DHS ANALYTICAL 

STUDIES 51

The Relationship between the

Health Service Environment

and Service Utilization: Linking

Population Data to Health

Facilities Data in Haiti and Malawi

September 2015

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Wenjuan Wang, Rebecca Winter, Lindsay Mallick, Lia Florey, Clara Burgert-Brucker, and Emily Carter

of ICF International.

DHS Analytical Studies No. 51 

The Relationship between the Health Service Environment 

and Service Utilization: Linking Population Data to Health 

Facilities Data in Haiti and Malawi 

Wenjuan Wang 

Rebecca Winter 

Lindsay Mallick 

Lia Florey 

Clara Burgert-Brucker 

Emily Carter 

ICF International 

Rockville, Maryland, USA 

September 2015 

Corresponding author: Wenjuan Wang, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: +1 301-407-6500; fax: +1 301-407-6501; 

email: wenjuan.wang@icfi.com
Acknowledgment: The authors would like to thank the reviewers of this report, Mai Do and Yazoume Ye, 

for their invaluable comments; Tom Pullum for methodological guidance; Paul Ametepi for expert advice 

on Service Provision Assessment (SPA) methodology; and Shireen Assaf for helpful discussion on 

analytical issues encountered. 

Editor: Sidney Moore 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about The DHS Program, contact 

The DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Wenjuan Wang, Rebecca Winter, Lindsay Mallick, Lia Florey, Clara Burgert-Brucker, and Emily Carter. 

2015. The Relationship between the Health Service Environment and Service Utilization: Linking 

Population Data to Health Facilities Data in Haiti and Malawi. DHS Analytical Studies No. 51. Rockville, 

Maryland, USA: ICF International. 
iii 

Contents 

Tables ......................................................................................................................................................... v 

Figures ........................................................................................................................................................ ix 

Preface ...................................................................................................................................................... xiii 

Abstract ....................................................................................................................................................... xv 

Executive Summary .................................................................................................................................. xvii 

1. Introduction and Literature Review ................................................................................................. 1 

1.1. Linking Household and Health Facility Data from the Same Survey ................................. 1 

1.2. Linking Data from Independent Household Surveys and Health Facility Surveys ............ 2 

1.3. Country Background and Health Systems in Haiti and Malawi ......................................... 5 

2. Data and Methods ............................................................................................................................ 7 

2.1. Data ..................................................................................................................................... 7 

2.2. Linking Clusters and Health Facilities ................................................................................ 8

2.3. Definitions of Key Variables .............................................................................................. 9 

2.4. Statistical Analysis ............................................................................................................ 13 

3. Results ............................................................................................................................................ 15 

3.1. Family Planning ................................................................................................................ 15 

3.2. Antenatal Care .................................................................................................................. 22 

3.3. Facility Delivery ............................................................................................................... 30 

3.4. Care Seeking for Children’s Diarrhea ............................................................................... 39 

3.5. HIV Testing ...................................................................................................................... 43 

3.6. Malaria-related Health Services ........................................................................................ 52 

4. Discussion and Conclusions .......................................................................................................... 69 

4.1 Key Findings on the Association between Service Environment and Use of Health 

Services ............................................................................................................................. 69 

4.2. Benefits of the Buffer Linkage Methodology ................................................................... 71 

4.3. Methodological Lessons Learned ..................................................................................... 72 

4.4. Study Limitations .............................................................................................................. 73 

4.5. Conclusions ....................................................................................................................... 74 

References ................................................................................................................................................... 75 

Appendix ..................................................................................................................................................... 79 

v 

Tables 

Table 1. Characteristics of health facilities in Haiti that provide family planning services, 

by residence, Haiti SPA 2013 ........................................................................................... 15 

Table 2. Results of multivariable logistic regressions of use of modern contraceptive 

methods among married women age 15-49 in Haiti, by residence ................................... 21 

Table 3. Characteristics of health facilities in Haiti that provide antenatal care services, 

by residence, Haiti SPA 2013 ........................................................................................... 22 

Table 4. Results of multivariable logistic regressions of having 4 or more ANC visits among 

women in Haiti who had a live birth in the five years preceding the survey, 

by residence ...................................................................................................................... 28 

Table 5. Results of multivariable logistic regressions of receiving high quality ANC among 

women in Haiti who had a live birth in the five years preceding the survey, 

by residence ...................................................................................................................... 29 

Table 6. Characteristics of all health facilities in Haiti with normal delivery services, 

facilities with BEmOC services, and facilities with CEmOC services, by residence, 

Haiti SPA 2013 ................................................................................................................. 31 

Table 7. Results of multivariable logistic regressions of facility delivery for the most recent 

birth among women in Haiti who had a live birth in the five years preceding the 

survey, by residence .......................................................................................................... 38 

Table 8. Characteristics of health facilities in Haiti that offer curative care services for 

children under five years, by residence, Haiti SPA 2013 ................................................. 40 

Table 9. Results of multivariable logistic regressions of care-seeking for children’s diarrhea 

at any public or private facility in Haiti among children with diarrhea in the two 

weeks preceding the survey, by residence ........................................................................ 43 

Table 10. Characteristics of health facilities in Haiti providing HIV counseling and testing 

services, by residence, Haiti SPA 2013 ............................................................................ 44 

Table 11. Results of multivariable logistic regressions of HIV testing in past 12 months among 

women age 15-49 in Haiti, by residence ........................................................................... 51 

Table 12. Characteristics of health facilities in Malawi that provide curative care services for 

children, by residence, Malawi SPA 2013-14 .................................................................. 52 

Table 13. Results of multivariable logistic regressions of finger/heel stick in children under 

five years with recent fever in Malawi, by residence ....................................................... 57 

Table 14. Characteristics of health facilities in Malawi that provide malaria diagnosis and/or 

treatment services, by residence, Malawi SPA 2013-14 ................................................... 58 

Table 15. Results of multivariable logistic regressions of being malaria positive among 

children age 6-59 months in Malawi, by residence .......................................................... 62 

Table 16. Characteristics of health facilities in Malawi that provide ANC services, by residence, 

Malawi SPA 2013-14 ........................................................................................................ 63 

Table 17. Results of multivariable logistic regressions of IPTp2 among women who had 

a live birth in the two years preceding the survey in Malawi, by residence ..................... 68 
vi 

Table A1. Percentage of family planning facilities in Haiti that provide specific family 

planning methods, by residence, Haiti SPA 2013 ............................................................. 79 

Table A2. Percent distribution of married women age 15-49 in Haiti, by selected family 

planning (FP) characteristics and background characteristics, according to residence .... 80 

Table A3. Percentage of married women age 15-49 in Haiti who are currently using a modern 

contraceptive method, by selected family planning (FP) characteristics, background 

characteristics, and residence, Haiti DHS 2012 ................................................................ 81 

Table A4. Percentage of ANC facilities in Haiti that have specific items available, by type of 

facility, Haiti SPA 2013 .................................................................................................... 82 

Table A5. Percent distribution of women in Haiti who had a live birth in the five years 

preceding the survey by selected ANC characteristics and background characteristics, 

according to residence, Haiti DHS 2012 and Haiti SPA 2013 .......................................... 83 

Table A6. Percentage of women in Haiti who had a live birth in the five years preceding the 

survey and had four or more ANC visits at the last birth, by ANC service provision 

environment, women’s background characteristics, and residence, Haiti DHS 2012 

and Haiti SPA 2013 .......................................................................................................... 85 

Table A7. Percentage of women in Haiti who had a live birth in the five years preceding the 

survey and received high quality ANC, by ANC service provision environment, 

 women’s background characteristics, and residence, Haiti DHS 2012 and 

Haiti SPA 2013 ................................................................................................................. 86 

Table A8. Among facilities in Haiti that offer normal delivery care, percentage that offer 

specific basic (BEmOC) and comprehensive (CEmOC) emergency care services, 

by residence, managing authority, and type of facility, Haiti SPA 2013 .......................... 87 

Table A9. Percent distribution of women in Haiti who had a live birth in the five years 

preceding the survey, by ANC and delivery care characteristics and women’s 

background characteristics, according to residence, Haiti DHS 2012 and 

Haiti SPA 2013 ................................................................................................................. 88 

Table A10. Percentage of women in Haiti who had a live birth in the five years preceding the 

survey and delivered the most recent birth in a facility, by availability of obstetric 

care services (BEmOC and CEmOC), number of ANC visits, women’s background 

characteristics, and residence, Haiti DHS 2012 and Haiti SPA 2013 ............................... 89 

Table A11. Percentage of facilities with selected items to support child curative care services 

among facilities in Haiti that offer curative care for children under five, by residence, 

type of facility, and managing authority, Haiti SPA 2013 ................................................ 90 

Table A12. Percent distribution of children under age five years in Haiti with reported diarrhea 

in the two weeks preceding the interview, by service readiness of facilities for treating 

childhood illness, whether care was sought for a public or private source, background 

characteristics of child, and background characteristics of mother, according to 

residence, Haiti DHS 2012 and Haiti SPA 2013 .............................................................. 91 

Table A13. Percentage of children in Haiti with diarrhea in the two weeks preceding the survey 

for whom care was sought from a public or private source, by level of facility readiness 

(service environment) for treating childhood illness, child’s background characteristics, 

mother’s background characteristics, and residence, Haiti DHS 2012 and 

Haiti SPA 2013 ................................................................................................................. 92 
vii 

Table A14. Percentage of facilities providing HIV counseling or testing services in Haiti that have 

HIV testing capacity and other items to support quality HIV testing and counseling 

services, by residence, Haiti SPA 2013 ............................................................................ 93 

Table A15. Percent distribution of women age 15-49 in Haiti, by level of facility service readiness 

(service environment) for HIV testing, whether woman had an HIV test in the 

12 months preceding the survey, and women’s background characteristics, 

according to residence, Haiti DHS 2012 and Haiti SPA 2013 .......................................... 94 

Table A16. Percentage of women age 15-49 in Haiti who were tested for HIV in the 12 months 

preceding the survey, by level of service readiness (service environment) for HIV 

testing and background characteristics, Haiti DHS 2012 and Haiti SPA 2013 ................ 95 

Table A17. Percentage of facilities in Malawi with specific indicators of malaria diagnostic 

capacity, by type of facility and residence, Malawi SPA 2013-14 ................................... 96 

Table A18. Percentage of children under five in Malawi who had fever in the two weeks 

preceding the mother’s interview, by whether the child used an ITN the previous night, 

background characteristics of the child, background characteristics of the mother, and 

residence, Malawi MIS 2012 ............................................................................................ 97 

Table A19. Percentage of children under five in Malawi with recent fever who had a finger/heel 

stick, by whether the child slept under an ITN the night before the interview, 

background characteristics of the child, background characteristics of the mother, 

and residence, Malawi MIS 2012 ..................................................................................... 98 

Table A20. Percent distribution of children age 6-59 months in Malawi, by whether household 

owns at least one ITN, background characteristics of child, and background 

characteristics of mother, according to residence, Malawi MIS 2012 .............................. 99 

Table A21. Percentage of children age 6-59 months in Malawi who tested positive for malaria 

via microscopy, by whether household owns at least one ITN, background 

characteristics of child, background characteristics of mother, and residence, 

Malawi MIS 2012 ........................................................................................................... 100

Table A22. Percentage of ANC facilities in Malawi that had specific items related to malaria 

diagnosis, prevention, and treatment available, by type of facility and residence, 

Malawi SPA 2013-14 ...................................................................................................... 101 

Table A23. Percent distribution of women age 15-49 in Malawi who had a live birth in the 

two years preceding the survey, by background characteristics, according to 

residence, Malawi MIS 2012 .......................................................................................... 102 

Table A24. Percentage of women age 15-49 in Malawi who had a live birth in the two years 

preceding the survey and who took at least two doses of SP for prevention of malaria 

at least once during ANC, by background characteristics and residence, 

Malawi MIS 2012 ........................................................................................................... 104

ix 

Figures 

Figure 1. Illustration of linking DHS clusters and SPA facilities ...................................................... 9 

Figure 2a. Percentage of FP facilities providing at least three modern contraceptive methods, 

by residence, Haiti SPA 2013 ........................................................................................... 16 

Figure 2b. Percentage of FP facilities providing at least three modern contraceptive methods, 

by facility type, Haiti SPA 2013 ....................................................................................... 17 

Figure 2c. Percentage of FP facilities providing at least three modern contraceptive methods, 

by managing authority, Haiti SPA 2013 ........................................................................... 17 

Figure 3. Percent distribution of DHS clusters in Haiti by level of access to facilities with 

three or more methods and residence ................................................................................ 18 

Figure 4. Map of bivariate relationship between the location of FP facilities in Haiti with 

at least three modern family planning methods and percentage of married women 

age 15-49 who are currently using a modern contraceptive method by department, 

Haiti DHS 2012 and Haiti SPA 2013 ............................................................................... 19 

Figure 5. Women’s use of modern contraceptive methods in Haiti by level of access to FP 

facilities with 3+ methods and residence .......................................................................... 20 

Figure 6. Percentage of ANC facilities with specific items for supporting ANC services 

by residence, Haiti SPA 2013 ........................................................................................... 23 

Figure 7. Boxplots of ANC readiness scores among clusters, by residence, Haiti SPA 2013 ......... 24 

Figure 8. Percentage of women age 15-49 who received selected components of ANC, 

by residence, Haiti DHS 2012 .......................................................................................... 25 

Figure 9. Map of bivariate relationship between facility service readiness to provide 

ANC services in Haiti and women’s receipt of 4 ANC visits, by department, 

Haiti DHS 2012 and Haiti SPA 2013 ............................................................................... 26 

Figure 10. Percentage of women in Haiti with 4 or more ANC visits by level of service readiness 

and residence ..................................................................................................................... 27 

Figure 11. Percentage of women in Haiti who received high quality ANC by level of service 

readiness and residence ..................................................................................................... 27 

Figure 12a. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by residence, Haiti SPA 2013 ...................................................... 32 

Figure 12b. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by type of facility, Haiti SPA 2013 ............................................. 32 

Figure 12c. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by managing authority, Haiti SPA 2013 ...................................... 32 

Figure 13a. Percent distribution of DHS clusters in Haiti by access to normal delivery service 

and BEmOC, according residence .................................................................................... 33 

Figure 13b. Percentage distribution of DHS clusters in Haiti by access to normal delivery 

service and CEmOC, according to residence .................................................................... 34 

Figure 14. Map of relationship between facilities in Haiti that provide delivery services and 

whether the facility offers BEmOC and percentage of women who delivered their 

most recent birth in a health facility, by department, Haiti DHS 2012 and 

Haiti SPA 2013 ................................................................................................................. 35 

Figure 15a. Percentage of women in Haiti rural areas who delivered the most recent birth in 

a facility, according to the availability of delivery services within the buffer .................. 36 
x 

Figure 15b. Percentage of women in Haiti other urban areas who delivered the most recent birth 

in a facility, according to the availability of delivery services within the buffer ............. 36 

Figure 15c. Percentage of women in Haiti metropolitan areas who delivered the most recent birth 

in a facility, according to the availability of delivery services within the buffer ............. 37 

Figure 16. Boxplots of child care readiness scores among clusters in Haiti, by residence ................ 41 

Figure 17. Percentage of children in Haiti for whom care was sought from a public or private 

source for recent diarrhea, by level of facility service readiness to treat childhood 

illness and residence ......................................................................................................... 42 

Figure 18a. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readinss to conduct HIV testing, according to residence, 

Haiti SPA 2013 ................................................................................................................. 45 

Figure 18b. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readinss to conduct HIV testing, according to facility type, Haiti SPA 

2013 .................................................................................................................................. 46 

Figure 18c. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readinss to conduct HIV testing, according to managing authority, 

Haiti SPA 2013 ................................................................................................................. 47 

Figure 19. Percent distribution of clusters in Haiti by access to HIV testing services, according 

to residence ....................................................................................................................... 48 

Figure 20. Map of relationship between facilities in Haiti that provide HIV testing services and 

percentage of women who were tested for HIV, by department, Haiti DHS 2012 and 

Haiti SPA 2013 ................................................................................................................. 49 

Figure 21. Percentage of women age 15-49 in Haiti who were tested for HIV in the 12 months 

preceding the survey, by the level of access to HIV testing services and residence ......... 50 

Figure 22a. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by facility type, Malaria SPA 2013-14 ............................................................... 53 

Figure 22b. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by residence, Malawi SPA 2013-14 .................................................................... 53 

Figure 22c. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by managing authority, Malawi SPA 2013-14 .................................................... 54 

Figure 23. Diagnostic capacity of facilities with sick child services to diagnose malaria, by 

residence, Malawi SPA 2013-14 ....................................................................................... 55 

Figure 24. Proportion of children in Malawi under five with recent fever who had a finger/heel 

stick by access to facilities with malaria diagnostic capacity, according to residence ..... 56 

Figure 25a. Percentage of malaria diagnosis/treatment facilities with ITNs available and 

observed, by type of facility, Malawi SPA 2013-14 ......................................................... 59 

Figure 25b. Percentage of malaria diagnosis/treatment facilities with ITNs available and 

observed, by managing authority, Malawi SPA 2013-14 ................................................. 59 

Figure 25c. Percentage of malaria diagnosis/treatment facilities with ITNs available and 

observed, by residence, Malawi SPA 2013-14 ................................................................. 60 

Figure 26. Distribution of access to facilities with ITNs available in Malawi clusters, 

by residence ...................................................................................................................... 60 

Figure 27. Proportion of children age 6-59 months with a positive malaria microscopy test in 

