UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ

PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO APLICADA

TATIANE ARAUJO MUNIZ LAUTERT

UNIDADES DE SAÚDE PÚBLICA EM CURITIBA: UMA ANÁLISEEXPLORATÓRIA E UM PROTÓTIPO DE DASHBOARD DE SAÚDEPARA APOIO À DECISÃO NO DOMÍNIO DA GESTÃO EM SAÚDE.

DISSERTAÇÃO

CURITIBA

2020

TATIANE ARAUJO MUNIZ LAUTERT

UNIDADES DE SAÚDE PÚBLICA EM CURITIBA: UMA ANÁLISEEXPLORATÓRIA E UM PROTÓTIPO DE DASHBOARD DE SAÚDEPARA APOIO À DECISÃO NO DOMÍNIO DA GESTÃO EM SAÚDE.

Public health units in Curitiba: an exploratory analysis and a healthdashboard prototype for decision support in health management domain.

Dissertação apresentada ao Programa de Pós-Graduação em Computação Aplicada (PPGCA)da Universidade Tecnológica Federal do Paraná(UTFPR) como requisito à obtenção do título de“Mestra em Ciências” - Área de Concentração:Engenharia De Sistemas Computacionais.

Orientador(a): Prof(a). Dr(a). Nádia PuchalskiKozievitchCoorientador(a): Prof(a). Dr(a). Monika Akbar

CURITIBA

2020

4.0 Internacional

Esta licença permite compartilhamento, remixe, adaptação e criação a partir do trabalho,

mesmo para fins comerciais, desde que sejam atribuídos créditos ao(s) autor(es). Con-

teúdos elaborados por terceiros, citados e referenciados nesta sobra não são cobertos

pela licença.

12/01/2021 -

https://utfws.utfpr.edu.br/aluno01/sistema/mpCADEDocsAssinar.pcTelaAssinaturaDoc?p_pesscodnr=202022&p_cadedocpescodnr=3790&p_cadedoc… 1/1

Ministério da Educação

Universidade Tecnológica Federal do Paraná Câmpus Curitiba

TATIANE ARAUJO MUNIZ LAUTERT

UNIDADES DE SAÚDE PÚBLICA EM CURITIBA: UMA ANÁLISE EXPLORATÓRIA E UM PROTÓTIPO DEDASHBOARD DE SAÚDE PARA APOIO À DECISÃO NO DOMÍNIO DA GESTÃO EM SAÚDE.

Trabalho de pesquisa de mestrado apresentado como requisitopara obtenção do título de Mestra Em Computação Aplicada daUniversidade Tecnológica Federal do Paraná (UTFPR). Área deconcentração: Engenharia De Sistemas Computacionais.

Data de aprovação: 17 de Dezembro de 2020

Prof.a Nadia Puchalski Kozievitch, Doutorado - Universidade Tecnológica Federal do Paraná

Prof.a Anelise Munaretto Fonseca, Doutorado - Universidade Tecnológica Federal do Paraná

Prof Artur Ziviani, Doutorado - Laboratório Nacional de Computação Científica

Documento gerado pelo Sistema Acadêmico da UTFPR a partir dos dados da Ata de Defesa em 17/12/2020.

“Dedico este trabalho ao meu pai Israel Araujo

Muniz (in memoriam), que me ensinou que para

aprender é preciso manter-se humilde.”

ACKNOWLEDGEMENTS

Primeiramente agradeço a Deus pela oportunidade em cursar este mestrado.

À minha orientadora, professora Dra. Nádia Puchalski Kozievitch, pelo todo o suporte,

motivação nos momentos difíceis, disponibilidade e incansáveis direcionamentos.

À minha co-orientadora, professora Dra. Monika Akbar, também pelas revisões e

contribuições dadas ao longo deste trabalho.

Aos professores e coordenadores da Pós, por todo o conteúdo ministrado, direciona-

mento e compartilhamento de conhecimento em cada matéria do curso.

Aos demais profissionais da instituição que direta e/ou indiretamente contribuem para

que tenhamos acesso a toda infraestrutura.

Aos meus familiares pela paciência, apoio e compreensão em momentos de ausência

para que este trabalho pudesse ser concluído.

Ao meu esposo, Filipe por suas palavras de ânimo, incentivo e encorajamento mesmo

nos momentos em quais eu mesma não acreditava possível continuar em frente.

Às minhas filhas, Melissa e Sophie pelos inevitáveis momentos de ausência, apoio e

por me fazerem querer ser uma pessoa cada vez melhor.

"There are many hypotheses in science that are

wrong. That’s perfectly alright; it’s the aperture

to finding out what’s right. Science is a

self-correcting process. To be accepted, new

ideas must survive the most rigorous standards

of evidence and scrutiny." Carl Sagan

RESUMO

LAUTERT, Tatiane Araujo Muniz. Unidades de Saúde Pública em Curitiba: Uma análiseexploratória e um protótipo de dashboard de saúde para apoio à decisão no domínio dagestão em saúde.. 2020. 70 f. Dissertação (Mestrado em Computação Aplicada) – UniversidadeTecnológica Federal do Paraná. Curitiba, 2020.

Garantir serviços de saúde adequados à população é um desafio, principalmente em países emdesenvolvimento, onde recursos limitados devem ser otimizados para atingir um percentual maiorda população. Para avaliar adequadamente os serviços de saúde e priorizar novos investimentos,é importante coletar, integrar e analisar grande quantidade de dados relevantes. Esta disser-tação apresenta um estudo agregando dados socioambientais, socioeconômicos, geográficos,de transporte e de saúde disponíveis ao público de diferentes fontes para a cidade de Curitiba,Brasil, com o objetivo de compreender a dinâmica das necessidades atuais de saúde e o uso dosistema de saúde pelos moradores da cidade. Esta dissertação apresenta uma análise detalhadadas consultas médicas, do bairro de procedência dos pacientes, das condições de saúde maisfrequentes relatadas nas fichas de atendimento médico. Também analisamos o aparecimento decertas doenças infecciosas nos bairros da cidade e as correlações entre essas doenças e diversosfatores socioambientais e socioeconômicos. Por fim, o artigo discute os resultados da análiseexploratória, destacando possíveis pontos que merecem atenção especial ou investimentos dagestão municipal para melhor atender o público.

Palavras-chave: Dados Abertos. Dados de Saúde. Análise Exploratória. Visualização de Dados.Dashboard.

ABSTRACT

LAUTERT, Tatiane Araujo Muniz. Unidades de Saúde Pública em Curitiba: Uma análiseexploratória e um protótipo de dashboard de saúde para apoio à decisão no domínio dagestão em saúde.. 2020. 70 p. Dissertation (Master’s Degree in Applied Computing) –Universidade Tecnológica Federal do Paraná. Curitiba, 2020.

Guaranteeing adequate health services to the population is a challenge, especially in developingcountries where limited resources must be optimized in order to reach a larger percentage of thepopulation. To properly assess health services and prioritize new investments, it is importantto collect, integrate, and analyze large amount of relevant data. This dissertation presentsa study aggregating publicly available socio-environmental, socio-economic, geographical,transportation, and health data from different sources for the city of Curitiba in Brazil, tounderstand the dynamics of current healthcare needs and healthcare usage by the city dwellers.This dissertation presents a detailed analyses on medical appointments, the neighborhood wherepatients come from, the most frequent health conditions reported from the medical assistancerecords. We also analyze the onset of certain infectious diseases in the city’s neighborhoodsand correlations between these diseases with various socio-environmental and socio-economicfactors. Finally, the dissertation discusses the findings from our exploratory analysis, highlightingpossible points deserving special attention or investments from the city management to betterserve the public.

Keywords: Open Data. Health Data. Exploratory Data Analysis. Data Visualization. Dashboard.

LIST OF FIGURES

Figure 1 – Research Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Figure 2 – Methodology steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Figure 3 – RDA COVID-19 Guidelines and Recommendations. . . . . . . . . . . . . . 21Figure 4 – Steps for investigating disease outbreaks . . . . . . . . . . . . . . . . . . . 23Figure 5 – Data Visualization and Explanatory Data Analysis (EDA) relationship. . . . 24Figure 6 – Steps taken in preparation for data analysis. . . . . . . . . . . . . . . . . . 28Figure 7 – Number of medical appointments across the years by month. . . . . . . . . 30Figure 8 – Distribution of appointments by age range for each gender. . . . . . . . . . 31Figure 9 – Population + Medical appointments per health units + Income per household. 32Figure 10 – Histogram showing the distance of the HU to the bus stops in meters. . . . . 33Figure 11 – The top 5 Health Units most visited by patients from other neighborhoods. . 34Figure 12 – Time comparison of general medical visits and top 5 health units visited by

patients from other neighborhood. . . . . . . . . . . . . . . . . . . . . . . 36Figure 13 – Number of appointments by hour per day of the week for all health units. . . 36Figure 14 – Number of appointments by hour per day of the week for top 5 health units

visited by patients from other neighborhoods. . . . . . . . . . . . . . . . . 36Figure 15 – Distribution of appointment by gender and age range in the top 5 health units 37Figure 16 – Influenza (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . . . 40Figure 17 – Dengue (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . . . . 40Figure 18 – Escherichia coli (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . 41Figure 19 – Hepatitis (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . . . 41Figure 20 – Yellow Fever (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . 41Figure 21 – Measles (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . . . . 41Figure 22 – Meningitis (Jan-2017 to Dec-2019). . . . . . . . . . . . . . . . . . . . . . 42Figure 23 – Health Dashboard Architecture. . . . . . . . . . . . . . . . . . . . . . . . . 45Figure 24 – Health Dashboard database tables and relationship. . . . . . . . . . . . . . 46Figure 25 – Interface of the Health Dashboard Prototype. . . . . . . . . . . . . . . . . . 47Figure 26 – Health Dashboard Filters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Figure 27 – Health Dashboard Map Layers. . . . . . . . . . . . . . . . . . . . . . . . . 48Figure 28 – Health Dashboard - Graphs available in the prototype. . . . . . . . . . . . . 49Figure 29 – Heat Map Layer for a given infection disease. . . . . . . . . . . . . . . . . 50Figure 30 – Region Layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Figure 31 – Neighborhood Layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Figure 32 – Health Unit Layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Figure 33 – Health Units and Neighborhood Layers. . . . . . . . . . . . . . . . . . . . 53Figure 34 – Users ability to complete usability testing scenarios. . . . . . . . . . . . . . 55Figure 35 – Evaluation Questionnaire Results - Easiness of Use and Graph Complexity. . 55Figure 36 – Evaluation Questionnaire Results - Relevance of Filters. . . . . . . . . . . . 56

LIST OF TABLES

Table 1 – Summary of challenges, guidelines and recommendations of RDA COVID-19Working Group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 2 – Types of Health Units from Curitiba . . . . . . . . . . . . . . . . . . . . . . 27Table 3 – Database table unidade_de_saude . . . . . . . . . . . . . . . . . . . . . . . 28Table 4 – Total of medical appointments over the years. . . . . . . . . . . . . . . . . . 29Table 5 – Quality standards for public transportation by bus in meters. . . . . . . . . . 32Table 6 – Top 5 ICD for medical visits performed outside the patient’s residential neigh-

borhood. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Table 7 – Top 5 ICD for all the medical visits. . . . . . . . . . . . . . . . . . . . . . . 35Table 8 – Selected Infectious Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . 39Table 9 – Cases of selected diseases over the years. . . . . . . . . . . . . . . . . . . . 39Table 10 – Medical appointments which triggered hospitalization . . . . . . . . . . . . 42Table 11 – Medical appointments which triggered hospitalization by Gender . . . . . . 42Table 12 – Usability testing scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Table 13 – Evaluation Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Table 14 – Suggestions for Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 56Table 15 – Medical Records Data Dictionary. . . . . . . . . . . . . . . . . . . . . . . . 66

