Examining Spatial Patterns of Primary Health Care Utilization in Southern Honduras

A Dissertation Submitted to the Division of Research and Advanced Studies

of the University of Cincinnati in partial fulfillment of the requirements for the degree of

Doctorate of Philosophy (Ph.D.)

In the Department of Geography of the College of Arts and Sciences

2005

By Jonathan B. Baker B.A. Skidmore College, 1981

M.B.A, Widener University, 1992 M.A., West Chester University, 2002

Lin Liu, Ph.D., Chairperson

Abstract:

Examining Spatial Patterns of Primary Health Care

Utilization in Southern Honduras By Jonathan B. Baker

Chairperson: Lin Liu, Ph.D.

Primary health care utilization is poorly understood in many parts of the developing world. This

is especially true in rural places, such as Santa Lucia, Intibuca, Honduras, where there are only

three primary health care facilities servicing almost 12,000 people, where the people are poor,

and generally speaking access to care is limited. This research project focuses on trying to

understand primary health care utilization patterns in this part of southern Honduras.

Specifically, this research project examines the utilization of three health clinics operating in and

around Santa Lucia.

The delivery of health care is dependent on many factors, including the availability, cost, and

capacities of the providers; the needs, resources and decisions of the patients; and the

characteristics of the region within which the patients live. This research focuses primarily on the

patients, and tries to understand their health seeking behavior. A better understanding of

utilization can be used by health service planners to improve primary health care delivery in this

and similar locations.

The findings of this research indicate that utilization patterns can be explained, to a large extent,

by factors relating to walking [travel] time, economic status, and the combined affect of health

service type and proximity to care. These findings are consistent with findings from prior

research: Both travel time and economic status are important factors in determining primary

health care utilization. In addition, a new variable is created to examine health decision-making.

This new variable has not been considered in previous research, and is found to very significant

determinant of health facility utilization in the study area.

A modified gravity model is used to estimate the level of utilization, and is tested through the use

of log linear transformation and multi-variate regression techniques. The results here, an R-

square of .644 for a model combining three different health clinic service areas, clearly indicate

a relationship between these independent variables and utilization.

Acknowledgements I would like to gratefully acknowledge the following individuals for their contribution to my

Doctor of Philosophy degree in Geography:

Lin Liu, Ph.D., University of Cincinnati, Geography Department, for his teachings in economic

geography; his support in the analysis and preparation of my dissertation and related papers; and

his guidance through the process of completing my Ph.D. He has been my dissertation committee

chair, and I am very grateful for his contribution to this research project and the development of

a clear agenda for my future research.

Howard Stafford, Ph.D., University of Cincinnati, Geography Department, for his teachings in

Economic & Marketing Geography and support in the research, development and design of my

dissertation. In addition, I would like to thank him for his many long discussions with me on

various topics related to geography, and his support of me as a Teaching Assistant and Adjunct

Instructor.

Andrew Bazemore, M.D., University of Cincinnati, Department of Family Medicine, Institute for

Health Policy and Health Services Research, for his teachings in the area of health services, and

public health policy, and his enthusiasm and support in the development of this research project.

Furthermore, I am very grateful for his support in helping me develop a niche in International

Health research, and his contacts at Shoulder-to-Shoulder, Inc. and Hombro á Hombro, without

which this research project would not have been possible.

Nicholas Dunning, Ph.D., for his teachings in the History and Philosophy of Geography, his

expertise in fieldwork in Latin America, and his support on my dissertation committee.

Chris Carr, for his expert teachings in the area of GPS, and his assistance in data collection in

Honduras and the development of methods for this project.

Miguel Coello, for his experience and discussions related to primary health care delivery in

Santa Lucia, Intibuca, Honduras.

The following University of Cincinnati affiliates for their kind financial support of this project;

The Graduate Student Governance Association (GSGA), The Institute for Global Studies and

Affairs (IGSA), and the University Research Council (URC).

La Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente (SERNA), for

making GIS and Honduran Census data files available for use in this project.

Part of this material is based upon work supported by the National Science Foundation under

Grant No. IIS-0081434. Any opinions, findings, and conclusions or recommendations expressed

in this material are those of the author(s) and do not necessarily reflect the views of the National

Science Foundation.

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TABLE OF CONTENTS Page List of Tables and Figures 6

List of Maps 7

Chapter One – Introduction 8

1.1 Problem Statement 9

1.2 Importance of Health Service Utilization Research 10

1.2.1 What affects utilization? 11

1.3 The Study Area 12

1.3.1 Location 12

1.3.2 History of the study area 17

1.3.3 Roads and Travel 18

1.3.4 Population 19

1.3.5 Employment 20

1.3.6 Income 21

1.3.7 Education 22

1.4 Health Services in Honduras 22

1.4.1 History on Health Services 22

1.4.2 Background on La Clinica Hombro á Hombro 23

1.4.3 Development of a rural health facility 24

1.5 Chapter Summary 25

Chapter Two – Literature Review 27

2.1 Theoretical Foundations of Prior Research 27

2.1.1 Access and Utilization to Health Care Services 27

2.1.2 Utilization 29

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2.1.3 Utilization and Distance 30

2.1.4 Factors influencing utilization 32

2.1.5 Unit of Analysis 36

2.1.6 Theoretical Framework 37

2.2 Methods of Studying Utilization 40

2.2.1 Regression Modeling 40

2.2.2 Gravity Modeling 40

2.2.3 Comparative Descriptive Analysis 41

2.2.4 Marketing Geography and Utilization 42

2.2.5 Geographic Information Systems 43

2.2.6 Global Positioning System 45

2.3 Empirical Studies 46

2.4 Social Equity in Access to Primary Care 47

2.4.1 Access and Equity 48

2.5 Chapter Summary 49

Chapter Three – Data Acquisition 51

3.1 Acquisition of Data for the GIS 51

3.1.1 Acquisition and development of a base map 51

3.1.2 Acquisition of GPS Data in Honduras 52

3.1.3 GIS and Census Data 55

3.2 Attendance Samples 56

3.3 Qualitative Sources of Data 59

3.3.1 Key Informant Data 59

3.3.2 Key Informant Interviews 61

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3.3.3 Informal Interviews 61

3.4 Challenges in conducting fieldwork in the developing world 61

3.5 Chapter Summary 65

Chapter Four – Descriptive Analysis 66

4.1 Unit of Analysis 66

4.2 Variables Used to Explain Utilization 66

4.2.1 Utilization Index 67

4.2.2 Distance 69

4.2.3 Walk-Time Estimate 70

4.2.4 Population Size 72

4.2.4.1 Reconciliation of Population Data 72

4.2.5 Attractiveness of Health Care Facility 76

4.2.5.1 Number of Doctors 77

4.2.5.2 Size of Health Facility 77

4.2.5.3 Operating Hours of Facility 77

4.2.6 Income 77

4.2.7 Employment Index 78

4.2.8 Economic Status 78

4.2.9 Education 79

4.2.10 Cost of Service 79

4.2.11 Road Quality 80

4.2.12 Health Choice 81

4.3 Analysis of Distance Decay 84

4.4 Comparative Analysis: Distance versus Walking-Time 85

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4.5 Data Analysis 87

4.6 Chapter Summary 91

Chapter Five - The Predictive Model 93

5.1 Selection of the Gravity Model 93

5.2 The Model 94

5.2.1 Calculation of the Dependent Variable 97

5.3 The Four Models 98

5.4 Chapter Summary 100

Chapter Six – Results 101

6.1 Correlation Analysis 101

6.2 Results 104

6.2.1 Stepwise Regression Methodology 104

6.2.2 System Model Results 105

6.2.2.1 Visual Inspection for System Model 106

6.2.3 Magdalena Health Center Model 107

6.2.4 Santa Lucia Health Center Model 109

6.2.5 Hombro á Hombro Clinic Model 111

6.3 Comparison of the Four Models 113

6.4 Analysis and Discussion of Explanatory Variables 115

6.4.1 Walking Time 115

6.4.2 Economic Status 116

6.4.3 Choice 118

6.4.3.1 Model Improvement 120

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6.5 Residuals Analysis 122

6.5.1 Residuals for Hombro á Hombro Model 123

6.5.2 Residuals for Overall Study Area 124

6.5.3 Residuals for Magdalena Health Center Model 126

6.5.4 Residuals for Santa Lucia Health Center Model 128

6.6 Summary of Results 129

6.7 Chapter Summary 131

Chapter Seven - Conclusions and Recommendations 133

7.1 Lessons learned in the field 135

7.2 Limitations of Fieldwork 136

7.3 Policy Implications 138

7.3.1 Accessibility Improvements 138

7.3.2 Community Assessments 139

7.3.3 Transportation Improvements 140

7.4 Future Research 140

7.5 Concluding Comments 142

Bibliography: 143

Appendix A: Bi-variate Correlations 149

Appendix B: Regression Output Table 153

Appendix C: Residuals 159

Appendix D: Key Informant Interview 162

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List of Tables and Figures Page Figure 1.1 Average Income Comparison 21

Figure 2.1 Initial Behavioral Model 38

Figure 2.2 Hypothetical Utilization Model 39

Table 4.1 Distance Table 70

Table 4.2 Estimated Walking-Time Calculation 71

Table 4.3 Population Aggregation Worksheet 75

Table 4.4 Average Speed Calculation Matrix 81

Figure 4.1 Choice Decision Model 84

Figure 4.2 Distance Decay of Utilization 85

Table 4.5 Correlations from Distance-Time Comparison 86

Figure 4.3 Histogram of Original Data 88

Figure 4.4 Scatter Plot of Original Data 88

Figure 4.5 Histogram for ln of Utilization 89

Figure 4.6 Scatter Plot for ln Utilization and ln Walking-Time 90

Table 4.6 Kolmogorov-Smirnov Test 91

Figure 5.1 Utilization Model 96

Table 6.1 Correlation to Utilization Summary 103

Table 6.2 Stepwise Regression Coefficients – System Model 106

Table 6.3 Stepwise Regression Coefficients – Magdalena Health Center 108

Table 6.4 Stepwise Regression Coefficients – Santa Lucia Health Center 110

Table 6.5 Stepwise Regression Coefficients – Hombro á Hombro Clinic 112

Table 6.6 Comparison of Models 114

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List of Maps Page

Map 1.1 Honduras National Map 15

Map 1.2 Honduras and Location of Study Area 16

Map 1.3 Santa Lucia Study Area – Detail 16

Map 1.4 Study Area Population Distribution 20

Map 3.1 Base Map of Study Area 56

Map 4.1 Village-Caserio Reconciliation 74

Map 4.2 Example of Population Reconciliation 76

Map 6.1 Utilization – System Model 107

Map 6.2 Utilization - Magdalena Health Center 109

Map 6.3 Utilization - Santa Lucia Health Center 111

Map 6.4 Utilization – Hombro á Hombro 113

Map 6.5 Residuals – Hombro á Hombro 121

Map 6.6 Residuals – Overall Service Area 125

Map 6.7 Residuals – Magdalena Service Area 127

Map 6.8 Residuals – Santa Lucia Service Area 129

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CHAPTER ONE - INTRODUCTION

The following research looks at utilization of primary health care services in a rural part of

southern Honduras. Specifically, this research examines health-seeking behavior and the

associated health care decision-making process. To accomplish this goal, key variables

explaining health-seeking behavior need to be identified, and their relationship to utilization

needs to be established. In this study several key determinants of primary health care

utilization are examined and compared to actual utilization patterns. Conclusions about

health-seeking behavior are then drawn based on these relationships.

The central aim of this research, then, is to examine the spatial patterns of health care

utilization, and to suggest a model that may be used to explain health-seeking behavior. Many

places such as rural parts of Central America are poorly understood by researchers and

medical planners, and it is for this reason that significant gains in knowledge may be achieved

as a result of this study.

Such research is inherently difficult. Many sources of secondary data do not exist for many

parts of the developing world. In this research project, it was very difficult to acquire the data

needed. It was necessary to conduct extensive fieldwork in the region to collect much of the

needed information for this research project. In the end, this research combines primary data

collected in the field with data from secondary sources into a dataset that was used to examine

utilization behavior in the study area. GPS receivers were used to locate roads, trails, villages

and other geographic features in the study area; health attendance at three area clinics were

sampled; interviews of several key informants were conducted; and available secondary

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information was gathered from sources such as the Honduran National Census. The collection

of data from these varied sources thus, enabled an opportunity to study utilization in the Santa

Lucia region of southern Honduras.

1.1 Problem Statement

The fundamental problem is to understand the spatial variation in utilization in the study area.

This problem includes the question of why certain places differ in the amount of medical care

consumed. The specific aims of this research project are threefold:

1. This study proposes that utilization can be understood by using a modified gravity model

to examine the determinants of primary health care utilization. The gravity model has not

been commonly employed in prior utilization studies.

2. This study prioritizes the factors influencing health-seeking behavior in a rural Latin

American setting. The health-seeking decision-making process is a complex process that

deserves attention, but has not been a common focus in past research. By prioritizing these

factors, health policy decisions should be more effective in improving health care

provision in this region.

3. This research tries to better understand the complex health-seeking process by creating a

variable that considers health decisions as a two-part process. It is hypothesized that a

compound variable will more accurately reflect the health-seeking decision-making

process.

The following chapter contains a description of the importance of health service research;

followed by the description of utilization; a description of the study area; a short history of the

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study area; a section describing the development of a rural health facility and Shoulder-to-

Shoulder, Inc./La Clinica Hombro á Hombro; and concludes with a brief summary.

1.2 Importance of Health Service Utilization Research

For most people health care is initiated when they or their relatives recognize that they are

sick, but the factors that result in a patient contact with health services is very complex (Habib

and Vaughan, 1986). Variations in response to sickness and in utilization exist from person to

person and for any one person at different times (Habib and Vaughan, 1986).

Good knowledge and understanding of how people use health facilities is vital for health

resource allocation and planning (Müller et al., 1998). Studies of health services utilization

often seek to understand the frequency and trends in health service use, and the possible

mechanisms that may determine this use (Habib and Vaughan, 1986). Utilization studies,

therefore, have a wide appeal to the policy makers, managers and providers of health care,

particularly when they are able to identify areas of improvement (Habib and Vaughan, 1986).

Health planners need to understand utilization in order to improve health services. They need

to identify the important characteristics that relate to health care use and their

interrelationships, in order to make more effective health policy decisions. Understanding

utilization enables better development decisions to be made, which should result in better,

more effective primary care for the people of the region in the long run. The ultimate

justification of health services utilization studies lies in the relationship of service use to

improving the health status of the population (Habib and Vaughan, 1986).

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Not much work has been done on health care use in developing countries since the early part

of the 1990s (Buor, 2002). During the 1960s and 70s, studies focusing on the determinants of

medical care utilization received a good deal of attention (Andersen and Newman, 1973). The

importance of this research comes from a set of social values and perceptions including: 1) a

growing consensus that all people have a right to medical care regardless of their ability to

pay; 2) the general belief that certain disadvantaged population groups are not receiving

medical care which is comparable to that available to the rest of the population; and, 3)

expectation that medical care can contribute to the general health level of the population

(Andersen and Newman, 1973).

1.2.1 What affects utilization?

The utilization of health services can be viewed as a type of individual behavior (Andersen

and Newman, 1973). According to Moore (1969), the behavioral sciences have attempted to

explain individual behavior as a function of characteristics of the individual himself,

characteristics of the environment in which he lives, and/or some interaction of these

individual and societal forces (Andersen and Newman, 1973).

Understanding health facility utilization involves trying to understand human behavior. This

is a complex and sometimes confounding process. There have been a number of key variables

used in past research to explain health care utilization. Many of these relationships have been

empirically tested, and some results are consistent, while other relationships are not. These

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sometimes inconsistent results lead us to a conclusion that we simply do not fully understand

some relationships.

In several studies distance was discovered to be one of the most important factors relating to

health care utilization (Buor, 2002-4; Müller et al., 1998; Stock, 1983). In particular, Buor’s

2002 paper “Distance as a predominant factor in the utilization of health services in the

Kumasi metropolis, Ghana” demonstrated the critical importance of distance as it relates to

utilization of health services in the developing world.

In addition to distance, there are other factors that affect health care utilization. These include

demographic characteristics of the patients such as income level, education level, gender, and

age; geographic characteristics such as the quality of roads; and service characteristics such as

number of doctors, number of office hours, cost of service, etc. All of these factors have been

examined and many have been determined to be significant in explaining health care

utilization, as we shall see in the literature review portion of this paper. These factors are

examined in this study in an effort to better understand their effect on health-seeking behavior

in the context of a rural, developing world study area.

1.3 The Study Area

1.3.1 Location

There are 18 States (Departamentos) in Honduras. The study area is in the state of Intibucá,

which is 316,562 hectares in size. Santa Lucia is the central town in a municipality

(Municipio) with the same name. The study area is located in the southern part of the state of

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Intibucá, within only a few miles of the El Salvador border. See Map 1.1, 1.2 and 1.3 for

spatial perspective on Honduras and the location of the study area within the country

The topography of the study area is quite mountainous with the town of Santa Lucia resting in

a narrow valley at 1,100 feet in altitude, and the nearby peak of Cerro Verde rising to 2,600

feet in altitude. The climate in the study area is semi-arid, with a weather pattern dominated

by the Pacific Ocean, yielding a rainy season from May to December and a dry season from

January to April. During recent fieldwork in the area during the January dry season,

temperatures ranged from mid 50s at night (during an unusually cold period) to mid 90s in the

afternoon.

The service area for the Hombro á Hombro health clinic was used to define the study area. A

sample of attendances from the Hombro á Hombro clinic was used to determine the study area

for this project. This sample, taken during a pilot study in December 2003, identified the

primary home locations for patients who attend the clinic.

The Hombro á Hombro clinic, a private sector (NGO) health facility, is located in the town of

Santa Lucia, the capital of the municipality of Santa Lucia. Most of the clinic’s patients come

from this municipality, though some come from the neighboring municipalities of Magdalena,

San Antonio, and Colomoncagua, and a few come from more distant locations such as El

Salvador, or Concepcion.

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In addition to the Hombro á Hombro health clinic, two Rural Health Centers operate in the

Santa Lucia service area, one in the town of Santa Lucia, and one in the nearby town of

Magdalena. These Rural Health Centers (Centros de Salud) are operated by the Honduran

Health Ministry.

The nearest hospital is in the city of La Esperanza, the state capital, 82 kilometers to the north

of Santa Lucia, and per Rodriguez (2003), a minimum of three hours drive by fast pickup

truck. The hospital in La Esperanza is a regional hospital with limited specialty services. For

many specialty services, patients must travel to Tegucigalpa, with no certainty of being seen

in short order for their concerns. Functionally, then, the vast majority of all health care

received is performed locally in Santa Lucia (Bazemore, 2005c).

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Map 1.1: Honduras National Map

Source CIA, 2004.

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Santa LuciaService Area

Olancho

Yoro

Colón

Gracias a Dios

El Paraiso

Cortés

Lempira

Atlántida

Copán

Francisco Morazán

Choluteca

Comayagua

Intibucá

La Paz

Santa Bárbara

Valle

Islas de la Bahía

Honduras

Departamentos IntibucaSant Lucia

N

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente

Map 1.2 Honduras and Location of Study Area

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#

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#

#ÊÚ

ÊÚ

ÊÚ

Espino

Llanitos

Palacios

San Jose

San Juan

Banaderos

El Aguila

Junquillo

Los Lomas

Los Pozos

Magdalena

San Pablo

Talquezal

El Barreal

El Castano

El Jicaral

La Montana

Las Aradas

Las Marias

San Marcos

San Rafael

Santa Rita

Cordoncillo

El Leoncito

El Mosquito

La Ceibilla

Santa Lucia

Los Horcones

Los Tablones

San Francisco

Las Marias Mag

Piedra Colorada

Santa Lucia Service Area N

0 2000 4000 Meters

Aldeas012

Municipios

Roads11 - 22 - 33 - 4

# PopulationÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region by Jonathan Baker, Chris Carr, and Andrew Bazemore.

Map 1.3. Santa Lucia Study Area Detail

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The size of the study area is at the micro scale, with an east/west width of

approximately 14.4 kilometers, by 8 kilometers north/south. The shape of the study

area is irregular, and these distances are at the greatest points. Much of the study area

fits within a 12 kilometer radius from the town of Santa Lucia, which is at its center.

The mountainous topography makes travel difficult within the service area. Per

fieldwork done in the study area, it takes on average over two hours to traverse the

study area from east to west by truck and, of course, it would take significantly longer

by foot. These measurements of the study area use a geographic information system

(GIS) to calculate the Euclidean (straight-line) distance measurements. The study area

contains the homes of over 90 percent of the patients who attend the Hombro á

Hombro clinic.

1.3.2 History of the Study Area

As we have discussed, this location is remote. As recently as the 1960s, travel from

Santa Lucia to La Esperanza required over a week using a donkey or by foot

(Rodriguez, 2003). In the 1970s, a primitive dirt road was built through the mountains

from La Esperanza to Magdalena. During the 1980s, the road was improved partly due

to the civil war in El Salvador, in an effort to assist the Honduran military with

transportation of equipment in and out of the border region (Rodriguez, 2003, 2005).

But not until 1998, with the relief efforts brought on by Hurricane Mitch, was the dirt

roadway improved as far south as Santa Lucia (Tepe, 2005).

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1.3.3 Roads and Travel

Today, all the roads in the service area are dirt (except in the central towns like Santa

Lucia where sections are cobbled), though some are improved so that a well equipped

vehicle can sustain 25 KPH. Most of the roads, particularly in the outlying areas, are

not improved and 5-15 KPH is a common rate of travel in a well equipped truck. Map

1.4 shows the distribution of the roads in the service area and indicates road

classifications from 1-4. Roads classified from 2-4 indicate relative travel speeds

observed during recent fieldwork in the area using a GPS with 4 being the best roads

and the highest commensurate average speeds. The road classification of 1 represents

a walking path or trail which is not accessible using a vehicle. Road classifications of

1 have the lowest observed average travel speed.

Several of the villages in the study area have no road leading to them, only a walking

path. This creates a particularly large challenge with regard to accessibility, not only

for health access issues, but also for basic commercial purposes such as getting

produce to the periodic market in the town of Magdalena.

Most people in the service area only have one mode of transportation – their feet. The

results of surveys performed at the Hombro á Hombro clinic during 2003 indicate that

70% of the people coming to the clinic arrived by foot (Kurak, 2003). In addition to

walking, Kurak's surveys indicate that 28% of the patient's surveyed arrived by car or

truck, 2% by horse, and 0% by bus.

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1.3.4 Population

According to the 2001 Honduran national census (INE, 2004), the population of the

study area is approximately 12,000 people. Census denominations are hierarchical,

with a structure similar to the U.S. census (US Census Bureau, 2005). At the top level

is the nation, then the State or Departamento. Our study area is located in the state of

Intibuca. Within each department are Municipalities (Municipio), similar to a U.S.

county. Santa Lucia and Magdalena are the two Municipalities within our study area.

Within each Municipality are the Aldeas, which vary in size, but are roughly 1-4

square kilometers in the rural locations. There are ten Aldeas in the study area. In the

context of this study, the term Aldea is used to mean a census-based areal unit, not to

be confused with the colloquial translation of the term as “village”. The word

“village” has a very specific meaning, as is defined later in this paper. The smallest

census denomination is the Caserio, similar to a neighborhood or barrio. There are

from four to 21 Caserios per Aldea, totaling 94 Caserios in the study area.