Malawi, by level of access to facilities with ITNs available and residence ...................... 61 

Figure 28a. Proportion of ANC facilities service ready for provision of IPTp, by type of facility, 

Malawi SPA 2013-14 ........................................................................................................ 64 
xi 

Figure 28b. Proportion of ANC facilities service ready for provision of IPTp, by managing 

authority, Malawi SPA 2013-14 ....................................................................................... 64 

Figure 28c. Proportion of ANC facilities service ready for provision of IPTp, by residence, 

Malawi SPA 2013-14 ........................................................................................................ 65 

Figure 29. Percent distribution of Malawi clusters by access to IPTp services, according to 

residence ........................................................................................................................... 66 

Figure 30. Women’s use of IPTp2 in Malawi by level of access to ANC facilities service ready 

for IPTp provision, by residence ....................................................................................... 67 

xiii 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several countries 

and applying multivariate statistical tools and models. These reports are also intended to illustrate research 

methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

The DHS Program 

xv 

Abstract 

Access to and quality of health services are key determinants in utilization of health services and 

consequently, health outcomes. By linking DHS household data and health facility survey data, this paper 

explores the relationship between the service environment (service availability and readiness to provide a 

service) and utilization and quality of health care obtained in several health areas in Haiti and Malawi. The 

health areas included are family planning, maternal health care, child care, and HIV testing services in 

Haiti, and malaria-related health care services in Malawi. Geographically appropriate buffer zones around 

each cluster were determined based on area of residence—metropolitan, other urban, and rural areas in 

Haiti and urban and rural areas in Malawi. For each health area, data from the facilities within the buffer 

were summarized to cluster level to measure the cluster’s service environment relative to the health area 

included in the buffer zone. These cluster-level data were then linked to individual women in the household 

surveys. As expected, for all the health services examined in Haiti, clusters in the metropolitan area were 

linked to a much greater number of health facilities than clusters in other urban and rural areas. For most 

health areas, a significant association was found in rural areas (and other urban areas in Haiti) between 

access to or readiness of service provision and utilization of health services. The relationship does not hold 

however in the metropolitan area in Haiti; more precise measurements of the health service environment 

would be needed for areas with both high density of health facilities and population. 

xvii 

Executive Summary 

Access to and quality of health services are thought to be key determinants in utilization of health services 

and health outcomes. However, analysis of the relationship between health services and health service 

utilization has its challenges. It requires data from both facilities and individuals in order to capture both 

service environment and service use outcomes. The DHS Program collects data at both levels through 

population surveys (DHS) and health facility surveys (SPA). A limited number of studies have linked the 

two, mostly by connecting clusters to the nearest health facility and analyzing how the physical access to 

health facilities, i.e. distance to the nearest facility, and/or the service provision in that health facility affects 

population use of health services. These studies have found an association between service environment 

and use of family planning (FP), maternal, curative, and HIV services. However, the geographic and 

temporal proximity of facility and household datasets, as well as the linking method employed, is limited 

and leaves room for improvement. 

This study aims to fill a major gap in the knowledge base on the relationship between provision of health 

services and utilization of health services in developing countries. By using a broader, service-environment 

linkage approach, errors due to GIS displacement of clusters in DHS datasets can be minimized. The 

relationship between the service environment and utilization of specific services was explored for a number 

of health areas including family planning (FP), maternal health care, child care, and HIV testing services in 

Haiti, while malaria-related health care services were examined in Malawi. 

Methods 

The analysis was based on linked population data and health facility data from Haiti and Malawi, drawn 

from household surveys—the 2012 Haiti DHS and the 2012 Malawi Malaria Indicator Survey (MIS)—and 

service provision assessment surveys—the 2013 Haiti SPA and the 2013-14 Malawi SPA. Instead of 

relating health services utilization to service provision in a single facility (the nearest one), we measured 

the service environment comprised of all the facilities in the formal sector of each country, within a 

specified buffer distance. The distances were operationalized by creating a group of facilities within a 

5-kilometer buffer distance from an urban cluster and a 10-kilometer buffer distance from a rural cluster. 

These buffer sizes were chosen to ensure that facilities within the same distance from the real location of 

the cluster were captured (given that urban clusters were displaced up to 2 km and most rural clusters were 

displaced up to 5 km by DHS). Facility-level variables for each health area, such as the facility’s capacity 

to provide a service or the facility’s readiness score to provide this service, were summarized at the cluster 

level to measure the cluster’s service environment within the specific distance. This cluster-level facility 

variable for each health area was then linked to clusters from the corresponding household survey. 

Results 

In Haiti, the results suggest that service readiness is a significant predictor of health care utilization, 

particularly in rural areas. Overall, the findings were not as strong in urban areas for most of the health 

areas covered. The results on family planning in Haiti indicate an association between the availability of 

contraceptive method choice and women’s use of modern contraceptive methods in rural and other urban 

areas. The presence of at least one facility offering a range of methods within the buffer distance from the 

community significantly increased adoption of modern contraceptive methods. 

Antenatal care (ANC) services are widely available at health facilities in Haiti. As a result, all clusters 

irrespective of location have at least one ANC facility within 5 km or 10 km. However, there is limited 

capacity to conduct urine and blood tests, little use of ANC guidelines, and poor availability of tetanus 
xviii 

vaccine. In addition, our analyses showed that service readiness is an important determinant of women’s 

receipt of four or more ANC visits and high quality ANC, although these associations are strongly 

significant only in rural areas. 

A significant association was found between facilities’ capacity to provide HIV testing and use of HIV 

testing services in rural areas of Haiti. In the metropolitan area, there was no association between the HIV 

service environment and use of HIV testing services. On the other hand, we found a significant positive 

association between the level of service readiness and care seeking for children’s diarrhea at health facilities 

in the metropolitan area but not in rural and urban areas. 

In Haiti, one-third of other urban clusters and half of rural clusters do not have any facility with basic 

emergency obstetric care services (BEmOC) within the 5 km or 10 km buffer and availability of 

comprehensive emergency obstetric care services (CEmOC) is even more limited. Our findings indicate a 

strong effect of having a BEmOC facility on facility delivery in the metropolitan area; however, the results 

in rural and urban areas suggest that being able to access a facility providing normal delivery services, with 

or without BEmOC, seems more important in rural and other urban areas. 

In Malawi, mixed results were found regarding availability of facilities providing malaria services and use 

of these services. Despite the high prevalence of malaria in rural areas of Malawi, over half of rural clusters 

do not have any facility within 10 km that is ready to provide intermittent preventive treatment of malaria 

in pregnancy (IPTp). High access to IPTp service-ready facilities is significantly associated with the IPTp2 

coverage (two doses) among pregnant women in rural areas. By contrast, the association between 

insecticide-treated net (ITN) availability and malaria parasitemia is significant in urban areas but not in 

rural areas. We did not find an association between either ITN use by households and ITN provision at the 

health facility or an association between children’s receipt of malaria diagnostic testing and diagnostic 

capacity at health facilities in either urban or rural areas. 

Conclusions 

The buffer linkage method can be applied in several health areas to examine how the service environment 

affects health services utilization when there are a household survey and a health facility census conducted 

around the same time period. This method has also been found to be useful in settings with low density of 

health facilities. For most health services, a significant association is found in rural (and other urban areas 

in Haiti) between access to or readiness of service provision and utilization of health services. The 

relationship does not hold in the metropolitan area in Haiti; more precise measurements of the health service 

environment would be needed for areas with both high density of health facilities and population. 
1 

1. Introduction and Literature Review 

Access to health services and quality of health services are a high priority for investments designed to 

increase health services utilization and eventually to improve health outcomes. However, understanding the 

role of health service environment on use and related outcomes has been constrained by limited data and 

methodological challenges in such an analysis. Analysis requires both data from facilities on service 

provision and data from households or the community on service use outcomes. Facility assessments can 

provide useful information on the availability and quality of services offered by providers, while household 

surveys provide information on population level need and demand for services. When available, combining 

data from representative household surveys and health facility assessments can be used to explore the 

relationship between service environment and use, including the role of service availability and quality in 

population-level use of health services. However, linking these two types of data is not straightforward 

primarily because of two reasons: 1) two types of surveys are independently conducted and are not 

necessarily designed for linking purposes; 2) the true location of clusters in household surveys is unknown 

due to geographic displacement of clusters for the purpose of protecting confidentiality. 

This study aims to fill the gap in the literature on the relationship between provision and utilization of health 

services by using a service environment linkage approach. Using data from independent household and 

health facility surveys in Haiti and Malawi, we linked households to health facilities within a specified 

distance (i.e. buffer) and used data from linked health facilities to measure the service environment. The 

relationship between the service environment and utilization of specific services was explored for a number 

of health areas including family planning (FP), maternal health care, child care, and HIV testing services in 

Haiti and malaria-related health care services in Malawi. 

1.1. Linking Household and Health Facility Data from the Same Survey 

Some studies have linked household and health facility data using information from the same survey. Early 

household surveys, including the World Fertility Surveys and the Demographic and Health Surveys (DHS) 

(Phase I to III) included a Service Availability Module (SAM). However, the SAM approach used key 

informants to generate data on service availability among a range of public and private providers and few 

visited actual facilities, raising questions about the validity of these data (Casterline 1987). Such data 

measured perceived access to services within a community rather than true access (Rose et al. 1999). 

A number of secondary analyses have linked facility characteristics and population data from these early 

DHS surveys that included SAM (Do and Koenig 2007; Magnani et al. 1999; Mroz et al. 1999; Pullum 

1991; Steele, Curtis, and Choe 1999). These analyses linked facility and individual data collected during 

the same survey in the same cluster. Pullum and colleagues developed a hierarchal model to assess the 

impact of service environment on contraceptive use. Their analysis of Guatemala 1987 DHS data found 

that increasing distance and travel time acted as a barriers to contraceptive use (Pullum 1991). Two similar 

analyses linked FP service availability data from the 1992 Morocco DHS with individual data for the 1992 

and 1995 DHS. These analyses found a significant association between the availability of FP methods at 

the closest public clinic and contraceptive use (Magnani et al. 1999) and proximity to a public health facility 

with increased adoption of modern FP methods after birth (Steele, Curtis, and Choe 1999). An analysis of 

the Vietnam 1997 DHS found that longer travel time to a commune health center was associated with 

significantly greater odds of discontinuing first and all methods of contraception. Individuals residing in 

communities with high quality health centers were also less likely to discontinue a method (Do and Koenig 

2007). However, Mroz and colleagues’ analysis of the 1992 Tanzania DHS and its 1993 facility supplement 

with additional service accessibility component found perceived facility quality, as reported by key 
2 

informants, was a better predictor of contraceptive use than time, distance, or perceived accessibility of a 

FP facility (Mroz et al. 1999). 

Rose and colleagues assessed the association between service environment and both use of FP and maternal 

health services in ten countries using data from the women’s questionnaire and SAM from DHS III surveys. 

This analysis found that across the ten countries representing the Latin American and Caribbean, Asia, and 

Africa regions, rural women living nearer to health services were not only more likely to use a modern 

contraceptive method, but also seek antenatal care (ANC) earlier and more frequently, deliver at a facility, 

and deliver with a skilled provider (Rose et al. 1999). 

1.2. Linking Data from Independent Household Surveys and Health Facility Surveys 

1.2.1. Household and health facility surveys used in linking analyses 

Multiple nationally representative stand-alone health facility surveys are now routinely conducted, 

including the Service Provision Assessment (SPA) by the DHS Program and the Service Availability and 

Readiness Assessment (SARA) by the World Health Organization (WHO 2014). Both the SPA and SARA 

collect nationally representative information on formal health facility readiness using an inventory scoring 

system. The SPA additionally collects data of adherence to standards of care through direct observation and 

client exit interview. These surveys typically collect information on a sample of formal sector health 

facilities. They do not collect data from community-based or informal points of care that may constitute the 

source of care for a significant proportion of health events. The SPA collects data from a census of health 

facilities in a limited number of countries (Hozumi et al. 2006). 

Beyond the SPA and SARA, there are additional mechanisms for collecting data on service provision. The 

World Bank has been conducting Service Delivery Indicator (SDI) surveys in several African countries to 

collect data on service delivery in education and health (The World Bank 2015). Zambia and Malawi have 

collected data on all public facilities and a sample of private facilities using the Japanese International 

Cooperation Agency’s (JICA) Health Facility Census tool (Hozumi et al. 2006). Population Council’s 

situation analysis has also been used in the past to capture health facility data on availability of reproductive 

services (Fisher et al. 1992). Other studies have been implemented to collect both population and facility 

data for specific programs or evaluation. 

1.2.2. Linking analyses in various health areas 

A number of analyses have linked population data from DHS and health facility data from SPA to look at 

the association between FP service availability, quality, and contraceptive use. Hong and colleagues looked 

at the association between service quality and IUD use, using data on household clusters from the Egypt 

2003 Interim DHS within 10 km of a public facility providing FP services sampled in the 2002 SPA. Their 

analysis found use of an IUD from a public facility was positively associated with living within the 

catchment area of a public facility with higher quality counseling and examinations. However, the authors 

note women may not have used the nearest facility (Hong, Montana, and Mishra 2006). Wang and 

colleagues analyzed FP data from four East African countries using regional administrative boundaries to 

link individual data and facility data on service environment, calculated as an index of quality of FP 

counseling, infection control, pelvic examination, and management practice. Their analysis found both an 

increase in the regional average number of available contraceptive methods and more favorable service 

environment were associated with greater odds of a woman in the region using a modern contraceptive 

method (Wang et al. 2012). 
3 

Other analyses have used targeted household and facility surveys to assess the influence of service 

environment on FP. Gage and Zomahoun used linked data from the 2009 Community Participation for 

Action in the Social Sectors project health facility and household surveys to look at the association between 

FP service environment and contraceptive outcomes in five Nigerian states. Their analysis found both better 

reproductive health staff training and stronger quality of provider-client interactions were associated with 

increased odds of modern contraceptive use within an local government area (Gage and Zomahoun 2011). 

Using data on individual reproductive health status and facility reproductive services from the 1999 

Delivery of Improved Services for Health (DISH) Evaluation Surveys in Uganda, Charles et al. found that 

proximity to a private health facility was associated with increased contraceptive use in urban areas; but 

none of the family planning service environment factors was independently associated with current use of 

modern contraception in rural areas (Katende, Gupta, and Bessinger 2003). Mensch and colleagues’ 

analysis of the 1992 Peru situation analysis and 1991-1992 DHS found increased contraceptive prevalence 

was associated with increased quality of facility care within the cluster. Quality of care was defined using 

an index of method availability, cleanliness, privacy, counseling, and provider method bias and 

restrictiveness (Mensch, Arends-Kuenning, and Jain 1996). 

A number of analyses have also looked at the relationship between service provision and maternal health 

services coverage. Wang and colleagues analysis of 2012 Haiti DHS and 2013 SPA data found higher 

average service readiness within 10 km of a cluster was associated with increased use of delivery services 

(Wang et al. 2014). In this study service readiness was assessed as a set of WHO-defined indicators of 

facility readiness to provide good-quality delivery services. A study by Gabrysch and colleagues using the 

Zambia DHS 2007 and national data from the Zambia Health Facility Census 2005 found increasing 

geographic distance was associated with decreasing odds of a woman delivering in a health facility. Access 

to higher-level health facilities was associated with increased odds of facility delivery. The authors noted 

the potential misclassification of level of obstetric care due to the difference in timing of the two surveys 

and imprecision in distance measures as a result of DHS cluster displacement and missing geo-coordinates 

(Gabrysch et al. 2011). Linking National Facility Censuses and DHS in Zambia and Malawi, Lohela et al. 

found that distance to the closest facility with delivery care was significantly associated with use of facility 

for delivery in both countries. Higher level of care in closet facility also increased use of facility delivery 

in Zambia but not in Malawi (Lohela, Campbell, and Gabrysch 2012). Kyei and colleagues’ analysis of the 

same datasets found no association between distance to a facility or level of service provided at a facility 

and timing of ANC or number of visits (Kyei, Campbell, and Gabrysch 2012). An analysis using the 1991 

Nepal FP and Health Survey with supplemental facility data from district records found use of ANC was 

associated with having a health post within the community and residing in the catchment area of a facility 

with higher structural quality, assessed as availability of trained staff, equipment, supplies and facilities 

(Acharya and Cleland 2000). 

Fewer analyses have looked at the association between service environment and use of HIV volunteer 

counseling and testing (VCT) services or curative health services. Hutchinson and Mahlalela used 

household data on adult health from the 2002-2003 Eastern Cape Primary Health Care Evaluation Survey 

and facility data from the 2002 Eastern Cape Facility Survey to measure the association between service 

provision and VCT use in Eastern Cape, South Africa (Hutchinson and Mahlalela 2006). The study found 

that among the rural population, increasing distance from a clinic offering VCT was associated with 

decreasing odds of an individual being tested. Among rural men, proximity to a clinic offering rapid testing 

and home visits for palliative care were associated with increased odds of being tested. Another study using 

a sample of married women and known information on primary health clinics in southern Mozambique 

found increasing distance to the closest clinic was associated with decreasing odds of HIV testing (Yao et 

al. 2012). One study using the Household and Health Facility Survey in four health districts Sri Lanka found 

distance to care was the most significant determinant in choice of facility for curative services, with 

increasing distance associated with decreasing likelihood of facility utilization (Akin et al. 1998). 
4 

1.2.3. Linking methods and limitations 

In the absence of surveys designed explicitly for linking, various methods may be used to associate data 

from population-based surveys, primarily the DHS, to stand-alone health facility survey data. Most analyses 

linked household or individual data to facilities using a measure of geographic proximity. Geographic 

linking methods include 1) linking households to the nearest one or more facilities either with (Hong, 

Montana, and Mishra 2006; Kyei, Campbell, and Gabrysch 2012) or without a limit on maximum distance 

to nearest facility (Akin et al. 1998; Gabrysch, Simushi, and Campbell 2011; Hutchinson and Mahlalela 

2006); 2) linking households to all facilities within a defined radius (Mensch, Arends-Kuenning, and Jain 

1996; Wang et al. 2014); 3) linking households to facilities within the same cluster (Acharya and Cleland 

2000; Katende, Gupta, and Bessinger 2003); and 4) linking households to facilities within the same 

administrative unit (Gage and Zomahoun 2011; Wang et al. 2012). 