LIST OF ACRONYMS

ACRONYMS

BHU Basic Health UnitsBSI British Standards InstitutionCIC Cidade Industrial de CuritibaCNES Cadastro Nacional de Estabelecimentos de SaúdeCDC Centers for Disease Control and PreventionDDL Data Definition LanguageDML Data Manipulation LanguageDQL Data Query LanguageECU Emergency Care UnitsEDA Explanatory Data AnalysisESRI Environmental Systems Research InstituteGIS Geographic Information SystemIBGE Brazilian Institute of Geography and StatisticsICD International Classification of DiseasesIPPUC Instituto de Pesquisa e Planejamento de CuritibaISO International Organization for StandardizationITU International Telecommunication UnionNNDSS National Notifiable Diseases Surveillance SystemPSAC Psychosocial Attention CentersRDA Research Data AllianceSARS Severe Acute Respiratory SyndromeSUS Sistema Único de SaúdeUTFPR Universidade Tecnológica Federal do ParanáWHO World Health Organization

CONTENTS

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.1 GENERAL OBJECTIVE . . . . . . . . . . . . . . . . . . . . . . . . . . 141.1.1 Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.2 METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.3 PUBLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.4 DISSERTATION STRUCTURE . . . . . . . . . . . . . . . . . . . . . . 16

2 BASIC CONCEPTS AND RELATED WORK . . . . . . . . . . . . . . . 172.1 GEOGRAPHIC INFORMATION SYSTEMS . . . . . . . . . . . . . . . 172.2 SMART CITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 OPEN DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.1 Public Health Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.2 Disease Outbreaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.4 EXPLORATORY DATA ANALYSIS . . . . . . . . . . . . . . . . . . . 232.5 CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3 DATA COLLECTION, PRE-PROCESSING, AND INTEGRATION . . 273.1 PREPARATION FOR EXPLORATORY DATA ANALYSIS . . . . . . 27

4 EXPLORATORY DATA ANALYSIS . . . . . . . . . . . . . . . . . . . . 294.1 MEDICAL APPOINTMENTS ACROSS THE MONTHS AND YEARS 294.2 GENDER AND AGE RANGE . . . . . . . . . . . . . . . . . . . . . . . 304.3 LOCATION ANALYSIS OF THE HEALTH UNITS . . . . . . . . . . . 314.4 PUBLIC TRANSPORT ACCESSIBILITY TO HEALTH UNITS . . . 324.5 DISPLACEMENTS TO ARRIVE AT HEALTH UNITS . . . . . . . . . 334.6 SELECTED INFECTIOUS DISEASES . . . . . . . . . . . . . . . . . . 374.6.1 Distribution of the selected diseases . . . . . . . . . . . . . . . . . . . . . . 394.7 HOSPITALIZATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5 HEALTH DASHBOARD PROTOTYPE . . . . . . . . . . . . . . . . . . 435.1 ARCHITECTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.1.1 Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.1.2 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.2 APPLICATION INTERFACE . . . . . . . . . . . . . . . . . . . . . . . 475.3 USABILITY TEST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.4 LESSONS LEARNT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

APPENDIX 65

12

1 INTRODUCTION

People are usually attracted to live in cities due to city services (such as health, education,

water), job perspectives, and living conditions (access to transportation, public facilities, etc.)

Cities influence people’s health and well-being through policies and interventions, including

those addressing social inclusion and social support; support for healthy and active lifestyles (for

example, the existence of cycling lanes); safety and environmental issues supporting children

and elderly population; working conditions; climate change preparedness; among others1.

According to the World Health Organization (WHO)2, today’s cities are facing a triple

health burden: infectious diseases (such as pneumonia, dengue, HIV/AIDS, tuberculosis, pneu-

monia); noncommunicable diseases (such as heart disease, stroke, asthma) and other respiratory

illnesses, cancers, diabetes and depression; and violence and injuries, including road traffic

injuries.

On the other hand, the increase in life expectancy and the aging of the world popu-

lation may increase demand in health systems which are already saturated in most countries

(BITTENCOURT; HORTALE, 2009). High population density combined with poverty and the

lack of adequate sanitation, can create conditions where infectious diseases can spread easily.

This increasing demand on the health system directly affects the quality of services provided.

Governments from several countries are taking initiatives to provide open health data,

such as the European Data Portal3 and United States4 to support primary health care and local

services5. In the meantime, since the approval of the Brazilian Law in Information Access in

20116, public agencies are making available their data in a variety of ways to citizens: through

transparency portals (e.g., the government of Paraná7 and API’s (Application Programming

Interface), (e.g., Brazilian Central Bank8). Along the same line, Curitiba, the largest city of the

Brazilian state of Paraná, is the eighth most populous city of Brazil, and has been participating in1 https://www.euro.who.int/en/health-topics/environment-and-health/urban-health/publications/2019/implementation-

framework-for-phase-vii-20192024-of-the-who-european-healthy-cities-network-goals-requirements-and-strategic-approaches-2019, last accessed 22-Jul-2020.

2 https://www.who.int/health-topics/urban-health, last accessed 22-Jul-2020.3 https://www.europeandataportal.eu/en/highlights/open-health-data-european-data-portal, last accessed 22-Feb-

2020.4 https://healthdata.gov/, last accessed 22-Feb-2020.5 https://www.euro.who.int/__data/assets/pdf_file/0003/376833/almaty-acclamation-mayors-eng.pdf, last ac-

cessed 22-Jul-2020.6 http://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm, last accessed 23-Jul-2020.7 http://www.transparencia.pr.gov.br/, last accessed 22-Jul-2020.8 https://dadosabertos.bcb.gov.br/, last accessed 22-Jul-2020.

13

Figure 1 – Research Areas.

Smart City

GeographicInformation

Systems(GIS)

Exploratory DataAnalysis

Data Analysis

Visualization

Is aggregatedwith

Other SourcesSocio-Economic

DataCensus

DataGeorreferenced

Data

Open DataHealthData

Health DashboardPrototype

Dashboard

Source: The Author.

open data initiatives along with several government stakeholders, such as Instituto de Pesquisa

e Planejamento de Curitiba (IPPUC)9 and the Municipality of Curitiba through its Open Data

Portal10.

Along with the data, the use of technologies, such as Geographic Information Systems

(GIS) assist in the analysis of services within a city. Initiatives such as Open Cities Project11 is

an example of that, where geographic and open data are combined to help urban planner and city

administrators make better informed decisions on natural disaster preparedness.

This dissertation presents a new analysis based on distinct data sources, including:

public health data, health units’ location, patients’ residence location and availability of bus stops

near the health units. Data about medical appointments from public Health Units of Curitiba is

investigated and integrated with the city’s georeferenced data. More specifically, it includes an

analysis of patient residence neighborhood versus the health units, categorizing the most frequent

diseases these patients were diagnosed with when the medical appointment took place, and the

spacial distribution of a group of diseases. The goal is to understand the medical appointments

and the diseases at public health units in Curitiba by doing an exploratory data analysis and relate

this information to other factors and data sources (such as health units location, accessibility of

bus stops).

The research areas of this dissertation is summarized in Figure 1.9 http://ippuc.org.br/, last accessed 22-Feb-2020.10 http://www.curitiba.pr.gov.br/DADOSABERTOS/, last accessed 22-Feb-2020.11 https://www.worldbank.org/en/region/sar/publication/planning-open-cities-mapping-project, last accessed 13-

Sep-2020.

14

1.1 GENERAL OBJECTIVE

The general objective of this dissertation is to understand the medical appointments at

public health units in Curitiba by doing an exploratory data analysis and relating this information

to other factors and data sources, such as health units location, accessibility of bus stops close

to these health units and displacements performed by the citizens to arrive at the health units,

aggregate it with socio-economic data and analyze the geographical distribution of selected

infectious diseases. Upon the exploratory data analysis, the objective is to also create a prototype

of a health data dashboard leveraging the code base developed by (PARCIANELLO, 2019).

1.1.1 Specific Objectives

To achieve the general objective, the following specific objectives are considered:

1. Explore related work on geographic information systems, smart cities, open data with

focus on health data, disease outbreaks and exploratory data analysis.

2. Review challenges of each area in order to understand their context.

3. Collect the raw data required and make it available for future researches.

4. Process, clean and aggregate the data to enable an exploratory data analysis.

5. Report any findings identified as part of the exploratory data analysis that might be useful

for health department to make decisions and hence contribute to public policies that can

be built to design or implement better services.

6. Develop a Health Dashboard prototype with geographic and temporal data leveraging the

code base developed by (PARCIANELLO, 2019).

7. Provide the solution for user testing and evaluation.

8. Analyse the usability testing and evaluation results and identify suggestions for improve-

ments based on the user evaluation.

15

Figure 2 – Methodology steps.

Basic Concepts &Related Work

Geographic information systemsSmart CitiesOpen data

Health dataDisease OutbreaksExploratory data analysis

Collect, Pre-process &Integrate Data

Provide a background on Curitibahealth systemDescribe the process to collect,pre-process and integrate thedata in preparation for theexploratory data analysis

Exploratory DataAnalysisPerform an exploratory data analysis on:

Medical appointments acrossmonths and yearsAge & gender distributionLocation analysis of health unitsPublic transport access to healthUnitsDisplacementsInfeccious diseasesHospitalization

Implement HealthDashboardPrototype

Create data model andconsolidate the dataDevelop a Health DashboardPrototype 

Testing &Evaluation

Provide the link of the prototypefor users testing and evaluationIdentify opportunities forenhacements

ConclusionReport any findings identified aspart of the exploratory dataanalysisAnalyze results of testing andevaluation of the HealthDashboard Prototype 

Source: The Author.

1.2 METHODOLOGY

The methodology of this dissertation has been divided into 6 steps, as shown in Figure 2.

The first step involved the research of the theoretical concepts related to the objectives intended,

therefore this work presents the basic concepts and related work on Geographic Information

Systems (GIS), Smart Cities, Open data with a focus on Public Health data, Disease Outbreaks,

and Exploratory Data Analysis, also it presents the challenges related to health data management.

The second step consisted of providing background about Curitiba health system, the process to

collect, pre-process, and integrate the data in preparation for the exploratory data analysis. As a

third step, a detailed exploratory data analysis is presented. The fourth step consisted of creating

a health dashboard prototype leveraging the code developed by (PARCIANELLO, 2019). At this

stage, the dashboard has been completely re-designed for the purpose of this work. Also, the

dashboard performance has been enhanced through the use of materialized views. As part of the

fifth step, the dashboard has been made available online and 7 users have been invited to perform

a usability test and evaluation of the dashboard. Also, users were asked to provide inputs for

future enhancements of the dashboard. In this step, the results of the usability test and evaluation

were presented. As a final and sixth step, the conclusion of this work is presented.