The three largest towns in the study area, Santa Lucia, Magdalena, and San Juan, have

population sizes of 917, 1054, and 990 respectively. Second tier towns are Las Lomas,

San Francisco, and San Jose, with population sizes of 754, 636, and 701 respectively.

Population distribution is depicted in Map 1.4.

Place names are frequently reused in Honduras, making demographic analysis

confusing. For example, the town of Santa Lucia is a Municipality, an Aldea, and a

Caserio. [It is important to know what level of the census hierarchy is being

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considered for a given analysis.] The scale of this study is the Village, which is a

synthetic geographic unit made up of one or more Caserios.

#

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#ÊÚ

ÊÚ

ÊÚ

Espino

Llanitos

Palacios

San Jose

San Juan

Banaderos

El Aguila

Junquillo

Los Lomas

Los Pozos

Magdalena

San Pablo

Talquezal

El Barreal

El Castano

El Jicaral

La Montana

Las AradasLas Marias

San Marcos

San Rafael

Santa Rita

Cordoncillo

El Leoncito

La Ceibilla

Santa Lucia

Los HorconesLos Tablones

San Francisco

Las Marias MagPiedra Colorada

Population Distribution N

0 2000 4000 Meters

Aldeas012

Municipios

Roads11 - 22 - 33 - 4

Population# 0 - 99# 100 - 217

# 218 - 431

# 432 - 754

# 755 - 1054

ÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region.

Map 1.4: Study Area Population Distribution

1.3.5 Employment

The primary occupation in the study area is agriculture, with many of the farmers

living a traditional subsistence life-style (Coello, 2003). Due to poor agricultural

conditions in the region, to supplement incomes many families have members working

as wage laborer in the major Honduran cities, or in the U.S. on a migratory basis. The

overall effect has been that few rural households exist independent of wage labor

(Stonich, 1991).

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1.3.6 Income

Basic income for most of the employed workers in the region is approximately $6 a

day, or approximately $1,500 a year (Coello, 2003). Many people are not employed

full-time, and therefore have lower incomes. The average income per capita (GDP per

capita-purchasing power parity) was $2,600 for the nation as of 2004 (CIA, 2004), but

in our study area it is far less. The average income per capita compares with the other

Central American countries of Guatemala ($4,100), El Salvador ($4,800), and

Nicaragua ($2,300). When contrasted on a global scale, Honduras income is above the

Republic of the Congo ($700), but is far below the U.S. at $37,800 (CIA, 2004).

Figure 1.1 highlights the contrast in per capita GDP between these countries.

-5,000

10,00015,00020,00025,00030,00035,00040,000

$US

Hon

dura

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Nic

arag

ua

Gua

tem

ala

ElSa

lvad

orC

ongo

U.S

.

Average Income (GDP/capita)

Figure 1.1 Average Income Comparisons

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1.3.7 Education

Basic education in the study area consists of local primary schools (grades 1-6), many

in rural village locations. Junior high schools (grades 7-9) are only available in the

relatively large central towns of Santa Lucia and Magdalena. Any students continuing

their education have two options for senior high school: A liberal arts program, which

prepares students for an advanced education at the university, or a trade/technical

school for business, accounting, computers, car mechanics, etc. Many students,

particularly in the rural villages, do not continue with their education beyond the 6th

grade (Diaz, 2005).

1.4 Health Service in Honduras

1.4.1 History on health services

The Honduran health system is made up of public and private sub sectors with the public

sector consisting of the Ministry of Public Health and the Honduran Social Security Institute

(IHSS), the National Water Supply and Sewerage Service and the National Institute for the

Prevention of Alcoholism, Drug Addiction and Drug Dependence (Black et al., 2004). During

a recent study, Black et al. (2004) estimated that the coverage for the Ministry of Public

Health was 60%; Social Security covered 10-12%; and the private sector covered 10% of the

population. The Ministry of Public Health is organized into 9 health regions, and is the main

provider of health services in the public sector with 1272 health centers distributed across the

country (Black et al., 2004). Included in this number are 28 hospitals, although modern

technology and complex health care interventions are concentrated in the six national

hospitals, five of which are located in the capital (Black et al., 2004). Of the 1272 health

- 23 -

centers throughout the country, 341 are Health Centers with an attending Physician and

Dentist, and 865 are Rural Health Centers (Black et al., 2004).

The Municipio of the Santa Lucia has a limited history of continuous primary health care

delivery. The first team of medical doctors from the University of Cincinnati came to the

Santa Lucia area in 1990, prior to which there had been no consistent physician presence in

this remote region (Bazemore, 2005c). The Shoulder-to-Shoulder/Hombro á Hombro clinic

was completed in 1993. Health service in the form of nursing staff was available in the towns

of Magdalena and Santa Lucia prior to 1990. The Health Centers in Magdalena and Santa

Lucia were built around 1997 (Heck, 2005), but physician support has been intermittent for

both of these rural health centers (Coello, 2003). Therefore, it has only been quite recently

that physician care has been available to the population of the region. Prior to this, and

continuing to some extent today, there were “traditional” health providers that included

Curanderos (healers), Sabadores (massagers), Parteras (midwives), and herbalists. In most

cases, these traditional healers lack formal medical training from a University.

1.4.2 Background on La Clinica Hombro á Hombro:

Hombro á Hombro is a non-profit NGO legally registered in Honduras since 1998, but has

been operating under the sister U.S. organization Shoulder to Shoulder since 1990. It

represents a successful partnership between the University of Cincinnati, College of

Medicine, the Honduran Ministry of Health, and the Community Health Board in Santa Lucia,

Intibuca. Its mission includes providing ongoing primary health care and public health

services to residents of Santa Lucia, Honduras, and to enhance the teaching mission of the

- 24 -

College of Medicine in International Health. Over the years, the project has expanded and

now includes provision of health care, dental and nutrition services. The clinic is fully staffed,

including two full-time physicians, one full-time dentist, and two nurses. It has examination

rooms, laboratory, pharmacy, X-ray equipment and an operating room (Shoulder-to-Shoulder,

2005).

1.4.3 Development of a Rural Health Facility

When developing a rural primary health service such as the Hombro á Hombro clinic, health

planners started with an assumption of need based on the population in a specified area with

lack of primary care resources to meet this need. In the case of the Hombro á Hombro clinic,

the location was established in a central town in the region, and was partly determined by

interest and commitment from the local community. Other towns were considered, but Santa

Lucia, was selected primarily due to this combination of socio-political and need-based

considerations (Bazemore, 2005a).

In the original plan the Hombro á Hombro clinic would service an area with a roughly 12

kilometers radius around the health clinic (Heck, 2005a). Though the original plan was to

serve as primary care provider for this whole service area, due to several factors including low

levels of access, it is questionable whether significant portions of the service area are actually

able to utilize the health clinic (Bazemore, 2005b).

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1.5 Chapter Summary

This chapter introduced the study of primary health care in Santa Lucia, Intibuca, Honduras.

This study focuses on utilization of primary care services in a poor, rural part of southern

Honduras. In order to better understand utilization patterns, this research creates a model

which can be used to predict utilization behavior in the service area. This is important for

several reasons – first, because the area is remote and poorly understood; second, because

understanding health-seeking behavior can help primary health care planners improve

services; and, third, because there are many other similar places around the world which can

benefit from the lessons learned in this research project.

This study is unique in several ways. First, it explores utilization in a new location not

previously studied. Utilization behavior has been studied in other rural developing world

locations such as Buor’s (2004) study in rural Ghana, but it has not been studied in rural parts

of Central America. Utilization behavior may vary from place to place for reasons such as

local culture, topography or political structure (to name a few). At the beginning of a research

project such as this, it is safe to say that we are not sure about how universal are the factors

that relate to utilization, and how well models will correspond to those developed in other

parts of the world.

This study differs from previous studies, which have focused on using linear models to

describe utilization behavior. This study uses a non-linear gravity model to explain utilization

behavior. It is believed that this model, which is significantly different than a linear model,

should perform better than the linear models used in past research. This is because

- 26 -

relationships such as distance to utilization may be described as non-linear (Müller, et al.,

2000), and non-linear models (such as the gravity model) are better able to predict these types

of relationships.

The next chapter will focus on a review of the research literature pertaining to health care

accessibility and utilization applied to the developing world.

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CHAPTER TWO - LITERATURE REVIEW

This chapter reviews the previous research conducted in the field of health service geography

in the area of accessibility and utilization. Linkages are made and gaps are noted between this

project and the literature so as to identify opportunities and justification for the current

research project. The first section of this chapter summarizes the theoretical research done in

this field and explains the processes and concepts involved. The second part of this chapter

summarizes the methodological approaches used in prior studies. The final section of this

chapter reviews the empirical studies that include and examination of the data, methods, and

findings of prior research.

2.1 Theoretical Foundations of Prior Research

2.1.1 Access & Utilization to Health Care Services

Access to health care is an important part of an overall health system and has a direct impact

on the burden of disease that affects many countries in the developing world. Unfortunately,

health care, like many public services, is not equally available to all people (Joseph and

Phillips, 1984), and limited physical access to primary health care continues to be a major

impediment to achieving the goal of health care for all (Perry and Gesler, 2000).

Access to health care is concerned with the ability of a population to obtain health care

services (Black et al., 2004). It is concerned with the ability and willingness of the population

of a given area to bridge the physical gap between their home and the location of a health

facility. Access to health services is influenced by many behavioral as well as cost and

distance factors (Buor, 2002; Müller et al, 1998). Utilization of health services is increasingly

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being viewed as a function of accessibility (Phillips, 1979; Noor et al., 2003). The distance

patients must travel in order to obtain health care service has long been recognized as a

primary determinant of the utilization of health care facilities (Stock, 1983). Health care

facilities are often geographically inaccessible to many who live in a rural area (Stock, 1983).

The distance element is particularly significant in rural parts of the developing world where

western-type health facilities are not common, and where patients are likely to make the

journey for treatment as pedestrians (Stock, 1983).

Probably the most important link or interaction in any health care delivery system is that

between consumer and provider (Meade and Earickson, 2000). An optimal distribution of

health care resources is possible only if this relationship is understood (Meade and Earickson,

2000). To improve access to health care, it is important to monitor how access varies across

both geography and subpopulations (Knickman, 1998). Assessing access across communities,

however, can be difficult (Halfon et al., 1999). Geographic information systems (GIS) can

help by combining and analyzing complex information from multiple sources and then

displaying it as maps (Phillips et al., 2000). Maps are very useful tools for investigative

research because they are visually compelling and in many cases easy to interpret. The old

adage that “a picture is worth a thousand words” relates very nicely to maps. The brain is

better able to assimilate a large amount of geographic information when it is presented in the

form of a map. Maps look at geographic data in such a way as to give features of the planet a

spatial perspective. Maps have been used for many years as a tool for examining health

concerns (Porter et al. 2004).

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2.1.2 Utilization

Love and Lindquist (1995) define utilization as the relationship between service providers and

surrounding populations. The spatial patterns of utilization are a combination of the location

of the service used and the frequency with which each patient uses the service (Hays et al.,

1990). Accessibility often plays an important role in determining who is able or willing to use

a health service. These concepts of “accessibility” and “utilization” are inter-related. There

are two basic approaches to measuring accessibility to primary health care services: the first

involves the measurement of potential physical accessibility based upon the location of

populations relative to that of health care facilities; the second, the measuring of actual or

revealed accessibility through the analysis of utilization data (Joseph and Bantock, 1982). In

the context of this study the focus will be on the second definition based on revealed or

actualized access to primary care. The main difference between these two approaches is that

one looks at the characteristics of a place for those persons who could use a facility, and the

other one looks at the characteristics for those who already use the facility. A common point

here is that one needs to have access to a public facility in order to use (or utilize) it. A

distinction between the two approaches is that the first focuses on facility location, whereas

the second focuses on the patient, looking at health-seeking behavior.

In the context of this study utilization (the dependent variable in this study) is viewed as a

combination of clinic attendance and population for a given geographic unit. Specifically, a

utilization index is created to test the spatial variation of primary health care use. This index is

calculated by taking the attendance numbers (of a health facility) for a given geographic unit

and dividing by the population for that geographic unit. Since the spatial unit of measure is

- 30 -

the village, both the attendance numbers and the population numbers are aggregated to this

spatial unit, i.e. the village. The resulting index is an attendance number that is normalized by

population. This normalization process is performed so as to neutralize the effect of

population size on the spatial patterns of attendance.

2.1.3 Utilization and Distance

Distance from patient to health care providers, which can be called proximity, is often cited as

one of the most important factors relating to accessibility and utilization of health care

resources (Powell, 1995). Closeness to a particular doctor or facility is one of the main

reasons for using a given resource (Meade and Earickson, 2000). In developing countries

studies have shown the important role of distance in reducing the use of health facilities,

especially in rural areas (Buor, 2003). Noor et al. (2003) suggest that distance is a crucial

feature of health service use, but its application and utility to health care planning have not

been well explored. Several researchers have focused on this issue of distance as being an

important factor determining health care facility utilization (Müller, et al., 1998; Buor, 2002-

3; Noor, et al., 2003; Habib and Vaughan, 1986; Stock, 1983; Egunjobi, 1983; Joseph &

Phillips, 1984). Their results have made clear that distance is a very important factor affecting

utilization in rural areas of the developing world. Despite the importance of distance, it has

often been overlooked in a planning and decision-making context (Meade and Earickson,

2000).

The link between provider and consumer weakens with distance (Meade and Earickson,

2000). In general, the further away a health facility is from a person’s residence, the less

- 31 -

likely he/she will use it. This is a distance decay concept, where the rate of interaction varies

inversely with distance. Distance decay is derived from the gravity model, which states that

the attractional force between two objects is directly proportional to their masses and

inversely proportional to the square of the distance between them (Meade and Earickson,

2000). According to Stock (1983), distance decay models have been employed frequently in

studies of health care behavior.

Distance decay is useful in determining central place hierarchies and functional

regionalization (Meade and Earickson, 2000). In places with rapid distance decay patterns,

health services should be decentralized and locally accessible. This usually applies to low-

order services such as first aid. High-order services such as heart transplants are not as

sensitive to distance. People are willing to travel further for these [high-level] services

(Meade and Earickson, 2000). This relationship is related to the body of theory known as

Central Place Theory (see Christaller, 1966), and can help health planners develop health

delivery systems that are effective, while considering constraints such as the cost of services

provided.

In health provision research, several utilization studies have been conducted in the developing

world, focusing on the spatial distributions of patients seeking medical treatment at particular

health facilities. These studies suggest that health care facilities in developing countries are

likely to effectively serve only a small area surrounding the facility (Stock, 1983). In Nigeria,

Stock found that at a distance of 5-km from the medical facility, the per capita utilization rate

fell to one third of that of the 0-km rate. In a study in Iraq, Habib and Vaughan (1986) found

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that after 5-km utilization rates declined to 60% of the 0-km rate. In a study in India,

Frederiksen (1964) found that the utilization rate declined by 50% for each half-mile between

the community and the facility (Buor, 2002). In a recent study in Kenya, Noor et al. (2003)

found that 60% of health facility users attended health facilities within 5-km of their home.

Buor (2002) found that most people patronize health services in metropolitan Ghana at a

distance of less than 3-km, and therefore recommends a 3-km model to study utilization.

2.1.4 Factors Influencing Utilization

The discussion from the previous section on distance and distance decay of utilization has

demonstrated its importance in health service research, but the distance issue cannot be

identified as the sole determinant. The delivery of health care is dependent on many factors,

including the capacities of the providers, the needs and capacities (socio-economic-

demographic factors) of the patients, and the characteristics of the region within which the

patients live (e.g., topography, transportation facilities and routes, settlement patterns,

government). The following discussion looks at the different variables used to explain

utilization behavior is past research.

Love and Lindquist (1995) focus on utilization, which they define as the relationship between

service providers (hospitals) and surrounding populations. This study examines actual

utilization patterns and forms conclusions about accessibility based on these patterns. Love

and Lindquist recognize that there are many factors, in addition to physical distance, that

influence the use of medical facilities. According to their study, insurance status, income,

education, occupation, age, gender, and individual preferences and perceptions all may be

- 33 -

involved in determining usage. Buor (2003) adds the factor for marital status to the studied

variables. Joseph and Phillips (1984) add social class and ethnicity as important factors

influencing utilization, but suggest they may be difficult to study given their relationship to

other studied variables such as income, occupation and education.

Income: Looking at income as an explanatory variable, Joseph and Phillips (1984) cited an

important New York study (Koos, 1954) as evidence that lower-income residents underutilize

health care services. Koos’ study suggests that lower income residents are often deterred from

seeking medical care because of cost and fear. At the same time, several studies have

suggested that lower-income residents tend to have a greater need for health care (Joseph and

Phillips, 1984). Wolfe’s (1999) research suggests a negative correlation between income and

health status. In her research, Wolfe found that from 1990 to 1995, the proportion of children

in the U.S. in very good or excellent health decreased. At the same time, this author found that

the proportion of non-poor children in very good or excellent health, increased. Wolfe's

research suggests a clear association between income and health, and that poor children are

now in worse general health than non-poor children (Wolfe, 1999). This issue of “more needy

lower income” residents underutilizing health service turned into a contentious debate in the

UK, with contradictory conclusions being made depending on the study cited (Joseph and

Phillips, 1984). The pattern of distance/utilization for low-income residents has not been

determined, and consequently offers the opportunity for further study.

Education: When looking at education as an explanatory variable, the results from Love and

Lindquist (1995), and Buor (2002), indicate there is a positive relationship between education

- 34 -

and utilization of health care facilities. It has been shown that people with higher levels of

education utilize health facilities more often than individuals with lower levels of education.

Age/Gender: Both age and gender of patients are important variables to study relating to

utilization of health care. It may be expected that as people age, and especially after

retirement, people will need health care services more for chronic and perhaps acute

conditions (Joseph and Phillips, 1984). Also, among younger age groups, utilization by

females can be increased for reasons associated with childbearing and conception. Several

studies have found that women have higher morbidity than men (Anderson and Andersen,

1972; Kohn and White 1976; Cleary et al. 1982). In spite of this higher level of morbidity,

women outlive men on average (Joseph and Phillips, 1984).

Race/Ethnicity: Joseph and Phillips (1984) suggest ethnicity may be important in

determining cultural preferences toward health care. They also suggest “diagnosis” or

“treatment” as another important variable to study. Availability of alternative sources of

medical treatment is included in this cultural preference issue. In many parts of Latin

America, people use traditional health providers known as Curanderos. They belong both

culturally and socially to the community and the local population supports their work (Perry

& Gesler, 2000).

Social Capital: Social capital may also influence utilization. This is a set of social

relationships that shape a group’s social interactions. Social capital is often connected with

places. The relative level of comfort or feeling of safety that is generated by a place or area is

- 35 -

often due to social connections, or social capital (World Bank, 2003). A person feels more

comfortable in a place he knows, or has been recommended to him/her. Family and cultural

patterns influence social capital and, hence, utilization of primary health care. Utilization of a

particular health care facility is often something that is passed on by word of mouth. In a

study of spatial attendance of health services in New Zealand, Hays et al. (1990) observed a

pattern of utilization with less spatial bounds when a family member recommended a health

service. This study concluded that people are willing to travel further to get health care if the

service has been recommended by a family member or relative.

Quality of Health Service: Received quality of health care service is an important issue in

determining health care utilization (Buor, 2002; Stock, 1983). The quality of the health

facility, the medical equipment available at the health facility, the treatment by the staff and

general relationship that the physicians have with their patients, all play an important role in

determining utilization of a health facility.

Complex Variables: As has been discussed, several variables have been used in past research

to examine utilization behavior, but human behavior is complex and not easily explained.

What determines the actual behavior may often lay in a combination of both consumer and

provider characteristics (Buor, 2003). Joseph and Poyner (1982) support this reasoning

arguing that both consumer and facility attributes interact to produce different reactions from

different persons (Buor, 2003).

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In Fotheringham’s (1983) paper, the author suggests many types of interactions may be

considered a result of a complex two-stage decision-making process. This is a way for a

model to simultaneously consider a combination of two or more variables, and this, in turn,

may more accurately reflect the human decision-making process. This is an intuitively

appealing concept, because human decisions such as primary care utilization may be the result

of two or more sub-decisions. Consequently, this study tests this idea by using a two-stage

variable to describe health choice decisions. In past research on health service utilization, the

effect of this type of variable on a utilization model has not been examined.

2.1.5 Unit of Analysis

The study of utilization can use several different units of measure. Buor’s (2002-4) studies use

individual level survey data to examine individual patient-provider interactions. As an

alternative approach, data can be aggregated at some predefined level. Müller et al. (1998),

for example, aggregated attendance data at the household level, and further grouped data

according to age and sex of patients.

When studying utilization, some variables are best applied to individuals, while others can be

aggregated or grouped according to the needs of the study. A variable such as sex, when

aggregated at some spatial level, may not offer insight into utilization behavior because once

aggregated the numbers become close to 50/50 for men versus women for most places.

Furthermore, meaningful generalizations based on aggregated data can run into an “ecological

fallacy” problem, where making generalizations about individuals should not be made based

- 37 -

on aggregated data. Nonetheless, sometimes it is necessary in studies such as this to aggregate

data and focus on the variables and conclusions that are appropriate for the unit of analysis.

In this study a synthetic unit of measure called the “Village” is used. This is necessary

because addresses, such as those used in the U.S., do not exist in the service area. In this study

clinic attendance records are listed according to the “Village” of residence, because no

detailed house location data are available. Furthermore, the Honduran National Census does

not release individual level data, only aggregated data, at a predefined spatial scale to preserve

individual anonymity. This makes comparisons with individual level socio-demographic data

available from the census impossible, and further restricts the type of model that can be used

in the study (such as the logit model which needs individual level data). As a result this study

uses aggregated data, and works to conduct the analysis according to the constraints offered

through the use of this type of data.

2.1.6 Theoretical Framework – Utilization Models

Frameworks for the study of utilization have been developed in prior research. The initial

utilization model, the model created during the 1960s (Andersen, 1968), is shown in the figure

2.1. Andersen (1995) describes the initial utilization model as a behavioral model. This model

suggests that people’s use of health services is a function of their predisposition to use

services, factors which enable or impede use, and their need for health care (Andersen, 1995).

Some individuals have a propensity to use services more than others. This propensity toward

use can be predicted by individual characteristics which exist prior to getting sick (Andersen

- 38 -

and Newman, 1973). People with these preexisting characteristics are more likely to use

health services, because they have a greater need. These characteristics can include

demographic, social and attitudinal-belief variables (Andersen and Newman, 1973).

Figure 2.1 Initial Behavioral Model: (taken from Andersen, 1995)

Even though an individual may be predisposed to use a health service, some means must be

available for him/her to do so (Andersen and Newman, 1973). Enabling conditions make

health service resources available to the individual. These can include income, health

insurance, availability of source of care, and accessibility of the source (Andersen and

Newman, 1973). Without these enabling resources, health care utilization may not occur.

If we assume the preexistence of both predisposing and enabling factors, the individual must

still perceive the need for health service. A perception of illness is necessary for use of a

health service (Andersen and Newman, 1973). An evaluated illness is also a source of

demand for health services. The illness level represents an immediate cause of health service

use (Andersen and Newman, 1973).

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The initial behavioral model of utilization has undergone several changes through the years.

