99% 100% 99% 97% 91% 99%

245 49,700 99% 99% 99% 96% 92% 99%

246 16,900 99% 98% 99% 98% 96% 99%

247 6,800 97% 100% 97% 93% 94% 100%

271 18,200 93% 100% 93% 96% 90% 98%

272 9,600 95% 100% 94% 97% 94% 99%

285 16,600 97% 99% 97% 98% 95% 98%

Mong Kok 220 1,200 95% 84% 99% 94% 95% 100%

221 83,400 99% 95% 99% 96% 94% 99%

222 21,000 99% 97% 98% 97% 93% 98%

227 18,500 97% 95% 99% 98% 95% 98%

228 14,800 98% 96% 99% 98% 94% 99%

229 15,900 99% 93% 99% 98% 96% 99%

253 33,900 99% 98% 99% 97% 95% 99%

254 19,900 98% 100% 96% 90% 90% 97%

Wong Tai Sin 281 206,100 96% 93% 97% 98% 93% 97%

282 56,700 97% 98% 97% 98% 94% 98%

283 65,800 96% 98% 97% 98% 93% 98%

284 16,500 99% 96% 97% 98% 96% 98%

287 18,500 94% 97% 97% 97% 96% 98%

288 & 289 66,300 94% 94% 96% 92% 92% 97%

Yau Tsim 211 14,800 99% 94% 99% 98% 95% 99%

212 16,600 96% 99% 96% 95% 92% 97%

214 4,700 98% 100% 98% 97% 93% 98%

217 6,200 99% 100% 95% 93% 97% 90%

225 39,000 99% 97% 99% 99% 95% 99%

226 10,500 99% 100% 98% 98% 95% 98%

251, 252 & 256 20,400 99% 100% 97% 92% 94% 99%


KEC Catchment

Appendix IV: Table 4 – Percentage of utilisation by TPU Group in KEC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

KEC Kwun Tong 280 & 286 71,800 99% 98% 99% 97% 95% 98%

290 56,200 94% 90% 94% 93% 94% 97%

291 85,600 97% 99% 96% 96% 96% 98%

292 51,000 97% 94% 97% 95% 95% 98%

293 & 296 85,400 96% 97% 96% 91% 89% 97%

294 & 295 168,100 97% 92% 97% 98% 96% 98%

297 103,900 96% 96% 96% 97% 95% 98%

298 53,900 95% 96% 94% 90% 90% 97%

Sai Kung 811-815 2,400 0% 0% 0% 93% 0% 0%

820 & 824 7,000 79% 48% 60% 89% 85% 66%

821 1,100 76% 0% 46% 89% 84% 46%

822 7,600 91% 63% 65% 92% 0% 72%

823 4,800 83% 100% 65% 86% 76% 80%

825 4,500 76% 100% 50% 96% 83% 77%

826 & 828 7,900 85% 64% 67% 96% 81% 77%

827 3,900 74% 18% 62% 97% 77% 78%

829 3,400 83% 86% 54% 95% 89% 71%

831 22,100 97% 87% 94% 92% 86% 93%

832 & 834 4,200 94% 96% 87% 58% 92% 93%

833 67,000 78% 82% 83% 17% 83% 80%

835 25,700 97% 89% 96% 89% 92% 97%

836 90,400 95% 94% 94% 89% 87% 96%

837 80,800 94% 96% 94% 89% 91% 95%

838 103,600 94% 88% 93% 85% 93% 96%

839 23,800 94% 95% 86% 73% 85% 93%


KWC Catchment

Appendix IV: Table 5 – Percentage of utilisation by TPU Group in KWC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