16

1.3 PUBLICATIONS

The following publications is directly related to this dissertation:

• LAUTERT, F. ; LAUTERT, T. ; Kozievitch, N. P. ; GOMEZ-JR, L. C. . Is the location of

Public Health Units in Curitiba meeting the citizen’s needs?. In: Workshop on Big Social

Data and Urban Computing, 2018, Rio de Janeiro. Proceedings of the Workshop on Big

Social Data and Urban Computing co-located with 44th International Conference on Very

Large Data Bases (VLDB 2018), 2018.

Additionally, during the Master Degree course the following research work, on distinct

subjects have also been produced and presented. The first has also been published as a book

chapter for the 10th Brazilian Workshop on Agile Methods (WBMA-2019), which is part of

Agile Brazil conference.

• LAUTERT, T. ; NETO, A. G. ; Kozievitch, N. P. A survey on agile practices and challenges

of a global software development team, 2019, Belo Horizonte. 10th Brazilian Workshop

on Agile Methods (WBMA-2019) (Agile Brazil 2019), 2019.

• LAUTERT, T ; How businesses are benefited from crowd source review systems, 2018,

Curitiba. III Workshop de Computação Social (UTFPR), 2018.

1.4 DISSERTATION STRUCTURE

This dissertation is organized as follows: Section 2 presents the concepts and gives an

overview on the related work. The data collection, pre-processing, and integration is presented

in Section 3, followed by the Exploratory Data Analysis on Section 4. Details about the Health

Dashboard prototype and its evaluation are presented in Section 5. Finally, conclusion is presented

at Section 6.

17

2 BASIC CONCEPTS AND RELATED WORK

Several health challenges can be listed as urgent for the next decade: (i) earning public

trust, (ii) harnessing new technologies, (iii) stopping infectious diseases, (iv) elevating health in

the climate debate, (v) delivering health in conflict and crisis, (vi) making healthy care fairer,

(vii) expanding access to medicine, (viii) preparing for epidemics, among others (World Health

Organization, 2020). This dissertation approaches the first three challenges, by using public

data from Curitiba public health system to provide and overview of the health system); by

using techniques such as Geographic Information Systems to better understand the data); and by

understanding the behaviour of infectious diseases in the public health system.

2.1 GEOGRAPHIC INFORMATION SYSTEMS

Geographic Information Systems (GIS) is a framework for gathering, managing, and

analysing data (ESRI, 2019). GIS integrates many types of data. It analyzes spatial location and

organizes layers of information into visualization using maps and 3D scenes, hence GIS helps to

reveal deeper insights into data, such as patterns, relationships, and situations. GIS can help in

the public health issues with maps and analysis to monitor and prevent pandemics, chronic or

infectious diseases, and monitor environmental quality that may affect community health.

The use of GIS techniques in the context of public health to investigate epidemiology is

of particular interest of a number of researchers. The most classical example is the one produced

in 1854 by (SNOW, 1856), who used maps to plot cholera cases and found a correlation with

the water supplied from a pump on a street called Broad in London, United Kingdom. In recent

studies, (NETO et al., 2014) demonstrate the use of GIS through a mobile application, which

provides maps, demographic data, health data and urban structure of some regions in the state

of São Paulo. (CAVALCANTE et al., 2018) describe an application with the location of both

public and private health units and also medical specialties offered by these health units. In

another study, (SOUSA et al., 2018) demonstrate a platform for preventing and combating

mosquito-borne diseases, in which the users can directly report cases of mosquito breeding sites

or disease cases.

Another example is presented by (VILAICHONE et al., 2020), with a community-based

study in Buthan, with the objective to assess antibiotic resistance patterns of H. pylori strains in

18

different geographical locations of the country to guide H. pylori treatment in order to reduce

gastric cancer mortality.

As highlighted by (HINO et al., 2006), analyzing disease distribution and determinants

in populations in space and time is an essential aspect of epidemiology. As per the literature

review of (RUSHTON, 2003), public health is now presented with the opportunity to examine

key relationships between the health characteristics of populations with both human and physical

environmental characteristics.

2.2 SMART CITIES

According to 2018 Revision of World Urbanization Prospects1, 55% of the world’s

population lives in urban areas today, and that is expected to increase to 68% by 2050.

As mentioned by (DAMERI; CAMILLE, 2014), in order to face the increasing problems

of urban areas, local public government, companies, not-for-profit organizations and the citizens

themselves embraced the idea of a smarter city, using more technologies, creating better life

conditions and safeguarding the environment.

As the effort of smart cities become a solution for managing the rapid urban growth

efficiently and effectively, there is a need of certain standardisation, with examples such as

International Organization for Standardization (ISO)2, British Standards Institution (BSI)3,

International Telecommunication Union (ITU)4, and others have published standards focused on

Smart Cities. BSI, for example, defines a smart city as one where there is “effective integration of

physical, digital and human systems in the built environment to deliver a sustainable, prosperous

and inclusive future for its citizens”5. For ISO6, a Smart City is one that dramatically increases

the pace at which it improves its social, economic and environmental (sustainability) outcomes.

In 2009, IBM published the IBM Smarter Cities campaign7, divided into 3 pillars:

planning and management, efficient daily management and infrastructure.

Data is also focused in smart cities (Harrison et al., 2010; BATTY et al., 2012). For

(Harrison et al., 2010) Smart Cities are urban areas that exploit operational data to optimize the1 https://population.un.org/wup/, last accessed 08-Jul-2019.2 https://www.iso.org/, last accessed 22-Feb-2020.3 https://www.bsigroup.com/, last accessed 22-Feb-2020.4 https://www.itu.int/, last accessed 22-Feb-2020.5 https://shop.bsigroup.com/upload/PASs/Free-Download/PAS180.pdf, last accessed 08-Mar-2020.6 https://www.iso.org/obp/ui/#iso:std:iso:37106:ed1:v1:en, last accessed on 08-Mar-2020.7 https://www.ibm.com/smarterplanet/us/en/, last accessed 16-Jun-2019.

19

operation of city services. (BATTY et al., 2012) describe that cities can only be smart if there are

intelligent functions that are able to integrate and synthesize the data to some purpose, in a way

of improving the efficiency, equity, sustainability and quality of life in cities.

2.3 OPEN DATA

As highlighted in (CALEGARI et al., 2016), we live in the age of data and the digitaliza-

tion of cities has led to produce massive data-sets and data streams related to urban environment.

Open data is a concept that governmental data should be available to anyone with a possibility

of redistribution in any form without any copyright restrictions (KASSEN, 2013). The overall

intention of the open data movement is to make local, regional and national data available in

machine readable format or direct processing form that allows for direct manipulation using

software tools for the purposes of cross tabulation, visualization, mapping and among others

(GURSTEIN, 2011).

As an example of data limitation, (SILVEIRA et al., 2015) refers to many mobility

models that are used to describe or predict urban mobility, but most of them are limited to a

single source of data to do such analysis.

Open data has been used in several contexts in the city of Curitiba. (KOZIEVITCH et

al., 2017) evaluate three decades of business activity through open data in the city of Curitiba and

in (VILA et al., 2016) an exploratory analysis was presented analyzing the bus service from the

perspective of pattern discovery, statistical analysis, data integration, and the use of connected

and open data. (NAKONETCHNEL et al., 2017) analyzes the open data of public transportation

in the cities of Curitiba and New York.

2.3.1 Public Health Data

Gathering relevant public health data is a challenge; health data are rarely available

although they are needed to monitor population health, prevent pandemics, chronic diseases, and

monitor environmental quality that may affect population’s health. Modern cities are flooded

with data and new information sources provide opportunities for novel applications that will

improve the citizen’s quality of life (KATAKIS, 2015).

Among the government systems aimed at managing and improving healthcare access to

20

Brazilians citizens (such as Cartão SUS 8, CNES9 and E-Saúde10), most of them do not provide

open data for research purposes. Although Brazil provides universal access to public healthcare

services11, data about specific issues such as immigrants or relation with transportation is rare.

Considering the public health data from Curitiba, several studies can be mentioned.

In the study of (LIMA et al., 2019), open data of Curitiba public health is aggregated with

transportation data, analysing the accessibility to Curitiba public health units via public trans-

portation. The study of (OLIVEIRA et al., 2018) presented a characterization of Paraguay’s

public health data, and using the information about the city of Asuncion a comparison was made

with Curitiba’s public health data. An approach based on evaluation to model the health care for

the elderly population in Curitiba health units is proposed by (SANTIN et al., 2017), segregating

the health data of the elderly population from the non-elderly.

If we consider the last pandemics, (DONG et al., 2019) present an interactive web-

based dashboard to track the novel coronavirus (COVID-19) in real time. In response to the

ongoing public health emergency, the researchers developed an online interactive dashboard12, to visualise and track reported cases of coronavirus. Further more, as a response to the

global COVID-19 Pandemic, the Global Research Data Alliance Community (RDA) (RDA

COVID-19 Working Groups, 2020) created a set of guidelines and recommendations for data

sharing of health data under the present COVID-19 circumstances. The report13 also includes

legal and ethical considerations, research software, community participation and indigenous data

(Figure 3).

Table 1 presents a summary of the challenges, guidelines and recommendations pre-

sented in the report of RDA COVID-19 Working Group.

8 http://portalsaude.saude.gov.br/index.php/oministerio/principal/secretarias/sgep/cartao-nacional-de-saude, lastaccessed 16-Nov-2018.

9 http://datasus.saude.gov.br/sistemas-e-aplicativos/cadastros-nacionais/cnes, last accessed 16-Nov-2018.10 http://esaude.curitiba.pr.gov.br/PortalSaude, last accessed 16-Nov-2018.11 http://portalms.saude.gov.br/index.php/sistema-unico-de-saude/sistema-unico-de-saude, last accessed 16-Nov-

2018.12 https://arcg.is/0fHmTX, last accessed 22-Feb-2020.13 https://www.rd-alliance.org/global-research-data-alliance-community-response-global-covid-19-pandemic, last

accessed on 06-May-2020.

21

Figure 3 – RDA COVID-19 Guidelines and Recommendations.

Source: Global Research Data Alliance(2020).

Table 1 – Summary of challenges, guidelines and recommendations of RDA COVID-19 WorkingGroup.