Aday’s Utilization model (Aday and Andersen, 1974) includes a health care system

component to the model in recognition of the importance of national health policy and the

resources in the health care system (Andersen, 1995). Dutton (1986) introduced structural

barriers to the model. Andersen (1995) introduces the factor of health outcomes into the

utilization model. Trying to incorporate both behavioral and spatial factors from the previous

models, Buor (2002) creates a model that combines distance and use of health services. This

author indicates that utilization is based on provider characteristics, user characteristics, and

restrictive factors (such as distance coverage, location of facilities, and service cost) which are

a result of government policy. See figure 2.2.

Figure 2.2 Hypothetical Utilization Model (taken from Buor, 2002)

The preceding models consider many of the variables that affect utilization behavior, but do

not consider the complex nature of the human decision-making process. As Fotheringham

- 40 -

(1983) suggests, many decisions may be the result of a two-stage decision-making process.

Incorporating a two-stage decision variable into the model may be a way to improve its

explanatory as well as its predictive capacity.

2.2 Methods of Studying Utilization

2.2.1 Regression Modeling

When looking at the demand for health service, Schultz (1975) focuses on analyzing the

characteristics of the people who are serviced by a given medical facility. Schultz (1975) uses

a multi-variate regression analysis to determine the relationships between medical service

supply and several demographic demand factors. Multi-variate regression analysis, also

known as multiple regression analysis, is a statistical method for studying the relationship

between a single dependent variable and one or more independent variables (Allison, 1999). It

is unquestionably one of the most widely used statistical techniques in the social sciences

(Allison, 1999). Schultz (1975) uses this measure of correlation to help explain the factors

that relate to demand for a given health service. Bour (2002, 2003) uses a multiple regression

analysis to examine the factors that effect health facility utilization.

2.2.2 Gravity Modeling

Gravity models have been used for years to account for a wide range of interactions between

people and places. The gravity model is derived from Newton’s Law of Gravitation, and has

been used as a way of combining accessibility and availability (Guigliardo, 2004). Gravity

models have been used to represent the potential interaction between any population point and

all service points within a specific distance (Guigliardo, 2004). In past research gravity

- 41 -

models have been used in several geographic studies on access to health care (Joseph and

Bantock, 1982; Scarpaci, 1984), but no known study has used this gravity modeling approach

in a study of primary care utilization.

Müller et al’s (1998) study suggests that the distance decay pattern of patient attendances is

non-linear. This suggests that the use of a non-linear model, such as the gravity model, would

constitute a better approximation of the true relationship of these variables to utilization.

Considering that this type of model has not been used in previous utilization studies and has

been suggested to be a better fit than non-linear models, an opportunity exists to both improve

utilization modeling and at the same time add to the understanding of health care utilization.

This is a gap in the knowledge base that needs to be filled. The use of the gravity model in

utilization studies may substantially improve the predictive capacity of a utilization model and

is therefore worthy of testing in a study such as this. Furthermore, use and testing of the

gravity model represents a significant contribution to knowledge relating to utilization of

health services.

2.2.3 Comparative Descriptive Analyses

In their study on health care utilization Phillips, et al. (2000) uses both the Griffith

Commitment Index and Location Quotient Index to describe the characteristics of places

relative to usage. The Griffith Commitment Index is an analytical measure used to show the

spatial patterns of patient utilization for a given health facility. This index is calculated by

taking the total number of visits for a geographic unit, and dividing this number by the total

number of visits for the study area as a whole. This index measures the extent to which

- 42 -

patients from defined geographic areas utilize a health care facility. The results can be ranked

and mapped in a GIS, and the authors suggest including only the top 60% of clinic visits, and

designating them as primary users (Phillips, et al., 2000).

Another comparative index employed by Phillips et al. (2000) is the Location Quotient Index.

Location quotients are calculated by dividing the utilization rate for a given geographic unit

by the utilization rate for the service area as a whole, thus allowing for a focus on specific

subpopulations. A Location Quotient Index can be used to determine the proportion of the

target population that uses a health clinic for a given geographic unit. The results are

calculated and mapped in a GIS (Phillips, et al., 2000). This index can be used to evaluate

service areas and to identify underserved and over-served populations.

2.2.4 Marketing Geography and Utilization

There are strong links between marketing geography and medical facility utilization. While

markets are most often associated with the buying and selling of goods and services in a

commercial sense, markets can also be applied to activity spaces that describe general

behavior (Ricketts, 2002), and the methods used in analyzing market areas can be very helpful

in improving medical service provision in the developing world. Market area analysis has

been used in health facility utilization studies (Martin and Williams, 1992). Being able to

identify the primary and secondary trade areas reveals the spatial extent of the service area,

and shows patterns of utilization. The proximal service area method, sometimes called

“Customer Spotting” (Jones and Simmons, 1990) is a way of putting your patients on a map.

This type of Pin map is helpful in delimiting the catchment area, identifying underserved

- 43 -

areas, and identifying potential future facility locations. Thiessen polygons, spider diagrams,

and concentric circles (buffers) are other popular ways to delimit proximal service areas.

In contrast to the deterministic, destination-based, proximal service area method just

mentioned, Huff (1963) developed a probabilistic, origin-based model of trade area

delimitation. This approach is derived from the gravity model and looks at shopping behavior

of the customer, and assigns a probability of attending a given retail store based on

mathematical computation of the attraction of a store and the distance to it. This method

focuses on the perspective of each customer’s home (origin) location, and creates a

continuous probability surface. This is in contrast to the store-based perspective of the

proximal service area method mentioned in the previous paragraph.

2.2.5 Geographic Information Systems

Geographic Information Systems (GIS) are computerized systems for the storage, retrieval,

manipulation, analysis, and display of geographically referenced data. Since they can include

physical, biological, cultural, demographic, or economic information, they are valuable tools

in the natural, social, medical, and engineering sciences, as well as in business and planning

(Mark et al., 2004). GIS is a young discipline. Although cartography and mapping go back

hundreds of years, GIS began in the 1960s, and is, thus, a relatively new form of computer

science (Mark et al., 2004).

Love and Lindquist (1995) recommend the use of GIS as an aid in the analysis of medical

access issues. These authors use a GIS and its related tools as a means of efficiently capturing,

organizing, storing, and retrieving spatial data. What distinguished a GIS from other types of

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information systems is that objects within the database are stored according to location. Thus,

hospitals and surrounding census districts [for example] can be assigned to specific locations

in the database. The GIS generates topological relationships where objects are locationally

related to one another in space, thereby enabling the researcher to conduct spatial

comparisons and other types of analytical procedures. These spatial operations and related

GIS capabilities, such as buffering and object overlay, make them ideally suited for measuring

accessibility to medical services (Love and Lindquist, 1995).

GIS has grown in popularity in public health through the past decade. Today, there are now

recurring conferences on GIS and health sponsored by organizations such as ESRI, the

American Public Health Association (APHA), the Agency for Toxic Substances and Disease

Registry (ATSDR), and others.

Geographic information systems have been helpful for understanding a variety of health care

issues such as defining hospital service areas, examining the affect of distance on access, and

disease patterns (Phillips et al., 2000). The use of GIS for the measurement of accessibility

has been popular in health care planning for several years (Parker & Campbell, 1998; Phillips

et al. 2000). A number of accessibility studies have been conducted in the developing world

utilizing GIS (Black et al. 2004; Perry & Gesler, 2000; Ayeni et al., 1987; Oppong &

Hodgson, 1994) and signify the contribution that GIS has made in health care planning and

provision research. Despite some early innovations in using GIS to understand health care

access within communities, it has remained largely underutilized (Phillips et al., 2000).

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The World Health Organization (WHO), through both its Evidence and Information for Policy

Cluster (EIP) and the Health Analysis and Information Systems (AIS) group has been

involved in a number of initiatives to measure and analyze physical accessibility to health

care using GIS. In research related to this initiative GIS has been used in conjunction with

sophisticated software products such as AccessMod and SEGEpi to examine accessibility

across large areas, and develop location-allocation models to be used in health care planning

(Black et al., 2004). In contrast to the extensive use of GIS in accessibility studies, the use of

GIS in developing world utilization studies has been limited. This study offers an opportunity

to expand the application of GIS into the area of primary health care utilization research, and

at the same time contribute to the body of knowledge.

2.2.6 Global Positioning Systems

The Global Positioning System (GPS) refers to the series of satellites developed and launched

by the U.S. Government for navigational purposes. Used in conjunction with a GPS receiver,

these satellites can determine your exact position anywhere on the earth. GPS provides

accurate information about latitude, longitude, altitude, speed, and direction of travel

(DeLorme, 2001). The GPS can be used in field research as an easy and effective way of

collecting primary data for health care research (Bernheisel et al., 2003; Brane et al., 2004).

Furthermore, the GPS data can be easily interfaced with GIS to provide maps and other

geographic analyses.

GPS has become a common tool in health related field research. In a recent study the GPS

proved to be an effective in determining the locations of mosquitoes carrying the West Nile

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virus (Gibbs and Emmanuel, 2005). In the developing world, GPS was used to identify the

spatial framework for an Indian village. This enabled an analysis of socio-economic,

demographic, and spatial factors related to a dengue fever outbreak in 2001 (Pratt, 2003).

GPSs have been used in other health geography studies including Tanser and Wilkinson’s

(1999) strategy for monitoring and control of tuberculosis in rural South Africa (Porter, 1999),

and in an analysis of access to primary care in Andean Bolivia (Perry and Gesler, 2000).

2.3 Empirical Studies

The impact of spatial factors on health care behavior and their implications for health care

delivery have been major themes in health service geography research (Shannon, et al., 1969;

Shannon and Defer, 1974; and Pyle, 1979). A number of studies have demonstrated the value

of geographical methodologies for health care planning purposes (Stock, 1983). In these

studies a number of different methods have been used to study accessibility and utilization.

These include a descriptive analysis of utilization (Phillips, et al, 2000); an analysis of

distance decay of utilization (Müller, et al., 1998); a population weighted average distance

analysis (ReVelle and Swain, 1970; Oppong and Hodgson, 1994), a maximal covering

analysis (Oppong and Hodgson, 1994); a regression, correlation analysis (Shultz 1975;

Gesler, 1999; Buor, 2002, 2003), negative exponential model (Stock, 1983), gravity model

(Joseph and Bantock, 1982; Scarpaci, 1984; Guagliardo, 2004), a service area analysis

(Martin and Williams, 1992); and an analysis of rural service centers using Central Place

Theory (Mallick and Routray, 2001). Geographic Information Systems (GIS) have been used

to implement several of these models and apply them to real-world case studies (Black et al.,

2004; Noor et al. 2003; Phillips et al., 2000; Perry and Gesler, 2000; Ayeni, et al., 1987;

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Oppong and Hodgson, 1994). In these studies spatial patterns of utilization have been

mapped, modeled, and the relationship of utilization to a host of variables has been tested.

The use of health services has been widely investigated in countries and cities in the

developed world (Bailey and Phillips, 1990). Many of the significant determinants of health

care utilization have been determined and tested in developed-world locations. In the cities

and countries of the Third World, the same cannot be said with any degree of confidence

(Bailey and Phillips, 1990). Many of the factors and relationships are simply unknown. As

Hellen (1986) points out, there is no exclusively Third-World health systems research to

parallel the clear demarcation that has been drawn around ‘tropical’ medicine. For this reason,

he suggests, many of the research methods developed and refined in the Western countries

may be applied in developing countries (Bailey and Phillips, 1990).

2.4 Social Equity in Access to Primary Care

Social equity can be most simply described as “a just distribution, justly arrived at” (Harvey,

1977). Social equity can be applied to the distribution of and access to health care resources.

The idea is to have health facilities located in such a way as to be fair to the majority of the

people in a given area. Therefore, when planning a health service area, health care authorities

need to consider social equity. One can use the population weighted average distance (Church

and ReVelle, 1974) as a measure of fairness, where lower average distance locations can be

viewed, not only as more efficient, but also more equitable.

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Noor et al. (2003) focus on the issue of equity in physical access to clinic services and use

GIS to define the population’s overall access to health care. They use regression to determine

equity of service. In this study the authors test the relationship between actual utilization and a

theoretical target level of physical accessibility (defined as percentage of population living 5-

km from nearest health facility), and conclude this is a good measure of equity (how well

actual utilization fits the theoretical target). Their results reveal a coefficient of determination

(r2) in the four tested districts between .71 and .99. The authors suggest that though there were

marked disparities in theoretical access between districts, particularly between rural and urban

areas, the predefined measures of theoretical access corresponds closely with actual usage

data. It is through the use of these and related tools that the authors believe health delivery

planning and monitoring can be conducted more equitably.

2.4.1 Access & Equity

Access has many components; geography is only one. Exploring equity and inequity in terms

of access to primary care is an important aspect that needs further study. Phillips (2000)

suggests there are two types of service areas; the “idealized” and the “actualized” service

areas. The “idealized” is what is originally planned for during the development of a health

service area; versus the “actualized” is what has actually taken place after some period of

time. These are not the same. This difference is one reason why we study issues like access to

care Bazemore (2005b).

The use of tools such as Geographic Information Systems (GIS) allows for the building of

models to test this equity issue. GIS enables us to examine how the original idealized plan is

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working. This tool allows us to understand the delivery of varying levels of care based on

access, by allowing us to understand the distribution of access across the study area and the

identification of pockets of under-served populations (Bazemore, 2005b).

An analysis of utilization data can be used to assess the “actualized” service area, and then

compare it with the “idealized” service area. In this type of analysis health facility attendance

records are examined, and a comparison is made between “actualized” and “idealized” service

areas. For examples see Noor, et al., 2003, Phillips et al., 2000, and Bazemore et al. 2003.

2.5 Chapter Summary

In summary, this chapter reviews the prior research on the topic of access to and utilization of

health care services. Access is described as the ability of a person to overcome the physical

space that exists between his or her home location and the location of the health care provider.

It focuses on the potential for people to access a given health service. This is contrasted with

utilization, which focuses on the actual past interactions between patient and provider. These

represent people who already have used a given health service, where the focus is on the

characteristics of those interactions.

Several variables have been used in past research to explain health utilization behavior.

Variables such as distance have been well researched and the relationship to utilization is

consistently negative throughout the literature. In contrast, other variables such as income and

education provide inconsistent results. There are many other variables that could explain

health seeking behavior, but have yet to be examined adequately in health care utilization

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studies, particularly in developing world locations. Variables such as size of facility, number

of doctors, number of operating hours, employment, type of facility, transport cost and mode

of transport all offer potential for explaining health seeking behavior and offer an opportunity

for further research. Researching these variables should offer an opportunity to better

understand the variables that help explain health seeking behavior in the developing world.

Finally, in this review several different approaches to the study of accessibility and utilization

are discussed and an opportunity for using a gravity model approach is identified. The

literature describes relationships such as distance to utilization to be non-linear. Nonetheless,

non-linear models, such as the gravity model, are not common in utilization studies.

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CHAPTER THREE - DATA ACQUISITION

This chapter summarizes the acquisition of data used in this study. This includes a section

describing the acquisition and development of a base map; the acquisition of village and

walking routes throughout the study area via GPS receivers, and the incorporation of these

data into a GIS; the acquisition of GIS political boundary shape files and census demographic

information; and a section describing the clinic attendance samples. The second part of this

chapter looks at the qualitative data used in this study and includes a section describing the

selection of the key informants. This is followed by a discussion of the challenges in

conducting fieldwork in the developing world.

3.1 Acquisition of Data for the GIS

3.1.1 Acquisition and development of base map

In order to start a geographic analysis of this type a good base map needs to be acquired or

developed. This is a good starting point for most types of spatial analyses. Having a good base

map gives a good frame of reference for physical features in the study area relative to other

features (such as villages relative to streams).

In most parts of the developed world formatted GIS files are available from a variety of GIS

vendors, but when working in the developing world, particularly rural parts such as southern

Honduras, these GIS files are often not available. As a surrogate, this research started by

acquiring a 1:50,000 scale topographic map available from the Instituto Geografico Nacional

de Honduras [the Honduran national geographic institute] (IGN, 1984). Though this map was

originally created in 1970, topographic features do not change much over 35 years, but human

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development features do. Nonetheless, it is the most detailed map available for this area, and

that is why it was chosen for this project. The appropriate map for the service area was

difficult to locate in the U.S., but eventually one was located at the Cornell University

Library.

This topographic map was then scanned using a large map scanner and saved in a digital

format called a TIF file. This map was later converted to a bil format that is usable in

ArcView, a common GIS software package. This process included geo-referencing nine

known points from the map and processing it with ENVI (a remote sensing software). This

processing enabled the GIS to use the known point locations as references and interpolate the

coordinates for all other places on the map. The result of this process is a GIS base map that

allows other geographic data (such as a road network) to be overlaid on top of this base map.

3.1.2 Acquisition of GPS Data in Honduras: Village locations and walking routes

The first step in the data acquisition phase involved primary collection of locations for each

village and the determination of all walking routes/trails used in the study area. The village

locations and walking routes were determined using a handheld GPS receiver. Using a local

guide to determine the location of each village, two field researchers traveled to each village

either by pick-up truck or on foot. A GPS device was used to determine the exact routes taken

and the coordinates of each village. This included all roads and pathways that the patients

used to travel from their home residence locations to the health facilities, as well as the exact

location of 31 important villages and towns in the service area. Data for each route and village

location were later downloaded into a GIS. (See Map 3.1 for more information.)

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Many of the routes were mapped using a good four-wheel drive pickup truck. This helped

data collection to progress quickly, allowing the researchers to get to most villages in the

study area in less than two weeks. Other villages not on roads had to be mapped with GPS on

foot. During this map-building phase, local guides were used to assist in finding needed routes

and village locations.

The GPS data were added to the GIS base map so that route distance calculations and spatial

references could be made. This research used Garmin eTrex Legend and Vista GPS receivers,

which have download capabilities and accuracies generally in the 20-30 foot range. Waypoint

and route data were downloaded via DNR Garmin, a freeware program, developed by the

Minnesota Department of Natural Resources (DNR Garmin. 2004), and imported into

ArcView GIS.

The GIS village/route file requires some processing to enable it to become a fully functioning

GIS network. The first procedure involves loading the GPS file into the GIS. Garmin’s track

files (which are used to show routes traveled) are basically a “cookie crumb” trail which gives

GPS location points along the trail every few seconds. The output of this file is therefore a

large assemblage of related points. These points are added to an ArcView project view using

the “Add Event Theme” function. This point theme needs to be converted into a line theme

through one of several ArcView script extensions, such as X-Tools (DeLaune, 2001) or it can

be drawn using heads-up digitizing methods.

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The next step in the GIS processing involves the linking of the route data and the village

waypoint data into a functioning network. This functionality is available in ArcToolbox,

which is available in ESRI’s ArcGIS 8 and 9 software packages, but not available in earlier

additions of ArcView 3.2. The first step of this process involved converting the route line

theme to a coverage using the “make shapefile into coverage” function in ArcToolbox under

conversion tools. The next step uses the “clean” function to create the topology used by the

network {Conversion Tools/Data Management Tools/Topology/Clean}. Use parameters –

Dangle length = 0; Fuzzy Tolerance = .0005 (snap distance) to insure proper connection of

dangling line segments and nodes. The final step, which creates a coverage usable by ArcGIS,

involved using ArcToolbox’s “Build” function to create the road network coverage. Be sure

to specify feature class = “line”. This last step is not necessary if ArcView is being used.

As a result of this procedure, we have a fully functioning network coverage for ArcView or

ArcGIS. ArcView’s Network Analyst is then used to calculate the shortest distance along the

road network from each village to the health clinic. {This is not to be confused with the travel

time estimation technique presented in chapter 3.} These distance measurements represent

varying levels of access in the distance decay model created for this study. This method of

measuring distance is a more accurate measure of travel impedance than simple straight-line

or Euclidean distance that has been often employed in past research such as Oppong and

Hodgson’s (1994) study on accessibility in Ghana. See map 3.1 on next page for an image of

the enhanced base map.

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3.1.3 GIS and Census Data:

In order to understand the distribution of the population and calculate the utilization index one

needs to have population data for each village. This research uses 2001 Honduran National

Census data for population counts of each village and this information is available at their

website (INE, 2004). There are some concerns, as with any census, as to the quality and

accuracy of these data. Considering though, that this is the only officially sanctioned census, it

is assumed to be accurate. None the less, caution is recommended when using these data and

drawing conclusions based on them.

INE makes these census data available in a tabular format, but what is more valuable to a

geographic analysis such as this is when the data are associated with corresponding GIS

shapefiles. These data were made available for use in this project by the Secretaria de Estado

en los Despachos de Recursos Naturales y Ambiente [The Secretary of Nature and the

Environment] (SERNA, 2005), located in Tegucigalpa, the Honduran capital. This made geo-

demographic analysis much simpler where the GIS could be used to group and analyze

spatially referenced demographic information at detailed levels. These data were available at

the Caserio level, which is akin to the U.S. Census’ Block Group level. See Map 4.1 on next

page for a detailed view of the study area.

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Basemap of Santa Lucia Study Area N

Topographic map information by Instituto Geografico Nacional (1984).Road and Village Locations by Jonathan Baker and Chris Carr

0 1000 2000 3000 4000 5000 Meters

Map 3.1: Base Map of Study Area

3.2 Attendance Samples

A systematic sample of patient attendance for each of the three health clinics in the service

area was performed during a two-week field trip to the study area in January of 2005. The

Honduran Health Ministry requires that all rural clinics keep records of the patients they are

seeing. These records, sometimes called log books, include detailed information on the

demographics (age, sex, weight, diagnosis, home location, etc.) of each patient who visits the

clinic. All three clinics in the service area keep this type of records, and samples were taken

from each clinic for the entire year of 2004. There were some challenges along the way, but

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this process went quite well considering that the three health clinics were under no obligation

to provide this information. The following section further explains this sampling process.

For each of the three clinics home location information was extracted in a systematic method

of sampling of the clinics’ patient attendance records. This involved sampling every 10th

record from the log books for the entire year of 2004. This represents 10% of all patients seen

by the clinic over that period. This yielded patient residence locations for a sample size of 477

patients from the Hombro á Hombro clinic, 320 patients from the Santa Lucia Health Center,

and 557 patients from the Magdalena Health Center. These records represented patients

residing in 31 villages or towns. Only the patients’ home location (village) was recorded. No

names or other personal information was taken to preserve confidentiality. The information

was tallied according to the patient’s home village. The same methodology was used for all

three clinic samples.

Acquiring permission to sample the clinic’s logs turned out to be a relatively easy process.

Since this researcher is associated with the University of Cincinnati’s Medical School and

supported by the director of the Hombro á Hombro health clinic, access to Hombro á

Hombro’s log books was straight forward. The sample was performed in the clinic and took

approximately two afternoons.

A similar experience occurred with gaining access to the Santa Lucia Health Center’s records.

The long-time nurse at the Health Center, who has a good amount of influence in the

community and strong ties with the Hombro á Hombro clinic, was very helpful in providing

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information and access to her clinic’s records. No resistance was given, and it seemed that she

understood the value of this research. This was a very positive experience and significant

amounts of other qualitative data were received during this process.