There are a number of limitations associated with these analytical methods. Different linking methods may 

introduce errors. For example, assigning individuals or households to the nearest health facility does not 

account for potential provider bypassing (Rose et al. 1999; Skiles et al. 2013). Additionally, straight-line 

distance may not accurately reflect true provider accessibility, due to varied road distance, quality, terrain, 

and seasonal variation (Gage and Zomahoun 2011; Hong, Montana, and Mishra 2006; Rose et al. 1999; 

Wang et al. 2014). The DHS displaces household cluster locations and use of these displaced cluster 

locations may result in misclassification and imprecision using straight-line distance linking methods 

(Perez-Heydrich et al. 2013). Cluster or administrative boundary linkages do not account for potential use 

of providers outside of the catchment boundaries (Gage and Zomahoun 2011). Skiles and colleagues 

explored the issues of linking DHS household and SPA facility data using data from the 2007-2008 Rwanda 

Interim DHS and 2007 Rwanda SPA to simulate the effect of cluster sampling and displacement. Both 

sampling and displacement resulted in misclassification of cluster links and estimated relative service 

environment. Additionally, assigning individuals to the closest facility resulted in greater misclassification 

than linking an individual to all facilities within an administrative boundary. Facility sampling in particular 

was shown to have the potential to underestimate service availability. These findings highlight the need for 

careful consideration when designing or selecting studies and linking methods for these types of analyses 

(Skiles et al. 2013). 

Stand-alone facility surveys may not be conducted at the same time as a population-based survey and 

sampled facilities may not correspond with the cluster locations collected through population surveys. This 

limits the potential viability of using these data to assess the impact of service provision on use, as facility 

data may not accurately reflect the service environment for a household at the time of the population survey. 

Additionally, any analysis that uses health facility data drawn exclusively from public health facilities 

excludes the contribution of private sector or community-based providers to the service environment. The 

potential level of contribution of non-public health facilities varies by setting and service being assessed. 

In summary, multiple studies have found an association between service environment and use of FP, 

maternal, curative, and HIV services. However, analyses that combine facility and household data require 

special consideration. The geographic and temporal proximity of facility and household datasets, as well as 

the linking method employed, must be considered during analysis and interpretation of study findings. In 

this study, we link nationally representative household surveys and health facility censuses in Haiti and 

Malawi with the buffer linkage method, which is shown to be less prone to misclassification errors due to 

cluster displacement (Skiles et al. 2013). The reason for choosing Haiti and Malawi is that both have a 

national facility census and a household survey (DHS in Haiti and MIS in Malawi) conducted within a year 

apart. Given the data available in DHS and MIS, we analyzed the use of multiple health services in Haiti 

including FP, maternal health care, child care, and HIV testing services, and the use of malaria related 
5 

health services in Malawi. The section followed overviews country background and health care systems in 

both countries. 

1.3. Country Background and Health Systems in Haiti and Malawi 

1.3.1. Haiti 

Haiti is a small Caribbean country occupying the western third of the island of Hispaniola. The country is 

organized into 10 departments. Haiti is the poorest nation in the Western Hemisphere, with a per capita 

GDP of $1800 (Dowell, Tappero, and Frieden 2011) and over 80 percent of the population living below the 

poverty line (World Bank 2015a). Haiti has experienced political instability over much of its history and is 

vulnerable to natural disasters. In January 2011, Haiti experienced a devastating earthquake that killed or 

displaced millions and destroyed already weak infrastructure (Dowell, Tappero, and Frieden 2011). 

Haiti has a population of 10.32 million (WHO 2015a). Despite improvements in maternal and child 

survival, Haiti’s under-five mortality rate (73 deaths per 1000 live births) and maternal mortality ratio (380 

deaths per 100,000 births) are the highest in the region (Cayemittes et al. 2013). High fertility (TFR 3.5) 

and mortality rates have contributed to a young age distribution with a median population age of 22 years 

(Cayemittes et al. 2013). Haiti’s life expectancy at birth is the lowest in the region at 63 years (WHO 2015a). 

Haiti experiences high food insecurity because of stagnant agricultural production and high food prices 

(Pan American Health Organization 2012). Literacy rates are low, particularly in rural areas. Less than twothirds of the population has access to improved water and only a quarter has access to improved sanitation 

facilities. Infectious disease continues to contribute to a significant proportion of morbidity and mortality 

in the country. Acute respiratory infection, low birth weight, and diarrhea are the leading causes of underfive mortality. Haiti is the only Caribbean country still affected by malaria. Moderate HIV prevalence (2 

percent of the population age 15-49 years) has contributed to increased rates of tuberculosis (Chatterjee 

2008; Ivers 2011; UNDESA 2012). 

Haiti has a chronically underfunded and understaffed health system (World Bank 2015a). The government 

health system is organized into 3 levels, including approximately 700 primary health facilities (community 

health centers, dispensaries, and communal hospitals), supported by 10 departmental hospitals and 4 

university hospitals (Ivers 2011). The government has limited capacity to administer health services; the 

public sector accounts for only 38 percent of health infrastructure (Chatterjee 2008). While government 

expenditure on health has increased, Haiti has the lowest health care spending in the region ($83.7 

purchasing power parity) accounting for 5.5 percent of total government expenditure (WHO 2015a). Much 

of the health system is dependent on donor funding or NGO support. Health infrastructure is limited with 

only 1.3 hospital beds per 1,000 population (Dowell, Tappero, and Frieden 2011). Additionally, emigration 

of health workers to other countries has left the country under-resourced (Cayemittes et al. 2013). The 

majority of health facilities and workers are concentrated in urban centers, with nearly half of health 

facilities concentrated in the Port-Au-Prince area. Traditional medicine plays a significant role in health 

care, constituting the first source of care for almost 80 percent of the population. A number of private 

providers and retail pharmacies also operate in the country. The majority of drug retailers are not registered 

and are not operated by licensed prescribers (Ivers 2011). 

Cost and geographic distance to care are key barriers in access to quality health services in the country 

(Ivers 2011). Low access has resulted in low utilization of maternal, child, and infectious disease health 

services. Contraceptive prevalence remains low at 35 percent. Only 67 percent of pregnant women receive 

four or more ANC visits and 36 percent of births take place in health facilities. Immunization coverage is 
6 

low and fewer than half of children with fever and one-third with diarrhea are taken for care (Cayemittes et 

al. 2013). 

1.3.2. Malawi 

Malawi is a small land-locked nation in southern Africa, divided into 28 districts. Malawi is comprised of 

multiple ethnic groups, each with its own language. English and Chichewa are the most commonly spoken 

languages. Malawi’s economy is primarily agricultural and periodic droughts and flooding have often 

resulted in famine. Political inconsistency, corruption, and mismanagement have limited economic 

development, making Malawi one of the least developed nations in the world with a per capita GDP of 

$800 (Ministry of Development Planning and Cooperation 2010). 

Malawi is a densely populated country, with a population of over 17.3 million in country of less than 

100,000 km2

. Malawi achieved Millennium Development Goal 4 by decreasing under-five mortality from 

245 deaths per 1000 live births in 1990 to 68 deaths by 2015 (National Statistical Office and ICF Macro. 

2011; WHO 2015b). The maternal mortality ratio remains high, having halved to 550 deaths per 100,000 

births over the same period. Malawi’s total fertility rate is high at 5.4 births per woman, which coupled with 

high mortality, has resulted in a young population (National Statistical Office and ICF Macro. 2011). 

Almost half of the population is younger than 15 years (Ministry of Development Planning and Cooperation 

2010).

Half of Malawi’s population is below the poverty line. Less than a fifth of the population resides in an urban 

area. The majority of the population works in the agricultural industry. Population growth, environmental 

degradation, periodic droughts and flooding, and barriers to importation make a large proportion of 

Malawians food insecure (Ministry of Development Planning and Cooperation). Almost half of children 

are stunted (National Statistical Office and ICF Macro. 2011). While two-thirds of the population has access 

to improved water, only 28 percent have access to improved sanitation facilities (Ministry of Development 

Planning and Cooperation 2010). Three-fifths of adults over the age of 15 are literate. Infectious diseases 

are the leading cause of morbidity and mortality in the country. Malaria, acute respiratory infection, and 

HIV/AIDS are the leading causes of under-five mortality (UNICEF 2015). Malaria is endemic in 95% of 

the country and accounts for 30 percent of all outpatient hospital visits according to the Malawi Ministry 

of Health (Ministry of Health 2005). 

The government health sector accounts for approximately 60 percent of health services, 37 percent are 

provided by Christian Health Association of Malawi (CHAM) facilities, and private or NGO facilities 

account for 3 percent of the formal health sector, as reported by the Malawi Ministry of Health (Ministry 

of Health 2011). The government health sector includes a professional cadre of community health workers, 

a range of primary community health posts and centers, district hospitals, and central hospitals located in 

large urban centers. The government of Malawi and other stakeholders have used a sector-wide approach 

to direct public health efforts over the past decade. Malawi provides an Essential Health Package free of 

charge through the public sector and service level agreements with CHAM and non-profit facilities (WHO 

2015b). Malawi is heavily dependent on aid funding, which accounts for over half of total health 

expenditure (World Bank 2015b). Government spending on health has increased to 18 percent of total 

government expenditure (WHO 2009). Malawi experienced one of the most severe health human resource 

crises in sub-Saharan Africa due to high mortality from HIV/AIDS, lack of training programs, and 

emigration (Ministry of Health [Malawi]and ICF International 2014). The number of health facilities 

increased from 600 in 2011 to over 1,000 in 2013 (Mueller et al. 2011), however the average number of 

hospital beds remains low at 1.3 beds per 1,000 population (Ministry of Development Planning and 

Cooperation 2010). Inadequate infrastructure, lack of essential medicines, and the distance and cost of 

reaching care remain barriers to health care utilization (Burgert and Prosnitz 2014). 
7 

2. Data and Methods

2.1. Data 

The analysis was based on linked population data and health facility data from Haiti and Malawi, drawn 

from household surveys—the 2012 Haiti DHS and the 2012 Malawi Malaria Indicator Survey (MIS)—and 

service provision assessment surveys—the 2013 Haiti SPA and the 2013-14 Malawi SPA. The DHS and 

MIS surveys provide data on the utilization of health services as well as respondents’ socio-demographic 

characteristics. The SPA surveys provide information on the availability of health services at health 

facilities and facilities’ readiness to provide good-quality services. Geographic coordinates data collected 

in both surveys were used to link DHS/MIS clusters and SPA facilities. 

2.1.1. Population data 

Population data came from the 2012 Haiti DHS and the 2012 Malawi MIS. The 2012 Haiti DHS is a 

population-based household survey that provides representative estimates for the country as a whole, for 

urban and rural areas, and for the 10 administrative departments of Haiti. The survey used a two-stage 

cluster sampling design. At the first stage, 445 clusters, including 45 from the temporary camps that house 

the population displaced by the 2010 earthquake, were selected with probability proportional to their 

population size from a master national sample frame. At the second stage, a systematic sample of 13,181 

households was drawn in the selected clusters. All women age 15-49 in the sampled households were 

eligible for individual interview, in which their socio-demographic characteristics and health services 

utilization information were collected. The 2012 Malawi MIS is designed to provide representative data for 

key malaria indicators at the national level and at the regional level. A similar two-stage sample design was 

used to select a sample of 140 clusters and 3,500 households. All women age 15-49 in the selected 

households were eligible for individual interview. All children age 6-59 months listed in the household 

were eligible for anemia and malaria testing. 

Both the DHS and MIS surveys georeferenced the locations of the sampled clusters using Global 

Positioning System (GPS) receivers to collect the coordinates of the center of the populated areas of the 

clusters. Prior to release of the geographic dataset, the cluster coordinates were verified and geographically 

displaced. Coordinates of urban clusters were displaced up to a maximum distance of 2 km. In rural areas, 

the displacement distance was up to 5 km with a further, randomly selected, 1 percent of rural clusters 

displaced up to 10 km (Burgert et al. 2013). 

For Haiti, eight clusters with missing GPS data were excluded from the analysis. We also excluded 45 

camps clusters from the analysis because respondents in these clusters were likely to reside in a different 

location at the time when they needed health care; therefore, the health care environment was not the same 

as where they were surveyed. In the end, 392 clusters (66 in the metropolitan area, 85 in other urban, and 

241 in rural areas) were included in the analysis for Haiti. All 140 clusters sampled (44 in urban and 96 in 

rural areas) in the Malawi MIS were included in the analysis. In both countries, the analysis for each specific 

health service was limited to the respondents who had potential need for the service. For example, the 

analysis sample for ANC and delivery care was women who had a live birth in the five years preceding the 

survey. For family planning (FP), we restricted the analysis to all married women interviewed. Analyses of 

malaria outcomes were restricted to children less than five years of age (case management outcomes) and 

to women with recent births (prevention of malaria in pregnancy outcome). The analysis sample size and 

characteristics are described in the results section for each service area studied. 
8 

2.1.2. Health facilities data 

Data on health facilities came from two SPA surveys—the 2013 Haiti SPA and the 2013-14 Malawi SPA—

which provide a census of all the formal-sector health facilities in each country. The SPA surveys collect 

data on availability and readiness to provide key health services in public and private health facilities, from 

hospitals at the highest level to dispensaries or health posts at the lowest level. In total, 905 health facilities 

in Haiti and 977 facilities in Malawi were interviewed. Both SPA surveys collected GPS data of the 

locations of the health facilities. Unlike the DHS data, facilities’ coordinates are not displaced. 

For each specific health area, only facilities providing the service were included in the analysis. For 

example, for delivery care, only data on 195 hospitals and health centers with beds were used in this study. 

Health centers without beds and dispensaries were excluded because they are not mandated to provide 

delivery care and are rarely used for delivery care. In analyzing FP services, only the 756 facilities that 

provide FP services were included in the analysis. 

Facility data for this study came primarily from the facility inventory interview, which was administered to 

the most knowledgeable person for each specific health service within each facility. The SPA facility 

inventory collects data on infrastructure, equipment, commodities, and medicines that are necessary for 

providing specific health services. 

2.2. Linking Clusters and Health Facilities 

The GPS locations of all health facilities were collected in the SPA surveys in both Haiti and Malawi and 

released without displacement. The DHS and MIS surveys, however, are sample household surveys and the 

GPS location data of sampled clusters are displaced to protect respondents’ confidential data. The 

displacement of clusters limits our ability to link a cluster to its closest facility because the closest facility 

identified based on the released geographic data may not actually be the nearest facility in reality; therefore, 

such linkage is subject to substantial misclassification and errors (Skiles et al. 2013). In this analysis, we 

linked each cluster to all of the health facilities within a specific distance from the cluster and used data of 

the linked facilities to create measures of the service environment surrounding the cluster. 

The steps to link DHS clusters and SPA facilities are illustrated in Figure 1. First, within a GIS1

, a matrix 

was created with the direct distance measurement from every DHS cluster location to every health facility. 

Second, the facility-level variable on service provision, for example, availability of service X and facility’s 

readiness score to provide this service, were linked to each cluster in the “long” table. The distances were 

operationalized, creating a group of facilities within a 5-kilometer “buffer” distance from an urban cluster 

and a 10-kilometer “buffer” distance from a rural cluster. These buffer sizes were chosen to ensure that 

facilities within the same distance from the real location of the cluster were captured (given that urban 

clusters were displaced up to 2 km and most rural clusters were displaced up to 5 km). Finally, data from 

the facilities within the buffer were summarized to the cluster level to measure the cluster’s service 

environment within the specific distance. Depending on the health service area, various summary indicators 

of facility-level data were used to obtain service measurements at the cluster-level. In the example shown 

in the aggregation table (Figure 1), whether there was at least one facility offering service X within a 10 

km buffer from a rural cluster and the median readiness score of facilities within 10 km were used to 

measure the cluster’s access to service X in rural areas. 

 

1

 “Near Table” tool in ArcInfo (ESRI, Redlands, CA) 
9 

Figure 1. Illustration of linking DHS clusters and SPA facilities 

2.3. Definitions of Key Variables 

In this section, we describe two groups of key variables for the analysis of each type of health services. The 

first group is the outcome variables, which comprise 1) utilization of a specific service, 2) quality of services 

received, and 3) specific health outcome (malaria positivity). The second group is the explanatory variables, 

which describe the service environment. These variables measure the availability of specific services and/or 

facilities’ readiness to provide the service within a defined area around each survey cluster. In assessing the 

service readiness, the WHO Service Availability and Readiness Assessment (SARA) Reference Manual 

(WHO 2014) is used to guide the selection and definitions of readiness indicators. The service environment 

variables are measured at the cluster level and are derived from the facility-level data based on the linkage 

between clusters and facilities. 