Summary of challenges, guidelines and recommendations of RDASub-groups /cross cuttingthemes

Challenges Guidelines for researchers Recommendations forfunders / policy makers

Clinical Promotion of clinical datasharing is important dueto many studies and tri-als being performed underenormous time pressure

Standardised clinicalterminologies shouldbe used and a fair bal-ance achieved betweentimely data sharing andprotecting privacy andconfidentiality

Measures should be takenin order to organise thesharing of data and trialdocuments in a suitable,trustworthy and securedata repository

Omics An increased need ofrapid openness for omicsdata to gain early insightsinto molecular biology ofthe processes at cellularlevel

Omics research should bea collaborative effort tolearn the genetic determi-nants of COVID-19 sus-ceptibility, severity andoutcomes

Promote use of domainspecific repositories to en-able standardisation ofterms and enforce meta-data standards

Epidemiology Data and models are fre-quently incomplete, pro-visional, and subject tocorrection under changingconditions

Data models must includeclinical data, disease mile-stones, indicators and re-porting data, contact trac-ing and personal risk fac-tors

Incentivise the publica-tion of situational data, an-alytical models, scientificfindings, and reports usedin decision making

22

Continuation of Table 1Sub-groups /cross cuttingthemes

Challenges Guidelines for researchers Recommendations forfunders / policy makers

Social Sciences Need equal inclusion ofsocial and economic is-sues with medical infor-mation to enable evidence-based decision making

Promote interoperablecross disciplinary andcross-cultural data useand collaboration formanaging social sciencedata during pandemics

Robust funding streamsfor social science researchfor understanding andmanaging the behavioural,and economic aspects

Community Need specific guidelinesfor enabling citizen scien-tists undertaking researchto contribute to a commonbody of knowledge

Encourage public and pa-tient involvement (PPI)throughout the data man-agement lifecycle from re-search question to finaldata sharing and usage

Balance between timelytesting and contact trac-ing, emergency response,community safety and in-dividual privacy concerns

IndigenousData Guide-lines

Indigenous data rights,priorities and interestsmust be recognised inCOVID-19 research activ-ities

Co-determination ofdata collection, owner-ship, sharing and usepriorities is the centralprinciple of Indigenousdata sovereignty

CARE Principles ofIndigenous Data Gov-ernance set minimalguidance for collectors,users and stewards of data

Legal and Eth-ical Considera-tions

Achieve a balance be-tween rights of people andinterests of researchersand policymakers

Ethical instrumentsshould be interpreted withthe law, and can guide theinterpretation of the law ifthe law does not address aparticular issue

During a pandemic, eth-ical review and approvalfor legally sharing datashould be expedited

Research Soft-ware

Need systems in placefor rapid dissemination ofdata and accelerated andreproducible research dur-ing a pandemic

It is critical for softwarethat is used in data analy-sis to produce results thatcan, if necessary, be repro-duced

Funders must allocate fi-nancial resources to sup-port the development andmaintenance of new re-search software

Source: Adapted from RDA (2020).

2.3.2 Disease Outbreaks

A disease outbreak is the occurrence of disease cases in excess of normal expectancy14.

The number of cases varies according to the disease-causing agent, and the size and type of

previous and existing exposure to the agent, and the definition may vary for countries, regions

and even cities.

In Brazil, this information can be found in Epidemiological Bulletins, which contains

detailed data and analysis15. In July, 2000 the Brazilian Ministry of Health implemented Episus-

Advanced, which is a Training Program in Applied Epidemiology. Professionals from this training

program created a Guide for Investigations of Outbreaks or Epidemics16. EpiSUS-Advanced14 https://www.who.int/environmental_health_emergencies/disease_outbreaks/en/, last accessed 19-July-2020.15 https://www.saude.gov.br/boletins-epidemiologicos, last accessed 19-July-2020.16 http://www.saude.gov.br/images/pdf/2018/novembro/21/guia-investigacao-surtos-epidemias-web.pdf, last ac-

23

Figure 4 – Steps for investigating disease outbreaks

Source: (Adapted from Episus-Advanced Guide. (2018))

has become one of the main response strategies to public health emergencies in nationwide.

According to the guide, there are 10 steps for investigation of a disease outbreak, as summarized

in Figure 4.

In USA the information is provided by the Center for Disease Control and Prevention

(CDC)17. The WHO outbreak definition (World Health Organization and others, 1999) states that

for a defined area, the average number of cases from previous years can be taken as a threshold.

All observations above that threshold should be considered as an outbreak. In order to detect the

outbreaks of these diseases, we are using a modified version of the World Health Organization

(WHO) outbreak definition. Instead of using the raw disease count, we set the threshold for

outbreak at two standard deviations in excess of the endemic channel (i.e., average) (BRADY et

al., 2015).

2.4 EXPLORATORY DATA ANALYSIS

Explanatory Data Analysis (EDA) is a branch of statistical analysis (TUKEY, 1977) .

It is an approach for analyzing data-sets in order to summarize the main characteristics of the

cessed 19-July-2020.17 https://wwwnc.cdc.gov/travel/destinations/traveler/none/brazil, last accessed 19-July-2020.

24

Figure 5 – Data Visualization and Explanatory Data Analysis (EDA) relationship.

Source: Schutt and O’Neil (2013).

data, often consisting in visual methods and graphical techniques to explore, insight into or

categorize the data. Figure 5 shows that EDA is a continuous approach in the context of Data

Science process and it is primarily used for seeing what the data can tell.

According to (Ferreira et al., 2013) with the increasing volume of urban data and more

data becoming available, new opportunities arise from data-driven analysis which can reveal

opportunities for improvements in the urban spaces. The data presents many challenges, due to the

fact that data are complex, contain geographical and temporal components in addition to multiple

variables. Similarly, (EDSALL, 2003) presents a system for exploring multidimensional health

statistics. The system makes use of GIS to explore health-statistics data of many dimensions, it

is an interactive system which allows multiple perspectives on complex information.

2.5 CHALLENGES

Several challenges can be listed within health data:

Huge volume of data: Many cities are taking initiatives to provide access to open data. The

challenge is how to make sense of such large amount of data, since the data is complex, and

might contain geographical and temporal components (Ferreira et al., 2013).

Data Integrity and consistency: When correlating multiple data sources, integrity and consis-

tency among the different sources of data is a challenge, specially in terms of inconsistencies

25

in data vocabulary, lack of common identifier across different data bases, missing data and

others(OLIVEIRA et al., 2018).

Prior knowledge required to make use of data: There are several limitations in the use of

open data(JANSSEN et al., 2012), which includes technologies, metadata and standardization.

Health data and privacy concerns: in the recent years health systems are being introduced,

with that patient records are becoming more electronic.Technologies helps improve the quality

of health care(Meingast et al., 2006), but there are still many concerns related to privacy when it

comes to health data. Data access, storage, and integrity are key challenges when it comes to

electronic patient records.

Health data and differences in data definitions and/or measurement methods: Health data

is derived from health information systems, including health-facility records, surveys or vital

statistics, and it may not be representative of the entire population of a country and in some

cases may not even be accurate. Comparisons between populations or over time can also be

complicated by differences in data definitions and/or measurement methods. Although some

countries may have multiple sources of data for the same year, it is more usual for data not to be

available for every population or year18.

The Brazilian Unified Health System (SUS)19 is one of the largest and most complex

public health systems in the world, encompassing individual treatment, ensuring full, universal

and free access for the entire population of the country. SUS was created in 1990, following

the change in the Federal Constitution of 1988 (CF-88), which article 19620 defines that health

is everyone’s right and the duty of the State. It is also presented that before the changes in the

CF-88, the public health system provided assistance only to workers linked to Social Security,

approximately 30 million people had access to hospital services, and philanthropic entities were

responsible for serving other citizens. (CASTRO et al., 2019) present how SUS has contributed

to improving the health and well-being of the Brazilian population since its 30 years of inception,

also shows how it has helped to reduce inequalities in health, however it also brings a study about

how recent political measures may present a threat to future expansion and sustainability of the

SUS.

From a data measurement perspective, there is still lack a fully integrated health system,

for example, when a patient goes to a private hospital his historical medical records are may18 https://www.who.int/gho/publications/world_health_statistics/2018/en/, last accessed 09-Jun-2019.19 http://www.saude.gov.br/sistema-unico-de-saude, last accessed 19-Aug-2019.20 https://www.jusbrasil.com.br/topicos/920107/artigo-196-da-constituicao-federal-de-1988, last accessed 16-May-

2020.

26

not be available in that hospital system, unless the patient has always been treated in that same

hospital. And the same happens when the patient goes to a public hospital, his full medical

records may not be available if the patient has been treated in a private hospital previously.

27

3 DATA COLLECTION, PRE-PROCESSING, AND INTEGRATION

For this dissertation, the dataset of medical services was used, basically focusing on

in Emergency Care Units and Basic Health Units. According to information from the city’s

Department of Transportation, Curitiba has an average of 250 bus lines and 9,940 bus stops (VILA

et al., 2016). The city also has 23 bus terminals and one intercity and interstate terminal which

also offers train services. The medical services dataset was integrated with the transportation

data. One of the major challenges of the data integration was the consistency of data within the

different sources (different description names, overlapping of data, among others). The following

section provides more details on the data cleaning and integration process.

Table 2 – Types of Health Units from Curitiba

Unit Type N. of UnitsBasic Health Units (BHU) 111Health Spaces 68Psychosocial Attention Centers (PSAC) 13Emergency Care Units (ECU) 9Medical Specialty Centers 5Therapeutic Residences 5Hospitals 2Clinical Analysis Laboratory 1Central of Vaccines 1Zoonosis Center 1

Source: The Author.

3.1 PREPARATION FOR EXPLORATORY DATA ANALYSIS

The data used in this dissertation is originated from Curitiba Open Data Portal. We used

the Medical appointments only, from Basic Health Units (BHU) and Emergency Care Units

(ECU).

The raw data is available in Comma Separated Values (CSV) format and for most of

this dissertation the data comprises from January-2017 to December-2019. Each file contained

three months of data, and the summary of the columns is presented in in Table 3. Additionally

socioeconomic and census data are also aggregated, such as income per household data published

by Curitiba Agency1 and population density published by IBGE2.1 http://www.agenciacuritiba.com.br/, last accessed 16-May-2020.2 https://www.ibge.gov.br/, last accessed 16-May-2020.

28

Table 3 – Database table unidade_de_saude

Column Name DescriptionCd_equip Code of the health unitNome_abrev Short name of the health unitNome_mapa Name of the health unit on the mapCd_bairro Code of the neighborhoodBairro NeighborhoodQuadr_equi Block of the health unitCd_regiona Code of the RegionRegional RegionFunc_manha Whether or not it is open in the morningFunc_tarde Whether or not it is open in the afternoonFunc_24hr Whether or not it is open 24 hours a dayDesativado Whether or not it is disabledCoord_e Coordinate eCoord_n Coordinate nGeom Geometry

Source: The Author.

Figure 6 – Steps taken in preparation for data analysis.

Source: The Author.

The following technologies were used: PostgreSQL3, PostGIS4, QGIS5, versions 2.14.11

(Essen) and 2.18.18 (Las Palmas), Gephi6, Tableau Desktop version7 and Microsoft Excel8.

Figure 6 shows the major steps of the data transformation. In the first phase, data from different

sources was collected. In the second phase, the data was cleaned and integrated, followed by the

analysis and visualization.

3 https://www.postgresql.org, last accessed 16-Nov-2018.4 https://postgis.net, last accessed 16-Nov-2018.5 https://www.qgis.org/en/site/, last accessed 16-Nov-2018.6 https://gephi.org/, last accessed 16-Nov-2018.7 https://www.tableau.com/pt-br, last accessed 14-Nov-2020.8 https://www.microsoft.com/pt-br/microsoft-365/excel, last accessed 14-Nov-2020.

29

4 EXPLORATORY DATA ANALYSIS

Three years of medical appointment data (2017, 2018 and 2019) from 31 different

medical procedures were used as input. Table 4 shows the number of appointment across these

months and years. Note that the month of February 2017 is incomplete, because the data was

missing at the portal.

4.1 MEDICAL APPOINTMENTS ACROSS THE MONTHS AND YEARS

Table 4 shows the number of appointments by month across the years of 2017, 2018

and 2019. January and December were the months with the lowest number of appointments and

May, August and October the months with the highest number. Note that the total of medical

appointments per month shows an increase over the years.