It was thought that the Magdalena clinic would be a different experience. Expectations for

trying to get a sample of the Magdalena Health Center’s records were not good from the

beginning. This clinic operates in another municipality, Magdalena. It is operated by a Cuban

social service doctor who was quite new to the area. There was some concern over the

doctor’s attitude towards American researchers coming into her clinic and asking to see their

health records. It was feared that geo-political tensions between the two respective countries

would cause a problem toward gaining access to the records. As it turned out, this was not the

case. The head clinician from the Hombro á Hombro clinic accompanied the research team to

the Magdalena clinic interview and worked as a facilitator, making the introductions and, in

general, smoothing the way for a very good, meaningful interview on the health conditions in

Magdalena. Towards the end of the meeting, the survey request was made, and access to the

records was granted.

The combined experience of these three survey experiences led to a better understanding of

the level of cooperation that exists between health facilities in this part of the world. Everyone

knows that conditions are difficult in and around Santa Lucia, and everyone tries to help if

they can with another person’s tasks. This is a very friendly, open, and constructive way to be.

When looking at the big picture, this experience was seen in sharp contrast to the overly

competitive, uncooperative way that many things happen in the US. Furthermore, by having

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samples for all three clinics a more thorough and robust utilization model could be developed

(compared to an analysis of only a single clinic’s utilization), one that considers all of the

primary health care options available to the citizens of the study area.

3.3 Qualitative Sources of Data

3.3.1 Key Informant Data:

A significant amount of qualitative data was compiled for this study. To get these data

involved performing several interviews with key informants in the field. These were

knowledgeable individuals whose expertise on the subject matter made them appropriate for

this research questioning. Key informant data were used primarily in two areas of this

research: Firstly, to better understand many of the socio-cultural and medical aspects of the

study area and the people who live there. And secondly, the key informant interviews helped

with the interpretation of the results. These key informant interviews help to improve the

quality of the study by adding insights from knowledgeable sources in the field, and experts in

the field of international health. Descriptions of the key informants are as follows:

1. Key informant #1 was the previous head clinician at the Hombro á Hombro health

clinic in Santa Lucia, Intibuca. He is a medical doctor with over 13 years experience at

the Hombro á Hombro clinic and his knowledge of the people and the conditions in

the study area were instrumental to this research. Furthermore, his knowledge of the

Honduran health system and sources of the other geographic information made him a

very important resource for this research.

2. Key informant #2 is the current director of the Shoulder to Shoulder, Inc., Hombro á

Hombro’s American sister organization. He is a medical doctor, health policy

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researcher, and faculty of a major university-based international health program. His

regional knowledge of medical needs, health care planning, and policy making was a

valuable resource to this research.

3. Key informant #3 was a project coordinator for PROMESA (a community

development corporation which offers community-based primary health care services

in several parts of the world) with a Masters in Social Work (MSW) and extensive

experience in health-based community assessments in Honduras.

4. Key informant #4 was the project site founder of the Hombro á Hombro health clinic,

with over 13 years experience in the Santa Lucia area. He is a medical doctor and is

the Director of the Division of Family Medicine at a major U.S. educational

institution. His background in both medical issues relating to international health, and

experience in the study area made him a very important contact.

5. Key informant #5 was the current head clinician at the Hombro á Hombro health

clinic. He is a medical doctor with over four years clinical experience in the Santa

Lucia service area. His knowledge of medical issues in the study area and contacts

throughout the community made him a very important resource.

6. Key informant #6 was the nurse practitioner at the Santa Lucia Health Center. She has

28 years experience servicing the medical needs to the people of Santa Lucia, much of

that period working without an M.D. Her knowledge of the people and the medical

situations in the service area was extensive.

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3.3.2 Key Informant Interviews:

Most key informant interviews were handled in an informal manner with questions pertaining

to their expertise and the goals of this research prepared ahead of time. If needed an

interpreter was present to help with translation. For some applicable interviews, summary

assessment surveys were administered at the beginning of the interview so as not to bias the

responses on the surveys. In Appendix D there is a detailed question and answer interview

with the former head doctor at the Hombro á Hombro clinic, who was one of the most

important key informants interviewed during this study. This serves as a good example of the

interview method used and the types of qualitative data acquired during this process.

3.3.3 Informal Interviews:

In addition to the above key informant interviews, many informal interviews were conducted

in the process of collecting data for this research. This included a number of interviews with

school teachers; the administrative head of Hombro á Hombro and director of the school

nutrition program; a key town leader; two board members of the Hombro á Hombro clinic; a

Honduran-American Social Worker; a Peace Corps Volunteer working in the area; and two

nurses and two doctors from the two government health centers. Other interviews were

performed in the Santa Lucia community on an informal basis to help with building the

foundation of this research.

3.4 Challenges in conducting fieldwork in the developing world:

There have been a number of challenges that have confronted this researcher during the

current study. One challenge related to the acquisition of GIS political boundary files that

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could be linked to the Honduran national census. It has taken almost two years and two trips

to Honduras to find and acquire the data for a good GIS analysis of this area. The following is

a description of the steps taken during this search.

At the beginning a thorough search of library, commercial, university and other internet-based

GIS resources (of which there are many) yielded little in the way of usable GIS files for the

study. Only country level shape files were acquired from ESRI, the publisher of ArcView.

This was in sharp contrast to the large amounts of secondary GIS information available for

most projects in a developed world location like the U.S. GIS information is easily accessible,

and its popularity so great, that it seemed surprising that no census based boundary files could

be found for Honduras, particularly considering that a good national census had been

conducted in Honduras in 2001. It was believed that the Honduran Census Institute (INE)

should have good, detailed maps to delineate the different parts of the country for the

purposes of collecting and analyzing data for the census. But where to get these GIS files?

Who to contact?

INE has a website and a good deal of census information available for researchers to

download (INE, 2004), but unfortunately none was linked to the polygon shapefiles needed to

make maps with GIS. Little progress was made contacting this Honduran agency or others

such as the Honduran Geographic Institute (IGN), directly via email, from addresses available

on several websites. Numerous emails were either not returned or were sent back as not

deliverable. It appeared that many of the links discovered during several Google searches

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were broken or not active. This process was repeated many times over the two year period,

and resulted in a good deal of frustration.

A decisive turn in this search occurred through networking, when a key informant was

established from MERTU, the Center for Disease Control’s Central American division

located in Guatemala City. In an email this informant revealed that these GIS files existed in

Tegucigalpa, Honduras’ capital city, at the Instituto Geografico Nacional (IGN - the

Honduran national geographic institute). This instigated further failed attempts to contact

them by email, but at least this researcher was able to conclude that these files existed.

The next break in this search occurred several months later with an email to a key informant

who was a native of Tegucigalpa, and a former head clinician of the Hombro á Hombro clinic.

He suggested that there were three potential sources for these data. In addition to IGN and

INE, he suggested contacting the Secretaria de Estado en los Despachos de Recursos

Naturales y Ambiente (SERNA, 2005) [The Secretary of the Nature & the Environment] as a

potential source for these GIS files.

At this point it was concluded that the best way to locate and procure these data was going to

be during an upcoming field trip to Honduras. A group of medical personnel was going to

Santa Lucia, Honduras during January of 2005, and going through Tegucigalpa. It was

therefore determined that going to Tegucigalpa a few days early would afford the best

opportunity to find and procure these GIS data.

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Finding addresses for these government agencies proved difficult prior to the trip to

Honduras. Upon arrival in Tegucigalpa, using a local phone book and some assistance from a

local phone operator, the addresses of these agencies were established. Also, in a valuable

phone conversation with key informant #1, the suggestion was made that the best place to

look first was SERNA. The following morning, a trip was made to SERNA’s office. This was

a very positive experience which yielded a contact with a GIS Analyst. This Analyst had the

data that was sought, but unfortunately, could not provide it without a letter of approval from

the Secretary’s office. At this point, she kindly discussed the steps needed to procure this

approval. This involved writing a proposal-type letter identifying the nature of the request,

and why this information was needed. It was explained that the request would take several

days to process. Luckily, this was done at the beginning of the trip, and upon return to

Tegucigalpa the data were provided.

Upon return to Tegucigalpa, this long search came to end when this researcher met with the

senior director of the GIS department, and was given a disk with all the data that was

requested. Upon discussion of the CD’s contents and the research plans, the director agreed to

provide additional information which linked the GIS shapefiles to the national census at the

lowest, and most detailed, spatial level. This has subsequently proven to be of great help,

since this research looks at several socio-demographic characteristics in the study area that are

available only from the national census. Furthermore, having this information at a detailed

level added to the analysis of the study area. In addition to providing a clear delineation of the

political units in the service area, this information enabled the accurate evaluation of the

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population distribution throughout the study area. It also enabled the creation of an

employment index that could be studied in this research.

3.5 Chapter Summary

In summary, this chapter has discussed the collection of data for this study. Portions of this

chapter discussed issues relating to developing a good base map; collecting data via a GPS

and developing a GIS with these data; the acquisition of census based shape files; the

collection of qualitative data from key informants; and the challenges of conducting research

in Honduras.

Data collection is a very important part of research, particularly in a developing world

location. In contrast to developed world locations, many secondary sources of geographic and

health information are not available. This often means having to perform substantial in-

country field work. Consequently, much of the data for this study have been collected in

Honduras, and the lessons learned there were instrumental in developing this and future field

work methodologies.

The next chapter, Chapter Four, is an exploratory chapter which looks at the data and tries to

come to some preliminary conclusions based on descriptive analyses which will be used to

justify the methods and models used in this paper.

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CHAPTER FOUR - DESCRIPTIVE ANALYSIS

This chapter is used to explore the data used in this study. This involves using descriptive

techniques to understand the variables studied, and this leads to the justification of the model

chosen for this study. This chapter starts out by giving a description of the spatial scale and a

full explanation of all of the variables used to study utilization. This is followed by an analysis

on distance decay of utilization used to describe the relationship of distance to utilization. The

final section of this chapter focuses on validating the data and focuses on determining the

normalcy of the dependent variable – the utilization index.

4.1 Unit of Analysis

The geographic unit of measure in this study is a synthesized point-based unit that will be

referred to as the “Village”. Attendance, population, utilization and other census data are

associated with the locations of these village points which were acquired with the help of

local guides and GPS receivers during fieldwork in the area. (See Maps 1.3 for the

distribution of the villages within the service area, or Map 3.1 for a more detailed topographic

map featuring these same geographic units). The population data for the village and the area

surrounding the village are estimated using a methodology described later in this chapter.

4.2 Variables Used to Explain Utilization

The next several sections detail the variables used to explain utilization behavior in the study

area. This research examines several key factors that may affect the patterns of demand for a

health facility, namely; a measure of the friction of distance from each village to each health

clinic in the service area (distance, walking time, and road quality); a measure of the

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population size for each village; a measure of the attraction for each health care facility; a

measure of the relative level of education for each village; a measure of the level of relative

wealth for each village; a measure of employment for each village; a measure of service cost

for each health facility; a measure of road quality; and a complex measure of choice.

The selected group of variables chosen is not intended to be all inclusive. As Habib and

Vaughan (1986) point out, a decision to highlight certain variables has to be made in most

studies because utilization is the outcome of many complex interactions with numerous

factors. It is practically impossible in most studies to observe the whole process in detail and

few utilization studies can be sufficiently comprehensive to encompass all possible factors

that can play a part in this process (Bailey and Phillips, 1990). The variables chosen for this

study are described in detail in the following sections.

4.2.1 Utilization Index

The utilization index is the dependent variable in the model and is a calculated ratio which

combines attendance numbers for the three clinic samples, groups them by village, and

normalizes them by population. The result is a utilization index number for each village-to-

clinic interaction.

The numerator in the utilization index is the total number of attendances for each clinic-

village interaction and is the result of a systematic sample of attendances for the three clinics

over a year (see section 3.2). A primary hypothesis in this study is that clinic attendances are

positively related to the size of a village. Larger villages should have higher attendances, and

- 68 -

smaller villages should have lower attendances. The connection between these two variables

is intuitive and leads us to the creation of an index ratio that considers the clinic attendances

relative to the population for a given village.

The denominator of the index is the underlying population for each village. The population

for each village can be seen as a proxy for ‘need’. If we assume that all people have the same

need for health services, then the distribution of the population in the study area should equate

to the distribution of need. The utilization index, then, becomes a relative measure of the

satisfaction of the need for health service.

The use of the population variable is important because when working with an analysis such

as this, we only know the people who come to a given clinic from a given village (via the

samples). What we don’t know is who does not come to the clinic. This is a limiting factor.

One way to get around this short-coming in the analysis is to normalize the attendance data by

the population. The result is a way to compare attendance, while adjusting for the size of the

population for a given village. By normalizing the attendance data by the population we create

an index ratio where higher index numbers relate to a higher proportion of a village that is

using the clinic. Lower index numbers indicate a lower proportion of the village population

using the clinic. One minus the index ratio number, by contrast, indicates the proportion of the

population that does not use the clinic. See section 5.2.1 for a detailed description of the

calculation of the utilization index.

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4.2.2 Distance

The following formula is used to measure two-dimensional distance. This includes only the

horizontal or flat distance between two points. GPS units were used to create a “cookie

crumb” trail network, where point locations were created at frequent intervals (determined by

the GPS every few seconds) along each trail. These GPS files were used to supply the

locational coordinates at a detailed level. X, and Y coordinates (longitude & latitude) are used

to obtain the network distances for routes throughout the study area. A UTM coordinate

system is used, with NAD-27 as the datum.

Each GPS point represents an “x”, and a “y” value. The distance formula is notated below:

( ) ( )2212

2112 yyxxd −+−= (4.1)

where,

x = the longitude coordinate (UTM Easting) of the GPS point

y = the latitude coordinate (UTM Northing) of the GPS point

for each point:

( )111 , yxP = (4.2)

( )222 , yxP = (4.3)

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Table 4.1 Distance Table

GPS Point DistanceP1P2 d 12P3 d 23… …Pn d n-1, n

An Excel spreadsheet was used to calculate the distance of each line segment between each

set of GPS points, and combine all of the line segments into a distance matrix for each town-

to-clinic trail combination.

4.2.3 Walk-Time Estimate

Elevation changes affect the friction of distance particularly for those who walk to the clinic.

In general, it is more difficult to hike up a grade than hike on a flat terrain, and it is logical to

assume that most people will hike slower up a grade. Since this is a very mountainous terrain,

the elevation change aspect of distance is important to include in an estimate of walking time,

so there needs to be a way to adjust travel distances to incorporate elevation changes.

In this study we adjust the distances developed in the prior section by an elevation change

factor (collected from the GPS logs) in order to more accurately reflect actual walking time.

This is accomplished by using the Appalachian Mountain Club's (AMC) (Daniell and Smith.

2003) formula to estimate the hiking time from each village to each clinic. The AMC formula

indicates that it takes 30 minutes to cover one mile on flat terrain, and adds 30 minutes for

every 1,000 feet of elevation gain. Downhill stretches are treated as if they were flat, which is

30 minutes per mile. This formula is generalizable for many places and types of terrain.

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Variations from estimates to actual time may be encountered depending on the individual’s

strength, fitness level and age. Based on preliminary research in the area, it is expected that

this formula should roughly estimate the time of travel by foot for the average person. This

methodology of estimating time of travel has been tested using a GPS to determine actual

time & distances for a few selected trail routes in the study area, and fairly estimates actual

travel time. In addition, it is believed that if inaccuracies occur, then they should occur

throughout the study area, thereby having little to no substantive impact on the study as a

whole.

This method for estimating travel time is operationalized by including a calculation in the

distance matrix spreadsheet that accounts for cumulative positive elevation changes over the

duration of each village-to-clinic trail, and adds the appropriate amount of time to the total

hiking time calculation. This should provide a very accurate identification of the elevation

change factor in the equation, and combined with the distance factor should provide a reliable

estimate of walking time from villages to clinics.

It is assumed that this method of estimating travel time is a more accurate representation of

reality for people who normally travel to the health clinic by foot (which is the majority of the

population in the service area) compared to simple distance measures. See following table for

an example of the estimated travel time calculation.

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Table 4.2: Estimated Walking Time Calculation

Trail Network Distance Matrix for Palacio to Santa Lucia Clinic

UTM Zone

X UTM

Easting

Y UTM

Northing Z

Altitude Distance AltitudeElevation Change

Elevation Adjustment

Travel Time

Cumulative Travel Time

(meters) (meters) (meters) (meters) (feet) (minutes) (feet) (feet) (minutes) (minutes) (minutes)

16 P 347941 1537732 513 168116 P 347939 1537730 514 3.20 11 0.060 1686 5 0.15 0.21 0.21 16 P 347934 1537730 514 5.02 16 0.094 1685 -2 - 0.09 0.30 16 P 347932 1537735 505 9.81 32 0.183 1658 -27 - 0.18 0.48 16 P 347948 1537735 505 16.01 53 0.298 1656 -2 - 0.30 0.78 16 P 347967 1537744 510 21.56 71 0.402 1672 16 0.47 0.87 1.66 16 P 347976 1537749 513 10.70 35 0.199 1681 10 0.29 0.48 2.14 16 P 347981 1537758 514 10.40 34 0.194 1686 5 0.15 0.34 2.48

Distance Time

Time Adjusted Distance

4.2.4 Population Size

Population size of each village and town is an important characteristic of the study area.

These data are acquired from the 2001 Honduran Census and is applied to the denominator of

the dependent variable for the gravity model. This factor helps to determine the spatial pattern

of the population which indirectly has an influence on the location of the three clinics. All the

clinics are located in the two major towns in the service area and their large populations

therefore have a strong effect on the models. A number of problems were incurred in this

study relating to the reconciliation of clinics’ attendance records with the locations used in the

census. The following section highlights this problem and suggests a method for allocating

population numbers to the villages covered in this study.

4.2.4.1 Reconciliation of population data

A big challenge arose when trying to associate the attendance data from the clinic with

population data from the Honduran 2001 Census data. Several Caserios (the smallest

geographical unit of measure of the Honduran national census) are not accounted for with any

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attendance numbers at the clinic. During registration at the clinic, patients are asked to

identify “the place from which they came” (this is the literal translation), and this is where the

problem arises – this is an ambiguous reference to “place”. Patients therefore, may identify

their home location as the town, village or general area that they live in. This makes it

difficult to accurately reconcile the clinic attendance data with the census data because “home

residence” could be interpreted as either a Caserio, Aldea or a Pueblo (town).

It is possible that people living in small rural places list the larger geographic unit (the Aldea),

or possibly the closest Caserio, as their home residence. It is hard to know exactly how this

“home residence” field is interpreted by the patients during admittance to the clinic,

particularly for patients residing in small rural locations. Map 4.1 offers a visual perspective

on this population reconciliation problem, where the blue dots represent places the patients’

identify as their home Villages and the red triangles represent census-based Caserios.

As a way of addressing this problem, the following methodology is used for reconciling the

attendance numbers from each clinic with the 2001 Honduran census, so that the village or

town is preserved as the unit of measure in this study, and the total population of the study

area is accounted for as accurately, and in as much spatial detail, as possible. The

methodology is as follows: A table is constructed for adding the population of each Caserio

(the smallest census unit) to the closest attending village, so that the total population for the

entire Aldea is taken into account. No assignment of Caserio population data is performed for

a Caserio located more than 1 kilometer from the nearest studied village. This helps ensure

census-based population data is representative of the proximal area of the studied village.

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Map 4.1: Village-Caserio Reconciliation

This procedure results in a population assignment for each Caserio according to its nearest

attending Village. Also, no across border (Aldea border) population assignments are made. In

summary, this procedure is a way of allocating the population of the smaller rural

communities to the larger (geographically known) locations, which we are calling Villages for

the purpose of this study.

As an example, the Aldea of Banaderos is used to illustrate this procedure. Banaderos is an

Aldea with seven Caserios, three of which are relatively large in size and which coincide with

the “Villages” identified in samples of clinics attendance records. According to the national

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census this Aldea has a population of 550 persons, with 118 persons not accounted for in the

Caserios which are identified with the Villages in this study (e.g. places with attendance

records from the clinics). These 118 persons are allocated to the three Caserios with

attendance records, via the nearest neighbor scheme described above. As an example, the

Caserio of El Aguila (one of the attending Caserios), which has a census population of 167,

rises by 30 to 197 persons after adding the Caserio of Las Delicias. This should more

accurately account for the small, outlying Caserios, such as Las Delicias, which is close to El

Aguila, but has no clinic attendance records. See following table and accompanying map for

more information.

Table 4.3: Population Aggregation Worksheet

Population Aggregation - Villages Plus Neighboring Caserios

Aldea NameCaserio Name (from Census)

Caserios Populations Village Name

Village Population

Banaderos El Aguila 167 El Aguila Banaderos Las Delicias 30 El Aguila 197Banaderos Banaderos 166 BanaderosBanaderos Cerro Las Tetas 20 BanaderosBanaderos La Florida 16 BanaderosBanaderos El Cohete 52 Banaderos 254Banaderos El Castano 99 El Castano 99

Aldea of Banaderos 550

Note: In the following map the geographic location of the studied villages have been

established through fieldwork in the area with a GPS and are represented by blue dots. The

locations of the census-based Caserios are the red triangles, from which the population

reassignment occurs. Additionally, the Village of El Castano is listed as being located in the

Aldea of Banaderos by the Honduran national census.

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#

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Banaderos

El Aguila

El Castano

Reyolar La Pita

Isletas

Bañaderos

El Zapote El Aguila

El Cohete

El Recod

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s

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Santa RitaCoyotera

a Barranca

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Las Delicias

Los Tablones

El Guanacaste

Cerro Las Tetas

Los Tablon

resa

Portillo de

Aldea of Banaderos

0 0.5 1 1.5 Kilometers

Aldeas01

Roads11 - 22 - 33 - 4

$ Caserios# Population

Map data from the Honduras Secretaria de Estado en los Despachos de RecursosNaturales y Ambiente, and fieldwork conducted in the area.Villages are blue dots, in bold print

N

Map 4.2: Example of Population Reconciliation

4.2.5 Attractiveness of Health Care Facility: The next group of variables studied relate to

the attractiveness of a health facility. This study looks at three separate ways of measuring

attraction, namely: the size of the facility, the number of doctors, and the cost of service. Each

is measured separately, and applied to the regression model as an independent variable, in

order to determine its effect in the final model. The general hypothesis that applies to all three

measures is the more attractive health facilities have higher utilization index rates. It is

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expected that these variables will be collinear; therefore only one will be used in the final

model. Details for each measure are as follows:

4.2.5.1 Number of Doctors: The number of full-time equivalent (FTE) doctors for

each health facility is an attraction factor tested in this study. It is hypothesized that health

facilities with a larger number of doctors would be more attractive than one with a smaller

number of doctors. Supporting information was acquired from key informants.

4.2.5.2 Size of the Health Facility: The size of all three health facilities is an

alternative measure of facility attraction tested for this study. The square footage of floor

space for each facility determines the size of each clinic, and this determines its relative

level of attraction. Jones and Simmons (1990) suggest using this method of measuring the

attraction of a facility such as a store. It is hypothesized that larger facilities have more

attraction than smaller ones. Measurements of clinic sizes were performed during

fieldwork in the area.

4.2.5.3 Operating Hours of Facility: The number of normal operating hours of a

health facility is the third measure of attraction tested in this study. It is hypothesized that

longer hours would be more attractive than shorter ones. Supporting information was

acquired from key informants.

4.2.6 Income

The levels of relative income for each village are another factor studied in this research. In

general, we know that the people of this region are poor by US standards where per capita

Gross Domestic Product (a measure of individual wealth) was $2,400 in 2004 (CIA, 2004).