KWC Kwai Tsing 326 160,000 98% 95% 97% 96% 93% 96%

327 109,600 95% 89% 96% 96% 91% 96%

328 55,700 98% 91% 97% 96% 95% 96%

350 108,900 97% 96% 97% 97% 91% 97%

351 82,600 95% 95% 96% 96% 91% 96%

Lantau Island 931 & 933 4,100 0% 0% 0% 0% 0% 0%

932 & 934 2,200 15% 5% 10% 10% 0% 35%

941-943 1,100 0% 0% 0% 47% 0% 0%

944 2,900 0% 0% 0% 95% 0% 0%

950 & 951 83,100 79% 35% 57% 85% 93% 83%

961 & 962 6,000 0% 0% 0% 94% 0% 0%

971-974 13,100 6% 34% 3% 48% 80% 44%

Sham Shui Po 255 & 269 60,900 97% 90% 97% 96% 96% 97%

260 46,400 94% 88% 92% 93% 92% 94%

261 10,700 96% 90% 96% 97% 96% 98%

262 17,400 99% 100% 99% 98% 95% 98%

263 56,600 97% 94% 96% 97% 95% 97%

264 62,700 98% 97% 98% 97% 95% 98%

265 22,700 99% 89% 98% 98% 97% 99%

266 51,700 98% 94% 98% 96% 94% 98%

267 36,400 98% 95% 99% 97% 95% 97%

268 28,600 96% 98% 96% 97% 95% 97%

Tsuen Wan 310 1,100 100% 0% 86% 94% 93% 94%

320, 324 & 329 102,400 97% 86% 97% 96% 96% 98%

321 7,500 98% 99% 92% 90% 94% 95%

322 4,100 89% 60% 71% 78% 75% 76%

323 82,500 93% 91% 91% 92% 89% 92%

325 57,100 97% 97% 97% 97% 93% 95%

331-334, 336 & 340 10,900 62% 56% 53% 45% 0% 52%

335 25,800 96% 91% 95% 90% 92% 95%

975 15,200 83% 69% 76% 69% 78% 77%


NTEC Catchment

Appendix IV: Table 6 – Percentage of utilisation by TPU Group in NTEC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