2.3.1. Family planning 

The outcome variable in the FP analysis is women’s use of any modern contraceptive method including 

female sterilization, male sterilization, pill, intrauterine device (IUD), injectables, implants, male condom, 

and lactational amenorrhea method, and other modern methods. Condom users are excluded from the 

analysis because the sources of condoms are often beyond the facilities that a SPA survey usually captures. 

More than 80 percent of male condom users in Haiti reported their most recent source of condoms was 

pharmacies, shops, or friends/relatives (Cayemittes et al. 2013). 
10 

Regarding service provision, we focused on the range of contraceptive method choice at facilities. This was 

assessed by the number of facilities offering three or more modern methods (excluding condom) within the 

specific area. 

At the facility level, facilities offering FP services were identified by asking the facility manager if the 

facility provides, prescribes, or counsels on each of a series of modern methods and if the facility has valid 

products observed on-site (including pills, injectables, implants, and emergency contraception). Thus, for 

each FP facility the total number of modern methods provided was determined. Again, condoms were 

excluded. 

After linking clusters and FP facilities within a buffer of 5 km around a cluster in metropolitan and other 

urban areas and a buffer of 10 km around a cluster in rural areas, we counted the number of facilities offering 

three or more methods and used this number to classify clusters into three groups: low, medium and high 

level of access to multiple method choices. These three levels of choice were defined by geographic areas 

because of the varying distribution and number of facilities. In rural and other urban areas, clusters linked 

to no facilities providing three or more methods within the buffer were classified in the low-level group; 

those linked to one such facility were classified in the medium-level group; and the rest, which were linked 

to more than one facility offering three or more methods, were classified in the high-level group. All clusters 

in the metropolitan area had one or more facilities offering three or more methods within the buffer; 

therefore, we divided the clusters into low-, medium-, and high-level groups based on the terciles of the 

numbers of facilities. 

2.3.2. Antenatal care 

The analysis of ANC focused on the care that women received for their most recent birth in the five years 

preceding the survey. Two outcomes were examined: having four or more ANC visits and the quality of 

ANC received during pregnancy. A woman was considered to have high quality ANC if she received 6 out 

of 8 types of services at least once during pregnancy: weight measured, blood pressure measured, urine 

sample collected, blood sample collected, iron tested, counseled on pregnancy complications, intestinal 

parasite treatment, and full tetanus coverage. 

The key independent variable is the readiness to provide good quality ANC among the facilities linked to 

a cluster within the specific distance. Similar to the FP service measurement, the indicator of ANC at the 

cluster level is the aggregation of the facility-level data after clusters and facilities are linked. First, at the 

facility level, according to the WHO SARA Reference Manual (WHO 2014), eight readiness indicators 

were constructed to reflect ANC service readiness in four domains: staff and training, equipment, 

diagnostic, and medicines and commodities; their definitions are presented in Appendix Table A4. A 

service readiness score was then created for each facility by giving equal weights to the four domains and 

equal weights to the indicators within the same domain. If the target readiness score is 100 points for a 

facility, each domain weighs 25 points and each indicator weighs 25 divided by the number of indicators 

in the domain. Each facility was assigned the appropriate points based on the presence or availability of 

each indicator. A facility receives a total score by summing up all indicators. The readiness score is a 

relative summary indicator of how ready a health facility is to provide good-quality ANC services. A higher 

score indicates better readiness and a lower score indicates poorer readiness compared with other facilities. 

After linking DHS clusters and SPA facilities as described above, the service readiness to provide ANC 

among facilities around a cluster was measured by the median readiness score of the facilities within the 

buffer. Given that the readiness score is a relative measurement, we divided the clusters into low-, medium-, 
11 

and high-level groups based on the score terciles at the cluster-level. The terciles were also generated 

separately for the three geographic areas. 

2.3.3. Facility delivery 

The outcome variable for the analysis on facility delivery is dichotomous, indicating whether or not a 

woman used a facility for delivery care for the most recent birth in the five years preceding the survey. 

In analyzing the service environment, we focused on the availability of normal delivery services, basic 

emergency obstetric care (BEmOC), and comprehensive emergency obstetric care (CEmOC) at health 

facilities because of their importance in reducing maternal mortality. As mentioned earlier, only hospitals 

and health centers with beds that are mandated to provide delivery services were included in the analysis. 

We first identified facilities that provide normal delivery services based on a question asked of the facility 

inventory respondent: if the facility provides normal delivery service. Using the latest definitions of 

emergency obstetric care (WHO et al. 2009), BEmOC is considered available if the facility is able to 

implement the following six signal functions: administration of antibiotics, administration of uterotonic 

drugs/oxytoxics, administration of anticonvulsants, manual removal of placenta, assisted vaginal delivery, 

and removal of retained products. A full package of CEmOC includes all six BEmOC functions plus ability 

to perform a caesarean section and blood transfusion. Using these criteria, facilities with BEmOC or 

CEmOC were identified. 

After linking clusters and health facilities, two service environment indicators at the cluster level were 

created—one on the availability of BEmOC and the other one on the availability of CEmOC. For the 

BEmOC indicator, clusters were categorized into three groups based upon the availability of normal 

delivery and BEmOC at health facilities within the specific distance from the cluster: 1) clusters linked to 

no facilities offering normal delivery, 2) clusters linked to a facility offering normal delivery but without 

BEmOC, and 3) clusters linked to a facility offering both normal delivery and BEmOC. The CEmOC 

indicator was constructed in a similar way with three categories: 1) clusters linked to no facilities offering 

normal delivery, 2) clusters linked to a facility offering normal delivery but without CEmOC, and 3) clusters 

linked to a facility offering both normal delivery and CEmOC. 

2.3.4. Child care 

Care seeking in health facilities for children’s diarrhea is the primary focus of the child care analysis. The 

outcome is whether the child with diarrhea was taken to a health facility. The denominator for this outcome 

is children under five who had diarrhea in the two weeks preceding the survey. 

In measuring the service environment for providing child health care, we examined the readiness to provide 

child curative care among the facilities linked to a cluster within the specific distance. The method used to 

create the readiness measurement for child curative care is similar to that used for ANC. First, among 

facilities that provide child curative care, a number of indicators representing a facility’s readiness to 

provide child curative care in four domains (staff and training, equipment, diagnostic, and medicines and 

commodities) were generated according to the WHO SARA Reference Manual (WHO 2014). These 

indicators and their definitions are presented in Appendix Table A11. With a similar approach to that used 

to create the ANC readiness score, a score reflecting the relative readiness to provide child curative care 

services was constructed for each facility. 
12 

The facility readiness score was aggregated to the cluster level after the DHS clusters and SPA facilities 

were linked. The median readiness score of the facilities within the buffer from the cluster was calculated 

and used to divide clusters into three groups with access to low, medium, and high level of readiness for 

child curative care based on the score terciles at the cluster-level. The terciles were generated separately for 

metropolitan, other urban, and rural areas. 

2.3.5. HIV testing 

In the analysis of HIV testing, we focused on the utilization of HIV testing services, defined as women who 

were tested for HIV in the 12 months preceding the survey. The denominator of this outcome is all women 

interviewed. 

Regarding the health facilities, among those that reported providing HIV testing and counseling services, 

each was examined regarding HIV testing capacity and the availability of items to support quality testing 

and counseling services. A facility is considered to have HIV testing capacity if the facility can conduct the 

test on-site with RDT kit or ELISA test, with functioning equipment and the reagents needed observed onsite (WHO 2014). Items that support quality HIV testing and counseling services include guidelines for 

HIV counseling and testing, availability of staff trained in HIV counseling and testing, visual and auditory 

privacy, and condoms. The selection of these indicators was guided by the WHO SARA Reference Manual 

(WHO 2014). 

We measured clusters’ access to HIV testing services with low, medium, and high levels relative to the 

geographic areas. In rural areas and other urban, a cluster is categorized as having low access to HIV testing 

services if it is linked to no facilities with HIV testing capacity, medium access if it is linked to a facility 

with HIV testing capacity but not having all the supportive items, and high access if it is linked to a facility 

with HIV testing capacity and all the supportive items. In the metropolitan area, all clusters were linked to 

at least one facility that demonstrated HIV testing capacity and 98 percent were linked to a facility with 

both HIV testing capacity and all items to support quality HIV testing and counseling services. Therefore, 

we classified clusters in the metropolitan area into three levels of access by using the terciles of the number 

of health facilities that provide HIV testing and counseling services. 

2.3.6. Malaria-related health services 

Three outcomes of malaria prevention and treatment were measured at the population level. The first was 

intermittent preventive treatment during pregnancy (IPTp2), defined as receipt of at least two doses of 

sulphadoxine-pyrimethamine (SP) for prevention of malaria, with at least one dose administered at an ANC 

visit, among interviewed women who had a live birth in the two years preceding the survey. The other two 

outcomes were measured in children: the coverage of malaria diagnostic testing in children with fever in 

the two weeks preceding interview; and malaria positivity as measured by microscopy among children age 

6-59 months. Information on malaria diagnostic testing is not collected directly through the Malawi MIS 

questionnaires. Instead, a proxy measure was used—whether a child under five years of age with a fever in 

the past two weeks had blood taken from a finger- or heel-stick. This information is collected for children 

of interviewed women. The other child health outcome is malaria parasitemia prevalence as measured via 

microscopy among all children age 6-59 months from interviewed households in the Malawi MIS. 

Health service capabilities related to each of the three outcomes were assessed in health facilities. These 

include facility’s readiness in providing IPTp, malaria diagnostic capacity, and the availability of 

insecticide-treated mosquito nets (ITNs). A facility was considered to be IPTp-ready if it had SP in stock, 

guidelines on IPTp, and a staff member trained in IPTp administration in the last 24 months. Malaria 
13 

diagnostic capacity refers to a facility having unexpired malaria rapid diagnostics test (RDT) kits in stock, 

a staff member trained in RDT within the past 24 months, and a malaria RDT protocol available or a 

functioning microscope with relevant stains and glass slides and a staff member trained in microscopy 

within the past 24 months. The availability of ITN at health facilities was assessed by asking if ITNs or 

vouchers for ITNs were available in the facility for distribution to clients on the day of the survey. 

The service environment at the cluster level was defined according to the number of health facilities within 

10 km in rural areas and 5 km in urban areas that met the criteria of readiness or availability. For example, 

for the cluster-level service readiness indicator about ITN availability, we counted the number of health 

facilities within the buffer that had ITNs available and used this number to divide clusters into low (no 

facility providing ITNs), medium (1-2 facilities providing ITNs), and high (3 or more facilities providing 

ITNs) level access to facilities with ITNs. Similarly, clusters were also classified into three levels in terms 

of IPTp readiness and malaria diagnostic capacity. Cutoff values for different levels were determined based 

on the distribution of the number of health facilities with service ready or available and varied among the 

indicators and sometimes between urban and rural areas. 

2.4. Statistical Analysis 

Separate analyses were conducted for Haiti and Malawi. For Haiti, a number of health service areas were 

studied including family planning, ANC, delivery care, care seeking for children’s diarrhea, and HIV 

testing. We focused on malaria-related services in Malawi because only malaria-related outcome data were 

collected in the Malawi MIS. The analysis was stratified by urban-rural residence because of substantial 

differences in the health service environment and in health care seeking behaviors between urban and rural 

areas. In Haiti, we further separated the Port-au-Prince metropolitan area (Port-au-Prince and urban zones 

of the Ouest region) from other urban areas because of the substantial differences in the density and types 

of health facilities2

. 

The analysis starts with descriptive statistics in three dimensions. First, at the health facility level, we 

described the background characteristics of health facilities that provide the service, the availability of 

services, commodities, or supplies at these facilities. Second, at the cluster level, after linking clusters to 

health facilities, the service environment indicators were constructed and summarized. Lastly, individual 

socio-demographic characteristics and other covariates that may be associated with the utilization of health 

services were described. Bivariate relationships between the individual’s utilization of health services and 

the service environment where they live were examined. We also visualized the relationships by showing 

selected outcomes of interest at the regional level and relevant facility-level variables on maps for Haiti. 

The effect of complex sample design was taken into account in the descriptive analysis. 

In the multivariable analysis, logistic regressions were used because all the outcome variables are 

dichotomous. The DHS and MIS data follow a hierarchical structure—that is, individuals are nested within 

households and households are nested within clusters. Respondents who live in the same cluster may not 

be independent of one another. Individual-level analysis ignores the nesting of people within clusters, which 

can result in underestimation of standard errors. Moreover, the outcome variable is at the individual level 

but the key explanatory variables of most interest—the service environment indicators—are at the cluster 

level. A multilevel analysis approach is more appropriate to allow for simultaneous investigation of the 

effects of the group-level and individual-level predictors on individual-level outcomes (Raudenbush and 

 

2

 Seven rural clusters were reallocated to the metropolitan group because they are located on the edge of Port-auPrince and are linked to a large number of health facilities. Two clusters next to Cap Haitien were reassigned to the 

“other urban” group for a similar reason. 
14 

Bryk 2002). Therefore, we applied multilevel (individual- and cluster-level) random intercept logistic 

regression models to investigate how the service environment affects the utilization of health services. 

In the regression models there were three possible outcomes: utilization of a specific service, receipt of a 

specific intervention, and a specific health outcome (for example, malaria status in children). The key 

predictors were level of access to the service and level of readiness to provide the service, both of which 

were measured at the cluster level. For each outcome, the regression adjusted for covariates that have been 

shown in the literature to be associated with the outcome. These typically include age, education, 

occupation, and household wealth. Other covariates, depending on the type of outcome, were also included. 

For example, in analyzing women’s use of modern contraceptive methods, number of living children and 

women’s exposure to FP messages were included in the regressions. Mother’s age at birth and birth order 

were important covariates and thus controlled for in the analysis of ANC and facility delivery. Number of 

ANC visits was adjusted for in the regression of facility delivery. In the analysis of HIV testing, in addition 

to individual socio-demographic variables, we controlled for the variable: having a birth in the 12 months 

before the interview, because women may have been tested for HIV during ANC visits. In the analysis of 

malaria parasite prevalence in children, household ownership of an ITN was included because ITNs are 

protective against malaria. In the analysis of IPTp coverage in women, exposure to malaria messages in the 

media was included as a covariate in the multivariable models. 
15 

3. Results 

Results are presented by health service area. In each section, we start with a description of health facilities 

including background characteristics of health facilities, availability of specific services, commodities, or 

supplies at health facilities. We then describe indicators of the service environment within the specific 

buffer around DHS clusters. Lastly, relationships between each outcome and the service environment are 

presented, controlling for relevant covariates. 

3.1. Family Planning 

3.1.1. Contraceptive methods choices in family planning facilities 

Among the 905 health facilities interviewed in the Haiti SPA, 756 were identified to provide FP services, 

the majority of which were located in rural areas. Table 1 shows the distribution of the FP facilities in each 

geographic location by type of facility, managing authority and department. In the metropolitan area, over 

60 percent of FP facilities are health centers without beds, while hospitals are 20 percent of the total. In 

contrast, in rural areas dispensary is the most common type of facility that provides FP services. The 

composition of FP facilities in other urban areas is more evenly distributed among different facility types. 

While the majority of FP facilities in rural and other urban are managed by the government or a mixed 

government and private non-profit managing author, private for-profit FP facilities are more commonly 

seen in the metropolitan area.

Table 1. Characteristics of health facilities in Haiti that provide family planning 

services, by residence, Haiti SPA 2013 

Characteristics Metropolitan Other urban Rural Total 

Type of facility 

Hospital 20.3 29.3 3.3 11.2 

Health center with beds 14.6 22.6 12.0 14.5 

Health center without 

beds 63.4 33.5 22.6 31.4 

Dispensary 1.6 14.6 62.1 42.9 

Managing authority 

Government 15.4 53.4 45.6 42.2 

NGO/private not-for-profit 25.2 9.4 14.7 15.4 

Private for-profit 46.3 13.3 17.0 21.0 

Mixed 13.0 23.9 22.8 21.4 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 13.3 21.9 32.9 

Sud-Est na 6.6 9.5 7.4 

Nord na 15.3 8.9 8.7 

Nord-Est na 3.3 5.8 4.4 

Artibonite na 8.6 19.0 13.9 

Centre na 6.6 5.4 4.7 

Sud na 20.6 6.6 8.3 

Grand-Anse na 10.0 5.0 5.1 

Nord-Ouest na 8.9 14.2 10.8 

Nippes na 6.6 3.9 3.8 

Total number of facilities 123 150 483 756 
16 

FP facilities in Haiti reported a wide range of modern contraceptive methods, with the most commonlyreported methods being pills, injectables, male condom and implant (Appendix Table A1). Overall, pill, 

injectables and male condom are provided by 77 percent or more of the facilities. Implant was reported by 

a considerable proportion of FP facilities (42 percent) in other urban areas, but by a much smaller group of 

facilities in metropolitan and rural areas. Figures 2a-c show the percentage of facilities that provide at three 

modern methods by residence, facility type and managing authority. The highest level of availability is in 

other urban areas: 51 percent of facilities in other urban areas provide at least three methods, followed by 

the metropolitan area at 28 percent and rural areas at 23 percent. By facility type, the percentage providing 

three or more methods is highest in hospitals and lowest in dispensaries. A limited proportion (25 percent) 

of health centers without beds reported to have at least three methods. About one–third of the government 

FP facilities reported having at least three methods, compared with 20 percent of the private for-profit 

facilities. 