Figure 7 shows the number of appointments by month across the years of 2017, 2018

and 2019. An average of 280,580 appointments were made by month in 2017, 313,075 in

2018 and 334,210 in 2019. January and December were the months with the lowest number of

appointments and May, August and October the months with the highest number. With this, we

can notice that the total of medical appointments per month shows an increase over the years.

Table 4 – Total of medical appointments over the years.

Months 2017 2018 2019Jan 229,452 276,070 281,713Feb 80 262,529 293,435Mar 282,615 316,269 299,559Apr 255,302 340,316 333,390May 303,252 350,874 361,254Jun 292,728 327,678 345,401Jul 272,112 309,117 346,419

Aug 302,229 345,852 367,456Sep 292,787 307,728 353,917Oct 310,991 349,514 398,133Nov 300,311 315,633 351,056Dec 244,601 255,320 278,788Total 3,086,460 3,756,900 4,010,521

Source: The Author.

30

Figure 7 – Number of medical appointments across the years by month.

Source: The Author.

4.2 GENDER AND AGE RANGE

According to the 2010 census data1, the gender distribution in Curitiba is 916,792

women and 835,115 men, that represents 52% of the population is female while 48% is male.

From the 3 years of medical appointments, we analyzed how the numbers of appointments are

distributed by gender, taking into consideration the ratio and proportion of male and female

population distribution. The results show that 60.62% were female patients while 39.38% were

male patients. According to studies presented by (BERTAKIS et al., 2000), women had a

significantly higher mean number of visits to their primary care clinic and diagnostic services

than men, this behavior is confirmed by the numbers presented in this dissertation.

The distribution of the number of appointments per age range among female and male

patients is shown on Figure 8. The only age range in which the number of male patients is higher

than the number of female patients is from 0 to 4 years old. While in all other age ranges the

number of female patients are always higher than male patients, even on non-reproductive age

ranges.1 https://censo2010.ibge.gov.br/sinopse, last accessed 09-Dec-2018.

31

Figure 8 – Distribution of appointments by age range for each gender.

Source: The Author.

4.3 LOCATION ANALYSIS OF THE HEALTH UNITS

From the 75 existing neighborhoods in Curitiba, 33 of them do not have a health unit.

Most of these 33 neighborhoods, are those with the highest income per household2. On the other

hand, in 29 neighborhoods there are 2 or more health units, and the Industrial District of Curitiba

(CIC) has the highest number of health units (a total of 16).

Figure 9-left shows a heat map with the distribution of the population according to data

from the 2010 census3. As a comparison, Figure 9-center shows the medical appointments per

health units for the year of 2017, and there is a notable overlap if compared to the heat map

on the left. Additionally, in Figure 9-right, there is an empty space around the downtown area,

which shows a heat map of income per household. Therefore, by looking at those maps it is

confirmed that the appointments at health units and the units themselves are located closer to the

population with the lowest income.2 http://www.agencia.curitiba.pr.gov.br/arquivos/regionais/perfil-economico-regional-matriz.pdf, last accessed

16-Nov-2018.3 http://www.ippuc.org.br/nossobairro/nosso_bairro.htm, last accessed 16-Nov-2018.

32

Figure 9 – Population + Medical appointments per health units + Income per household.

Source: The Author.

4.4 PUBLIC TRANSPORT ACCESSIBILITY TO HEALTH UNITS

According to (FERRAZ; TORRES, 2014) there are twelve main factors that influence

quality of urban public transportation, being them: accessibility, frequency of service, time of

journey, capacity, security, vehicle resources, stop resources, information system, connectivity,

operator behavior and road conditions. In this section, is analyzed the existence of bus stops near

the health units from the perspective of the distance between the health unit to the bus stop or

bus station. Table 5, shows the quality standards defined by (FERRAZ; TORRES, 2014) for the

accessibility factor, considering the walking distance parameter from the start to the end of the

trip.

Considering the quality of standards presented, all the 125 health units considered in

this dissertation have either a bus stop or a bus station well located, within a walking distance

of less than 300 radius in meters. Figure 10 shows a histogram with the number of health units

within each defined bucket of distance from the health units to bus stops or bus stations.

Table 5 – Quality standards for public transportation by bus in meters.

Factor Parameter Good Regular BadAccessibility Walking distance < 300 300-500 > 500

Source: Adapted from Ferraz and Torres (2004).

33

Figure 10 – Histogram showing the distance of the HU to the bus stops inmeters.

Source: The Author.

4.5 DISPLACEMENTS TO ARRIVE AT HEALTH UNITS

In this section, the displacements made by the citizens to arrive at the public health

units is analyzed. For this analysis, only the data from 2017 is used, due to the great amount of

data and computer processing limitation.

The results show that 12.34% of the 3,086,460 medical appointments made in 2017

were performed outside the neighborhood where the patients reside. It means that a daily average

of 1,130 people went to another neighborhood to have a medical appointment. Figure 11

shows the displacement made by the patients from their residential neighborhood to the 5 most

visited health units by patients from outside the neighborhood where the unit is located. These 5

most visited health units are emergency care units (ECU) that provide service 24 hours a day,

being: Cajuru, Boqueirão, Boa Vista, Campo Comprido and Sítio Cercado. Figure11 shows a

network graph which highlights the intensity of the flow for those patients that had their medical

procedures outside their residential neighborhood - thicker lines represent a greater flow of

people. Looking at the numbers of those 5 units, a total of 28.64% of the medical appointments

were performed by people from other neighborhoods in 2017.

Note that ECU Boqueirão is one of the top 5 that most receive people from other neigh-

borhoods but it does not have a bus stop in the 200 meter area. For these medical visits performed

outside the patients’ residential neighborhood in 2017, the top 5 International Classification

of Diseases (ICD) codes are presented in Table 6 and compared to the 5 ICD codes of all the

medical visits of the same period for comparison purposes as shown in Table 7.

34

Figure 11 – The top 5 Health Units most visited by pa-tients from other neighborhoods.

Source: The Author.

35

Table 6 – Top 5 ICD for medical visits performed outside the patient’s residentialneighborhood.

Top 5 outside residential neighborhood TotalAcute upper respiratory infections, unspecified 14512Acute tonsillitis, unspecified 9229Acute nasopharyngitis [common cold] 8969General Medical Examination 8309Other gastroenteritis and colitis of infectious andunspecified origin

8098

Source: The Author.

Table 7 – Top 5 ICD for all the medical visits.

Top 5 ICDs for All Medical Visits TotalGeneral Medical Examination 309366Issue of Repeat Prescription 174749Acute upper respiratory infections , unspecified 124942Essential (primary) hypertension 108797Acute nasopharyngitis [common cold] 95349

Source: The Author.

Based on this information, it is possible to notice that the great majority of the medical

visits made outside the patients’ residential neighborhood consist of complications of the res-

piratory tract, which usually get aggravated at night. To confirm this information, the time of

these medical visits were checked in Figure 12. The numbers on the left represent the number of

medical appointments in general, the blue line shows the number of medical appointments by

hour at the health units and the numbers on the right represent the number of medical appoint-

ments at the top 5 most health units most visited by patients from other neighborhoods, in the

red line it is possible to see the number of medical appointment at the top 5 health units by hour.

In summary, this analysis points out that the number of medical visits is constant from 14:00

to 23:00 in the top 5 health units most visited by patients from other neighborhood, while the

number of general medical appointments drops sharply after this period. Figures 13 shows the

number of appointments by hour per day of the week for all health units while Figure 14 shows

the same view but only for the top 5 health units visited by patients from other neighborhoods.

Figure 13 and Figure 14 also reinforce the finding that when top 5 facilities get more patients,

the number of appointments in the general facilities declines proportionally.

In regards to medical appointments distribution by gender, looking only at the top

5 health units most visited by patients from other neighborhood it was found that female

patients represent 54% while male patients represent 46%. Comparing to the overall distribution

36

Figure 12 – Time comparison of general medical visits and top 5 healthunits visited by patients from other neighborhood.

Source: The Author.

Figure 13 – Number of appointments by hour per day of the week for all health units.

Source: The Author.

Figure 14 – Number of appointments by hour per day of the week for top 5 health units visited by patientsfrom other neighborhoods.

Source: The Author.

37

Figure 15 – Distribution of appointment by gender and age range in the top5 health units

Source: The Author.

mentioned in sub-session 4.1, the distribution is quite different. And finally, on Figure 15, the

gender distribution per age range on these top 5 health units, age ranges 0-4 and 0-14 show the

same pattern as the overall distribution on figure 3, while in all other ranges the differences in

the number of appointments decrease.

4.6 SELECTED INFECTIOUS DISEASES

The diseases were selected based on the following criteria:

1. A list of the most important diseases was ranked, using their symptoms and the TFIDF

(term frequency–inverse document frequency) as a quantifier based on more than 7 million

bibliographic records from PubMed4.

2. Once identified, they were checked against the National Notifiable Diseases Surveillance

System (NNDSS)5.

3. The diseases selected in items 1 and 2, were ranked with the highest occurrence in the

open data of the health units in Curitiba.4 https://pubmed.ncbi.nlm.nih.gov, last accessed 13-Mar-2020.5 https://www.cdc.gov/, last accessed 13-Mar-2020.

38

The occurrences of the selected diseases are shown in Table 8 for the period from

January 2017 to December 2019. The selected ICDs are grouped into 7 categories of infectious

diseases and occurrences by geographical distribution is plotted by neighborhood.

Escherichia coli: Also known as E. coli is a bacteria commonly found in the lower

intestine of humans and warm-blooded animals. Most E. coli strains are harmless, but some can

cause severe diarrhea. E. cole is transmitted by contaminated water or food. The Environmental

Institute of Parana6 performs monitoring of the waters used for recreation, places can be flagged

as appropriate or inappropriate for bathing. The quality of the water indicates the amount of

sewage present in the water. For this, the indicator Escherichia coli is used, the greater the number

of the bacteria in the water, the greater the amount of sewage and, consequently, the greater the

probability of the existence of pathogenic organisms (causing disease).

Dengue and Yellow Fever: are transmitted by a mosquito called Aedes aegypti

mosquito7. The transmission of dengue is done by the female mosquito, because the female

needs a blood protein for the maturation of the eggs. The Municipal Dengue Control Program is

part of the routine of the Environmental Health Center. Dengue manifests in two ways: Classical

Dengue and Hemorrhagic Dengue8. In December 2019, for example, Paraná state had 2,631

dengue cases and 10 municipalities in epidemic9.

Yellow Fever can be divided into wild yellow fever and urban yellow fever, they are

caused by the same virus, but transmitted by different mosquitoes. Wild fever is transmitted by

Haemagogus or Sabethes vectors and the urban fever is transmitted by Aedes aegypti.

Measles: is a highly contagious viral disease, transmitted from person to person through

coughing, sneezing, close personal contact or direct contact with infected secretions10. The most

common complications are otitis, pneumonia, diarrhea, encephalitis and neurological problems.

The disease can, in some cases, lead to death.

Hepatitis: viral hepatitis is a serious public health problem in Brazil and worldwide11.