Key informants estimate average income in the study area at about $1500 for those with

- 78 -

paying jobs. The literature suggests there is a significant positive relationship between income

and utilization, but a detailed examination of income levels by household or small census

units such as a Caserio was not available for this research. Secondary sources such as the

Honduran Census do not provide this information. As surrogate measures for income,

employment index and economic status variables were created to test their ability to explain

utilization behavior.

4.2.7 Employment Index

An employment index is created to examine the effect of employment on the utilization of

health services. An index value is created for each village using a composite of two 2001

Honduran Census data fields. The first field indicates the number of person’s in a Caserio that

“found work last week”, while the second field indicates the number of persons in a Caserio

that “did not find work last week”. This variable is calculated by taking the first field, and

dividing it by the summation of the two fields {a/(a+b)}. The results are an employment index

for each village, which is tested in the regression analysis.

4.2.8 Economic Status

This research uses another surrogate measure to evaluate relative income levels for each of

the villages. This involves developing a summary assessment in order to categorize the

economic status of the different villages. This summary assessment categorizes economic

conditions for each of the studied villages on a high-medium-low basis. In order to convert

the categories into a numeric format the following scheme will be used: high = 3; medium =

2; and low = 1. The five assessments are combined into an average score for each village, and

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the natural log was applied to the result before applying this to the regression analysis. Five of

these “economic status” assessments were completed by key informants in the study area

during December of 2003.

4.2.9 Education

Education is an important variable in determining health facility utilization and is studied in

this research project. The municipality of Santa Lucia has many rural lower level schools, but

only two high-schools. This research uses 2001 Honduras national Census data to determine

percentages of a population that have attained a 6th grade education (the end of primary

education in Honduras). These data are used to determine which villages have had higher

percentages of their pupils graduate from the 6th grade. This is accomplished by taking the

number of 6th grade graduates and dividing it by the population for the village as a whole.

This higher level of educational attainment may, in turn, help explain areas of higher and

lower health facility utilization. It is a hypothesis of this study that there is a positive

relationship between the percentage of primary school graduates and clinic utilization rates.

4.2.10 Costs of Service

The cost of health service may affect utilization patterns. The cost of service variable works in

a similar way to the other previously mentioned attraction variables, only this can be thought

of more as a deterrent. It is hypothesized that higher cost private facilities, such as the

Hombro a Hombro clinic, are less attractive than cheaper public ones. This variable was

treated as a separate independent variable in the model, and the hypothesis was tested in the

regression analysis. Data were collected from key informants in the study area.

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4.2.11 Road Quality

The quality of the road from the village to the health clinic may help determine access via

alternative means of transport. It is hypothesized that better roads lead to more truck traffic,

which leads to greater accessibility via collective pick-up trucks. If it is true that more people

are traveling by truck to the clinic, then this factor may help explain certain areas of increased

clinic utilization.

A good way of measuring the quality of the roads is through the analysis of the GPS logs, and

developing an average speed associated with each route or trail. It is logical to assume that

better roads mean higher speeds, which will be more attractive to collective truck drivers

(because they can more efficiently transport their passengers). For the purposes of this study,

a categorical ranking is created based on average speed. The following scheme is used: A zero

is given to walking trails where it was not possible to drive a truck; a one is given to average

travel velocity of 0-10 KPH; a two is given to average velocities of 11-20 KPH; and a three is

given to 21+ KPH velocities. These data are used in the gravity modeling to determine

whether there is a relationship between utilization and road quality, which has been suggested

in the literature. (See abbreviated example below.)

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Table 4.4 Average Speed Calculation

Road/Trail Network Average Speed for Palacio to Santa Lucia Clinic

UTM Zone

X UTM

Easting

Y UTM

NorthingZ

Altitude Distance

Percentage of Total

Distance

Speed for Route

Segment

Weighted Average Speed

(meters) (meters) (meters) (meters) (kph) (kph)

16 P 347941 1537732 513 0.7000 16 P 347939 1537730 514 3.20 0.0008 1.0000 0.0007716 P 347934 1537730 514 5.02 0.0012 0.1000 0.0001216 P 347932 1537735 505 9.81 0.0023 3.9000 0.0091616 P 347948 1537735 505 16.01 0.0038 4.7000 0.0180216 P 350095 1539199 362 12.37 0.0030 3.9000 0.0115516 P 350097 1539194 362 5.39 0.0013 6.4000 0.0082516 P 350099 1539187 365 8.21 0.0020 3.9000 0.0076716 P 350102 1539184 365 4.24 0.0010 3.0000 0.0030516 P 350104 1539189 366 5.41 0.0013 3.8000 0.00492Total 4,175.82 1.00 8.66

SpeedDistance

4.2.12 Health Choice:

The Choice factor is a two-decision variable created to identify the spatial patterns of health-

seeking behavior made by people in the study area. Health choice decisions are often complex

and may include more than one decision before actual interaction occurs. A composite

variable is created for this study that tries to combine two key health choice decisions. We

will call this variable the Choice factor. This variable attempts to capture the spatial effect of a

combination of two key decisions in the health choice decision – the decision for type of

health service (private versus public health care); and proximity to provider.

The idea for this variable was first conceived after a visual inspection of the two public health

centers’ utilization maps. When looking at the spatial distribution patterns of utilization for

the two government health centers, it is apparent that the Magdalena Health Center services

primarily people living in the eastern part of the greater service area, while the Santa Lucia

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Health Center primarily services people living in the western part. This creates a basic

division in the attendance patterns for public health centers, and may in part be due to the

political/administrative boundaries of the municipalities of Magdalena and Santa Lucia and

the spatial organization of the government run health centers.

The logic behind the decision-making behavior is borrowed from Fotheringham’s (1983)

paper where the author suggests many types of interactions can be considered a result of a

two-stage decision-making process. This research suggests viewing the health-seeking

behavior in the study area as a two-stage decision process involving choices. The first

decision is whether to choose a private or a public health facility, and the second is whether to

choose a health provider that is within your ‘local’ municipality, or not. The first decision

may be based on your ability to afford a private health service, or the perceived need for the

higher quality service that can be associated with a private health care service. And the second

decision assumes that you have not chosen the private health provider, and then relates to

choosing one of the government health facilities. It is believed that much of this second

decision is based on “proximity” of provider relative to the home locations of the patients, and

suggests that people have a strong bias toward patronizing the closer facility which is usually

in their local municipality.

When creating a variable to perform in the system-wide model, the first step looks at all

utilization decisions as a specific type of service choice. A value of one is assigned to all

decisions that utilize the private Hombro á Hombro clinic, and if selected no further decision

needs to be made. If the private health facility is not chosen, then a choice is made to select

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one of the two public health centers, where a value of two is given to any village-clinic

interactions that are outside of the patient’s “home” municipality, or a value of one is given to

interactions within the “home” municipality. This distinction helps specify utilization outside

of one’s home municipality, which is an uncommon occurrence. This second decision we will

call the administrative separation factor.

In the development of this Choice factor this research made two assumptions and the results

indicate these assumptions worked to help explain health-seeking behavior (utilization). The

first of the assumptions is that the administrative separation factor is not an important factor

for the private health clinic. This assumption is investigated by mapping the residuals for the

Hombro á Hombro clinic. A visual inspection of the residuals for the private clinic supports

this assumption that there is no east/west administrative separation in the private clinic’s

utilization.

The second assumption is that this administrative separation factor is important to the public

health centers’ utilization. This is clearly indicated in the visual inspection of the public health

centers’ utilization maps showing an east/west bias in their two service areas. This

observation helps provide validation of the second assumption. More will be discussed on this

variable in the results and conclusions chapters.

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The following figure describes the health choice decision-making process.

Figure 4.1: The Choice Decision Model:

4.3 Analysis of Distance Decay:

This paper now proceeds with an analysis that looks at the distance decay patterns in

utilization across the study area, trying to form a better understanding of the relationship

between distance and utilization. In the following analysis a graph is created to show how the

utilization index rates vary at different distances from a clinic. In this analysis the Hombro á

Hombro clinic was used as a base, and distances from this base were calculated via

ArcView’s Network Analyst functionality. Clinic attendances were then grouped by village

and aggregated at 2-km intervals. The aggregated attendance numbers were then divided by

the population for that distance interval.

The resulting graph shows a strong distance decay pattern for the utilization index. In

addition, these results suggest that the decay pattern is not linear. This is an important finding

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because it indicates that the relationship between these variables would be best approximated

using a non-linear model such as the gravity model. The justification for using the gravity

model in this study is therefore made by looking at the figure 4.2 which describes the actual

distance decay of utilization in the study area.

Access: The Distance Decay of Utilization

0%1%2%3%4%5%6%7%8%9%

10%

1 km 3 km 5 km 7 km 9 km 11 kmDistance

Util

izat

ion

Figure 4.2 – Distance Decay of Utilization

The conclusions from this analysis of distance decay are consistent with Müller et al’s (1998)

study of the effect of distance on health attendances in Papua New Guinea, namely, that

attendance decreases substantially with distance, and the decrease is non-linear.

4.4 Comparative Analysis: Distance versus Walking-Time

The preceding argument focuses on the distance decay of utilization, and justifies the use of a

non-linear gravity model based on this analysis. Other measures of the friction of distance

may be substituted for this distance measure. This study focuses on using an alternate

- 86 -

measure, namely walking time. Given the above justification, it is important to know whether

there are substantial differences in these measures, and whether walking time can be

substituted for distance in the above analysis.

In past studies several authors have chosen to focus on the issue of distance as being the main

factor determining health care facility utilization (Stock, 1983; Joseph & Phillips, 1984;

Müller, et al., 1998; Buor, 2002). Though these measures of distance and their effect on

utilization have been documented in this research, only Buor (2002) has chosen to compare

the relationships between these two related variables.

The following correlation matrix indicates that the Ln of network distance cross-correlates

with the Ln of travel time (estimated walking time) at a .996 level. Furthermore, this

relationship is highly significant at a .000 level. It is clear from these statistics that the

variables are very similar and strongly related.

Table 4.5: Correlations from Distance-Time Comparison

Ln UtilizationLn Network

DistanceLn Travel

TimeLn Utilization Pearson Correlation 1.000 -0.440 -0.433

Sig. (p-value ) - 0.015 0.017Ln Network Distance Pearson Correlation -0.440 1.000 0.996

Sig. (p-value ) 0.015 - 0.000Ln Travel Time (Walking) Pearson Correlation -0.433 0.996 1.000

Sig. (p-value ) 0.017 0.000 -

Given the close relationship that exists between the Ln of Network Distance and the ln of

Travel Time (walking), it is logical to conclude that walking time may be substituted for

- 87 -

distance in the distance decay analysis, with little variance in the results. Furthermore, these

results infer a similar non-linear relationship between utilization and walking time.

4.5 Data Analysis

It is important to analyze the distribution of data for the dependent variable, particularly if you

are going to use it in a regression analysis. A good descriptive analysis can be accomplished

in two simple tests. The first involves creating a scatter plot of the dependent variable

(utilization) to estimated walking time, and second, a histogram of the dependent variable.

The main question we are trying to answer is whether the data are normally distributed. The

results of this analysis can then be used to determine which models are appropriate for this

analysis and which are not.

Looking at Figure 4.3, it is clear that the original data for the dependent variable (utilization

index) is not normally distributed. The lack of a “bell” shape in the histogram and the lack of

any discernable pattern in the scatter plot both confirm the original data for the dependent

variable (utilization index) is not normally distributed.

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Figure 4.3 Histogram of Original Data

Figure 4.4: Scatter Plot of Original Data

It is a known requirement of regression modeling that the dependent variables must be

normally distributed. Therefore, in order to use multiple regression methods to test the final

model, it is important that the dependent variable have a normal distribution. So, the solution

- 89 -

is to perform a logarithmic transformation of the utilization index values in order to normalize

their distribution.

After a natural log transformation of the data, the next step is to rerun both of the previous

analytical graphs (the histogram and the scatter plot) to see if the data are now normal. When

looking at the histogram of the natural log of the utilization index, the distribution now

appears to be close to approximating a normal curve. Also, when looking at a scatter plot of

these transformed variables, a negative, relationship appears to exist between the natural log

of walking time and the natural log of utilization variables. See Figure 4.5 and 4.6 for more

information.

Figure 4.5: Histogram for ln Utilization

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Figure 4.6: Scatter Plot for ln Utilization and ln Walking-Time

As a further test of normalcy, a one-sample Kolmogorov-Smirnov (K-S), Goodness-of-Fit

Test, is performed. The results of this test indicate a significance level of .499, which is

considered normal. {Large significance levels (p-values > .05) indicate that the observed

distribution corresponds to the theoretical [normal] distribution (SPSS, 2000)} Now a

regression analysis can be used with confidence that the distribution of the natural log of the

utilization index variable is normally distributed. See Table 4.1 for more information.

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Table 4.6 Kolmogorov-Smirnov Test

One-Sample Kolmogorov-Smirnov Test

74-3.439461.107229

.096

.051-.096.828.499

NMeanStd. Deviation

Normal Parametersa,b

AbsolutePositiveNegative

Most ExtremeDifferences

Kolmogorov-Smirnov ZAsymp. Sig. (2-tailed)

LN_UTIL

Test distribution is Normal.a.

Calculated from data.b.

4.6 Chapter Summary

The preceding chapter explores the data used in this study, starting with several variables used

to study utilization. In addition to distance and travel (walking) time, several socio-economic-

demographic and service-based variables are considered. The development of a new

compound choice variable is discussed in some detail, and a model for this variable is

presented. This new variable is expected to improve the explanatory power of the model and

represents a substantial contribution to utilization research.

The next section of this chapter performs an analysis of distance decay of utilization and

concludes that the relationship between utilization and distance is non-linear. This observation

justifies the use of the non-linear gravity model used in this study. It is suggested that the

application of the gravity model to this area of research should result in a significant

contribution to utilization research.

- 92 -

A section of this chapter compares walking time estimates with distance as a measure of the

friction of distance. Both measures correlate highly with utilization, and it is concluded that

either measure adequately measures the distance decay effect. Thus, a justification is made

that either may be used in an analysis such as this.

The last section of this chapter looks at the dependant variable and determines through several

statistical procedures that the original data is not normally distributed and therefore

inappropriate for the planned regression analysis. This analysis is compared with a similar

analysis using naturally log transformed variables. The results indicated that the log

transformed variables adequately represent the normal distribution and therefore are

appropriate for the planned regression analysis.

This chapter justifies the use of the gravity model in this present research. The next chapter,

Chapter 5, discusses this model in more detail.

- 93 -

CHAPTER FIVE - THE PREDICTIVE MODEL

This chapter describes the model used in this study – a gravity model. The chapter is

organized as follows: The first section discusses the selection of the gravity model and why it

is used in this study. The second section describes the model and justifies the natural-log data

transformation used on the data.

5.1 Selection of the Gravity Model

When considering the type of model to use, past research has included the use of the linear

regression model (Buor, 2002-4; Shultz, 1975), the negative-exponential model (Stock, 1983),

and non-linear models such as the gravity model (Shannon et al. 1969). Müller, et al. (1998)

concludes that a relationship, such as the distance relative to utilization, is non-linear.

The conclusions drawn from the previous chapter on the analysis of the utilization data

confirm Müller et al’s (1998) conclusion, and suggest that the relationship between distance

and utilization in this study is also non-linear. The previous chapter recommends the use of

the gravity model as a better way to approximate the relationship between the studied

variables and utilization because the gravity model is a non-linear model and can account for

the curvilinear relationships, such as the distance decay of utilization described in the

previous chapter. Though the gravity model has been popular in past research relating to

accessibility to health care, no known study has used the gravity model to study primary

health care utilization. This is a new approach to studying utilization, and based on the

exploratory analyses, has the potential to yield a model that improves upon those used in past

research.

- 94 -

5.2 The Model

This research creates a complex model that tries to explain the health-seeking behavior for the

people who live in the Santa Lucia study area. This model is based on the gravity model.

The gravity model comes from Newton’s law of gravitation, which states that any two objects

in space attract one another according to a force that is proportional to the product of their

masses and inversely proportional to the square of the distance separating them (Stutz and de

Souza, 1998). Newton’s law of gravitation can be expressed as the force of attraction F, which

is equal to Mi (the mass of the first body), times Mj (the mass of the second body), divided by

the distance separating i and j (Stutz and de Souza, 1998). The basic formula of the gravity

model is as follows:

2

*

ij

ji

dMM

F = (5.1)

The gravity model has been applied to flows between two points in several areas in

geographic research, and has become popular in transportation and migration studies. A

modified form of the traditional gravity model used in this study can be expressed as follows:

)*******(17654321 αααααααβ ChoiceRQCostEconEduEmpAT

PopIY =+

=

(5.2)

- 95 -

Where

Yij = The utilization of the Hombro a Hombro clinic from village i;

Iij = Interaction from village i to clinic j. (Measured though sample of

attendances at each clinic);

Tij = Estimated walking time from village i to clinic j (measure of the friction of

distance);

Popi = The population size for each village

A1-3j = Health facility attraction measured in terms of number of doctors, number of

office hours, and size (in square footage) of facilities.

Ii = Income;

Empi = Employment (number employment as a ratio of total workforce);

Edui = Education;

Econi = Economic status (relative measure of wealth for a village);

RQij = Road Quality

Choice = A compound variable use to describe health choice decisions;

71−α = Exponent values that control their respective variables;

i = Village identifier;

j = Clinic identifier.

A symbolic representation of the model used to study the utilization of health services

in Santa Lucia, Honduras is as follows:

- 96 -

Access Factors:Travel Time (Walking)

Road Quality

Provider Characteristics:Size of Clinic

Number of DocsCost of Service

Health Choice-Type of Care:

Private vs Public

Village Characteristics:Economic Status,

Education, EmploymentHealth Status (Need)

Utilization

Health Choice-Proximity of

Health Facility

Figure 5.1 Utilization Model

This research uses a data transformation technique that converts the values for all non-linear

variables into their linear form, and tests the model using multi-variate regression analysis.

The rationale for this decision is based on four considerations. First, a multi-variate non-linear

model (such as the gravity model) should better approximate the patterns of utilization than

the standard linear model. The nature of a gravity model is different than a linear model. It is

able to accurately reflect curvilinear relationships, whereas a linear model cannot. Second, it

is impossible to assess the statistical significance of any gravity model, or variables contained

within one. Third, though a multivariate non-linear model is difficult to accurately calibrate

and interpret, the transformation of the model into its linear form should make the model

- 97 -

more operational and intuitive to interpret. Both the second and third points are necessary to

accurately calibrate the gravity model. And fourth, the preceding chapter concludes that the

dependent variable, utilization, is not normally distributed. A logarithmic transformation is

recommended to “normalize” this variable. The log-linear transformation discussed in this

section will accurately normalize the dependent variable so that it does not violate the

“normalcy” requirement of the regression model. It is believed that this preceding section has

adequately justified the log-linear transformation of all variables used in this current study.

The result is a transformed gravity model into its linear equivalent form suitable for use in a

multiple regression analysis.

With natural logarithmic transformations, the original model can be rewritten as:

ChoiceRQCostEconEduEmpATY lnlnlnlnlnlnlnlnln 7654321 αααααααβ +++++++=

(5.3)

5.2.1 Calculation of the Dependent Variable

The utilization index is the dependent variable in the gravity model and subsequent regression

analysis. The utilization index variable calculation is based on a ratio of aggregated

attendance numbers from village i to clinic j, divided by the population for village i. The

attendance numbers were taken from a systematic sample for each clinic (see the patient

samples section below for more information). The formula can be expressed as follows:

i

ijij P

IY

1+= (5.4)

- 98 -

where, Y is the utilization from village i to clinic j; I is the interaction (attendance number)

from village i to clinic j; and P is the population for village i.

Since we earlier conclude that a natural logarithmic transformation of this variable is needed,

all interaction (attendance) numbers need to be increased by one. This allows for proper

consideration of all 93 cases in the regression analysis, and keeps the regression software

(SPSS) from excluding any cases for villages with zero interactions. This is important because

zero interactions are valid cases and help to explain places with no clinic attendance. These

places need to be considered in the analysis, and not be excluded. Therefore, by adding one to

the attendance number for each case in the study we are able to keep the relative integrity of

the samples, and at the same time allow for a natural logarithmic transformation to be

performed on the utilization index for all 93 cases. The resulting utilization index can be used

to compare relative levels of utilization throughout the study area.

5.3 The Four Models:

The modified gravity model used in this study is presented in the previous section (5.2). We

can call this our system model because it encompasses all the elements needed to examine the

entire primary health care system for the Santa Lucia service area. Looking at it from the

perspective of the people who live in the service area, the system model represents all the

options available for the primary health care service. This includes one private health clinic,

and two public health centers. In this study we choose to examine all four different

configurations of our gravity model – one system model, and three individual clinic sub-

models - because they all offer a different perspective on the phenomena presented in the

- 99 -

study area. This section discusses why we need four models for this analysis, and what their

main differences are.

Why is this needed? Since the study area is composed of three individual clinic service areas,

the characteristics of the system model are derived from the characteristics of the three sub-

models. Therefore, an examination of the three individual clinic models should offer clues

about utilization behavior in the greater system-wide model. It is believed that a comparison

of the significant variables, their Betas, and their coefficients of determination, should provide

these clues.

What are the similarities/differences? The four different models all are derived from the

gravity model; all use the same formulation and the same group of explanatory variables (see

equation 5.2), and all use the same testing methodology. All four models incorporate natural

log transformations to all the variables as described and justified in the prior section (see

section 5.2).

In addition to these similarities, the individual clinic’s service areas also have several

differences. For example, the spatial distribution of the three clinics is not even. Two of the

clinics (one private, one public) are located in the large central town of Santa Lucia, while the

other public clinic is located by itself in another large town. As another example, the

topography of the study area also varies across the study area. This area is mountainous, and

as you go west of the town of Santa Lucia you encounter a large mountain. This type of

accessibility barrier is not present in the eastern portion of the study area. A third distinct

- 100 -

difference is the road quality. It is clear after conducting fieldwork in the area that the road

quality, in general, gets worse as you go south and west. These are just three of the many

differences that exist within the study area. These differences make a clear case for needing to

examine each clinic’s utilization and subsequent service area separately in order to determine

the important factors that effect utilization for each clinic.

In general, we can say that the individual clinic models, compared to the system model,

answer different sets of questions. The individual clinic models focus on the characteristics of

each individual clinic’s utilization and the differences between them, while the system model

focuses on health-seeking behavior for the entire service area and explaining these important

relationships. The first leads to the second, but different questions are asked. The latter, for

example, considers questions such as which type of health facility to attend (public versus

private choice) that aren’t pertinent when looking at an individual clinic’s model.

5.4 Chapter Summary

This chapter has provided a summary of the model used in this research project. Through the

use of the gravity model, log linear transformation, multiple regression analysis, and the

analysis of several key independent variables, a more thorough understanding of utilization

behavior is obtained for the Santa Lucia service area.

The next chapter, Chapter 6, reports on the results obtained from this model.

- 101 -

CHAPTER SIX - RESULTS This chapter summarizes the results of this research, followed by a section analyzing the

variables used to study utilization. This chapter first looks at the correlations of the variables

involved in this study. This is followed by a definition of the stepwise regression procedure

and the results of the system model and the three individual clinic models. An examination of

the performance of each model is made through the comparison of coefficients of

determination (R2), and an examination of the significance, direction, and strength of each of

the independent variables. Standardized coefficients from the regression output are examined

for similarities and differences throughout the various models studied.