Figure 2a. Percentage of FP facilities providing at least three modern contraceptive methods, 

by residence, Haiti SPA 2013 

27.6

50.5

23.1

29.3

0

20

40

60

80

100

Metropolitan Other urban Rural Total
17 

Figure 2b. Percentage of FP facilities providing at least three modern contraceptive methods, 

by facility type, Haiti SPA 2013 

Figure 2c. Percentage of FP facilities providing at least three modern contraceptive methods, 

by managing authority, Haiti SPA 2013 

3.1.2. Access to contraceptive method choices in DHS clusters 

In the Haiti DHS, after excluding 45 camp clusters and 8 clusters with missing GPS data, 392 clusters 

remained in the analysis including 66 in metropolitan areas, 85 in other urban, and 241 in rural areas. 

Among the linked facilities within the buffer, we counted the number of facilities that provide at least three 

methods and used this number to categorize clusters. Figure 3 shows the distribution of clusters by the level 

of access. In rural areas, 17 percent of the clusters have low level of method choices, meaning no facility 

with 10 km from the cluster offers at least three modern methods, 29 percent and 53 percent have medium 

and high level of choice, respectively. Out of the 85 clusters in other urban areas, 42 percent of the clusters 

have high level of choice, i.e. linked to two to five facilities that reported availability of at least three 

62.4

40.9

24.8

20.0

0

20

40

60

80

100

Hospital Health center with

beds

Health center

without beds

Dispensary

34.1

30.1

20.1

28.4

0

20

40

60

80

100

Government NGO/private notfor-profitPrivate for-profit Mixed
18 

methods. All clusters in metropolitan areas have at least one (up to 21) facilities offering three or more 

methods within 10 km; therefore, we divided the clusters into low, medium, and high levels of choice of 

access based on the terciles of the number of facilities. 

Figure 3. Percent distribution of DHS clusters in Haiti by level of access to facilities with three or 

more methods and residence 

3.1.3. Modern contraceptive use and its association with the FP service environment 

Overall, 7,339 married women were analyzed for their use of modern contraception. Background 

characteristics of these women in each geographic location are shown in Appendix Table A2. Modern 

contraceptive prevalence rate (except condom use) ranges from 22 percent in the metropolitan area to 30 

percent in rural and other urban areas. Figure 4 visualizes the bivariate relationship between the location of 

FP facilities with at least three family planning methods (excluding condom) available (green dots) and 

percent of married women 15-49, by department, who are currently using a modern contraceptive method 

(excluding condom) ranging from low (light pink) to higher (dark pink). Overall, areas with more facilities 

providing three or more methods (higher density of dark green dots) coincide with areas where there are a 

higher percentage of women using modern contraception (dark pink departments). 

38.6

16.7 17.2

35.3

41.6

29.4

26.1

41.7

53.4

0

20

40

60

80

100

Metro Other urban Rural

Low Medium High
19 

Figure 4. Map of bivariate relationship between the location of FP facilities in Haiti with at least 

three modern family planning methods and percentage of married women age 15-49 who are 

currently using a modern contraceptive method by department, Haiti DHS 2012 and Haiti SPA 2013 

Specifically, Figure 5 highlights the bivariate relationship between women’s use of modern contraception 

and the level of access to multiple FP methods. In rural areas, modern contraceptive use increases with the 

level of access to modern methods. Among women who lived in areas with high level of access to multiple 

method choices, 35 percent reported current use of a modern contraception, while among those who were 

from clusters in which no facility within the service environment offered three or more methods, only 20 

percent used a modern method. In other urban areas, the medium and high-level groups show high use of 

contraception than the low-level group. The bivariate relationship between modern contraceptive use and 

the level of access to methods is not obvious in the metropolitan area. The bivariate associations between 

contraceptive use and other covariates are presented in Appendix Table A3. 
20 

Figure 5. Women’s use of modern contraceptive methods in Haiti by level of access to FP facilities 

with 3+ methods and residence 

Using multilevel, multivariable logistic regression models, we assessed how women’s contraceptive use is 

associated with the level of method choice within the service environment. We ran a multilevel model for 

each of the three geographic areas. Table 2 presents odds ratios and 95% confidence intervals for the level 

of access to contraceptive methods as well as for the covariates adjusted for in the models. In rural areas 

the level of method choice is positively and significantly associated with women’s use of modern 

contraception after controlling for women’s characteristics. Compared with the women who lived in a 

cluster with low level of access to multiple methods, the odds of using a modern contraceptive method is 

73 percent higher for women from a cluster with medium level of access, and over two times higher for 

those from a community with high level of access. A similar positive relationship is also found in other 

urban areas. However, in the metropolitan area we found no significant association between women’s use 

of modern contraception and their communities’ access to facilities that provided three or more 

contraceptive methods. Among women’s individual characteristics, woman’s age, the number of living 

children she had, and her contact with FP provider are consistently associated with her use of contraception 

in all geographic areas. Household wealth and household wealth do not seem to be important predictors of 

contraceptive use. 

 

0%

20%

40%

60%

80%

100%

Metro Other urban Rural

Low Medium High
21 

Table 2. Results of multivariable logistic regressions of use of modern contraceptive 

methods among married women age 15-49 in Haiti, by residence 

 Metropolitan Other urban Rural 

Variables OR 95% CI OR 95% CI OR 95% CI 

Level of access to FP 

methods 

Low 1.00 1.00 1.00 

Medium 0.84 0.54 - 1.30 1.77** 1.19 - 2.63 1.73** 1.23 - 2.45 

High 0.91 0.56 - 1.46 2.01** 1.33 - 3.05 2.23*** 1.61 - 3.09 

Age 

15-24 1.00 1.00 1.00 

25-34 0.84 0.55 - 1.30 0.78 0.54 - 1.13 1.04 0.83 - 1.31 

35-44 0.33*** 0.19 - 0.58 0.52** 0.33 - 0.81 0.81 0.61 - 1.06 

45-49 0.22*** 0.10 - 0.50 0.25*** 0.13 - 0.47 0.29*** 0.20 - 0.42 

Number of living kids 

0-1 1.00 1.00 1.00 

2-3 2.99*** 2.05 - 4.37 2.10*** 1.54 - 2.86 2.70*** 2.15 - 3.38 

4-5 2.96*** 1.66 - 5.26 1.78** 1.15 - 2.75 3.32*** 2.52 - 4.37 

6+ 1.92 0.68 - 5.47 2.27** 1.23 - 4.20 3.19*** 2.32 - 4.38 

Education 

None 1.00 1.00 1.00 

Primary 1.54 0.86 - 2.74 1.08 0.71 - 1.64 1.09 0.91 - 1.31 

Secondary or higher 1.71 0.93 - 3.12 1.02 0.65 - 1.61 1.13 0.87 - 1.46 

Occupation 

Unemployed 1.00 1.00 1.00 

Agriculture 2.30* 1.14 - 4.65 1.07 0.57 - 2.02 1.27 0.98 - 1.63 

Professional 1.17 0.83 - 1.66 1.23 0.93 - 1.63 1.28** 1.08 - 1.52 

Other 0.56 0.26 - 1.21 1.76 0.97 - 3.18 1.17 0.67 - 2.04 

Wealth Quintile 

Lowest na na 1.00 

Second na na 0.93 0.78 - 1.11 

Middle na na 1.01 0.80 - 1.29 

Lowest-Middle 1.00 1.00 na 

Fourth 1.05 0.63 - 1.74 0.86 0.63 - 1.18 1.22 0.83 - 1.79 

Highest 0.71 0.41 - 1.22 0.64* 0.43 - 0.93 0.77 0.40 - 1.46 

Religion 

Catholics 1.00 1.00 1.00 

Protestant 1.04 0.75 - 1.44 0.79 0.61 - 1.02 0.78** 0.67 - 0.92 

Vaudousant/other 1.36 0.76 - 2.42 0.58 0.32 - 1.05 0.94 0.70 - 1.25 

Exposure to FP messages 

No exposure 1.00 1.00 1.00 

Weekly or more 0.86 0.62 - 1.19 0.91 0.71 - 1.17 1.03 0.88 - 1.20 

Contact with FP provider 

No 1.00 1.00 1.00 

Yes 2.29*** 1.61 - 3.26 1.89*** 1.45 - 2.46 1.82*** 1.55 - 2.13 

Number of women 1181 1402 4079 

Number of clusters 66 85 241 

***p<0.001 **p<0.01 *p<0.05
22 

3.2. Antenatal Care 

3.2.1. Antenatal care service readiness at health facilities 

Table 3 shows the distribution of the ANC facilities in Haiti in each geographic location by type of facility, 

managing authority and department. There are 832 facilities that provide ANC services, the majority (63 

percent) of which are located in rural areas (Table 3). Most often the government is the managing authority 

in other urban and rural areas, while close to half of the ANC facilities in the metropolitan area are private 

for-profit. Dispensaries account for almost 60 percent of facilities in rural areas, yet they represent only 4 

percent of the facilities in the metropolitan area. 

Figure 6 shows the indictors used to create the ANC service readiness score by metropolitan, other urban, 

and rural areas. The difference in indicators by residence is minimal. The largest disparity is in the 

availability of a staff member trained in ANC; 60 percent of facilities in the metropolitan area have at least 

one staff providing ANC services trained in some aspects of ANC in the last two years, while less than 40 

percent in rural areas have at least one. The service readiness indicators are similar between facility types 

(Appendix Table A4), with the largest discrepancy again being in trained staff. Only 27 percent of all 

dispensaries have at least one trained staff available. 

Table 3. Characteristics of health facilities in Haiti that provide antenatal 

care services, by residence, Haiti SPA 2013 

Characteristics Metropolitan Other Urban Rural Total 

Type of facility 

Hospital 21.5 29.8 4.2 12.2 

Health center with beds 11.4 23.6 13.4 15.0 

Health center without beds 63.1 31.8 23.8 32.4 

Dispensary 4.0 14.9 58.6 40.4 

Managing authority 

Government 14.1 50.4 43.7 39.7 

NGO/private not-for-profit 26.8 10.5 15.9 16.8 

Private for-profit 46.3 14.9 17.8 22.3 

Mixed 12.8 24.2 22.6 21.1 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 13.0 24.3 35.6 

Sud-Est na 8.1 9.6 7.6 

Nord na 18.0 8.8 9.0 

Nord-Est na 3.1 5.5 4.1 

Artibonite na 8.7 18.0 13.0 

Centre na 6.8 6.3 5.3 

Sud na 18.6 6.1 7.4 

Grand-Anse na 9.3 4.8 4.8 

Nord-Ouest na 7.6 13.3 9.8 

Nippes na 6.8 3.4 3.5 

Total number of facilities 149 161 522 832 
23 

Figure 6. Percentage of ANC facilities with specific items for supporting ANC services 

by residence, Haiti SPA 2013 

3.2.2. Access to ANC facilities and service readiness in DHS clusters 

Based on geographic location, clusters were linked to between three to 89 health facilities in the 

metropolitan area, one to twenty in other urban, and one to 22 in rural areas. The ANC service environment 

measure was created from the median of all facility readiness scores within the specified cluster’s service 

environment. Figure 7 shows the range, median, and outliers of readiness scores among DHS clusters by 

residence. The median score is very similar across all residence areas; however, the distribution of the score 

is much narrower in the metropolitan area compared to other urban and rural, which have a much broader 

range of scores. The distributions in rural and other urban areas are similar. 

0

20

40

60

80

100

National

ANC

guidelines

available

At least

one

trained

staff*

Blood

pressure

apparatus

Urine

protein

testing

Iron

testing

Iron

tablets

Folic acid

tablets

Tetanus

vaccine

Metro

Other Urban

Rural
24 

Figure 7. Boxplots of ANC readiness scores among clusters, by residence, Haiti SPA 2013 

3.2.3. Recommended levels of ANC services that women received and determinants 

Among all women surveyed in the 2012 Haiti DHS, 4,921 gave birth in the five years preceding the survey. 

Appendix Table A5 shows the background characteristics of these women. The majority of women reside 

in rural areas (59 percent), followed by the metropolitan area (25 percent), and then other urban areas (17 

percent). Of the women in the metropolitan area, 70 percent reside in a service environment with medium 

or high-level access to facilities with ANC readiness. In other urban areas, 64 percent reside in the same 

service environment. In rural areas, 62 percent of women have a medium or high level ANC service 

environment. Both outcomes, having four or more ANC visits during their last pregnancy and having high 

quality ANC, are more prevalent among women from urban areas compared to those from rural areas. 

High quality ANC was defined as having any six out of eight of the following: weight measured, blood 

pressure measured, urine sample taken, blood sample taken, 90 days of iron supplementation, tetanus 

vaccine given, informed of signs of complications, intestinal parasites treatment given during pregnancy. 

Figure 8 shows there does not appear to be a large disparity in the receipt of individual components of ANC 

care across residence areas. While some components are widely received across all localities, for example 

weight and blood pressure measurements; others are less prevalent, like parasite treatment, in all three areas. 

The exception is receipt of the full 90-day iron supplementation: there is a noticeably higher prevalence of 

iron supplementation in other urban areas compared with both rural and metropolitan areas. 

20

40

60

80

100

Median readiness score

Metropolitan Score Other Urban Score

Rural Score
25 

Figure 8. Percentage of women age 15-49 who received selected components of ANC, 

by residence, Haiti DHS 2012 

The relationship between facility service readiness to provide ANC services and the use of ANC services 

by department is illustrated in Figure 9. Service readiness of facilities is represented by green dots, range 

from low readiness (light green) to high readiness (dark green). The percentage of women who gave birth 

in last five years who had 4 or more ANC visits at last pregnancy by department ranges from low (light 

pink) to high (dark pink). The Aire Metropolitaine and Nord-Est have the highest density of dark green dots 

(high readiness) as well as the departments with the highest percentage of women having 4 or more visits 

(dark pink), though many of the other departments have fairly high density of medium and high readiness 

among their facilities. 

0 20 40 60 80 100

Weight measured

Blood pressure measured

Urine sample taken

Blood sample taken

Iron supplemment, 90 days or more

Informed of signs of complications

Intestinal parasite treatment

Full tetanus protection

Received 6 of 8 components

Rural Other Urban Metro
26 

Figure 9. Map of bivariate relationship between facility service readiness to provide ANC services 

in Haiti and women’s receipt of 4 ANC visits, by department, Haiti DHS 2012 and Haiti SPA 2013 

Figures 10 and 11 show women’s receipt of four or more ANC visits and high quality ANC, by level of 

ANC service readiness within the service environment. In rural areas for both outcomes, it appears a higher 

proportion of women in areas with better ANC service readiness received the recommended ANC services. 

The relationship, however, is not obvious in metropolitan and other urban areas. Appendix Tables A6 and 

A7 show the bivariate relationships between women’s background characteristics and the two ANC 

outcomes. 
27 

Figure 10. Percentage of women in Haiti with 4 or more ANC visits by level of service readiness 

and residence 

Figure 11. Percentage of women in Haiti who received high quality ANC by level of service 

readiness and residence 

0%

20%

40%

60%

80%

100%

Metro Other urban Rural

Low Medium High

0%

20%

40%

60%

80%

100%

Metro Other urban Rural

Low Medium High
28 

These two variables, four or more ANC visits and high quality of ANC care, were used as outcomes in 

logistic regression models to test the association between the respective outcome and service readiness 

environments while controlling for other covariates. Separate models were fitted for metropolitan, other 

urban, and rural areas for each outcome. As seen in Table 4, having a higher quality service environment 

was associated with having four or more ANC visits in rural and other urban areas. In rural areas, women 

in clusters with facilities with a high readiness score have almost two times the odds (OR=1.99, 95% 

CI=1.480–2.668) of having four or more ANC visits compared to women in clusters with facilities with a 

low readiness score. In other urban areas, women in clusters with high-level service environment had 68 

percent higher odds (OR=1.68, 95% CI=1.041–2.710) of having had four or more ANC visits, compared to 

women in clusters with low-scoring facilities. Service readiness was not significantly associated with 

having four or more visits in the metropolitan area. Other covariates including woman’s age at birth, birth 

order, household wealth, and mass media exposure were also found to be significant predictors of receiving 

4 or more ANC visits and the associations are in the expected direction. 