The most relevant etiological agents in Curitiba are viruses A, virus B and virus C. It is estimated

that 1.9% of the Curitiba population has hepatitis C virus and that 60% of those infected have6 http://www.iap.pr.gov.br/pagina-297.html, last accessed 05-Jul-2020.7 http://www.saude.curitiba.pr.gov.br/orientacao-e-prevencao/dengue.html, last accessed 04-Jul-2020.8 http://www.saude.curitiba.pr.gov.br/images/MS%20Guia-febre-amarela-2018.pdf, last accessed 04-Jul-2020.9 http://www.aen.pr.gov.br/modules/noticias/article.php?storyid=105013&tit=Parana-tem-2.631-casos-de-

dengue-e-10-municipios-em-epidemia, last accessed 19-Jul-2020.10 http://www.saude.curitiba.pr.gov.br/noticias/1169-tire-todas-as-duvidas-sobre-a-vacinacao-contra-o-

sarampo.html, last accessed 05-Jul-2020.11 http://www.saude.curitiba.pr.gov.br/images/PROT.HEPATITES%20VIRAIS%20-2018%201.pdf, last accessed

05-Jul-2020.

39

not yet been diagnosed12.

Meningitis: is an inflammatory process of the meninges, the membranes that surround

the brain and spinal cord13. It can be caused by various infectious agents, such as bacteria, viruses,

parasites and fungi, or also by non-infectious processes. Bacterial and viral meningitis are the

most important from the point of view of public health, due to their magnitude, ability to cause

outbreaks, and in the case of bacterial meningitis, due to the severity of cases.

Influenza: Every year there are vaccination campaigns against influenza promoted

throughout Brazil. The goal of the campaign is focused on groups of people more susceptible to

the disease.

Table 8 – Selected Infectious Diseases

ICD DescriptionA04* Escherichia coliA90 DengueA95* Yellow FeverB05* MeaslesB15* HepatitisB16* HepatitisB17* HepatitisG008, G009 MeningitisJ10* Influenza

Source: The Author.

4.6.1 Distribution of the selected diseases

Table 9 – Cases of selected diseases over the years.

Year Influenza Dengue E. Coli Hepatitis Yellow Fever Measles Meningitis2017 3853 786 100 79 12 2 232018 3886 525 143 117 96 2 212019 3150 737 141 132 108 51 8Total 10889 2048 384 328 216 55 52

Source: The Author.

Table 9 presents the the distribution over the years for the previous selected diseases.

For normalization, the occurrence index for ratio and proportion, as presented in the equation 1,12 https://www.curitiba.pr.gov.br/noticias/centro-de-referencia-tem-97-de-cura-para-casos-de-hepatite-c/51698, last

accessed 05-Jul-2020.13 http://www.saude.curitiba.pr.gov.br/12-vigilancia/460-meningites.html, last accessed 11-Jul-2020.

40

Figure 16 – Influenza (Jan-2017 to Dec-2019). Figure 17 – Dengue (Jan-2017 to Dec-2019).

Source: The Author.

was used, considering the population density by neighborhood (based on census data from 2010).

Figures 12, 13, 14, 15, 16, 17, 18 presents the neighborhoods that have the highest occurrences

of certain infectious diseases, where the darker colors represent the highest number of cases per

neighborhood.

Among the selected diseases, Influenza and Dengue have a greater presence in Curitiba,

in addition it is possible to observe a sign of high correlation in the geographical distribution

of dengue and meningitis cases (52 cases). According to the Human Symptoms and Diseases

Network presented by (ZHOU et al., 2014), Dengue and Meningitis share some symptoms,

meningitis contains 57% of dengue symptoms. There are reports in the literature that indicate

that meningitis is a rare complication of dengue, as reported in (PUCCIONI-SOHLER et al.,

2013).

𝑂𝑐𝑐𝑢𝑟𝑒𝑛𝑐𝑒𝐼𝑛𝑑𝑒𝑥 =𝑇𝑜𝑡𝑎𝑙𝑂𝑓𝑂𝑐𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑒𝑠

𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝐵𝑦𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑* 1000 (1)

4.7 HOSPITALIZATION

Within the selected data, it is also possible to see how many medical appointment trig-

gered hospitalization. Table 10, shows that about 0.7% of the medical visit require hospitalization.

41

Figure 18 – Escherichia coli (Jan-2017 to Dec-2019). Figure 19 – Hepatitis (Jan-2017 to Dec-2019).

Source: The Author.

Figure 20 – Yellow Fever (Jan-2017 to Dec-2019). Figure 21 – Measles (Jan-2017 to Dec-2019).

Source: The Author.

42

Figure 22 – Meningitis (Jan-2017 to Dec-2019).

Source: The Author.

Table 10 – Medical appointments which triggered hospitaliza-tion

Year Total Hosp TotalPerYear %Total Hosp2017 23681 3086460 0.77%2018 28185 3756900 0.75%2019 27516 4010521 0.69%

Source: The Author.

Table 11 – Medical appointments which triggered hospitalization by Gender

Year Female Hosp Male Hosp %Female Hosp %Male Hosp2017 11732 11949 46% 55%2018 14296 13889 47% 53%2019 14143 13373 47% 53%

Source: The Author.

In the context of gender distribution, it is possible to see in Table 11 that a higher percentage of

male patients required to be hospitalized, these numbers are also taking into consideration the

ratio and proportion of male and female population distribution in the Curitiba.

43

5 HEALTH DASHBOARD PROTOTYPE

The exploratory data analysis presented in session 4 of this dissertation was a manual

process which demanded a great amount of effort and required knowledge in different tools as

shown in Figure 6, previous knowledge in Data Definition Language (DDL), Data Manipulation

Language (DML), Data Query Language (DQL) in order to create the database tables needed,

insert the data into the database tables, read and aggregate the data with different sources,

and show the data in a way that facilitates the visualization of a great amount of data. This

shows that although open data is available for the general public to use, the process of making

use of a great amount of data and transforming data into information is complex. With this

in mind, this session presents a Health Data Dashboard prototype which leverages the code

produced by (PARCIANELLO, 2019). The purpose of (PARCIANELLO, 2019)’s prototype,

named Origin-destination was to understand the patterns of public transportation systems in

Curitiba1, therefore for this dissertation, the code has been redesigned to the purpose of analyzing

Curitiba public health data and enable the analysis of the health data in a dynamic way. From a

Database design point of view, Origin-destination uses indexes and table partitioning in order

to improve performance while the Health Dashboard uses Materialized Views. In regards to

application-database access management, Health Dashboard has been enhanced to use prepared

statements or parameterized statements.

5.1 ARCHITECTURE

This session presents details of the technologies used and presents the database design.

5.1.1 Technologies

The prototype enables end-users to easily access and analyze Curitiba public health

data, without the need for any software installation, data manipulation, or previous knowledge in

the data. Also, the prototype uses only open-source technologies to avoid any kind of spending

on software acquisition and/or contracting.

Database Server: the database server is a Linux server with Debian 9 distribution2 with1 https://github.com/yussefparcianello/OrigemDestinoStpCuritiba, last accessed 18-Oct-2020.2 https://www.debian.org/index.pt.html, last accessed 05-Nov-2020.

44

two cores of AMD EPYC 7401 processors and 1.5G of memory, the database system used was

PostgreSQL 9.5 x643 with PostGIS 2.1.1 extension4.

Application Server: the application server where the online prototype has been made

available for testing and evaluation is a Linux server with 12 Cores, 16GB of RAM and Ubuntu

distribution5 running the following software: Apache Server6 and PHP7, while the development

environment of application is a Intel Core i3-6006U x64 2.0GHz, 8GB RAM with Windows 10

64 bits, running Apache Server and PHP.

Maps and Libraries: Open Street Map8 combined with Leaflet library9 and Heatmap

puglin10 have been used for Mapping and Data visualization

Other Libraries such as: JQuery v. 3.3.111, Bootstrap v. 3.3.712, and Chart.js v. 2.7.313.

Figure 23 shows the technologies used and how they are segregated from Client and

Server side perspectives. The source-code of the prototype is available in GitHub14 and video

showing the prototype usage is available in Youtube15.

5.1.2 Database

To return the database query results in a fast and efficient way, materialized views have

been created. Materialized views are database objects that contain the results of a query, they

are useful to allow faster access to data or for making the data available in a way that facilitates

the displaying of the data in a graph without the need of querying multiple sources of data at

the time of processing. The use of materialized views in this prototype is an enhancement to

(PARCIANELLO, 2019)’s work.

Health Data Source Table: Similar to the data used in the exploratory data analysis

session, only the Medical appointments data has been used, from Basic Health Units (BHU) and

Emergency Care Units (ECU). A total of 14,382,414 medical records from 2016 to 2020 have3 https://www.postgresql.org/, last accessed 05-Nov-2020.4 https://postgis.net/, last accessed 05-Nov-2020.5 https://ubuntu.com/, last accessed 05-Nov-2020.6 https://httpd.apache.org/, last accessed 05-Nov-2020.7 https://www.php.net/, last accessed 05-Nov-2020.8 https://www.openstreetmap.org/, last accessed 18-Oct-2020.9 https://leafletjs.com/, last accessed 18-Oct-2020.10 https://github.com/Leaflet/Leaflet.heat, last accessed 18-Oct-2020.11 jquery.com/, last accessed 18-Oct-2020.12 getbootstrap.com/, last accessed 18-Oct-2020.13 www.chartjs.org/, last accessed 18-Oct-2020.14 https://github.com/TatianeLautert/dadosAbertosSaudeCuritiba, last accessed on 18-Oct-2020.15 https://youtu.be/47KMZO-N91A, last accessed 08-Nov-2020.

45

Figure 23 – Health Dashboard Architecture.

Server-Side           Client-Side

Architecture: Health Dashboard - Prototype

Data Visualization

leaflet.js OpenStreetMap

Filter Panel

bootstrap.js

Graphs

jquery.js chart.js

Web Server

Apache HTTP Server PHP

js librariesand

plugins

Database Server

Postgres PostGIS

Source: The Author.

been inserted into this database table. Details of database columns are shown in Figure 24.

Geodata Tables: 3 geo-referenced database tables were used to plot the maps in

the prototype, details of these database table are shown in Figure 24. Database table limites_-

legais.divisa_de_bairros stores the geo-referenced data of Curitiba neighborhoods. Table limites_-

legais.divisa_de_regionais stores the geo-referenced data of Curitiba macro regions. Table

saude.unidade_saude stores the geo-referenced data of Curitiba Health Units.

The purpose of storing consolidated information from Health Data Source table in the 4

Materialized Views was in terms of (i) number of medical appointments by month and year, (ii)

number of medical appointments by age range and gender, (iii) number of medical appointments

by hour and day of the week and (iv) number of medical appointments by selected infectious

disease per week number.

46

Figure 24 – Health Dashboard database tables and relationship.

DatabaseGeodata Tables 

limites_legais.divisa_de_regionaisgidcodigonometiposhape_areashape_lengeom

limites_legais.divisa_de_bairrosgidcodigonometiposhape_areashape_lengeom

saude.unidade_saudegidcd_equinomecoord_ecoord_ngeom

Health Data Source Table

saude.tatiane_atendimento_unidade_saudedt_atendimentodt_nascimentosexocod_tipo_unidadetipo_unidadecod_unidadedesc_unidadecod_procedimentodesc_procedimentocod_cbodesc_cbocod_ciddesc_cidsolicitacao_examesqt_prescrita_farmacia_ctbanaqt_dispensada_farmacia_ctbanaqt_medicamento_nao_padronizadoencaminhamento_atendimento_especialistaarea_atuacaodesencadeou_internamentodt_internamentoestab_solicitanteestab_destinoestab_destinoestab_destinotratamento_domicilioabastecimentoenergia_eletricatipo_habitacaodestino_lixofezes_urinacomodosem_casodoencagrupo_comunitariomeio_comunicacaomeio_transportemunicipiobairronacionalidadecod_usuarioorigem_usuarioresidentecod_profissional

Source: The Author.