6.1 Correlation Analysis

Reviewing the correlations that exist between pairs of variables, one begins to get an

understanding of the relationships that exist between each of the studied variables. In this

analysis the focus is on the ln of utilization, which is the dependent variable in the gravity and

subsequent regression models. Looking at the following summary table, these results indicate

there is a strong negative correlation between walking time and utilization. In each of the

related models this variable is indicated between the .05 and .001 significance levels. The

Choice variable also exhibits a very strong negative correlation to utilization. In every

applicable model the Choice variable is significant at the .001 level. Both of these variables

will be discussed in greater detail later in the regression analysis.

- 102 -

Other significant variables include cost of service, facility attraction measured in square

footage of clinic floor space, road quality, and educational attainment to the 6th grade level.

These variables vary in significance level but most are significant at the .05 level or better.

This analysis reveals that the facility attraction variable (determined by size of health facility),

is significant on a bi-variate basis for the system-wide model, but is later excluded from the

multiple regression models because the value of the variables becomes a constant for each of

the individual clinic models. A similar situation occurs for the cost of service variable. It is

noted that cost of service and facility attraction are cross-correlated at the .99 level. This

suggests that the two variables may be explaining the same thing, therefore only one of these

variables should be used in any final model.

This analysis indicates the economic status variable, a variable significant at the .05 level in

the final system-wide regression model, is not significant in any of the bi-variate correlations.

This research does not offer evidence on this matter, but suggests that this should be

investigated further in future research.

The road quality variable is marginally significant on a bi-variate basis for three out of the

four models, including the System-wide model, the Santa Lucia Health Center model and the

Hombro á Hombro model. The road quality is an important variable that helps to explain the

patterns of utilization for two of the three clinics, but in the context of the multi-variate

models it is not significant.

- 103 -

When visiting Santa Lucia it is apparent that collectivo truck transport is a growing mode of

transport for people of the region. The town of Santa Lucia functions as a service center for

the region with two health clinics, a high school, several markets, and a bank. Santa Lucia has

several good quality roads leading from it to several of the more remote village locations. A

number of villages to the west of Santa Lucia have a barrier to access by foot, namely Monte

Verde. Compared to the flatter eastern portions of the study area, travel by foot in the western

portion of the study area is difficult. These factors may encourage truck transportation in the

area, but primarily to places with better roads. Road quality may therefore be seen as a

surrogate for the amount of relative truck transport to a given village, and improved access

that it offers. This in turn may help to explain the spatial patterns of utilization for the region.

See the following summary table of bi-variate correlations comparing the different models, or

reference Appendix A for a complete listing of the bi-variate correlations for the entire study:

Table 6.1 Correlation to ln Utilization - Summary

VariableSystem-Wide

Model Sig.Magdalena

Health Center Sig.Santa Lucia

Health Center Sig.Hombro a

Hombro Clinic Sig.Ln Travel Time -0.550 **** -0.682 **** -0.444 ** -0.465 ***Ln Choice -0.712 **** -0.790 **** -0.700 **** -Ln Cost of Service 0.269 ***Ln Road Quality 0.181 * 0.324 * 0.393 **Ln Economic StatusLn Education 6th Gr. -0.301 *Ln Attraction (Facility Size) 0.284 **** significant at the .10 level or better; ** significant at the .05 level or better;*** significant at the .01 level or better; **** significant at the .001 level or better.

- 104 -

6.2 Results

6.2.1 Stepwise Regression Methodology

This study uses a stepwise multi-variate regression methodology available from the statistical

software package SPSS to determine the optimal mix of variables for the final regression

equation. Variables are included and excluded in the final equation according to the following

methodology: According to the SPSS documentation of the technique, at each step in the

computation, the independent variable not in the equation which has the smallest probability

of F is entered [most significant variable], if that probability is sufficiently small. Variables

already in the regression equation are removed if their probability of F becomes sufficiently

large. [The parameters for entry were set at .05, and removal at .10.] The method terminates

when no more variables are eligible for inclusion or removal (SPSS, 2000).

The stepwise regression methodology serves a second purpose. The output from the stepwise

regression gives us a ranked list of the significant variables tested in this study. This enables

us to determine the relative importance of each of the factors that influence utilization

behavior, which is one of the specific aims of this research project. This method of ranking

the predictors relative to importance was used in Buor’s (2002) study of utilization in Ghana.

The ranking of the results should help health planners determine the most important factors to

focus on when trying to improve health care provision in the region, because these are the

most important factors that effect utilization.

- 105 -

6.2.2 System Model Results

With the results of the regression analysis, we are able to summarize this relationship with the

following regression equation:

ln Y = .527 - 2.055 ln Choice -.853 ln T -.385 ln Econ

R2 = .644

Where Y equals the natural log of the utilization index, and ln Choice is the natural log of the

Choice variable, ln T equals the natural log of the estimated walking time variable, and ln

Econ equals the natural log of the economic status variable. The coefficients in the above

equation are unstandardized and the variables are ordered according to importance in the

model.

The coefficient of determination (R2) for this model is .644, and can be interpreted as meaning

that 64.4% of the variance in the ln utilization index variable is explained by the combined

variance in the ln choice, ln walking time, and ln economic status variables. This relationship

can be summarized by stating there is a statistically significant relationship between the

independent and dependent variables, and one that is moderately strong. In particular, the

variables for Choice and walking time are extremely significant. A summary of the stepwise

model development with standardized (Beta) coefficients, p-values (a measure of statistical

significance), and coefficients of determination (R2) follows:

- 106 -

Table 6.2: Stepwise Regression Coefficients - System Model

Step Variable Beta

Coefficient Sig (p-value) R2 1 Ln Choice -0.712 0.000 0.507 2 Ln Choice -0.593 0.000 0.612 Ln Time -0.346 0.000 3 Ln Choice -0.544 0.000 0.644 Ln Time -0.489 0.000 Ln Econ Status -0.222 0.006

6.2.2.1 Visual Inspection for the System Model:

This is the general system-wide model (for all three clinics) used to explain the overall health-

seeking behavior of the people in the service area. As you can see from the following map,

there are higher levels of utilization near central large towns & main roads. This is not

surprising considering access should be at its highest levels in these areas.

In contrast, points in extreme periphery exhibit low levels of utilization. Many of these

villages are south and west of Santa Lucia, over Monte Verde and along the Torola River,

which is the border with El Salvador. There are some low utilization areas in the extreme

eastern portion of the service area. These places deserve further investigation, since they may

be accessing an alternate health clinic further to the east, which is closer to them, but out of

this study area.

- 107 -

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Los Horcones

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San Francisco

Las Marias Mag

Utilization - Combined for All Clinics N

0 2000 4000 Meters

Aldeas012

Municipios

Roads11 - 22 - 33 - 4

Utilization# 0 - 0.047# 0.047 - 0.091

# 0.091 - 0.131

# 0.131 - 0.188

# 0.188 - 0.325

ÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region.

Map 6.1: Utilization - System Model

6.2.3 The Magdalena Health Center Model:

This is the model used to explain the overall behavior of the people attending the government

sponsored (public) Magdalena Health Center. We are able to summarize this model with the

following regression equation:

ln Y = .490 – 2.633 ln Choice -.711 ln T

R2 = .719

- 108 -

Where Y equals the natural log of the utilization index, and ln Choice is the natural log of the

Choice variable, and ln T equals the natural log of the walking time variable. The coefficients

in the equation are unstandardized.

This model has an R2 of .719, which is considered strong in explaining utilization behavior,

and is the strongest of the utilization models presented, including the system model. For this

model variables for choice, and travel time are very significant. The economic status variable,

significant in the System Model, and the Santa Lucia Health Center model, is not significant.

A summary of the stepwise model development with standardized (Beta) coefficients, p-

values (a measure of statistical significance), coefficients of determination (R2), and a map of

utilization for the Magdalena Health Center follows:

Table 6.3: Stepwise Regression Coefficients - Magdalena Health Center Model.

Step Variable Beta

Coefficient Sig (p-value) R2 1 Ln Choice -0.790 0.000 0.625 2 Ln Choice -0.596 0.000 0.719 Ln Time -0.364 0.005

- 109 -

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El Aguila

Junquillo

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Los Pozos

Magdalena

San Pablo

TalquezalEl Castano

Jicaral

La Montana

Las AradasLas Marias

San Marcos

San Rafael

Santa Rita

Cordoncillo

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La Ceibilla

Santa Lucia

Los Horcones

Los Tablones

San Francisco

Las Marias Mag

Utilization - Magdalena Health Center N

0 2000 4000 Meters

Aldeas012

Municipios

Roads11 - 22 - 33 - 4

Magdalena HC# 0 - 0.002# 0.002 - 0.016

# 0.016 - 0.04

# 0.04 - 0.168

# 0.168 - 0.296

ÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region.

Map 6.2: Utilization Magdalena Health Center Model

Notice the concentration of high utilization villages in the eastern portion of the study area.

6.2.4 Santa Lucia Health Center Model:

This is the model used to explain the overall behavior of the people attending the government

sponsored Santa Lucia Health Center. We are able to summarize this model with the

following regression equation:

Y = .856 - 1.961 ln Choice - .966 ln T - .584 ln Econ

R2 = .631

- 110 -

Where Y equals the natural log of the Utilization Index, ln Choice is the natural log of the

Choice variable, ln T equals the natural log of the Walking Time variable, and ln Econ equals

the natural log of the Economic Status variable. The coefficients in the equation are

unstandardized, and the variables are ranked according to importance in the model.

This model has an R2 of .631, which is considered moderately strong in explaining utilization

behavior. The significant explanatory variables are – ln Choice, ln Walking Time, and ln

Economic Status. A summary of the stepwise model development with standardized (Beta)

coefficients, p-values (a measure of statistical significance), coefficients of determination

(R2), and a map of utilization for the Santa Lucia Health Center follows:

Table 6.4: Stepwise Regression Coefficients – Santa Lucia Health Center Model.

Step Variable Beta Coefficient Sig (p-value) R2 1 Ln Choice -0.700 0.000 0.490 2 Ln Choice -0.625 0.000 0.558 Ln Time -0.271 0.047 3 Ln Choice -0.554 0.000 0.631 Ln Time -0.512 0.004 Ln Econ Status -0.355 0.029

- 111 -

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Banaderos

El Aguila

Junquillo

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Los Pozos

Magdalena

San Pablo

TalquezalEl Castano

Jicaral

La Montana

Las AradasLas Marias

San Marcos

San Rafael

Santa Rita

Cordoncillo

El Leoncito

La Ceibilla

Santa Lucia

Los Horcones

Los Tablones

San Francisco

Las Marias Mag

Utilization - Santa Lucia Health Center N

0 2000 4000 Meters

Aldeas012

Municipios

Roads11 - 22 - 33 - 4

Santa Lucia HC# 0 - 0.004# 0.004 - 0.016

# 0.016 - 0.036

# 0.036 - 0.072

# 0.072 - 0.11

ÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region.

Map 6.3: Utilization - Santa Lucia Health Center Model

Notice the concentration of high utilization places in the western portion of the study area,

and low utilization places in the east.

6.2.5 Hombro á Hombro Clinic Model:

This is the model used to explain the behavior of the people attending the Hombro á Hombro

clinic. We are able to summarize this model with the following regression equation:

Y = -.883 - .509 ln T

R2 = .216

- 112 -

Where Y equals the natural log of the utilization index, and ln T equals the natural log of the

walking time variable. The coefficients in the equation are unstandardized.

This model has a modest R2 of .216, which is considered weak in explaining utilization

behavior, and in contrast to relatively strong results of .719 and .631 for the public health

center models. This indicates that less of the variation in utilization can be explained by

variation in the independent variables. Part of this sub-performance can be explained by lack

of the Choice variable that was significant in the other models. This variable was excluded by

the stepwise refinement procedure because there was no variation in the value of the variable

(a constant) for all the village-to-clinic interactions. In this model the only significant

explanatory variable was ln walking time. A summary of the stepwise model development

with standardized (Beta) coefficients, p-values (a measure of statistical significance), and

coefficients of determination (R2) follows:

Table 6.5: Stepwise Regression Coefficients of Independent Variables - Hombro a Hombro

Clinic.

Step Variable Beta

Coefficient Sig (p-value) R2 1 Ln Time -0.465 0.008 0.216

- 113 -

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San Pablo

TalquezalEl Castano

Jicaral

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Las AradasLas Marias

San Marcos

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Cordoncillo

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La Ceibilla

Santa Lucia

Los Horcones

Los Tablones

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Utilization - Hombro a Hombro Clinic N

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Aldeas012

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Roads11 - 22 - 33 - 4

Hombro# 0 - 0.01# 0.01 - 0.031

# 0.031 - 0.046

# 0.046 - 0.074

# 0.074 - 0.107

ÊÚ Clinics

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region.

Map 6.4: Utilization - Hombro á Hombro Clinic.

6.3 Comparison of the Four Models

This next section compares the four models used in this study. These models all use the

gravity model as their foundation, so therefore should be similar in their mathematical

structure. The methods that were used to develop them are the same. There are some

differences too. These differences include; different spatial extents, different mixes of

significant variables, different proximal location factors, different attendance samples or

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groups of samples to test the overall model. These all lead to different results as the preceding

sections have shown.

As an indication of the overall “fit” of a model to the actual utilization data, the coefficient of

determination, or R2, is a good statistic to use to compare different models. The R2 for the

overall system model is .644. This is similar to the R2 of .719 and .631 of the two Health

Center models. The Hombro á Hombro clinic model is somewhat lower than the others with

an R2 of .216. See table 6.5 below.

Table 6.6: Comparison of Models

Model Model Name

Number of Sig.

Factors R2

1 System Model 3 0.644

2 Magdalena Health Center 2 0.719

3 Santa Lucia Health Center 3 0.631

4 Hombro a Hombro Clinic 1 0.216

The reasons for the differences in the models’ performance, other than what has already been

specified, are a matter of speculation. The system model with its R2 of .644 could be

considered a benchmark since it represents all three samples and has three significant

variables (two of which are very significant). The wildcard factor appears to be the inclusion

(or exclusion) of the Choice variable as a determinant of level of the R2. The System Model,

the Magdalena Health Center Model and the Santa Lucia Health Center Model all indicate the

Choice factor is highly significant, and all have strong R2s. The Hombro á Hombro Clinic

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Model does not include the Choice factor (it was excluded during the stepwise refinement

process), and subsequently its R2 is relatively weak. These results therefore lead to the

conclusion that the model estimates real world utilization better for a combined service area,

or for public health centers such as the Magdalena and Santa Lucia Health Centers. The

Hombro á Hombro Clinic’s utilization patterns appear to be more complex, and its model

needs further improvement in order to compare with the predictive capacity of the other

models.

6.4 Analysis and Discussion of Explanatory Variables:

6.4.1 Walking-Time

All models tested in this study indicate the Walking Time variable is very significant at the

.01 significance level or better, with a negative or inverse relationship to utilization. This

suggests a strong “distance-decay” effect relating to walking time. The standardized

coefficient (Beta) for walking time is -.489 for the system model, -.465 for the Hombro a

Hombro clinic model, -.512 for the Santa Lucia Health Center model, and -.364 for the

Magdalena Health Center model. These results indicate that walking-time occupies a

significant position in the utilization model.

All of the models exhibit a similar magnitude and direction in their coefficients within the

-.364 to -.512 range, with the system model being near the middle of the range at -.489. This

indicates a generally strong distance decay of utilization pattern based on walking time to the

clinics, and is consistent with other rural developing world health service research. Buor

(2004) for example found that the distance/utilization relationship in a rural part of Ghana had

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a beta coefficient of -.504. Habib and Vaughan (1986) found that the distance/utilization

relationship in Iraq had a beta coefficient of -.300. The results of these studies may be

considered similar to the walking time decay patterns from this study since the variables for

walking time and distance are highly correlated (see Chapter 4, section 4.4, for more detail).

The Santa Lucia Health Center’s coefficient for walking time is -.512, exhibiting the sharpest

distance (time) decay pattern. This finding suggests the smallest effective service area in the

study. This is possibly due to the competition from the Hombro á Hombro clinic, located in

the same town, causing a division in the clientele base along socio-economic lines, where the

poorer members of the community attend this government health center. This conclusion is

consistent with data collected from key informants and may suggest that people who patronize

this clinic are more spatially bound, with fewer options like transportation that would allow

them to overcome the friction of distance. It seems likely that these issues stem from low

income and poverty which is endemic in the service area.

6.4.2 Economic Status:

The Economic Status variable is significant in the System-wide model and the Santa Lucia

Health Center model. Studies such as Buor (2003) have suggested a significant relationship

between income and utilization. In most prior research this relationship has been positive,

where higher incomes enable greater utilization (closes the access barrier through better

transportation, affordability of service, etc.), and lower incomes generally make access to care

difficult, if not impossible. This relates to Andersen's (1968) enabling factors, where the cost

of care, cost of transport, and other affordability issues enable or inhibit use of health services.

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In contrast, the results of this research indicates that in the context of the model the Economic

Status variable exhibits an inverse relationship to Utilization in the study area as a whole (the

system-wide model), where it was significant at the .006 level; and at the .029 level for the

Santa Lucia Health Center, in particular. This variable was found not to be significant in the

other two models. The general conclusion drawn from the two significant tests suggests that

as economic levels decline, utilization increases...but why?

When trying to explain this relationship, this research suggests that need plays an important

role in determining utilization. There is an obvious connection between economic status and

income levels, and low income levels with poverty. It has been shown that poverty can be

associated with decreased health status. Wolfe’s (1999) economic research suggests there is a

strong [positive] correlation between poverty and poor health. Joseph and Phillips (1984)

suggest this may be related to need, where lower income residents have a greater need for

health care services. This research suggests that Joseph and Phillips' connection is valid and

that higher 'need' may be driving higher utilization in poorer parts of the study area after

considering the effect of walking time and the Choice factor.

This research suggests that the small fee associated with the two government Health Centers

service is not inhibiting utilization, otherwise the poorer members of the community would

not be utilizing the service (and there would not be this significant inverse relationship). This

evidence suggests that the service is affordable, even to the poorer members (though not

necessarily the poorest) of the service area. Furthermore, this evidence suggests that many

patients from lower economic statuses may indeed be accessing the Santa Lucia Health

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Center’s service, in contrast to possibly a broader distribution of economic statuses for the

Hombro á Hombro clinic. Several key informants have suggested this dynamic – that the

poorer members of the community go to the government Health Centers, while the wealthier

members go to the Hombro á Hombro clinic (which is considerably more expensive).

6.4.3 Choice:

The results of this research clearly indicate that there is a very significant relationship

between the Choice factor and health facility utilization, and that health-seeking behavior is

better approximated using a two-tier decision-making approach. The implication is that the

Choice factor is the only other variable studied that compares with walking time in explaining

such a high proportion of utilization. Thus, the Choice factor, like the walking time variable,

occupies a significant position in the overall utilization model.

These results point to a strong spatial patterning in the study area associated with the two

government health centers, which appear to suggest a tendency for people who attend the

government health centers do so in the municipality in which they reside. This is not always

the case, and some people prefer to attend the health center that is the closest to, but not in,

their municipality. But this does help confirm one conclusion drawn from the economic status

section that suggests that the poor are more spatially bound due to lack of transportation

options and therefore attend the closest government health center, usually in their

municipality.

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The results of this research do not lead to a clear conclusion as to why this variable is

significant. There are a number of theories that may suggest why health choice decisions are

linked to the home administrative unit (municipality). One theory might suggest that people

come to the main town in their municipality for various economic reasons in addition to

servicing their health care needs. This could be considered the “activity space” (Jones and

Simmons, 1990) for these individuals. Since the government health center is also located in

this large central town, then it is within their normal weekly travels or “activity space”.

Young people also could follow a similar “activity space” pattern explaining their health

choice decisions, but not necessarily for the same reasons. Many young people come to the

central towns of Santa Lucia and Magdalena for high-school. These are the only two high-

schools in the area, and travel for these individuals to these central towns becomes a common

almost daily occurrence. Once again, since the health center is located in this central town,

then it is within their “activity space”, and patronage of the local health center should be

considered the easiest place to get primary health care service.

Another related way to explain this relationship would be to suggest that the choice variable

helps explain the patterns of health-seeking behavior relating to proximity of clinic and home

residence. This could relate to Fotheringham’s (1983) “competing destinations” concept

where people choose to attend the clinic that is closest to their home residence when given the

choice of more than one service provider. This theory suggests there is a higher utility

associated with utilization of a service that is closer to the patient, than utilization of a service

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that is further away. For the most part, with a few exceptions, the closest government health

center would be located in the central town for each municipality.

The Hombro á Hombro clinic’s patient utilization patterns do not exhibit the same

municipality-based pattern of utilization. People who attend this clinic come from all parts of

the service area regardless of the municipality from which they reside. As has been mentioned

previously, this clinic’s clientele is more socio-economically diverse and patients come from a

broad array of locations. Patients who attend this clinic (which is considerably more

expensive than the government Health Centers) may choose this provider because of the

higher quality of service associated with this private clinic. As a visual analysis of the

Hombro á Hombro clinic’s utilization shows there is no east/west spatial patterning in their

utilization. {See Hombro á Hombro clinic utilization map, Map 6.4}. This is confirmed by a

map of the residuals from the regression analysis of the Hombro á Hombro’s utilization model

that furthers this observation. See map 6.5 for more information.

6.4.3.1 Model Improvement: Does the addition of the Choice variable really improve the

model’s performance? When considering this question, a comparison of the residuals from the

model (both before and after the addition of the Choice variable) seems to be in order. An

average of the absolute value of the residuals from the regression analysis is compared for

both iterations of the model. The results indicate that the average deviation in residuals for the

model without the Choice factor is .73, compared to .57 if we include the Choice factor. This

amounts to a 21% reduction in deviation from the estimated regression line, and is a clear

indication of the model’s improvement.

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This research has tested other measures including a modified Huff model, and simple

proximity measures to nearest health facility. Neither of these measures has been able to

capture the level of significance, and improvement in the explanatory power of the model

(indicated by the R2) that this Choice variable has. This is partly due to the compound nature

of the Choice variable. It considers a two-tier decision-making process, which more

accurately reflects the complex nature of the health-seeking decision-making process in the

context of this rural, Central American region.

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Los Horcones

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Hombro a Hombro Clinic Residuals N

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Roads11 - 22 - 33 - 4

ÊÚ Clinics

Hombro a Hombro Residuals

% -2.8 - -1.4

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# 0 - 1

# 1 - 2

Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region by Jonathan Baker, Chris Carr, and Andrew Bazemore.

Map 6.5: Residuals – Hombro á Hombro Clinic

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In summary, the Choice variable helps determine health choice decisions within the study

area. This variable considers both type of service, and proximity to provider as important

variables in the decision-making process. This composite variable is constrained so that the

decision reflects a two-tier decision-making process. This variable describes the first tier of

the health choice decision as being a-spatial (private versus public primary care service),

which is based on quality of service and other needs based considerations. After the first tier

of the decision has been made, then the decision turns to a spatial-type decision, based loosely

on proximity, through the consideration of municipality of residence.

6.5 Residuals Analysis:

The following section looks more closely at the maps of the residuals generated for this study.