Table 4. Results of multivariable logistic regressions of having 4 or more ANC visits 

among women in Haiti who had a live birth in the five years preceding the survey, by 

residence 

 Metro Other Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Facility service readiness 

Low 1.00 1.00 1.00 

Medium 0.71 0.42 - 1.19 1.09 0.70 - 1.70 1.26 0.94 - 1.69 

High 0.77 0.43 - 1.40 1.68* 1.04 - 2.71 1.987*** 1.48 - 2.67 

Maternal age at first birth 

<20 1.00 1.00 1.00 

20-34 2.92*** 1.67 - 5.10 2.26** 1.26 - 4.04 1.84*** 1.39 - 2.43 

35-49 3.57** 1.60 - 7.95 1.93 0.87 - 4.27 1.88** 1.29 - 2.74 

Birth Order 

1 1.00 1.00 1.00 

2-3 0.49** 0.30 - 0.82 0.62 0.37 - 1.03 0.67** 0.52 - 0.85 

4-5 0.43* 0.22 - 0.85 0.47* 0.26 - 0.88 0.70* 0.52 - 0.94 

6+ 0.20*** 0.08 - 0.46 0.78 0.34 - 1.76 0.60** 0.42 - 0.85 

Education 

None 1.00 1.00 1.00 

Primary 1.08 0.58 - 2.03 1.62 0.93 - 2.81 1.34** 1.09 - 1.65 

Secondary or higher 1.65 0.83 - 3.27 2.90*** 1.57 - 5.36 2.24*** 1.67 - 3.01 

Occupation 

Not Employed 1.00 1.00 1.00 

Agriculture 0.96 0.34 - 2.68 0.65 0.26 - 1.61 0.94 0.71 - 1.24 

Professional 1.25 0.82 - 1.90 1.17 0.80 - 1.71 1.44*** 1.20 - 1.74 

Other 0.79 0.38 - 1.67 0.68 0.32 - 1.46 0.78 0.44 - 1.36 

Wealth quintile 

Lowest na na 1.00 

Second na na 1.10 0.91 - 1.33 

Middle na na 1.87*** 1.41 - 2.47 

Lowest-Middle 1.00 1.00 na 

Fourth 1.27 0.73 - 2.21 1.44 0.98 - 2.13 2.83*** 1.61 - 4.94 

Highest 2.78** 1.44 - 5.36 3.48*** 1.94 - 6.24 1.95 0.73 - 5.23 

(Continued)
29 

Table 4. − Continued

 Metro Other Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Religion 

Catholics 1.00 1.00 

Protestant 0.97 0.64 - 1.48 1.12 0.77 - 1.63 1.08 0.90 - 1.29 

Vaudousant/other 0.41** 0.22 - 0.76 0.47* 0.26 - 0.85 0.89 0.64 - 1.22 

Exposure to media 

Less than one time per week 

Weekly or more 1.22 0.70 - 2.12 1.51* 1.02 - 2.23 1.54*** 1.29 - 1.83 

Number of women 746 952 3087 

Number of clusters 66 85 241 

***p<0.001 **p<0.01 *p<0.05 

Associations with quality of ANC are presented in Table 5. Here, only the rural model showed that women 

in clusters with a high level service environment were significantly associated with having high quality 

ANC care (OR=1.84, CI=1.344–2.508) compared to women in a low level service environment. Service 

environment was not significantly associated with high quality ANC in other urban or metro areas. 

Household wealth and women’s education seem to be more important for women receiving high quality of 

ANC service in rural and other urban areas than in the metropolitan area. 

Table 5. Results of multivariable logistic regressions of receiving high quality ANC1 among 

women in Haiti who had a live birth in the five years preceding the survey, by residence 

 Metro Other Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Facility service readiness 

Low 1.00 1.00 1.00 

Medium 1.17 0.78 - 1.79 1.10 0.66 - 1.82 1.21 0.88 - 1.67 

High 1.57 0.95 - 2.58 1.22 0.73 - 2.05 1.84*** 1.34 - 2.51 

Maternal age at first birth 

<20 1.00 1.00 1.00 

20-34 2.06** 1.27 - 3.35 1.33 0.79 - 2.23 1.27 0.95 - 1.69 

35-49 2.40* 1.20 - 4.78 1.96 0.94 - 4.12 1.40 0.94 - 2.07 

Birth order 

1 1.00 1.00 1.00 

2-3 0.79 0.54 - 1.16 0.99 0.65 - 1.50 1.03 0.81- 1.32 

4-5 1.26 0.70 - 2.29 0.50* 0.29 - 0.86 0.97 0.72 - 1.30 

6+ 0.96 0.40 - 2.31 0.86 0.40 - 1.85 0.93 0.65 - 1.32 

Education 

None 1.00 1.00 1.00 

Primary 1.29 0.68 - 2.48 1.33 0.73- 2.43 1.40** 1.12 - 1.74 

Secondary or higher 1.42 0.73 - 2.76 2.16* 1.14 - 4.07 2.33*** 1.73 - 3.16 

Occupation 

Catholics 1.00 1.00 1.00 

Agriculture 1.91 0.82- 4.46 0.89 0.37 - 2.10 1.04 0.77 - 1.41 

Professional 1.26 0.89 - 1.79 1.78** 1.25 - 2.53 1.29** 1.07 - 1.56 

Other 0.86 0.46 - 1.63 1.17 0.56 - 2.41 0.72 0.40 - 1.30 

(Continued)
30 

Table 5. − Continued

 Metro Other Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Wealth quintile 

Lowest na na 1.00 

Second na na 0.96 0.78 - 1.17 

Middle na na 1.30 0.98 - 1.72 

Lowest-Middle 1.00 1.00 1.00 

Fourth 1.46 0.84 - 2.52 1.52* 1.03 - 2.27 2.52*** 1.52 - 4.17 

Highest 1.12 0.62 - 2.03 1.87* 1.14 - 3.08 2.02 0.83 - 4.95 

Religion 

Catholics 1.00 1.00 1.00 

Protestant 0.92 0.65 - 1.30 1.07 0.76 - 1.50 0.92 0.77 - 1.11 

Vaudousant/other 0.56 0.31 - 1.00 0.48* 0.26 - 0.89 0.92 0.65 - 1.29 

Exposure to media 

No exposure 1.00 1.00 1.00 

Weekly or more 1.30 0.77 - 2.20 1.21 0.80 - 1.81 1.10 0.92 - 1.33 

Number of women 703 916 2733 

Number of clusters 66 85 241 

1 High quality ANC is defined as having any 6 out of 8 of the following: weight measured, blood pressure 

measured, urine sample taken, blood sample taken, iron supplementation, tetanus vaccine given, informed of 

signs of complications, intestinal parasites treatment given during pregnancy. 

***p<0.001 **p<0.01 *p<0.05 

3.3. Facility Delivery 

3.3.1. Availability of basic and comprehensive emergency obstetric care at health facilities 

We examined all hospitals and health centers with beds in Haiti that provide normal delivery services. Of 

the 195 facilities, 61 offer basic emergency obstetric care (BEmOC) and 36 of these offer comprehensive 

emergency obstetric care (CEmOC). Of the 36 CEmOC facilities, 8 are located in the metropolitan area, 23 

are in other urban areas, and 5 are in rural areas. Table 6 provides information on the distribution of these 

facilities by background characteristics, and Appendix Table A8 provides additional information on the 

distribution of the key functions included in BEmOC and CEmOC. 
31 

Table 6. Characteristics of all health facilities in Haiti with normal delivery services, facilities with 

BEmOC services, and facilities with CEmOC services, by residence, Haiti SPA 2013 

Facilities with normal 

delivery services BEmOC Facilities CEmOC Facilities 

Characteristics Metro 

Other 

Urban Rural Total Metro 

Other 

Urban Rural Total Metro 

Other 

Urban Rural Total 

Type of facility 

Hospital 74.4 57.3 24.3 48.2 83.3 73.7 36.4 68.9 87.5 87.0 60.0 83.3 

Health center with bed 25.6 42.7 75.7 51.8 16.7 26.3 63.6 31.1 12.5 13.0 40.0 16.7 

Managing authority 

Government/public 17.9 62.2 35.1 43.1 50.0 71.1 36.4 60.7 50.0 60.9 20.0 52.8 

NGO/ Private not for 

profit 23.1 7.3 16.2 13.8 33.3 7.9 27.3 16.4 25.0 8.7 40.0 16.7 

Private for profit 56.4 12.2 25.7 26.2 16.7 7.9 18.2 11.5 25.0 13.0 20.0 16.7 

Mission/ faith-based 2.6 18.3 23 16.9 0.0 13.2 18.2 11.5 0.0 17.4 20.0 13.9 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 13.4 28.4 36.4 100.0 7.9 36.4 31.1 100.0 8.7 40.0 33.3 

Sud-Est na 6.1 2.7 3.6 na 5.3 0.0 3.3 na 8.7 0.0 5.6 

Nord na 15.9 10.8 10.8 na 13.2 18.2 11.5 na 8.7 0.0 5.6 

Nord-Est na 4.9 8.1 5.1 na 7.9 0.0 4.9 na 4.3 0.0 2.8 

Artibonite na 12.2 20.3 12.8 na 26.3 9.1 18.0 na 21.7 0.0 13.9 

Centre na 9.8 10.8 8.2 na 7.9 18.2 8.2 na 13.0 40.0 13.9 

 Sud na 13.4 4.1 7.2 na 10.5 0.0 6.6 na 13.0 0.0 8.3 

Grand-Anse na 7.3 2.7 4.1 na 7.9 18.2 8.2 na 4.3 20.0 5.6 

Nord-Ouest na 9.8 8.1 7.2 na 10.5 0.0 6.6 na 13.0 0.0 8.3 

Nippes na 7.3 4.1 4.6 na 2.6 0.0 1.6 na 4.3 0.0 2.8 

 

Total number of facilities 39 82 74 195 12 38 11 61 8 23 5 36 

Among the 195 facilities, 43 percent are government/public-run, 26 percent are private for-profit, and the 

remainder are mission/faith based (17 percent) and NGO/private not for profit (14 percent). Overall 

hospitals and health centers each account for half the facilities; but most facilities offering normal delivery 

services in the metropolitan area are hospitals and most are health centers in rural areas. The majority of 

BEmOC and CEmOC facilities in Haiti are hospitals (69 percent and 83 percent, respectively) and most are 

public/government run (61 percent and 53 percent, respectively), although as Table 6 shows, these 

distributions vary across metropolitan, other urban and rural areas. 

In the metropolitan area, 31 percent of facilities with normal delivery services offer BEmOC and 21 percent 

offer CEmOC services (see Figure 12a). In other urban areas, 46 percent offer BEmOC and 28 percent offer 

CEmOC services. Availability of emergency care is lowest in rural areas, where just 15 percent of facilities 

with normal delivery care offer BEmOC and 7 percent provide CEmOC services. Figures 12b and 12c 

highlight the availability of BEmOC and CEmOC services by the type of facility and the managing 

authority. BEmOC and CEmOC availability is most common in hospitals and in government/public and 

NGO/private not for profit facilities. 
32 

Figure 12a. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by residence, Haiti SPA 2013 

Figure 12b. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by type of facility, Haiti SPA 2013 

Figure 12c. Among facilities that offer normal delivery services, the percentage that provide 

BEmOC and CEmOC, by managing authority, Haiti SPA 2013 

 

31

46

15 21

28

7

0

20

40

60

80

100

Metro Other urban Rural

Place of Residence

BEmOC CEmOC

45

19

32

6

0

20

40

60

80

100

Hospital health center with bed

Facility Type

BEmOC CEmOC

44 37

14

23 22 21

12 15

0

20

40

60

80

100

Gov/public NGO/ Private not

for profit

Private for profit Mission/ faithbased

Managing Authority

BEmOC CEmOC
33 

3.3.2. Access to normal delivery, basic and comprehensive obstetric care services in DHS clusters 

Access to health facilities within a cluster is closely related to the population and facility density, with urban 

areas generally having greater access than rural areas. Nearly all metropolitan clusters are linked to at least 

one facility with normal delivery services, but 14 percent of other urban clusters and 18 percent of rural 

clusters do not have any facility with normal delivery services within the 5 or 10 kilometer buffer (Figures 

13a-b). Proximity to a BEmOC facility is also nearly universal in metropolitan clusters, with 98 percent of 

clusters linking to a BEmOC facility within 5 km. However, only 67 percent of other urban clusters and 48 

percent of rural clusters have a BEmOC facility within the buffer distance. Overall, the percentage of 

clusters near a CEmOC facility is lower than the percentage near a BEmOC facility, with 80 percent of 

metropolitan clusters, 56 percent of other urban clusters, and 33 percent of rural clusters linking to a 

CEmOC facility within the buffer distance. 

Figure 13a. Percent distribution of DHS clusters in Haiti by access to normal delivery service and 

BEmOC, according residence 

1.0 13.9 17.7 1.5

19.5

34.7

97.5

66.6

47.6

0

20

40

60

80

100

Metro Urban Rural

None Normal Delivery (no BEmOC) BEmOC
34 

Figure 13b. Percentage distribution of DHS clusters in Haiti by access to normal delivery service 

and CEmOC, according to residence 

3.3.3. Utilization of facility-based delivery services and determinants 

Of the 4,921 women with a live birth in the last five years for whom complete data are available, only 39 

percent delivered the most recent birth at a health facility. Delivery in a health facility is far more common 

in the metropolitan and other urban areas (60 percent and 59 percent, respectively) compared with rural 

areas (24 percent). Appendix Table A9 shows the distribution of background characteristics of women who 

were analyzed. 

Figure 14 shows the facilities that provide delivery services in Haiti and whether or not the facility offers 

BEmOC (dark green dots). Additionally, the percent of women who delivered their most recent birth in a 

health facility (among women with a birth in five years preceding the survey) is shown by department, 

ranging from low percentage of facility delivery (light pink) to high percentage of facility delivery (dark 

pink). Overall there are very few facilities in Haiti that provide BEmOC services (dark green dots), and a 

high number of these are concentrated in the Aire Metropolitaine, which corresponds to the department 

with the highest percentage of women delivering in a facility. 

1

13.9 17.7 19

29.8

49.7

80

56.3

32.6

0

20

40

60

80

100

Metro Urban Rural

None Normal Delivery (no CEmOC) CEmOC
35 

Figure 14. Map of relationship between facilities in Haiti that provide delivery services and 

whether the facility offers BEmOC and percentage of women who delivered their most recent birth 

in a health facility, by department, Haiti DHS 2012 and Haiti SPA 2013 

Figures 15a-c highlight the bivariate association between the availability of delivery services and the 

percentage of women who delivered their most recent birth in a facility, separately for women in rural, other 

urban and metropolitan areas. In rural clusters without access to a facility with normal delivery services 

within 10 kilometers, only 8 percent of women delivered the most recent birth in a facility (Figure 15a). 

Coverage of facility delivery was significantly higher in clusters with at least one facility offering normal 

delivery services within the buffer distance, but there was no difference in coverage between clusters with 

only normal delivery services available, compared with clusters where emergency obstetric care was 

available within the buffer (either a BEmOC or CEmOC). 
36 

Figure 15a. Percentage of women in Haiti rural areas who delivered the most recent birth in a 

facility, according to the availability of delivery services within the buffer 

Figure 15b. Percentage of women in Haiti other urban areas who delivered the most recent birth in 

a facility, according to the availability of delivery services within the buffer 

8 8

29 27 27 29

0

20

40

60

80

100

BEmoC CEmoC

No facilities with normal delivery services

At least one facility with normal delivery but no EmOC

EmOC

37 37

53

57

66 66

0

20

40

60

80

100

BEmoC CEmoC

No facilities with normal delivery services

At least one facility with normal delivery but no EmOC

EmOC
37 

Figure 15c. Percentage of women in Haiti metropolitan areas who delivered the most recent birth 

in a facility, according to the availability of delivery services within the buffer 

In other urban areas, facility delivery coverage increased incrementally with the availability of delivery and 

emergency obstetric services (see Figure 11b). In other urban clusters with no normal delivery services 

available, 37 percent of women delivered in a facility, while in clusters with normal delivery services 

available 53 percent delivered in a health facility, and in areas with a BEmOC or CEmOC within 5 

kilometers, 66 percent delivered in a facility. 

In the metropolitan area, nearly all clusters have at least one facility with normal delivery services available. 

In clusters with normal delivery services but no BEmOC services available within the buffer, 41 percent of 

women delivered the most recent birth in a health facility, while in clusters with at least one BEmOC within 

the buffer, 61 percent delivered in a facility. There was no difference in facility delivery coverage between 

clusters with and without access to a CEmOC within the 5 kilometer buffer. The unadjusted relationships 

between facility delivery and other covariates are shown in Appendix Table A10. 

Using multilevel models, we assessed how women’s utilization of facility delivery is associated with the 

availability of health facilities with normal delivery, BEmOC, and CEmOC services within the cluster 

service environment. We ran six multilevel models to examine BEmOC and CEmOC availability separately 

for metropolitan, other urban and rural areas. 

Table 7 presents odds ratios and 95% confidence intervals for the indicators of availability of delivery 

services, as well as for the covariates adjusted for in the models. For rural and other urban clusters, we 

examined three levels of availability within their respective buffer: having no facility with normal delivery 

services, having a facility with normal services but no emergency care, and having at least one facility with 

emergency obstetric care available. Clusters with no facilities with normal delivery care were used as the 

reference category. Since 99 percent of metropolitan clusters were linked to at least one health facility with 

normal delivery care within 5 km of the cluster, in the models for metropolitan clusters, having normal 

delivery services but no emergency care are used as the reference category, and we examined the advantage 

of having at least one emergency obstetric care available within the buffer. 