47

Figure 25 – Interface of the Health Dashboard Prototype.

Source: The Author.

5.2 APPLICATION INTERFACE

In the prototype, all interaction takes place on a single screen. As shown in Figure 25,

the web interface is divided vertically in two: an interactive map with a search panel and map

layer options is available on the right; on the left, the results of the search are available in the

form of dynamic graphs. Through the search panel users can filter the data according to their

needs, and through the map layer option users can select the maps layers they wish to see on the

map.

Figure 26 shows the filter options available in the search panel. The first filter option

allows users to select the data by year, the second filter option allows users to filter the data based

on each Health Unit, the third filter allows users to filter by Month, the fourth option can be used

to filter the day of the week, and the last option allows users to select by a particular Infectious

Disease.

Figure 27 shows the mapping layers available in the prototype. By default, layer options

show Health Units, Disease Outbreak Heat Map, and Neighborhood are selected, Region layer is

not selected by default and this is to make the mapping visualization cleaner. The user can select

the desired mapping layers to be shown by clicking on the icon and then checking or unchecking

each mapping layer checkbox.

48

Figure 26 – Health Dashboard Filters.

Source: The Author.

Figure 27 – Health Dashboard Map Layers.

Source: The Author.

Figure 28 shows the 4 graphs available in the prototype, graph A from Figure 28 shows

the number of medical appointments over the months. Data is displayed according to the filter

criteria selected by the user. In case the user hovers the mouse over the bars of the graphs, a

message is displayed with the exact number of medical appointments for that given month. In

this example, it is possible to see that a filter by year 2020 has been selected by looking at the

title of the graph.

As part of the exploratory data analysis in session 4, the same information was presented

in the form of a table and a graph in Figure 7 for years 2017, 2018, and 2019. In that session, the

data presented was fixed and it was required previous knowledge of the data source, database

queries, visualization tool to display the information. With the prototype, the user can access the

49

Figure 28 – Health Dashboard - Graphs available in the prototype.

Source: The Author.

information without any previous knowledge, and data is dynamically displayed according to the

user’s preference and needs.

Graph B from Figure 28 shows the number of medical appointments by age range and

gender. The data is displayed according to the filter criteria defined by the user. Similarly, the

same information was presented in session 4, Figure 8, however with the prototype, the data can

be dynamically displayed according to the user’s preference and needs.

Graph C from Figure 28 shows the number of medical appointments by hour per day of

week. Data is displayed according to the filter criteria defined by the user. It allows the users

to identify the volume of medical appointment by hour for a given day of the week and it is

also possible to select a specific health unit. This information was also presented in session 4

Figure 13 and Figure 14, however with the prototype, it becomes very easy for the user to apply

50

Figure 29 – Heat Map Layer for a given infection dis-ease.

Source: The Author.

different filters. It is also possible to check the peak times of appointments for a specific health

unit for example.

Graph D from Figure 28 shows number of a given infectious disease by week number.

Data is displayed according to the filter criteria defined by the user. The red bars show that there

has been an outbreak of the given infectious disease in those weeks. In this graph, the modified

WHO definition of disease outbreak described in session 2.3.2 has been used to detect an

outbreak, i.e. the threshold for an outbreak in a given week is defined at two standard deviations

in excess. Also, the heat map outbreak is based on the same method, however, the criteria are

re-applied at each neighborhood level. As an example, Figure 29 shows the heat map where

the outbreak criteria have been applied by neighborhood, areas in red on the heat map means

that there has been a great number of outbreaks for the given period. As opposed to the other

graphs available on the Dashboard, Graph D is a new form of data visualization when compared

to session 4.

The selected infectious diseases available for user selection in Graph D of the prototype

were the same infectious diseases presented in the exploratory data analysis session, being

them Escherichia coli, Dengue, Yellow Fever, Measles, Hepatitis. Additionally considering the

ongoing COVID-19 pandemics the following are also added for use selection: Confirmed-Covid

cases, Suspected-Covid cases, and Severe Acute Respiratory Syndrome (SARS).

51

Figure 30 – Region Layer.

Source: The Author.

Besides the heat map for infectious disease, there are also other options of map layers

available on the prototype. Figure 30 shows the Region Layer, Figure 31 shows the Neighborhood

Layer, Figure 32 shows the Health Units Layer. Users are also able to select and view multiple

layers at the same time, Figure 33 shows a combined view of both the Neighborhood Layer and

the Health Units layer.

5.3 USABILITY TEST

An experiment was conducted between 10-Oct-2020 to 23-Oct-2020 with seven partici-

pants, 1 participant from the Health Science area, 1 from Architecture, and 5 from Computer

Science area (1 IT Architect, 1 IT Quality Engineer, 1 IT Manager, 1 System Analyst, 1 Post

Doctoral Student). It is important to mention that given the current COVID-19 pandemic scenario,

it was not possible to perform the usability test scenario in deeper detail or in person due to the

current social distancing guidelines.

In order to test the usability of the prototype, 3 usability testing scenarios as presented in

Table 12 and an evaluation questionnaire with 4 questions as presented in Table 13 were defined.

The participants were asked to execute 1 low, 1 medium, and 1 high complexity usability testing

52

Figure 31 – Neighborhood Layer.

Source: The Author.

Figure 32 – Health Unit Layer.

Source: The Author.

53

Figure 33 – Health Units and Neighborhood Layers.

Source: The Author.

scenarios. For each scenario, participants were asked to provide their responses based on the

results they could see either on the Dashboard Graphs or on the Dashboard Map. The purpose

of this usability test is to measure the user success rate, which is defined by the percentage of

tasks that users complete correctly, as explained by Nilsen Normam Group16. After executing

the usability testing scenarios, participants were asked to respond an evaluation questionnaire

to assess their perception in the following aspects (i) easiness of use, (ii) relevance of filters

available, (iii) complexity in understanding the graphs, (iv) suggestion for improvements, in case

of any.

The instructions on how to access the prototype was provided to each user by email

and each email contained a user-specific link to a Google Sheet17 with the description of each

usability testing scenario and the questionnaire where users could input their answers.

Figure 34, shows the results of the users’ ability to complete each usability testing

scenarios. It shows that all 7 users executed the usability testing scenario and provided the correct

answer to the question. For scenario 2, 4 users were able to complete the scenario successfully,

2 users executed the scenario but provide a partially right answer to the question, and 1 user16 https://www.nngroup.com/articles/success-rate-the-simplest-usability-metric/, last accessed 01-Nov-2020.17 https://docs.google.com/spreadsheets/, last accessed 14-Nov-2020.

54

Table 12 – Usability testing scenarios

Scenario ID Scenario Complexity Usability Testing Scenario1 Low How many medical visits were there

in March 2019?2 Medium In October 2018, was there any age

range in which there was a greaternumber of medical appointments formale patients?

3 High Based on the heat map, using allthe data available, which regionor neighborhood presented a greatnumber of outbreaks for confirmedcases of Covid?

Source: The Author.

Table 13 – Evaluation Questionnaire

Question ID Evaluation Question Options1 Was it easy to use the prototype? Yes / No2 Do you think the filter options were relevant? Yes / No3 How do you assess the complexity in understanding the graphs? Easy / Medium / Difficult4 Do you have any suggestion for improvements? Open-ended Question

Source: The Author.

executed the scenario but provide a wrong answer to the question. For scenario 3, all 7 users

executed the scenario and provided the correct answer to the question. This shows that for the

low complexity usability testing scenario the user success rate of the prototype usability was

at 100%, for the medium complexity scenario the success rate was at 57%, and for the high

complexity scenario, the success rate is at 100%.

Figure 35 shows the results of the users’ evaluation of the prototype in terms of easiness

of use and graph complexity. All 7 users evaluated the prototype as easy to use, 6 uses evaluated

the complexity in understanding the graph as easy and 1 user evaluated as difficult.

Figure 36 shows the results of the users evaluation of the relevance of the filters available

in the prototype. All 7 users evaluated the filter options as relevant.

Table 14 shows the suggestions for improvement for the prototype given by the users.

There were 7 suggestions in total and some of these suggestions can be implemented as a future

work.

Suggestions 1, 2, 4, 5, and 6 can be easily implemented and would bring value to user

experience. Suggestion 3 needs to be further analyzed and tested on what would be the best

approach. Suggestion 7 would require additional data to be aggregated to our existing database.

55

Figure 34 – Users ability to complete usability testing scenarios.

Source: The Author.

Figure 35 – Evaluation Questionnaire Results - Easiness of Use and GraphComplexity.

Source: The Author.

5.4 LESSONS LEARNT

Several factors need to be considered when designing a health dashboard prototype

such as:

1. Analysis of the technology to be used in terms of cost and libraries compatibility. In this

prototype, only open source technologies were used and as mentioned by (PARCIANELLO,

2019) due to libraries incompatibility, many times when you want to add new functionality

it is required to redesign the application to replace conflicting plugins with others.

56

Figure 36 – Evaluation Questionnaire Results - Relevance of Filters.

Source: The Author.

Table 14 – Suggestions for Improvement

ID Suggestion for Prototype Improvement1 Show labels and units of x and y axis of the graphs.2 The first bar graph (graph A in Figure 28) has many colors, it would be easier to understand if only

one color is used.3 The last bar chart (graph D in Figure 28) shows no data when some options in the Search Panel are

selected. This might be confusing for some users. An alternative could be to hide this graph when isempty.

4 The top right icon has a light color that makes hard to identify it at first sight. The suggestion is tochange the color and move the Search Panel at the same level (to the left). You can also put a floatingdiv tag and put these two options inside of it and add a legend ("menu"). This could help to user tolocate these options.

5 The Search Panel is fixed, making it flexible would help to give the users more functionalities to viewthe data.

6 Lack of legend for the heat map and the number of cases could also appear when hovering the mouseover the map, just as it appeared in the bar graphs; I had difficulties to answer question 3, because theanswer varied according to the zoom I used to look at the map (when I zoomed in a lot, I couldn’tdistinguish which neighborhood had the most reddish coloring, so I preferred to use less zoom andanswer by region).

7 If possible add a lethality rate of the coronavirus in the form of a graph, for example, in one week xnumber of people have been tested positively versus how many people died.

Source: The Author.

57

2. Previous knowledge of the data is required, therefore an exploratory data analysis is an

important step to understanding what the data can tell and it serves as a basis on what

visual information to show on the prototype.

3. Identification of relevant filters for end-users to explore the data in an efficient manner.

Ideally, before implementing such filters it is important to assess the users’ requirements.

4. Analysis of what are the best ways to display the data and what statistical method to apply.

For example, one of the challenges faced during this research was to find an appropriate

method for identifying disease outbreaks. It has even been found imprecision between

national and international methods for publication of disease outbreaks, while the CDC

presented disease outbreak information easily on their website, in Brazil such information

is published in the Brazilian Epidemiological Bulletins.