Residuals are the difference between an observed value and the value predicted by the model

(SPSS, 2000). An analysis of the residuals from the estimated equation provides an

opportunity to compare the individual cases that lie the furthest from the estimated

relationship. The residual is the vertical distance an instance of the sample is away from the

calculated regression line. If the model is a close fit to the observed data then the residuals

will be small, but if it is not, then the residuals will be large. The coefficient of determination

(R2) is a measure of “goodness of fit”, and can help support an examination of the residuals

from the regression analysis.

In this research, residuals are mapped, and used to establish how well the model explains the

overall distribution of the data. In the case of the Choice variable, residuals mapping helps to

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confirm an even distribution of residuals exists for the Hombro á Hombro clinic throughout

the study area. This is important in establishing the validity of the first assumption used in the

Choice variable, namely that there is no structural bias in the model with regard to the private,

Hombro á Hombro clinic utilization.

6.5.1 Residuals from Hombro á Hombro Model

Looking at Hombro á Hombro’s residuals map (Map 6.5) may help to identify things

occurring in the study area that are not explained well in the model. The Hombro á Hombro

utilization model has an R2 of .22 which indicates that only 22% of the deviation in utilization

is explained by the model. At the same time, this also implies that 78% of the deviation is not

accounted for in the model. Therefore, in an effort to try to explain the two greatest residuals

(largest deviations) may give some direction on how to improve the model for future research.

The two villages with the most extreme residuals (and they are quite far from the regression

line compared to the others) are Bañaderos and San Marcos, Colomoncagua. This research

suggests this error can be explained.

Bañaderos, in the extreme western periphery has a very strong positive residual, indicating a

high level of utilization relative to the model’s predicted value. This may be explained by a

new roadway servicing this once remote and isolated area. It is believed there has been a

certain amount of pent-up demand for health service in this area causing a higher than normal

level of utilization at the Hombro á Hombro clinic. After the addition of a roadway, citizens

are now able to rent a truck to transport them to the Hombro á Hombro clinic. This was

confirmed by a key informant (Suarez, 2005) at the clinic as a common way for people from

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Bañaderos to travel the 12 plus kilometers to the Hombro á Hombro clinic. It is expected that

this utilization index rate will move towards a more normalized rate sometime in the future.

San Marcos, Colomoncagua, was a different story. This village is in the extreme eastern

periphery of the study area and has a strong negative residual, indicating a very low utilization

relative to the model’s predicted value. This research suggests that this is due to a new health

facility now operating to the east of San Marcos. This health facility is another university

sponsored health clinic (similar to Hombro á Hombro), and is closer to the people who live in

San Marcos, Colomoncagua, than any of the study area clinics. Since our study area was

originally defined in December 2003 based on attendance at the Hombro á Hombro clinic, it

is understandable that things may have changed, and people who once came from San Marcos

to attend the Hombro á Hombro clinic, no longer do so.

6.5.2 Residuals for Overall Study Area

The following section looks at a map of the residuals from the regression modeling for the

overall study area. See maps 6.6 for a visual representation of the geographical arrangement

of the regression residuals, where negative residuals [the red squares] represent areas of over-

estimation by the model, and positive residuals [the blue circles] represent areas of under-

estimation by the model. This map clearly suggests that there is an east/west bias in the

residuals for the overall service area (even after the addition of the Choice variable), that is

not accounted for by the model. One possible explanation for this is that the value of the

choice variable is assigned arbitrarily. In improving the model, future research may wish to

focus on assigning weights to the values given to the Choice variable. This, in turn, may more

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adequately describe the east/west spatial patterns that exist in the study area, and improve the

predictive capacity of the model.

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Overall Service Area Residuals N

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Total Residuals

% -2.6 - -1.25

% -1.25 - 0

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Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region by Jonathan Baker, Chris Carr, and Andrew Bazemore.

Map 6.6: Residuals – Overall Service Area

Other than the east/west patterning previously mentioned, conclusions from the residual

mapping of the overall service area are difficult to make because there is no other spatial

patterning apparent in the study area. Therefore, this map does not suggest additional

variables to study. This leaves us with a simple conclusion; the additional error in the model

could be derived from other non-spatial sources, or is random.

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6.5.3 Residuals for Magdalena Health Center Model

The following map represents the residuals for the Magdalena Health Center. San Marcos,

Colomoncagua stands out with exceptionally low utilization relative to the predicted value

from the regression equation. Following a similar logic from the Hombro á Hombro model,

the people from San Marcos may be accessing a new clinic further to the east and outside of

this service area, thus causing their low utilization. La Ceibilla, in the southern portion of

Municipality of Magdalena, also stands out with low utilization. This may simply be

explained by relatively easy access to the town of Santa Lucia and the two clinics there. This

village has relatively high utilization for both the Hombro á Hombro clinic and the Santa

Lucia Health Center, but low utilization for the Magdalena Health Center. This would be

considered one of the exceptions, where people in this village prefer to travel to the closer

clinic, though outside of their municipality, to received primary health care.

Looking at another village with a strong negative residual, San Jose, in the western portion of

the study area, exhibits a very low utilization level relative to the Magdalena Health Center

model. When considering possible explanations, it is important to understand that San Jose is

located over seven kilometers and 2.5 hours estimated walking time from Santa Lucia, and

further from Magdalena. This friction of distance factor alone may explain this low

utilization, but it may also be more complicated than this.

When considering other factors, it is necessary to understand San Jose in the context of the

study area. Firstly, San Jose is in a different Municipio, namely San Antonio. Secondly, the

village of San Jose has its own private health clinic, but according to key informants (Coello,

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2003) it has no resources (staff or supplies) and is normally closed. Thirdly, the town of San

Antonio has its own government health center (5.25 kilometers to the west of San Jose), so it

is likely that some of the people in San Jose are going [west] to the town of San Antonio for

their health service needs. With three alternative options for primary health care it is therefore

less likely that people will travel to Magdalena for health service.

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Magdalena Residuals

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Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region by Jonathan Baker, Chris Carr, and Andrew Bazemore.

Map 6.7: Residuals – Magdalena Health Center

In contrast, Los Pozos has a utilization index level that is higher than the model predicts. This

may be explained by access from the best roadway in the area leading almost directly to the

Magdalena Health Center. It is expected that collective truck transport is both efficient and

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cheap for people residing in this village. This village may be used as a good example of how

things might be if the transportation network is improved in the service area. See Map 6.7 of

the Magdalena Health Center residuals for more information.

6.5.4 Residuals from Santa Lucia Health Center Model

The Santa Lucia Health Center residuals map displays a pattern that can be explained with a

focus on access. The high residuals villages of Las Aradas, La Ceibilla, and San Juan, all are

relatively close to Santa Lucia and offer no major physical barrier (like a mountain) in their

pathway. Access to the Santa Lucia Health Center is relatively good for all three villages,

even if traveling by foot. The very negative residual indicated for San Francisco, on the other

hand, may be explained by the existence of two other clinics closer to this village and the

better access they provide. See Map 6.8.

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Santa Lucia HC Residuals

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Map data from the Honduras Secretaria de Estado en los Despachos de Recursos Naturales y Ambiente, and fieldwork conducted in the region by Jonathan Baker, Chris Carr, and Andrew Bazemore.

Map 6.8: Residuals – Santa Lucia Health Center

6.6 Summary of Results

From an examination of the utilization and residuals maps, we are able to conclude the

following:

• High utilization areas may be the result of good roads resulting in cheap efficient

collective truck transportation. This reduces the barrier of distance. Such may be the

case for Magdalena and Los Pozos in the system model.

• High utilization areas may be a result of new improvements in access, where before

there were none. This may be the case with the village of Bañaderos.

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• Low utilization areas may be the result of competition from other clinics outside the

Santa Lucia service area. This may be the case with San Marcos, Colomoncagua, or

San Jose.

• There is an east/west spatial pattern in the service areas for the two public Health

Centers, one servicing the eastern municipality of Magdalena, and one servicing the

western municipalities of Santa Lucia and San Antonio.

• There is no (east/west) spatial patterning in the Hombro á Hombro clinic’s service

area.

Based on the regression analysis of the presented gravity model, this research has come to the

following general conclusions:

• There is a strong relationship between primary health care utilization and walking

time, economic status and the Choice factor. The R2 of the system-wide model is .64,

which indicates that 64% of the variance in utilization index can be explained by the

combined movement of these variables. This model may be improved with further

empirical study.

• Distance and estimated walking time are both highly related. Both variables indicate a

negative or inverse relationship with utilization, a relationship that is expected. This

inverse relationship to utilization represents a travel-time decay pattern that is

consistent with distance decay patterns from other similar developing world research

(see Buor, 2002, for comparison).

• The Choice factor is highly significant and provides an instrumental improvement in

the system-wide model’s performance. This research shows that by creating a variable

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that combines health service choice (private versus public service) and the

municipality of residence (proximity to care) a better estimate of health-seeking

behavior results. The value of combining simple variables into a complex variable

such as this indicates the complexity of human decisions, and shows potential

direction for future research.

• The principles of the model developed in this study can be generalizable throughout

the developing world, but the parameters need to be recalibrated to reflect the unique

aspects of the new study area.

• Utilization analyses such as those performed in this study can be completed in a

relatively short period of time, given expertise in geographic field methods (including

GPS & GIS) and availability of in-country resources.

• This study yields a large amount of useful information for health service planners

working in the region.

• This model provides for a unique way of analyzing utilization in the developing world

which may be applied to other similar locations.

6.7 Chapter Summary

This chapter shows that utilization patterns can be explained to a large extent by factors

relating to walking time, economic status, and the combined affect of health service type and

proximity to care (the Choice factor). This chapter illustrates that it is difficult to have one

model that works equally well for different types of clinics. Public clinics appear to have

different factors effecting utilization patterns, compared to a private clinic.

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In this study a modified gravity model is constructed to help explain utilization behavior in

the study area, and is tested using log transformed variables and multiple regression analysis.

Utilization patterns for the combined service area are analyzed, as are the utilization patterns

for the three individual clinics. The explanatory variables for walking time and choice were

found to be very significant in explaining utilization behavior.

The following chapter summarizes this research project and expands on the conclusions

identified in this chapter.

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CHAPTER SEVEN: CONCLUSIONS AND RECOMMENDATIONS

This research project studies health service provision by focusing on health facility utilization

in a poor region of southern Honduras. More specifically, this study uses a combination of

spatial-interaction modeling and multiple regression techniques to develop and test a model

used to predict the spatial patterns of utilization. This study examines actual utilization

patterns from three health clinics, relates them to several socio-economic and geographic

characteristics existing in the study area, and forms conclusions about health-seeking behavior

based on those relationships.

The results of this research are consistent with other research done in the field of health

facility utilization. Utilization behavior clearly exhibits an inverse relationship to travel time.

The variable for health choice, a variable not previously studied, was found to be very

significant. Furthermore, variables for education and economic status are also found to be

significant factors explaining utilization. Since prior research provides inconsistent results

with regard to education and economic status, this research helps confirm their relationship to

utilization in a rural developing world location.

The Choice factor is a new variable not found in previous research. This is a compound

variable that considers health choices as a two-part decision process, including the selection

of service type (private versus public) and the selection of a health facility associated with

one’s home municipality. Given this variable's highly significant results, this study suggests

that health-seeking behavior is complex and is best represented by a compound variable of

this kind. This is a significant contribution to the literature not only because it helps to

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identify spatial-interaction patterns, but also because it leads the way for future studies to

explore other combinations of variables that may further explain this complex health care

decision-making process.

This research also explores the use of a way to estimate walking travel time. In a service area

where most people walk to health services, the intuitive appeal of using walking time as an

alternative measure of the friction of distance is great. No prior research has used this method

(an established method for estimating hiking times in mountainous locations), in health

service research. Given the large amount of time committed to calculating the walking time

estimates, this research concludes that network distance, being easy and efficient to calculate

using a GIS, is the more efficient measure for future studies. Other measurements of the

friction of distance may also be considered for future research. One alternative may be to

refine the walking-time variable to a measure of "effort expended" (see Zipf, 1949) - using

calories or economic cost.

This project also contributes to the body of research through its extensive fieldwork. A great

deal of information was collected and compiled for this study through primary and secondary

sources. Techniques were developed to better assist in the collection of data in a poorly

understood place such as southern Honduras. The GPS proved to be an invaluable tool in

locating places, roads and trails in the service area, and its ability to download this collected

information for use in a GIS. Interview and sampling techniques were also developed to assist

in attaining important characteristics relating to health-seeking behavior.

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Challenges were encountered during this study. Of particular interest was the challenge of

reconciling the clinic attendance records with the national census. A method of reconciling

the census point data with geo-referenced “Village” locations was devised using a proximal

area method of census point unit assignment. This method is flexible and offers an

opportunity to use data gathered at different scales to be aggregated to a scale that is

consistent with other sources and allows for further analysis. This is a methodological

achievement that can be used in future fieldwork applications throughout the world.

The following section describes lessons learned in the field, followed by a general discussion

on fieldwork in Honduras. This is followed by a section on policy implications geared toward

improving health services in the study area. The chapter and paper concludes with some

suggested questions for future research.

7.1 Lessons learned in the field:

The lessons learned while conducting fieldwork in Honduras were as follows:

• Organize the trip (as much as possible) in advance. Write up anticipated questions for

interviews; know you goals; research design; and what data you will need for your

research. Develop a task list and time-line. This will help ensure that your time in the

field is well spent and that you are able to get as much information as possible from a

given trip. A good research proposal should help in this regard.

• Communicate and network: talk with as many knowledgeable persons as possible to

try to understand existence, form and availability of data.

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• The best information is not always available through internet, commercial or other

common secondary resources.

• Be patient, but persistent. Don’t give up trying to find your data.

• If all else fails, go there – it is sometimes more effective if you are in-person, rather

than a faceless name on an email.

• Above all, when conducting research in Latin America, be flexible, to accommodate

the more “relaxed” Latino way of doing things. Remember the key word for us

Gringos when in Latin America – “Tranquilo”.

In general the fieldwork experience was a very good. Without exception the Hondurans that

were encountered during the fieldwork were friendly and helpful. This researcher was

particularly impressed with the attitudes of the employees of La Clinica Hombro á Hombro,

including the doctors and staff and several key members of the Santa Lucia community, in

their willing assistance in the collection and interpretation of data needed for this research.

Furthermore, the employees of La Secretaria de Estado en los Despachos de Recursos

Naturales y Ambiente in Tegucigalpa were also very helpful in providing the very much

needed Honduran GIS/Census files.

7.2 Limitations of Fieldwork:

The limitations of the fieldwork include the following:

1. Key informant interviews – may be biased in a way based on experiences and

perceptions. Nonetheless, key informants were selected based on their knowledge of

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the study area and expertise in the subject matter, and to reflect a balance of leadership

qualities, genders, economic strata, age, etc.

2. Economic Status variable – based on survey summary assessments. These may be

biased in some way based on perceptions of “wealthier” versus “poorer” villages. Key

informants were selected based on their comprehensive knowledge of the study area.

Basic economic data such as average household income were not available for this

study. Many families in the study area live a traditional subsistence lifestyle. In these

families there is a lack of a currency-based economy. Furthermore, there was lack of

funds to collect this type of information.

3. Census data may be inaccurate. Inaccuracy of the population counts for each village

can cause errors in the analysis. Also, the educational attainment and employment

variables were based on census data. These variables also may contain inaccurate data.

Nonetheless, the 2001 Honduran National Census is the best possible source for this

type of information because it gives detailed, unbiased demographic information.

Furthermore, the 2001 census is the most current one available for this type of

research.

4. Attendance samples do not consider repeat visits. Some chronic conditions may need

repeated visits to the clinic. The attendance samples do not consider this possibility. If

there is nonrandom distribution of frequent visitors by village, then these places can

skew the results of this analysis.

5. Utilization was not broken down by type of visit, nor chief complaint, which may

impact utilization patterns differently than the aggregate measure. Attendance samples

do not consider type and severity of illness. Chronic problems such as headaches are

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commonly not life threatening. Other medical problems such as Dengue or Malaria

can be life threatening. Since this analysis does not consider the type or severity of

illness, it is not able to distinguish between the more important and less important

medical issues.

6. The dependant Variable (utilization) does not capture people who do not come to the

clinics, and is therefore not a direct measure of access to care. Utilization looks at the

characteristics of those who come to a health facility, whereas access looks at the

characteristics that enable or disable use of a health service. Utilization studies

consider access factors (such as distance to care) as one determinant of utilization, but

the utilization index such as the one used in this study only measures access indirectly.

The calculated utilization index ratio can infer access because it is a measure of the

relative use of the health facility (attendance divided by population). See section 4.2.1

for details on the calculation of the utilization index. This analysis is based on

utilization and does not directly measure the characteristics of those who do not come

to the clinics and is therefore a potential source of error in the study.

7.3 Policy Implications:

This section makes a few recommendations aimed at improving health care service in the

study area.

7.3.1 Accessibility Improvements:

One way to improve accessibility is to offer periodic field-clinics at auxiliary sites to be

conducted in underserved communities on a regular basis. One auxiliary site could be located

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southwest of the main clinic (Santa Rita), another northwest (San Jose) and one southeast (Las

Lomas). The main idea here is to bring the doctors to the people, rather than to transport the

people, to the clinic. The Hombro á Hombro clinic has the infrastructure needed to get the

doctors to these auxiliary sites. These auxiliary-clinics could be acquired with little or no cost.

In the southwestern and northwestern auxiliary locations, there are already health clinics, but

no staff to run them. In the eastern auxiliary service area, an old school house or church could

be used. The treatments are often relatively simple, so no need for sophisticated equipment.

Also, the Hombro á Hombro clinic has a sufficient amount of meds to supply auxiliary site

needs.

7.3.2 Community Assessments:

This research also recommends the use of community medical assessments to evaluate high

utilization areas. If an assumption is made that high utilization places are such because of

higher than normal need, then a logical conclusion would be that there may be some medical

challenge in the village or community that needs to be addressed. An analysis similar to this

current research could be used to identify villages with abnormally high clinic utilization.

This could be combined with medical assessment expertise from visiting medical brigades. A

community assessment could then be combined with a GIS analysis to help to identify “hot

spots” which may lead to identifying situations like a contaminated water supply. Identifying

places such as these could have a large positive impact on the health status of people who live

in the area.

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7.3.3 Transportation Improvements:

This research also recommends the investigation of improvements to the transportation

network. Significant improvements in access to care can be achieved through improvements

in the transportation network. One potential solution is targeted subsidized truck

transportation. Privately owned collective trucks are becoming more common in the Santa

Lucia service area, offering improved access to primary health care providers such as the

Hombro á Hombro clinic. A portion of the cost of this transportation could be subsidized by

the clinic and paid directly to the transportation operators. This approach could focus on those

least able to pay, such as subsistence farmers, or those living in the most remote locations.

7.4 Future Research:

The following is a list of potential research projects that can be done in Santa Lucia area or

other rural developing world locations that will help improve the understanding of health

services utilization.

The Choice variable has been shown to be effective in explaining utilization behavior in the

service area, but it can be improved. This research suggests a future project examining the

effect of weightings on the Choice variable, and the testing in other similar locations. Other

two-part choice variable may also be explored. One such combination may include questions

such as: To seek treatment or not? To use a Curandero or a clinic?

Another interesting project would be to investigate alternative variables that may help explain

utilization behavior. A variable indicating diagnosis has good potential for future research.

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This could be used to separate chronic from acute conditions receiving attention at the

clinic(s), and might help identify variations in utilization based on medical condition. A

subsequent analysis of distance decay patterns by medical condition would provide for an

improved understanding of the friction of distance.

Another study would focus on one specific type of medical treatment such as dental treatment

at a facility such as the Hombro á Hombro clinic. The new dental facility at the Hombro á

Hombro clinic has been very successful and an investigation of utilization patterns for it

would be a very interesting enhancement to this research project.

Another very interesting project would be the investigation of improvements to the

transportation network. It is clear that improvements to the transportation network will

improve access to care issues throughout the service area, but this area is poor and

government-based transportation projects are not a probability. Nonetheless, private

transportation operators (collective truck transport) are already finding their way to the region.

As the number of these operators increase access issues should improve. An interesting future

research project would involve conducting a longitudinal study of changing utilization

patterns across the study area. A focus of this research could be on the changing patterns of

access.

An additional interesting study would examine the effect of traditional health care providers

(Curanderos) on health care utilization in the area.

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One more suggested research project would analyze the effect of new health facilities on the

patterns of utilization in the service area. There are two new private clinics not far from Santa

Lucia. These new clinics at Santa Ana, and San Marcos de la Sierra could offer an

opportunity to further study utilization in a larger study area. This could help to support this

research project through confirmation of the models and methods used.

7.5 Concluding Comments

This research project has accomplished its goals. It has successfully developed a model for

use in primary care utilization research. Several variables, both old and new, have been

examined and tested in a micro-scale rural developing world location. This has yielded an

improved understanding of the determinants of utilization and health-seeking behavior, in

general, for the study area.

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149

Appendix A: Bi-variate Correlations – System-wide Model

Variable Stat LN UTILLN

TIME LN_A4 LN EMPLN % EDU 6

LN ECON

LN COST LN RQ

LN CHOICE

LN_UTIL r 1 -0.55 0.284 -0.054 0.021 0.054 0.269 0.181 -0.712 (Utilization) Sig . 0.000 0.006 0.606 0.840 0.605 0.009 0.082 0.000

N 93 93 93 93 93 93 93 93 93LN_TIME r -0.55 1 -0.102 0.13 -0.252 -0.566 -0.091 -0.321 0.344

Sig 0.000 . 0.331 0.213 0.015 0 0.385 0.002 0.001N 93 93 93 93 93 93 93 93 93

LN_A4 r 0.284 -0.102 1 0 0 0 0.992 0 -0.527 (Attraction) Sig 0.006 0.331 . 1 1 1 0 1 0.000

N 93 93 93 93 93 93 93 93 93LN_EMP r -0.054 0.13 0 1 0.016 -0.059 0 -0.466 0.000

Sig 0.606 0.213 1 . 0.877 0.576 1 0 1N 93 93 93 93 93 93 93 93 93

LN % EDU 6 r 0.021 -0.252 0 0.016 1 0.123 0 0.18 0Sig 0.840 0.015 1 0.877 . 0.241 1 0.085 1N 93 93 93 93 93 93 93 93 93

LN_ECON r 0.054 -0.566 0 -0.059 0.123 1 0 0.152 0Sig 0.605 0 1 0.576 0.241 . 1 0.146 1N 93 93 93 93 93 93 93 93 93

LN_COST r 0.269 -0.091 0.992 0 0 0 1 0 -0.5Sig 0.009 0.385 0 1 1 1 . 1 0N 93 93 93 93 93 93 93 93 93

LN_Road r 0.181 -0.321 0 -0.466 0.18 0.152 0 1 0 Quality Sig 0.082 0.002 1 0 0.085 0.146 1 . 1

N 93 93 93 93 93 93 93 93 93LN CHOICE r -0.712 0.344 -0.527 0 0 0 -0.5 0 1

Sig 0.000 0.001 0 1 1 1 0 1 .N 93 93 93 93 93 93 93 93 93

150

Appendix A (continued): Bi-variate Correlations –Magdalena Health Center

Variable Stat LN UTILLN

TIME LN_A4 LN EMPLN % EDU 6

LN ECON

LN COST LN RQ

LN CHOICE

LN_UTIL1 r 1 -0.682 . -0.212 0.274 0.14 . -0.014 -0.79 (Utilization) Sig . 0 . 0.252 0.135 0.452 . 0.941 0

N 31 31 31 31 31 31 31 31 31LN_TIME r -0.682 1 . 0.28 -0.401 -0.473 . -0.163 0.535

Sig 0 . . 0.127 0.025 0.007 . 0.38 0.002N 31 31 31 31 31 31 31 31 31

LN_A4 r . . . . . . . . . (Attraction) Sig . . . . . . . . .