41

61 60 61

0

20

40

60

80

100

BEmoC CEmoC

No EmOC EmOC
38 

Table 7. Results of multivariable logistic regressions of facility delivery for the most recent birth 

among women in Haiti who had a live birth in the five years preceding the survey, by residence 

 Metropolitan Other Urban Rural 

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 

Variables OR 95%CI OR 95%CI OR 95%CI OR 95%CI OR 95%CI OR 95%CI 

Availability of Basic Emergency 

Obstetric Care Services1 

None 1.00 1.00 

Any facility with normal delivery 

services but no BEmOC 1.00 2.15* 1.05 - 4.39 2.95*** 1.80 - 4.83 

Any BEmOC 2.92* 1.28 - 6.68 3.38*** 1.77 - 6.47 2.89*** 1.77 - 4.72 

 

Availability of Comprehensive 

Emergency Obstetric Care Services1 

None 1.00 1.00 

Any facility with normal delivery 

services but no CEmOC 1.00 2.92** 1.48 - 5.76 2.90*** 1.79 - 4.68 

Any CEmOC 1.31 0.83 - 2.06 2.89** 1.48 - 5.65 2.97*** 1.78 - 4.97 

Maternal age at birth 

<20 1.00 1.00 1.00 1.00 

20-34 1.36 0.82 - 2.23 1.34 0.81 - 2.22 1.49 0.88 - 2.52 1.48 0.87 - 2.51 1.30 0.94 - 1.80 1.30 0.95 - 1.80 

35-49 1.91 0.95 - 3.85 1.95 0.96 - 3.96 2.49* 1.18 - 5.23 2.45* 1.17 - 5.16 2.09** 1.29 - 3.39 2.09** 1.29 - 3.39 

 

Birth order 

1 1.00 1.00 1.00 1.00 

2-3 0.57** 0.38 - 0.86 0.57** 0.38 - 0.86 0.39*** 0.25 - 0.59 0.38*** 0.25 - 0.58 0.33*** 0.25 - 0.44 0.33*** 0.25 - 0.44 

4-5 0.40** 0.22 - 0.70 0.39** 0.22 - 0.69 0.41** 0.24 - 0.71 0.40** 0.23 - 0.70 0.20*** 0.13 - 0.29 0.20*** 0.13 - 0.29 

6+ 0.41* 0.18 - 0.93 0.39* 0.17 - 0.89 0.36** 0.17 - 0.77 0.35** 0.16 - 0.76 0.17*** 0.10 - 0.27 0.17*** 0.10 - 0.27 

 

Education 

None 1.00 1.00 1.00 1.00 

Primary 2.60** 1.36 - 5.01 2.59** 1.34 - 5.00 0.93 0.51 - 1.68 0.92 0.51 - 1.67 1.33 0.97 - 1.81 1.33 0.97 - 1.81 

Secondary or higher 4.54*** 2.34 - 8.80 4.49*** 2.30 - 8.79 1.76 0.95 - 3.27 1.73 0.93 - 3.22 2.17*** 1.51 - 3.12 2.17*** 1.51 - 3.12 

Wealth quintile 

Lowest na na 1.00 1.00 

Second na na 1.68*** 1.29 - 2.19 1.68*** 1.29 - 2.18 

Middle na na 2.61*** 1.89 - 3.60 2.60*** 1.89 - 3.60 

Lowest-Middle 1.00 1.00 na na 

Fourth 1.12 0.68 - 1.85 1.14 0.69 - 1.90 1.19 0.80 - 1.76 1.26 0.85 - 1.86 5.23*** 3.18 - 8.59 5.21*** 3.17 - 8.56 

Highest 2.24** 1.30 - 3.88 2.24** 1.28 - 3.92 2.60*** 1.56 - 4.33 2.88*** 1.72 - 4.80 7.94*** 3.28 - 19.20 7.90*** 3.26 - 19.13 

 

Number of ANC visits 

0 1.00 1.00 1.00 1.00 1.00 

1 3.37 0.97 - 11.74 3.12 0.89 - 10.88 1.38 0.39 - 4.91 1.40 0.39 - 4.98 2.47* 1.06 - 5.77 2.47* 1.06 - 5.76 

2-3 2.22 0.95 - 5.18 2.21 0.94 - 5.20 1.71 0.70 - 4.17 1.70 0.70 - 4.12 3.49*** 1.85 - 6.59 3.49*** 1.85 - 6.58 

4+ 4.11*** 1.94 - 8.73 4.09*** 1.91 - 8.75 4.25*** 1.88 - 9.57 4.19*** 1.86 - 9.44 7.14*** 3.90 - 13.08 7.13*** 3.90 - 13.06 

DK/Missing 2.91 0.52 - 16.24 3.24 0.58 - 18.18 1.19 0.21 - 6.66 1.25 0.22 - 6.94 7.53 0.18 - 316.28 7.61 0.18 - 319.67

 

Number of women 754 754 962 962 3,089 3,089 

Number of clusters 66 66 85 85 241 241 

***p<0.001 **p<0.01 *p<0.05 

1 Availability is assessed within 10 kilometers for rural clusters and 5 kilometers for metropolitan and other urban clusters. Since nearly all metropolitan clusters have at least one 

facility with normal delivery services, the reference category for the metropolitan models combine “None” with “Any facility with normal delivery services but no EmOC.” 

2 For metropolitan and other urban areas, the first 3 quintiles were combined and used as the reference category. 

In the metropolitan area, the availability of any BEmOC facility within the service environment was 

positively and significantly associated with women’s use of facility delivery care after controlling for 

women’s characteristics. Women in clusters with a BEmOC facility available within 5 kilometers had a 2.9 

times higher odds of facility delivery, compared with women in clusters with normal delivery services but 

no BEmOC available within the buffer, after controlling for background characteristics of the woman. 

Having a CEmOC facility available within the buffer was not associated with significantly greater odds of 

facility delivery, compared with the odds among women in clusters with normal delivery services but no 

CEmOC facility available within the buffer. 

In both other urban areas and rural areas, the availability of normal delivery services within the buffer was 

significantly associated with women’s use of facility delivery care after controlling for women’s 
39 

characteristics. In other urban areas, women in clusters with normal delivery services available within the 

5 kilometer buffer were 2.2 times more likely to go to a facility for delivery, and women in clusters with a 

BEmOC facility available within the buffer were 3.4 times more likely to deliver in a facility compared 

with women in clusters with no delivery services available. Similarly, in the model examining availability 

of CEmOC facilities, in other urban areas women in clusters with normal delivery services available (but 

no CEmOC) within the buffer were 2.9 times more likely to go to a facility for delivery, and women in 

clusters with a CEmOC facility available within the buffer were also 2.9 times more likely to deliver in a 

facility compared with women in clusters with no delivery services available. The results were similar in 

rural areas. 

As expected, household wealth, maternal education, and the number of ANC visits were positively and 

significantly associated with women’s use of facility delivery care, while the child’s birth order was 

negatively associated with the use of facility delivery care. 

3.4. Care Seeking for Children’s Diarrhea 

3.4.1. Service readiness in providing child curative care services at health facilities 

We examined the 848 facilities in Haiti that offer curative care services for children under five. Among 

these, over 40 percent are dispensaries, one third are health centers without beds (32 percent), and the 

remainder are health centers with beds (15 percent) and hospitals (13 percent) (Table 8). The type of facility 

varies widely between metropolitan, other urban, and rural areas. In rural areas, dispensaries are the most 

prevalent type of facility with curative care services for children (59 percent of all facilities with child care) 

while in the metropolitan area they represent only 3 percent of facilities. Child health services in Haiti are 

offered by a mix of government (29 percent), NGO/private not for profit (17 percent), private for-profit (23 

percent), and mission/faith-based (21 percent) facilities. Government/public facilities are more common in 

rural and other urban areas, while private for-profit facilities are more common in the metropolitan area. 
40 

Table 8. Characteristics of health facilities in Haiti that offer curative care 

services for children under five years, by residence, Haiti SPA 2013 

Characteristics Metro Other Urban Rural Total 

Facility type 

Hospital 22.7 29.6 4.1 12.4 

Health center with bed 12.7 21.8 13.3 14.8 

Health center without bed 61.3 32.2 23.5 31.9 

Dispensary 3.3 16.3 59.1 41.0 

Managing authority 

Government/public 14.7 49.2 43.2 39.3 

NGO/Private not for profit 27.3 11.5 15.6 16.8 

Private for profit 44.7 15.7 19.0 22.9 

Mission/ faith-based 13.3 23.6 22.3 21.0 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 12.7 23.9 35.2 

Sud-Est na 6.1 8.4 6.5 

Nord na 19.4 9.9 10.0 

Nord-Est na 3.6 5.4 4.1 

 Artibonite na 8.5 18.7 13.4 

Centre na 6.7 6.2 5.2 

Sud na 19.4 5.8 7.4 

Grand-Anse na 9.7 4.9 4.9 

Nord-Ouest na 7.4 13.2 9.7 

Nippes na 6.7 3.6 3.5 

Total number of facilities 150 165 533 848 

3.4.2. Clusters’ access to child care facilities and service readiness 

To evaluate the service environment for curative child care, a readiness score was generated at the health 

facility level based on four domains of facility readiness: staff and training, equipment, diagnostic capacity, 

and the availability of medicines and commodities (see Appendix Table A11 for a detailed summary of the 

components of the readiness score). Each of the 392 clusters included in the 2012 Haiti DHS was linked to 

all facilities within a 5 or 10 kilometer buffer; and an overall readiness score was generated for each cluster 

based on the median score of all facilities linked to that cluster. Metropolitan clusters linked with an average 

of 54 health facilities offering curative child care, other urban clusters linked with an average of 6 facilities, 

and rural clusters linked with an average of 8 facilities.

Figure 16 shows the distribution of readiness scores among DHS clusters. While the median score was 

roughly similar across metropolitan, other urban, and rural areas (median values were 48.2, 53.0, and 52.3, 

respectively), the distribution of scores differed across the three groups of clusters. Scores in the 

metropolitan area were quite homogenous while the interquartile range was wider in other urban and rural 

clusters. The cluster-level service readiness score was divided into terciles, separately for metropolitan, 

other urban, and rural areas, to designate clusters as having either a low, medium, or high level of readiness 

for treating childhood illness. 
41 

Figure 16. Boxplots of child care readiness scores among clusters in Haiti, by residence 

3.4.3 Care seeking at health facilities for children’s diarrhea and determinants 

The analysis of care seeking for children’s diarrhea included 1,255 children under age five years who were 

reported to have had diarrhea in the two weeks preceding the mother’s interview, according to the 2012 

Haiti DHS. Appendix Table A12 shows the background characteristics of these children. Care seeking for 

children’s diarrhea is low in Haiti. Care was sought from any public or private provider for roughly one 

third of these children (35 percent). The children are roughly equally spread across the three levels of the 

service readiness environment, as we would expect given that these were constructed as cluster-level 

terciles. 

Figure 17 shows the bivariate association between the level of service readiness and the percentage of 

children for whom care was sought from a public or private source for recent diarrhea. In the metropolitan 

area, care seeking coverage increased with increasing service readiness among facilities. Care for diarrhea 

was sought from a public or private facility for 29 percent of children in clusters with low facility-level 

readiness, for 40 percent of children in clusters with medium readiness, and for 60 percent for children in 

clusters with high readiness. In other urban areas and rural areas, however, there was no difference in the 

level of care seeking across levels of service readiness (see Figure 16). The bivariate associations between 

seeking care for diarrhea at a health facility and other covariates can be found in Appendix Table A13. 

0 20 40 60 80 100

Median Readiness Score

Metro other urban rural
42 

Figure 17. Percentage of children in Haiti for whom care was sought from a public or private 

source for recent diarrhea, by level of facility service readiness to treat childhood illness and 

residence 

Using multilevel models, we assessed how care seeking for children’s diarrhea is associated with service 

readiness at health facilities within the specified buffer of the cluster. We ran separate multilevel models 

for metropolitan, other urban, and rural areas. Table 9 presents odds ratios and 95% confidence intervals 

for the level of service readiness, as well as for the covariates adjusted for in the models. In the metropolitan 

area, the level of service readiness of the facilities is positively and significantly associated with care 

seeking from a public or private facility for children’s diarrhea, after controlling for background 

characteristics. The odds that children with recent diarrhea received care from a public or private facility 

were 3.3 times greater among children in clusters with a high level of service readiness among facilities, 

compared with the odds among children in clusters with a low level of service readiness. However, in other 

urban and rural areas there was no association between the level of service readiness among facilities and 

the odds of care seeking for children’s diarrhea. Few background characteristics were associated with the 

odds of care seeking for children’s diarrhea. Household wealth was positively associated with care seeking 

for diarrhea in other urban and rural areas, and the child’s age was negatively associated with the odds of 

care seeking in the metropolitan area. 

0

20

40

60

80

100

Metropolitan Area Other Urban Areas Rural Areas

Low Middle High
43 

Table 9. Results of multivariable logistic regressions of care-seeking for children’s diarrhea at 

any public or private facility in Haiti among children with diarrhea in the two weeks preceding the 

survey, by residence 

 Metropolitan Other Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Facility-level service readiness for 

treating childhood illness1 

Low 1.00 1.00 1.00 

Medium 1.92 0.67 - 5.45 1.35 0.65 - 2.80 1.16 0.74 - 1.80 

High 3.29* 1.05 - 10.30 1.43 0.73 - 2.79 1.41 0.91 - 2.17 

Age 

<1 1.00 1.00 1.00 

1-2 0.75 0.31 - 1.78 1.38 0.66 - 2.85 1.17 0.79 - 1.73 

2-3 0.31* 0.11 - 0.89 1.17 0.51 - 2.64 1.12 0.71 - 1.78 

3-4 0.08*** 0.02 - 0.35 0.89 0.33 - 2.34 0.96 0.55 - 1.69 

4-5 0.09* 0.01 - 0.58 1.57 0.50 - 4.96 1.15 0.62 - 2.13 

Sex of child 

Female 1.00 1.00 1.00 

Male 0.90 0.44 - 1.81 0.86 0.48 - 1.52 0.76 0.56 - 1.04 

Mother’s age 

<20 1.00 1.00 1.00 

20-34 1.46 0.41 - 5.17 1.00 0.38 - 2.62 0.78 0.46 - 1.33 

35-49 1.29 0.27 - 6.31 0.72 0.24 - 2.20 1.03 0.56 - 1.91 

Mother’s education 

None 1.00 1.00 1.00 

Primary 1.57 0.41 - 6.03 0.63 0.21 - 1.92 1.17 0.79 - 1.74 

Secondary or higher 1.67 0.40 - 6.95 0.71 0.23 - 2.23 1.51 0.91 - 2.48 

Wealth quintile 

Lowest na na 1.00 

Second na na 0.91 0.64 - 1.30 

Middle na na 0.89 0.53 - 1.48 

Lowest-Middle 1.00 1.00 na 

Fourth 0.83 0.30 - 2.31 1.21 0.63 - 2.31 2.46* 1.06 - 5.71 

Highest 1.13 0.36 - 3.56 2.70* 1.15 - 6.35 1.58 0.33 - 7.57 

Number of children 301 197 757 

Number of clusters 60 74 219 

***p<0.001 **p<0.01 *p<0.05 

1 To evaluate the service environment for curative child care, a readiness score was generated at the health facility 

level based on four domains of facility readiness: staff and training, equipment, diagnostic capacity, and the availability 

of medicines and commodities (see Appendix Table 1 for a detailed summary of the components of the readiness 

score). DHS clusters were then linked with the service readiness scores for facilities within a 5 or 10 kilometer buffer, 

and an overall readiness score was generated for each cluster based on the median score of all facilities linked to that 

cluster. These scores were divided into terciles, which we refer to as low, medium, and high readiness. 

3.5. HIV Testing 

3.5.1. HIV testing capacity at health facilities 

In the 2013 Haiti SPA, 480 facilities reported to provide HIV counseling and testing services. The 

distribution of these facilities in each geographic area by facility type, managing authority, and regional 

location is presented in Table 10. In metropolitan and other urban areas, health centers without beds and 

hospitals are major providers of HIV testing and counseling services. In contrast, in rural areas, dispensaries 

account for 39 percent of the facilities that provided HIV counseling and testing services. Facilities’ 
44 

managing authority varies among three locations. In the metropolitan area, private for-profit accounts for 

45 percent of the health facilities providing HIV testing and counseling services, while other urban areas 

primarily rely on government facilities. Various management types are relatively evenly distributed in rural 

areas. 

Table 10. Characteristics of health facilities in Haiti providing HIV counseling 

and testing services, by residence, Haiti SPA 2013 

Characteristics Metropolitan Other urban Rural Total 

Type of facility 

Hospital 32.6 35.9 8.9 23.1 

Health center with beds 14.7 21.5 21.6 19.7 

Health center without beds 51.2 34.0 30.7 37.2 

Dispensary 1.6 8.6 38.7 20.0 

Managing authority 

Government 14.7 51.8 32.6 33.4 

NGO/private not-for-profit 28.7 15.2 21.3 21.5 

Private for-profit 45.0 15.1 23.0 26.6 

Mixed 11.6 17.9 23.1 18.5 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 10.0 33.9 44.7 

Sud-Est na 7.2 5.2 4.4 

Nord na 20.1 10.3 10.4 

Nord-Est na 4.3 8.9 5.2 

Artibonite na 9.3 11.3 7.7 

Centre na 9.3 7.1 5.8 

Sud na 17.2 3.8 6.7 

Grand-Anse na 8.6 3.3 3.9 

Nord-Ouest na 8.9 13.0 8.3 

Nippes na 5.0 3.3 2.9 

Total number of facilities 129 139 212 480 

The availability of HIV testing capacity and other items to support quality testing services is reported in 

Appendix Table A14. Overall, more than 64 percent of facilities that reported providing HIV counseling 

and testing services have full HIV testing capacity, which means they either have HIV diagnostic test kits 

or ELISA tests with essential equipment and materials for conducting the test. The availability of other 

items to support quality testing and counseling services is limited. In only 8 percent of facilities, all 

supportive items including HIV testing and counseling guideline, trained staff, visual and auditory privacy, 

and condoms are available. A facility is considered to be well-prepared for conducting HIV testing if it has 

all of these items. The HIV testing capacity and the availability of supportive items vary by geographic 

area. Only half of the facilities in rural areas have HIV testing capacity, compared to more than 70 percent 

in metropolitan and other urban areas. In all three areas, the majority of health facilities are equipped to 

provide visual and auditory privacy; but low percentages of facilities have guidelines on HIV counseling 

and testing and staff trained in HIV counseling and testing. 