5. The prototype must provide a good user experience, functionalities need to be intuitive

and simple.

6. Use of techniques to enhance the system performance in fetching and retrieving the data

from the database.

7. Use of data security measures to avoid malicious SQL injections. In our health dashboard

prototype, the security measure chosen was the use of prepared statements to access the

data from the database server.

8. Public health data must always be anonymized to be displayed in this kind of dashboard.

The public health data available in Curitiba Open Data Portal was already anonymized,

but in scenarios where such type of dashboard accesses the data directly from the health

system, data anonymization is a crucial step to be taken.

9. Consideration of user feedback for prototype enhancement. During the evaluation ques-

tionnaire the users provided good suggestions which can enhance the user experience of

our prototype.

Lastly, it would be helpful if there were standards defined for data sharing and use of

technologies by governing bodies which would allow scaling of Health Dashboards by

any city, in a plug and play mode. For example, the open data could be available via

Application Programming Interfaces (APIs) with appropriate documentation to facilitate

58

interaction between multiple applications, and template applications which any cities could

simply leverage the template code and deploy a new Dashboard instance without the need

to developing it from scratch.

59

6 CONCLUSION

There are several challenges related to health data, mainly faced by large urban centers.

New technologies, infectious diseases, public trust, delivering health in conflict and crisis,

making healthy care fairer, expanding access to medicine are some of the problems that impact

the development and planning of a city (World Health Organization, 2020). Ideally, data must be

accompanied by openly accessible metadata so that it can be discovered, interpreted correctly,

and reused for subsequent research. If crisis such as COVID-19 pandemic are considered,

data combined with context and meaning turns into knowledge for informing public health

response (RDA COVID-19 Working Groups, 2020), and the use of common metadata standards,

as well as vocabularies, are recommended.

In this sense, this dissertation presented an exploratory analysis using sociopolitical,

geographical, transportation and health data in Curitiba, relating this information to other factors,

such as health units locations, bus stops accessibility, displacements performed by the citizens to

arrive at the health units and analysis of selected infectious diseases distribution across the cities’

neighborhoods.

In order to have a better understanding on the context of each related area, this disserta-

tion presented a summary of the basic concepts about GIS, smart cities, open data with focus on

health data, among others. The main challenges of each area have also been presented.

In our preliminary data analysis, the results show that the bus stops are well located and

allow citizens to arrive at the majority of health units without the need of long walks, fulfilling

its function of being located where the population needs. But it is also observed that there is a

relevant amount of displacements from outside the citizens’ neighborhood to arrive at the top 5

ECU, especially looking for treatment of respiratory problems. It is required further research on

related works and with the municipality of Curitiba to understand if those numbers are expected

and why. As future work, it would be useful to analyse if the displacements are caused by

specific demands per regions, so that the results of the analysis could be used for enhancement

of specialty center units redistribution according to the demands. The results also showed some

differences in gender behaviour towards medical visits, with the majority (60.62%) being of

female visits, except in the age range from 0 to 4 years old. Similar behaviour could also be seen

in related work explored.

The initial data analysis also showed that on the 7 categories of selected infectious

60

diseases, influenza and dengue have a greater presence in Curitiba when compared to the other

categories considered in this dissertation, also a sign of correlation in the geographical distribution

of dengue and meningitis could be observed. Further research is needed to confirm this behaviour

in more details.

Our Health Data Prototype showed that it can facilitate the analysis of the health data

aggregated to other sources to either the general public or professionals of the Health Science

departments. It also showed that even with the use of a great amount of data the dashboard

performance to show the graphs and maps is not affected with the appropriate techniques of

data modeling design. And that, from a user point of view no prior knowledge of the data or

technology are required to perform the search and analysis of the data through the dashboard.

As future work, we can mention the enhancement of the Health Data prototype based

on the users’ inputs, the inclusion of other data-sets such as air quality data and how it correlates

with diseases of respiratory tract, enhancement of data visualization and statistical methods to

allow for prediction of health service demands per region.

As the population increases its important to conduct further analysis to identify if

the health services are still adequate to the needs of the population, in this dissertation the

demographic data source is from 2010 census. In Brazil demographic census is conducted every

10 years, in 2020 as a result of the Covid-19 Pandemics, IBGE has decided to postpone the

census to 2021 as per communication published in March 2020 1. With this scenario, as future

work, newer census data can be used as it becomes available to analyse the how the increase

in population affects the quality and demands in health services. Also, other methods such as,

leveraging crowd source data could be used to bring insights on the population’s perception of

quality of the health services, for example by using Google rating and review data of the health

units available on Google Maps.

The Municipal government of Curitiba could use the results of this research to assess

whether the different data sources administered by the city can be normalized in order to facilitate

future research. Other studies using data from different cities could use the numbers from this

dissertation as a baseline for comparison. The results can also be used as a starting point for

professionals in the public health field to obtain insights for a more detailed future research.

1 https://www.ibge.gov.br/novo-portal-destaques/27161-censo-2020-adiado-para-2021.html, last accessed Dec-27-2020.

61

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APPENDIX

66

APPENDIX A - MEDICAL RECORDS DATA DICTIONARY

Table 15 – Medical Records Data Dictionary.Medical Records Data Dictionary

Column Name Port Column Name Eng Description Type Sizedt_atendimento Date of Appointment Date when the medical ap-

pointment occurredDATE

dt_nascimento Date of Birth Date of birth of the patient DATEsexo Gender Gender of the patient VARCHAR2 1cod_tipo_unidade Code of Unit Type Code of the health unit type NUMBER 5tipo_unidade Type of Unit Type of the health unit type VARCHAR2 50cod_unidade Code of Unit Code of the health unit VARCHAR2 150desc_unidade Description of the

UnitDescription of the health unit VARCHAR2 80

cod_procedimento Code of Procedure Code of procedure per-formed

VARCHAR2 12

desc_procedimento Description of Proce-dure

Description of procedure per-formed

VARCHAR2 255

cod_cbo Code of CBO Code of professional occupa-tion

VARCHAR2 8

desc_cbo Description of CBO Description of professionaloccupation

VARCHAR2 200

cod_cid Code of CID Code of diagnostic VARCHAR2 4desc_cid Description of CID Description of diagnosis VARCHAR2 150solicitacao_exames Request for Exam Indicates whether an exam

request has occurredVARCHAR2 3

qt_presc_farm_ctbana Qty Medicine Pre-scribed

Quantity of medicine pre-scribed at the Curitibanapharmacy

NUMBER 10

qt_disp_farm_ctbana Qty MedicineReleased

Quantity of medicine re-leased from the Curitibanapharmacy

NUMBER 10

qt_med_nao_padron Qty Non-StandardizedMedicine

Quantity of Non-Standardized Medicine

NUMBER 10

enc_atendimento_espec Referral to SpecialistService

Indicates whether referralwas made to Specialist Care

VARCHAR2 3

area_atuacao Practice Area Area of practice VARCHAR2 255desencadeou_intern Triggered Hospital-

izationIndicates whether hospital-ization was triggered

VARCHAR2 3

dt_internamento Date of Hospitaliza-tion

Date of the patient’s hospital-ization

DATE

estab_solicitante Requesting Facility Facility that requested thehospitalization

VARCHAR2 80

estab_destino Targeted Facility Facility in which hospitaliza-tion occurred

VARCHAR2 80

cid_internamento CID of Hospitaliza-tion

Code of the diagnosis of hos-pitalization

VARCHAR2 4

tratamento_domicilio Home Treatment Type of Water Treatment athome

VARCHAR2 30

abastecimento Supply Type of Water Supply athome

VARCHAR2 40

energia_eletrica Electricity Indicates whether there iselectricity in the household

VARCHAR2 3

tipo_habitacao Type of Housing Type of housing VARCHAR2 60destino_lixo Garbage Disposal Waste destination at home VARCHAR2 30

67

Continuation of Table 15Name of the Column Description Type Sizefezes_urina Faeces/Urine Destination of faeces / urine

at homeVARCHAR2 30

comodos Rooms Quantity of rooms at home NUMBER 5em_casodoenca In Case of Illness Services sought in the event

of illnessVARCHAR2 40

grupo_comunitario Community Group Community Group in whichthe patient participates

VARCHAR2 40

meio_comunicacao Means of Communi-cation

Communication media usedat home

VARCHAR2 40

meio_transporte Means of Transporta-tion

Means of Transport used athome

VARCHAR2 40

municipio Municipality Patient municipality VARCHAR2 50bairro Neighborhood Patient neighborhood VARCHAR2 72nacionalidade Nationality Patient’s nationalitycod_usuario user_code Unique user code NUMBER 10origem_usuario user_origin 1 - Resident in the munici-

pality 2 - Non resident in themunicipality

NUMBER 1

residente resident 1 - With definitive regis-tration at BHU 2 - With-out definitive registration atBHU

NUMBER 1

cod_profissional professional_code Professional unique code NUMBER 10Source: Adapted from Curitiba Open Data Portal (2019)

68

APPENDIX B - SCRIPTS FOR MATERIALIZED VIEWS

CREATE MATERIALIZED VIEW

public.qtd_atendimento_mes_ano AS

select

cod_unidade,

desc_unidade,

EXTRACT(MONTH from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as month,

EXTRACT(YEAR from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as year,

count(*)

from saude.tatiane_atendimento_unidade_saude

group by 1, 2, 3, 4;

CREATE MATERIALIZED VIEW

public.qtd_atendimento_faixa_etaria_genero AS

select date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) as age_range,

EXTRACT(MONTH from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as month,

EXTRACT(YEAR from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as year,

sexo as gender,

cod_unidade,

count(*),

case when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 4 Then ’0 to 4’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 4

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 14 Then ’5 to 14’

when date_part(’year’,age(to_date(dt_nascimento,

69

’DD-MM-YYYY’))) > 14

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 24 Then ’15 to 24’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 24

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 34 Then ’25 to 34’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 34

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 44 Then ’35 to 44’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 44

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 54 Then ’45 to 54’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 54

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 64 Then ’55 to 64’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 64

and date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) <= 74 Then ’65 to 74’

when date_part(’year’,age(to_date(dt_nascimento,

’DD-MM-YYYY’))) > 74 Then ’75+’

end

from atendimento_unidade_saude

group by 1,2,3,4,5

CREATE MATERIALIZED VIEW

public.qtd_atendimento_hora_dia_semana AS

SELECT

70

cod_unidade,

desc_unidade,

EXTRACT(HOUR from to_timestamp(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as hour,

EXTRACT(DOW from to_timestamp(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as day_of_week,

EXTRACT(YEAR from to_timestamp(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as year,

EXTRACT(MONTH from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as month,

count(*)

FROM saude.tatiane_atendimento_unidade_saude

group by 1, 2, 3, 4, 5, 6;

CREATE MATERIALIZED VIEW

public.qtd_doenca_semana_ano AS

SELECT

cod_cid,

desc_cid,

EXTRACT(YEAR from to_timestamp(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as year,

EXTRACT(WEEK from to_timestamp(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as Week_Number,

EXTRACT(MONTH from to_date(dt_atendimento,

’DD/MM/YYYY HH24:MI:SS’)) as month,

bairro,

count(*)

FROM saude.tatiane_atendimento_unidade_saude

group by 1, 2, 3, 4, 5,6;