N 31 31 31 31 31 31 31 31 31LN_EMP r -0.212 0.28 . 1 0.016 -0.059 . -0.466 0.244

Sig 0.252 0.127 . . 0.931 0.754 . 0.008 0.187N 31 31 31 31 31 31 31 31 31

LN % EDU 6 r 0.274 -0.401 . 0.016 1 0.123 . 0.18 -0.332Sig 0.135 0.025 . 0.931 . 0.511 . 0.334 0.068N 31 31 31 31 31 31 31 31 31

LN_ECON r 0.14 -0.473 . -0.059 0.123 1 . 0.152 -0.014Sig 0.452 0.007 . 0.754 0.511 . . 0.415 0.942N 31 31 31 31 31 31 31 31 31

LN_COST r . . . . . . . . .Sig . . . . . . . . .N 31 31 31 31 31 31 31 31 31

LN_Road r -0.014 -0.163 . -0.466 0.18 0.152 . 1 0.14 Quality Sig 0.941 0.38 . 0.008 0.334 0.415 . . 0.453

N 31 31 31 31 31 31 31 31 31LN CHOICE r -0.79 0.535 . 0.244 -0.332 -0.014 . 0.14 1

Sig 0 0.002 . 0.187 0.068 0.942 . 0.453 .N 31 31 31 31 31 31 31 31 31

151

Appendix A (continued): Bi-variate Correlations – Santa Lucia Health Center

Variable Stat LN UTILLN

TIME LN_A4 LN EMPLN % EDU 6

LN ECON

LN COST LN RQ

LN CHOICE

LN_UTIL1 r 1 -0.444 . 0.14 -0.301 -0.043 . 0.324 -0.7 (Utilization) Sig . 0.012 . 0.452 0.1 0.816 . 0.076 0

N 31 31 31 31 31 31 31 31 31LN_TIME r -0.444 1 . 0.061 -0.141 -0.623 . -0.456 0.276

Sig 0.012 . . 0.743 0.451 0 . 0.01 0.132N 31 31 31 31 31 31 31 31 31

LN_A4 r . . . . . . . . . (Attraction) Sig . . . . . . . . .

N 31 31 31 31 31 31 31 31 31LN_EMP r 0.14 0.061 . 1 0.016 -0.059 . -0.466 -0.244

Sig 0.452 0.743 . . 0.931 0.754 . 0.008 0.187N 31 31 31 31 31 31 31 31 31

LN % EDU 6 r -0.301 -0.141 . 0.016 1 0.123 . 0.18 0.332Sig 0.1 0.451 . 0.931 . 0.511 . 0.334 0.068N 31 31 31 31 31 31 31 31 31

LN_ECON r -0.043 -0.623 . -0.059 0.123 1 . 0.152 0.014Sig 0.816 0 . 0.754 0.511 . . 0.415 0.942N 31 31 31 31 31 31 31 31 31

LN_COST r . . . . . . . . .Sig . . . . . . . . .N 31 31 31 31 31 31 31 31 31

LN_Road r 0.324 -0.456 . -0.466 0.18 0.152 . 1 -0.14 Quality Sig 0.076 0.01 . 0.008 0.334 0.415 . . 0.453

N 31 31 31 31 31 31 31 31 31LN CHOICE r -0.7 0.276 . -0.244 0.332 0.014 . -0.14 1

Sig 0 0.132 . 0.187 0.068 0.942 . 0.453 .N 31 31 31 31 31 31 31 31 31

152

Appendix A (continued): Bi-variate Correlations – Hombro á Hombro Clinic

Variable Stat LN UTILLN

TIME LN_A4 LN EMPLN % EDU 6

LN ECON

LN COST LN RQ

LN CHOICE

LN_UTIL1 r 1 -0.465 . -0.068 0.038 0.059 . 0.393 . (Utilization) Sig . 0.008 . 0.714 0.84 0.753 . 0.029 .

N 31 31 31 31 31 31 31 31 31LN_TIME r -0.465 1 . 0.039 -0.202 -0.634 . -0.38 .

Sig 0.008 . . 0.835 0.277 0 . 0.035 .N 31 31 31 31 31 31 31 31 31

LN_A4 r . . . . . . . . . (Attraction) Sig . . . . . . . . .

N 31 31 31 31 31 31 31 31 31LN_EMP r -0.068 0.039 . 1 0.016 -0.059 . -0.466 .

Sig 0.714 0.835 . . 0.931 0.754 . 0.008 .N 31 31 31 31 31 31 31 31 31

LN % EDU 6 r 0.038 -0.202 . 0.016 1 0.123 . 0.18 .Sig 0.84 0.277 . 0.931 . 0.511 . 0.334 .N 31 31 31 31 31 31 31 31 31

LN_ECON r 0.059 -0.634 . -0.059 0.123 1 . 0.152 .Sig 0.753 0 . 0.754 0.511 . . 0.415 .N 31 31 31 31 31 31 31 31 31

LN_COST r . . . . . . . . .Sig . . . . . . . . .N 31 31 31 31 31 31 31 31 31

LN_Road r 0.393 -0.38 . -0.466 0.18 0.152 . 1 . Quality Sig 0.029 0.035 . 0.008 0.334 0.415 . . .

N 31 31 31 31 31 31 31 31 31LN CHOICE r . . . . . . . . .

Sig . . . . . . . . .N 31 31 31 31 31 31 31 31 31

153

Appendix B: Modified Gravity Model Output Tables - System-wide model:

Model Summary

.712a .507 .501 .87609129

.783b .612 .604 .78105657

.802c .644 .632 .75285974

Model123

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), LN CHOICEa.

Predictors: (Constant), LN CHOICE, LN TIMEb.

Predictors: (Constant), LN CHOICE, LN TIME, LNECON

c.

Coefficientsa

-3.128 .111 -28.117 .000-2.689 .278 -.712 -9.672 .000

-.331 .574 -.576 .566-2.240 .264 -.593 -8.486 .000

-.604 .122 -.346 -4.949 .000.527 .632 .834 .407

-2.055 .263 -.544 -7.816 .000-.853 .147 -.489 -5.787 .000-.385 .137 -.222 -2.805 .006

(Constant)LN CHOICE(Constant)LN CHOICELN TIME(Constant)LN CHOICELN TIMELN ECON

Model1

2

3

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LN UTIL+1a.

154

Appendix B (continued): Modified Gravity Model Output Tables - System-wide model

Excluded Variablesd

-.346a -4.949 .000 -.463 .882-.126a -1.469 .145 -.153 .722-.116a -1.366 .175 -.143 .750.181a 2.533 .013 .258 1.000

-.054a -.734 .465 -.077 1.000.021a .287 .775 .030 1.000.054a .736 .464 .077 1.000

-.089b -1.153 .252 -.121 .715-.079b -1.039 .301 -.110 .743.079b 1.138 .258 .120 .883

-.009b -.139 .890 -.015 .981-.071b -1.044 .299 -.110 .928-.222b -2.805 .006 -.285 .636-.074c -.985 .327 -.104 .711-.064c -.867 .388 -.092 .738.066c .977 .331 .104 .878

-.004c -.055 .956 -.006 .980-.081c -1.230 .222 -.130 .926

LN TIMELN_A4LN_COSTLN_RQ_1LN_EMPLN_P_ED6LN ECONLN_A4LN_COSTLN_RQ_1LN_EMPLN_P_ED6LN ECONLN_A4LN_COSTLN_RQ_1LN_EMPLN_P_ED6

Model1

2

3

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), LN CHOICEa.

Predictors in the Model: (Constant), LN CHOICE, LN TIMEb.

Predictors in the Model: (Constant), LN CHOICE, LN TIME, LN ECONc.

Dependent Variable: LN UTIL+1d.

155

Appendix B (continued): Model Output Tables – Magdalena Clinic

Model Summary

.790a .625 .612 .92835441

.848b .719 .699 .81755448

Model12

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), LN CHOICEa.

Predictors: (Constant), LN CHOICE, LN TIMEb.

Coefficientsa

-2.629 .280 -9.393 .000-3.493 .503 -.790 -6.948 .000

.490 1.047 .468 .643-2.633 .524 -.596 -5.023 .000

-.711 .232 -.364 -3.065 .005

(Constant)LN CHOICE(Constant)LN CHOICELN TIME

Model1

2

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LN UTIL+1a.

Excluded Variablesc

-.364a -3.065 .005 -.501 .713.099a .854 .401 .159 .980

-.021a -.175 .863 -.033 .941.013a .107 .915 .020 .890.129a 1.144 .262 .211 1.000.011b .104 .918 .020 .901.038b .358 .723 .069 .909

-.085b -.761 .453 -.145 .819-.057b -.469 .643 -.090 .696

LN TIMELN_RQ_1LN_EMPLN_P_ED6LN ECONLN_RQ_1LN_EMPLN_P_ED6LN ECON

Model1

2

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), LN CHOICEa.

Predictors in the Model: (Constant), LN CHOICE, LN TIMEb.

Dependent Variable: LN UTIL+1c.

* The Ln A4, and Ln Cost variables were excluded because there is no variation for this model.

156

Appendix B (continued): Model Output Tables – Santa Lucia Health Center Model Summary

.700a .490 .473 .86701710

.747b .558 .527 .82135881

.794c .631 .590 .76485364

Model123

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), LN CHOICEa.

Predictors: (Constant), LN CHOICE, LN TIMEb.

Predictors: (Constant), LN CHOICE, LN TIME, LNECON

c.

Coefficientsa

-3.168 .194 -16.343 .000-2.479 .470 -.700 -5.280 .000

-.794 1.158 -.685 .499-2.213 .463 -.625 -4.782 .000

-.512 .246 -.271 -2.077 .047.856 1.295 .661 .514

-1.961 .445 -.554 -4.408 .000-.966 .303 -.512 -3.191 .004-.584 .254 -.355 -2.300 .029

(Constant)LN CHOICE(Constant)LN CHOICELN TIME(Constant)LN CHOICELN TIMELN ECON

Model1

2

3

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LN UTIL+1a.

157

Appendix B (continued): Model Output Tables – Santa Lucia Health Center

Excluded Variablesd

-.271a -2.077 .047 -.365 .924.230a 1.784 .085 .320 .980

-.032a -.232 .818 -.044 .941-.077a -.540 .594 -.101 .890-.034a -.251 .803 -.047 1.000.142b 1.007 .323 .190 .792.005b .037 .971 .007 .923

-.158b -1.154 .258 -.217 .831-.355b -2.300 .029 -.405 .575.087c .644 .525 .125 .762.017c .139 .890 .027 .921

-.176c -1.389 .177 -.263 .828

LN TIMELN_RQ_1LN_EMPLN_P_ED6LN ECONLN_RQ_1LN_EMPLN_P_ED6LN ECONLN_RQ_1LN_EMPLN_P_ED6

Model1

2

3

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), LN CHOICEa.

Predictors in the Model: (Constant), LN CHOICE, LN TIMEb.

Predictors in the Model: (Constant), LN CHOICE, LN TIME, LN ECONc.

Dependent Variable: LN UTIL+1d.

* The Ln A4, and Ln Cost variables were excluded because there is no variation for this model.

158

Appendix B (continued): Model Output Tables – Hombro á Hombro Clinic

Model Summary

.465a .216 .189 .72375628Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), LN TIMEa.

Coefficientsa

-.883 .857 -1.030 .311-.509 .180 -.465 -2.828 .008

(Constant)LN TIME

Model1

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LN UTIL+1a.

Excluded Variablesb

.253a 1.452 .158 .265 .856-.050a -.301 .765 -.057 .998-.058a -.343 .735 -.065 .959-.394a -1.941 .062 -.344 .598

LN_RQ_1LN_EMPLN_P_ED6LN ECON

Model1

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), LN TIMEa.

Dependent Variable: LN UTIL+1b.

* The Ln A4, Ln Cost, and Ln CHOICE variables were excluded because there is no variation for the Hombro á Hombro clinic.

159

Appendix C: Residuals from the Regression Equation (Unstandardized)

VILLAGE CLINICRES_1

(System)RES_2 (Magda

RES_3 (S.Lucia

RES_4 (HáH)

Aguila Magdalena Health Center 0.28340 0.79388Banaderos Magdalena Health Center -0.51961 -0.03325Barriel Magdalena Health Center -0.01737 -0.23970Cordoncillo Magdalena Health Center -0.36033 -0.34295El Castano Magdalena Health Center 1.94595 2.06758Espino Magdalena Health Center 0.31432 0.08249Junquillo Magdalena Health Center -0.03789 0.02806La Ceibilla Magdalena Health Center -0.60861 -0.70289La Montana Magdalena Health Center -1.21183 -0.87915Las Aradas Magdalena Health Center -0.10421 -0.23080Las Lomas Magdalena Health Center -0.25349 -0.67478Las Marias, Magdelena Magdalena Health Center 0.61337 0.26574Las Marias, Santa Lucia Magdalena Health Center -0.34078 -0.52448Leoncito Magdalena Health Center 1.48306 1.17104Llanitos Magdalena Health Center -0.49310 -0.52722Los Horcones Magdalena Health Center 0.71262 0.81620Los Pozos Magdalena Health Center 1.70491 1.38919Magdelena Magdalena Health Center -0.35163 -0.77183Palacio Magdalena Health Center -0.29563 -0.39351San Francisco Magdalena Health Center 0.00052 -0.20048San Jose Magdalena Health Center -1.12282 -1.35116San Juan Magdalena Health Center 0.48659 0.04322San Lorenzo Magdalena Health Center 0.25436 0.21136San Marcos, Colomoncag Magdalena Health Center -1.27030 -1.71437San Marcos, Santa Lucia Magdalena Health Center 0.27423 0.06459San Pablo Magdalena Health Center 0.68565 0.41749San Rafael Magdalena Health Center 0.75587 0.37897Santa Lucia Magdalena Health Center 1.07468 0.67027Santa Rita Magdalena Health Center 0.75803 0.61275Tablones Magdalena Health Center -1.21916 -0.67132Talquezal Magdalena Health Center 0.29954 0.24508

160

Appendix C (continued): Residuals from the Regression Equation-Unstandardized

VILLAGE CLINICRES_1

(System)RES_2 (Magda

RES_3 (S.Lucia

RES_4 (HáH)

Aguila Sant Lucia Health Center 0.23457 0.05484Banaderos Sant Lucia Health Center -0.40534 -0.56862Barriel Sant Lucia Health Center -0.22985 -0.13682Cordoncillo Sant Lucia Health Center -0.43416 -0.39650El Castano Sant Lucia Health Center -0.11345 -0.08140Espino Sant Lucia Health Center -1.07901 -0.96904Junquillo Sant Lucia Health Center 0.91964 0.92574La Ceibilla Sant Lucia Health Center 1.69593 1.49860La Montana Sant Lucia Health Center -0.19352 -0.32561Las Aradas Sant Lucia Health Center 0.65128 0.70635Las Lomas Sant Lucia Health Center -0.63973 -0.55919Las Marias, Magdelena Sant Lucia Health Center 0.00868 0.05327Las Marias, Santa Lucia Sant Lucia Health Center -0.23869 -0.17108Leoncito Sant Lucia Health Center 0.80535 0.84063Llanitos Sant Lucia Health Center 0.28551 0.38464Los Horcones Sant Lucia Health Center 0.10376 -0.05737Los Pozos Sant Lucia Health Center -0.49398 -0.45205Magdelena Sant Lucia Health Center -0.52940 -0.34736Palacio Sant Lucia Health Center 0.02019 0.07815San Francisco Sant Lucia Health Center -1.80299 -1.80669San Jose Sant Lucia Health Center -0.39990 -0.20882San Juan Sant Lucia Health Center 1.18615 1.16617San Lorenzo Sant Lucia Health Center -0.63480 -0.58878San Marcos, Colomoncag Sant Lucia Health Center -0.90052 -0.80176San Marcos, Santa Lucia Sant Lucia Health Center 1.12740 1.28251San Pablo Sant Lucia Health Center 0.18509 0.34990San Rafael Sant Lucia Health Center 0.39931 0.46576Santa Lucia Sant Lucia Health Center -0.55088 -0.45176Santa Rita Sant Lucia Health Center 0.79001 0.91712Tablones Sant Lucia Health Center -0.41061 -0.62210Talquezal Sant Lucia Health Center -0.24170 -0.17873

161

Appendix C (continued): Residuals from the Regression Equation-Unstandardized

VILLAGE CLINICRES_1

(System)RES_2 (Magda

RES_3 (S.Lucia

RES_4 (HáH)

Aguila Hombro á Hombro 0.48491 0.91569Banaderos Hombro á Hombro 0.79781 1.15279Barriel Hombro á Hombro -0.49133 -0.21910Cordoncillo Hombro á Hombro -0.33807 -0.30298El Castano Hombro á Hombro 0.84810 0.81387Espino Hombro á Hombro -0.02038 0.17258Junquillo Hombro á Hombro 0.07979 0.30995La Ceibilla Hombro á Hombro 0.35619 0.74330La Montana Hombro á Hombro -1.09442 -0.59748Las Aradas Hombro á Hombro -0.48959 -0.43012Las Lomas Hombro á Hombro -1.02528 -1.20818Las Marias, Magdelena Hombro á Hombro 0.11053 0.04752Las Marias, Santa Lucia Hombro á Hombro -0.45830 -0.41243Leoncito Hombro á Hombro 0.38834 0.35720Llanitos Hombro á Hombro -0.53289 -0.67088Los Horcones Hombro á Hombro -0.32557 0.04289Los Pozos Hombro á Hombro 0.07583 0.02182Magdelena Hombro á Hombro 0.50195 0.30742Palacio Hombro á Hombro -0.42725 -0.38746San Francisco Hombro á Hombro -0.18164 -0.07642San Jose Hombro á Hombro 0.29718 0.07655San Juan Hombro á Hombro 0.28914 0.33773San Lorenzo Hombro á Hombro 0.47691 0.52306San Marcos, Colomoncag Hombro á Hombro -2.37515 -2.61787San Marcos, Santa Lucia Hombro á Hombro 0.14818 0.07250San Pablo Hombro á Hombro 0.91532 0.88259San Rafael Hombro á Hombro -0.17692 -0.31316Santa Lucia Hombro á Hombro -0.80856 -0.37572Santa Rita Hombro á Hombro 0.32965 0.18488Tablones Hombro á Hombro -0.34016 0.21717Talquezal Hombro á Hombro 0.43097 0.43228

162

Appendix D: Key Informant Interview

The following is a summary of Key Informant #1 Interview which can be used as an

example of interviews used during fieldwork for this study. The interview took place in

Santa Lucia, Honduras during December 2003, and uses a basic question and answer

format with questions prepared before the interview, and follow-up question presented

when appropriate.

Question #1: Describe the basic characteristics of the most common patients: Most

patients come from the two major nearby towns of Magdalena, and Santa Lucia (more

than 50%).

• 50% of patients are less than 5 years old, mostly female.

• 10-12 years ago most patients came from the rural villages, now mostly from two

major towns (Magdalena, & Santa Lucia). Though Magdalena has its own clinic it

does not have as good a facility (medical equipment), nor does it have as good a staff

(Doctor not always present). Consequently, many people now are choosing to travel

45 minutes by foot to come to the Santa Lucia Clinic. Good reputation of Shoulder-to-

Shoulder/Hombro a Hombro Clinic also a factor. Patients coming by car from San

Antonio also similar situation. San Antonio has clinic, but not as good a service.

Question #2: Describe the characteristics of the most serious patients: Some of the most

problematic areas are the most remote places. People coming from far away (remote)

villages, often wait too long before coming to clinic. Many go to local traditional healers,

who often give out poor advice. This delays the treatment of the patients by the clinic

163

(people think they will get better from traditional healer). Often these people come to the

clinic after the disease/medical problem gets very advanced, and sometime too late for the

clinic doctor to help (or at least help easily/quickly). If the patients came in quickly then

the Doctors would be more effective. He recommended efforts to work with local healers,

to educate them, about medical problems that should be referred to the clinic. In some

cases, this has been implemented and is working.

• This is where improving accessibility would probably help patient outcomes. If the

clinic were more accessible, then people would probably use the clinic more

frequently.

• Many patients with serious medical issues have the following characteristics: They

have a poor education (do not read); many are malnourished; come from big families

(10-12 children); and prevalence of alcoholism in the family.

Question #3: What other characteristics keep people from utilizing clinic?

• Personal biases, preferences, and misinformation:

• Hypothetical example of a prospective patient who had a friend or family

member who had a bad experience with a western doctor. This lead to a

personal bias against the western clinic.

• Patients think they need money to come to clinic. Though the clinic charges

people for the service, many people don’t have money. So they think they

cannot go to the clinic when they are sick. This is not true, and people need to

be educated to understand this.

164

• Alternative sources of health service: Traditional healers are a problem as described

above.

• Midwives are good, but only for “normal” births. Complicated births need clinic or

hospital attendance.

• Government clinic in Santa Lucia cheaper, but doesn’t always have doctor. It’s a

cheaper place to get drugs, but it doesn’t always have them. When they have drugs,

many people go there. This causes uneven flow of patients to Hombro a Hombro

clinic.

Question #4: What recommendations can be made for improving service?

• Having a doctor, nurse, and two health workers/promoters who travel out to the

villages every 2 weeks (need to be on a regular schedule). This, in effect, brings the

health care to the people. The Hombro a Hombro clinic has offered to provide

transportation and medicine, but needs funding for the salaries of the staff.

• Also discussed the possibility of having the social service doctor spend two days a

week out at the Santa Rita clinic and the San Jose clinic, both of which have a

building, but don’t have a doctor or staff. This is a good idea, but one that runs into

political problems in Tegucigalpa with the National Health Ministry.

#5: What are your thoughts on educational efforts out in the villages?

• Teach people about preventive medicine – childhood inoculations, etc.

• Brigades and traveling doctors/nurses can help with educational efforts.

165

• Health promoters in each of the villages know basic health issues. Helps refer people

to clinic if needed.

Question #6: What are the towns with local traditional healers? San Juan, Santa Lucia,

Palacios, Condoncillo, San Rafael, Junquillo, San Marcos, and Santa Rita.

Question #7: What form of transportation do most patients use to get to clinic?

• Key informant 1 and 5 collaborated on this issue and estimated that 80-85% of their

patients come to the Hombro a Hombro clinic by foot.

• When the patient is very sick, often a car/truck is hired.

Question #8: What are the most common medical problems?

• Years ago infectious diseases were the most common, but as the health of these people

improves, chronic diseases have become the most prevalent. These include: diarrhea,

upper respiratory infection (from cooking fires inside), headaches, muscle aches,

hypertension, diabetes, arthritis & pregnancy.

Question #9: What is the average number of children in a family? The average number of

children is 5.

Question #10: What is the ethnic makeup of most of the people in the service area? The

ethnic makeup of most of the people is mixed (mestizo).