NTEC North 545 & 546 2,700 95% 93% 88% 98% 94% 93%

610 & 621 15,300 59% 99% 55% 74% 82% 53%

620 & 622 3,000 0% 0% 0% 0% 0% 0%

623 1,200 100% 100% 89% 58% 100% 100%

624 & 629 56,600 94% 87% 91% 94% 90% 90%

625 57,200 91% 88% 88% 92% 87% 87%

626 24,300 95% 97% 92% 94% 91% 90%

627 35,800 93% 90% 91% 94% 93% 86%

628 88,200 91% 87% 87% 90% 87% 85%

634 16,400 72% 90% 52% 83% 65% 69%

641 3,800 76% 100% 66% 87% 84% 97%

642 3,000 92% 0% 65% 62% 0% 71%

651-653 8,000 0% 0% 0% 66% 0% 0%

Sha Tin 731, 733 & 754 64,300 95% 94% 95% 90% 84% 95%

732, 751 & 753 44,400 94% 91% 93% 90% 86% 96%

755 66,500 98% 97% 97% 94% 90% 98%

756 & 761-762 98,500 97% 98% 97% 95% 94% 97%

757 216,500 96% 92% 95% 92% 85% 96%

758 50,700 97% 98% 97% 98% 92% 97%

759 130,700 98% 98% 97% 96% 93% 97%

Tai Po 631-633 10,400 93% 79% 81% 91% 90% 93%

711-712, 721 & 728 16,100 77% 85% 59% 0% 89% 76%

720 4,800 82% 84% 76% 86% 81% 83%

722 & 727 50,200 92% 92% 91% 93% 94% 90%

723 77,300 95% 91% 92% 94% 94% 91%

724 10,700 81% 98% 79% 79% 80% 89%

725 5,600 91% 100% 80% 81% 86% 83%

726 105,500 94% 95% 93% 95% 94% 93%

729 14,800 91% 100% 81% 83% 90% 82%

741-744 8,300 0% 0% 0% 0% 0% 0%


NTWC Catchment

Appendix IV: Table 7 – Percentage of utilisation by TPU Group in NTWC


Cluster


2015

Population

Acute IP

< 25 min

Non-acute IP

< 25 min

SOP

< 25 min

PHC

< 20 min

A&E

< 20 min

AHOP

< 25 min

NTWC Tuen Mun 411-416 & 427 7,800 89% 89% 79% 83% 82% 76%

421 & 422 89,600 87% 94% 87% 92% 87% 88%

423 & 428 181,000 90% 91% 90% 93% 89% 91%

424 107,600 90% 88% 90% 93% 90% 90%

425 73,500 91% 92% 87% 92% 90% 89%

426 16,700 77% 79% 74% 71% 68% 82%

431-434 2,100 91% 100% 74% 86% 0% 76%

441 17,800 88% 90% 82% 88% 88% 86%

442 8,000 89% 93% 80% 82% 86% 86%

Yuen Long 510 291,400 86% 91% 86% 93% 87% 88%

511 6,600 80% 93% 77% 93% 87% 94%

512 6,900 92% 74% 84% 89% 93% 95%

513 1,900 70% 100% 52% 23% 36% 24%

514 2,700 82% 91% 84% 90% 78% 92%

515 & 517 10,800 92% 75% 82% 92% 92% 87%

516 3,100 65% 100% 77% 53% 76% 84%

518 2,400 97% 100% 88% 90% 92% 90%

519 13,100 85% 75% 78% 89% 87% 77%

521 10,000 96% 73% 86% 85% 85% 88%

522 19,400 87% 81% 83% 90% 78% 88%

523 8,800 98% 99% 87% 93% 94% 81%

524 64,600 93% 93% 88% 95% 94% 93%

525 & 526 4,900 97% 89% 93% 97% 96% 90%

527 67,500 91% 95% 90% 97% 93% 93%

528 6,500 92% 100% 87% 92% 88% 93%

529 21,000 83% 93% 76% 93% 89% 89%

531 15,600 88% 62% 61% 44% 76% 71%

532 10,700 46% 21% 28% 61% 83% 53%

533 5,800 69% 56% 32% 31% 88% 60%

541 19,700 75% 59% 71% 87% 78% 83%

542 11,300 74% 34% 68% 55% 83% 85%

543 3,100 35% 17% 24% 4% 68% 81%

544 4,200 45% 22% 80% 89% 64% 83%

Annex I: TPU Maps by Planning Department Page | 339

Annex I: TPU Maps by Planning Department

The Tertiary Planning Unit (TPU) maps as shown in the document “Projections of

Population Distribution 2015 – 2024” published by the Government’s Planning

Department in December 2015 are illustrated below.

List of Abbreviations Page | 345

List of Abbreviations

A&E Accident and Emergency

Acute IP Acute Inpatient

AHOP Allied Health Outpatient

BDO Bed Days Occupied

C&SD Census and Statistics Department

CCGs Clinical Commissioning Groups

CPH Castle Peak Hospital

DCCA District Council Constituency Area

DS Designated Services

FCA Floating Catchment Area

GIS Geographic Information System

HA Hospital Authority

HKEC Hong Kong East Cluster

HKWC Hong Kong West Cluster

IRAMD SC Internal Resource Allocation Model Development Steering Committee

KCC Kowloon Central Cluster

KEC Kowloon East Cluster

KWC Kowloon West Cluster

KWH Kwong Wah Hospital

NDH Northern District Hospital

NLTH North Lantau Hospital

Non-acute IP Non-acute Inpatient

List of Abbreviations Page | 346

NTEC New Territories East Cluster

NTWC New Territories West Cluster

OLMH Our Lady of Maryknoll Hospital

PHC Primary Care

PWH Prince of Wales Hospital

QEH Queen Elizabeth Hospital

QMH Queen Mary Hospital

S&P Strategy and Planning

S&WP Statistics and Workforce Planning

SOP Specialist Outpatient

TMH Tuen Mun Hospital

TPU Tertiary Planning Unit

WTSH TWGHs Wong Tai Sin Hospital

Prepared by:

Finance Division, Hospital Authority

Hospital Authority Building, 147B, Argyle Street, Kowloon, Hong Kong

www.ha.org.hk

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All rights reserved. No part of this publication may be reproduced, stored in a

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