Figures 18a-c present the distribution of the facilities that reported providing HIV testing and counseling 

by availability of HIV testing capacity and preparedness to provide the HIV testing services. In rural areas, 

about half of the facilities do not have HIV testing capacity and only 7 percent have testing capacity and 

also are well prepared to provide the services. Although more than 70 percent of facilities in metropolitan 

and other urban areas have HIV testing capacity, but few are prepared to provide the services. In Over 70 

percent of dispensaries in rural areas, the major provider of HIV testing services in rural areas, do not have 
45 

Aire metropolitaine/Reste-Ouest HIV testing capacity. Hospitals are more prepared to provide HIV testing 

services than health centers with or without beds. Government facilities have the highest percentage without 

HIV testing capacity (43 percent). 

Figure 18a. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readiness to conduct HIV testing, according to residence, Haiti SPA 2013 

27.1

20.9

49.1

64.3

68.3

44.3

8.5 10.8 6.6

Metropolitan Other urban Rural

No Testing capacity With testing capacity Testing capacity & well-prepared
46 

Figure 18b. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readiness to conduct HIV testing, according to facility type, Haiti SPA 2013 

15.3

29.5 30.3

71.9

69.4

63.2 63.5

22.9

15.3

7.4 6.2 5.2

Hospital health center with bed health center without bed dispensary

No Testing capacity With testing capacity Testing capacity & well-prepared
47 

Figure 18c. Percent distribution of facilities providing HIV counseling and testing services by 

capacity and readiness to conduct HIV testing, according to managing authority, Haiti SPA 2013 

3.5.2. Access to HIV testing services in DHS clusters 

Clusters were classified into low, medium, and high access to HIV testing services based on if there is a 

health facility within the specific buffer with HIV testing capacity and level of readiness to provide the 

service. Figure 19 presents the distribution of clusters by the level of access. In rural areas, only 29 percent 

of the clusters have high access to HIV testing within 10 km, meaning there is a facility with both HIV 

testing capacity and also well prepared to provide HIV testing services. Moreover, 14 percent of the clusters 

have no facility with HIV testing capacity within 10 km from the cluster. In other urban clusters, a similar 

distribution is observed, with slightly higher percentage of clusters (33 percent) have high access to HIV 

testing services in other urban areas. Almost all clusters in the metropolitan area have one or more (up to 

nine) facilities that have full HIV testing capacity and also are well-prepared to provide the service. We 

divided the clusters into low-, medium-, and high-access groups based on the terciles of the number of 

facilities with full HIV testing capacity and readiness to provide HIV services. 

43.1

27.2 26.6

41.7

49.4

64.1 66.4

47.1

7.5 8.7 7 11.2

Government/public NGO/ Private not for profit Private for profit Mission/ faith-based

No Testing capacity With testing capacity Testing capacity & well-prepared
48 

Figure 19. Percent distribution of clusters in Haiti by access to HIV testing services, according to 

residence 

3.5.3. Utilization of HIV testing services and its association with the service environment 

We examined prior HIV testing experience among all women 15-49. Background characteristics of these 

women in each geographic location are shown in Appendix Table A15. In rural areas, only 19 percent of 

women reported to be tested for HIV in the 12 months prior to the survey. The coverage is about 30 percent 

in the metropolitan and other urban areas. 

The relationship between facilities that provide HIV testing services and women who were tested for HIV 

is shown in Figure 20. Level of testing capacity and preparedness is displayed in light green (provide HIV 

testing and counselling services without testing capacity) to dark green (testing capacity and fully prepared). 

The percentage of women who were tested for HIV in the last 12 months preceding the survey by 

department is shown in lighter pink for lower values and darker pink for higher values. Departments with 

more facilities with testing capacity and higher preparedness (dark and medium green dots) also have higher 

percentage of women who have had an HIV test in the last 12 months (darker pink). 

33.8

15.4 14.3

38.9

51.4 56.6

27.4 33.2 29.1

Metropolitan Other urban Rural

Low Medium High
49 

Figure 20. Map of relationship between facilities in Haiti that provide HIV testing services and 

percentage of women who were tested for HIV, by department, Haiti DHS 2012 and Haiti SPA 2013 

Figure 21 highlights the bivariate relationship between women’s recent experience of HIV testing and the 

level of access to HIV testing services. In rural areas, HIV testing coverage increases with the level of 

access to HIV testing service. The coverage in clusters with high access to HIV testing service is 22 percent, 

which is twice the coverage in clusters with low access. The minimal difference in coverage between 

clusters with medium access and those with high access may suggest that HIV testing capacity is more 

important than the presence of the items to support the service. In other urban areas, HIV testing coverage 

incrementally increases with the level of access, from 24 percent in the low-access group to 35 percent in 

the high-access group. All three groups in the metropolitan area have similar percentages of women who 

were tested for HIV in the 12 months prior to the survey. The bivariate associations between prior HIV 

testing experience and other covariates are presented in Appendix Table A16. 
50 

Figure 21. Percentage of women age 15-49 in Haiti who were tested for HIV in the 12 months 

preceding the survey, by the level of access to HIV testing services and residence 

In the multilevel logistic regressions models, we assessed how women’s HIV testing in last 12 months is 

associated with the HIV testing service environment at health facilities within a specified buffer of the 

cluster. We ran separate multilevel models for each of the three geographic areas. Table 11 presents odds 

ratios and 95% confidence intervals for the level of access to HIV testing services as well as for the 

covariates adjusted for in the models. 

In rural areas, the level of access is positively and significantly associated with women’s likelihood of being 

tested for HIV after controlling for women’s characteristics. Compared with the women who live in a cluster 

with low access (no facility with HIV testing capacity), the odds of having a HIV test is 58 percent higher 

for women from a community with medium access (there is a facility with HIV testing capacity), and more 

than twice as likely for those from a community with high access (there is a facility with HIV testing 

capacity and fully ready to provide the service). Similar results are shown in other urban areas. In the 

metropolitan area, we found no significant association between women’s HIV testing experience and their 

communities’ access to HIV testing services. 

 

0%

20%

40%

60%

80%

100%

Metro Other urban Rural

Low Medium High
51 

Table 11. Results of multivariable logistic regressions of HIV testing in past 12 months among 

women age 15-49 in Haiti, by residence 

 Metropolitan Urban Rural 

Variables OR 95%CI OR 95%CI OR 95%CI 

Service readiness level for HIV testing 

Low 1.00 1.00 1.00 

Medium 1.10 0.84 - 1.45 1.50* 1.02 - 2.21 1.58* 1.10 - 2.28 

High 1.06 0.80 - 1.41 1.77** 1.18 - 2.66 2.09*** 1.40 - 3.11 

Age 

15-24 1.00 1.00 1.00 

25-34 1.17 0.91 - 1.49 1.45** 1.16 - 1.83 1.12 0.94 - 1.35 

35-44 0.70* 0.52 - 0.95 1.00 0.75 - 1.33 0.69** 0.55 - 0.87 

45-49 0.56* 0.36 - 0.88 0.64* 0.42 - 0.98 0.48*** 0.34 - 0.67 

Education level 

None 1.00 1.00 1.00 

Primary 1.05 0.69 - 1.59 1.91** 1.27 - 2.88 1.48*** 1.20 - 1.82 

Secondary or higher 2.03*** 1.35 - 3.06 3.21*** 2.12 - 4.86 3.08*** 2.42 - 3.93 

Occupation 

Unemployed 1.00 1.00 1.00 

Agriculture 1.59* 1.03 - 2.46 1.29 0.81 - 2.07 1.03 0.78 - 1.36 

Professional 1.15 0.93 - 1.43 1.38** 1.13 - 1.69 1.32*** 1.12 - 1.54 

Other 1.17 0.80 - 1.72 0.99 0.64 - 1.53 1.88** 1.21 - 2.93 

Wealth quintile 

Lowest na na 1.00 

Second na na 1.24* 1.03 - 1.48 

Middle na na 1.86*** 1.49 - 2.32 

Lowest-Middle 1.00 1.00 na 

Fourth 1.11 0.77 - 1.60 1.08 0.84 - 1.39 2.56*** 1.85 - 3.53 

Highest 1.43 0.99 - 2.08 1.37* 1.04 - 1.81 3.16*** 2.05 - 4.86 

Marital status 

Never in union 1.00 1.00 1.00 

Currently married 2.99*** 2.32 - 3.85 1.96*** 1.55 - 2.48 2.61*** 2.13 - 3.20 

Former married 2.49*** 1.73 - 3.59 1.37 0.96 - 1.96 1.92*** 1.37 - 2.68 

Religion 

Catholics 1.00 1.00 1.00 

Protestant 0.78* 0.64 - 0.95 0.86 0.72 - 1.02 0.85* 0.73 - 0.99 

Vaudousant/other 0.82 0.56 - 1.20 1.19 0.79 - 1.78 0.81 0.60 - 1.09 

Birth history 

No birth in past year 1.00 1.00 1.00 

Had a birth in past year 4.72*** 3.38 - 6.60 6.03*** 4.40 - 8.28 6.06*** 5.07 - 7.25 

Number of women 2592 3169 7066 

Number of clusters 66 85 241 

***p<0.001 **p<0.01 *p<0.05 

As expected, having a recent live birth in last year is positively associated with being tested for HIV. These 

women possibly received HIV testing through ANC visits. More educated women are more likely to be 

tested for HIV. Household wealth seems to be important for getting HIV tested in rural areas but not in 

other two locations. Women coming from wealthier households are more likely to report a HIV test in last 

12 months. 
52 

3.6. Malaria-related Health Services 

Using data from the Malawi MIS and SPA, we examined the relationship between three malaria prevention 

and treatment outcomes and the relevant health service environment variables. This section describes the 

results for each outcome. 

3.6.1. Association between children’s receipt of malaria diagnostic testing and malaria diagnostic 

capacity at health facilities 

3.6.1.1. Malaria diagnostic capacity at health facilities 

In the 2014 Malawi SPA, 915 facilities provide child curative care services for children less than five years 

of age. Among these facilities, over 50 percent are health centers or maternity facilities, 32 percent are 

clinics, 12 percent are hospitals, five percent are dispensaries and only 1 percent are health posts (Table 

12). The type of facility varies widely between urban, and rural areas. In rural areas, health centers/maternity 

facilities are the most prevalent type of facility with curative care services for children (66 percent of all 

facilities with child care), while in the urban areas they represent only 11 percent of facilities. Conversely, 

67 percent of urban facilities offering curative care for children are clinics whereas only 18 percent of rural 

curative care facilities are clinics. Health posts are only found in rural areas. Child health services in Malawi 

are offered by a mix of government (49 percent), CHAM/faith-based (18 percent), private (22 percent), 

NGO (5 percent), and company facilities (6 percent). Government/public facilities are more common in 

rural areas, while private facilities are more common in urban areas. 

Table 12. Characteristics of health facilities in Malawi that 

provide curative care services for children, by residence, 

Malawi SPA 2013-14 

Characteristics Urban Rural Total 

Type of facility 

Hospital 20.5 8.1 11.7 

Health centre/maternity 11.0 66.2 50.2 

Dispensary 1.2 6.4 4.9 

Clinic 67.2 17.7 32.1 

Health post 0.0 1.6 1.1 

Managing authority 

Public 23.7 59.5 49.1 

CHAM/other faith-based 8.5 21.3 17.6 

Private 50.7 10.1 21.9 

NGO 12.0 2.5 5.3 

Company 5.1 6.5 6.1 

Region 

Northern 14.4 18.4 17.2 

Central 39.9 35.8 37.0 

South 45.7 45.8 45.8 

Total number of facilities 265 649 915 

Diagnostic capacity for malaria was examined for each health facility based on several indicators: 

availability of the valid Rapid Diagnostic Test (RDT) for malaria in conjunction with staff trained in 

administering RDT and presence of an RDT protocol; or equipment for testing blood slides for malaria 

parasites under a microscopy along with staff trained in malaria microscopy (see Appendix Table A17 for 

a detailed summary of the components of the diagnostic capacity definition). Results are shown in Figures 
53 

22a-c. In urban areas, 34 percent of child curative care facilities have malaria diagnostic capacity and 36 

percent of facilities in rural clusters. Hospitals are much more likely to have malaria diagnostic capacity 

(73 percent) than are dispensaries (18 percent), clinics (18 percent) or health posts (11 percent). 

Figure 22a. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by facility type, Malaria SPA 2013-14 

Figure 22b. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by residence, Malawi SPA 2013-14 

72.6

39.5

18.3 17.9

11.1

0

20

40

60

80

100

Hospital Health

centre/maternity

Dispensary Clinic Health post

34 35.5 35

0

20

40

60

80

100

Urban Rural Total
54 

Figure 22c. Percentage of facilities with sick child services that have the capacity to diagnose 

malaria, by managing authority, Malawi SPA 2013-14 

3.6.1.2 Access to malaria diagnostic capacity in MIS clusters 

Each of the 140 clusters included in the 2012 Malawi MIS were then linked with the total number of 

facilities with diagnostic capacity for malaria within a 5 or 10 kilometer buffer. These counts were divided 

into three categories representing low, medium and high access to facilities with the capacity to diagnose 

malaria. This process was done separately for urban and rural clusters. Figure 23 shows the distribution of 

low (no facility within the buffer with malaria diagnostic capacity), medium (1-2 facilities for rural and 1-

5 facilities for urban) and high access (3+ for rural and 6+ for urban) to child curative care facilities with 

malaria diagnostic capacity among MIS clusters, by urban and rural residence. 

While approximately half of all clusters fell within the medium access category for both urban and rural 

clusters, only 7 percent of urban clusters had low access compared to 33 percent of rural clusters. In contrast, 

43 percent of urban clusters had high access to malaria diagnosis compared to only 13 percent of rural 

clusters. 

0

20

40

60

80

100

Government CHAM/faith-based Private NGO Company
55 

Figure 23. Diagnostic capacity of facilities with sick child services to diagnose malaria, 

by residence, Malawi SPA 2013-14 

3.6.1.3. Malaria diagnostic testing among children with fever and determinants 

The individual-level outcome of interest in this analysis is whether or not a child had a finger or heel stick 

as part of the case management response to a recent fever. This outcome is a proxy measure for a child 

receiving a malaria test (either an RDT or a blood slide for microscopy). Standards of care for children with 

fever in Malawi include testing for malaria before providing treatment. The analysis of finger/heel stick 

included 750 children under age five years who had fever in the two weeks preceding the mother’s 

interview, according to the 2012 Malawi MIS. Appendix Table A18 shows the background characteristics 

of these children. Diagnostic testing for malaria in children with fever is relatively low in Malawi. Roughly 

a third of urban children (35 percent) and 19 percent of rural children less than five years old with recent 

fever reported having a finger/heel stick (Appendix Table A19). 

Figure 24 shows the bivariate association between the level of malaria diagnostic capacity and the 

percentage of children for whom a finger or heel stick was performed for recent fever. In the urban clusters, 

testing coverage decreased with increasing numbers of facilities with diagnostic capacity. Finger/heel sticks 

were received by 50 percent of children in clusters with low facility-level diagnostic capacity, by 36 percent 

of children in clusters with medium readiness, and by 27 percent of children in clusters with high readiness. 

This association was not significant, however. In rural areas there was also no significant difference in the 

level of finger/heel stick across levels of diagnostic capacity (see Figure 24). 

6.8

33.3

50

54.2

43.2

12.5

Urban Rural

Low Medium High
56 

Figure 24. Proportion of children in Malawi under five with recent fever who had a finger/heel stick 

by access to facilities with malaria diagnostic capacity, according to residence 

Using multilevel models, we assessed how diagnostic testing for malaria (as measured through the proxy 

finger/heel stick variable) in children with fever is associated with diagnostic capacity at health facilities 

within the specified buffer of the cluster. We ran separate multilevel models for urban and rural areas. 

Table 13 presents odds ratios and 95% confidence intervals for the level of diagnostic capacity, as well as 

for the covariates adjusted for in the models. In both the urban and rural areas, no significant association 

was found between the level of malaria diagnostic capacity of the facilities and the likelihood that a child 

had a finger/heel stick in response to recent fever, after controlling for background characteristics. 

Only one background characteristic was associated with the odds of finger/heel stick in children with recent 

fever. Children in rural areas whose mothers completed secondary or more education had 3.6 times the odds 

of having a finger/heel stick performed in response to recent fever, compared with children whose mothers 

had no formal education (OR=3.6; 95% CI=1.4–9.1). No significant associations were seen between 

finger/heel stick in children and background characteristics in urban areas. 

 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Urban Rural

Low Medium High
57 

Table 13. Results of multivariable logistic regressions of finger/heel stick in 

children under five years with recent fever in Malawi, by residence 

 Urban Rural 

Variables OR 95%CI OR 95%CI 

Diagnostic capacity 

Low 1.00 1.00 

Medium 0.27 0.04 - 2.00 1.27 0.71 - 2.26 

High 0.1300 0.01 - 1.39 1.08 0.458 - 2.55 

Use of ITN 

No 1.00 1.00 

Yes 0.87 0.37 - 2.05 0.83 0.52 - 1.37 

Age 

<1 1.00 1.00 

1-2 1.95 0.52 - 7.34 1.36 0.63 - 2.97 

2-3 1.47 0.43 - 5.03 0.75 0.32 - 1.76 

3-4 1.06 0.27 - 4.18 0.47 0.18 - 1.23 

4-5 0.54 0.12 - 2.39 0.83 0.31 - 2.20 

Birth order 

1 1.00 1.00 

2 0.57 0.27 - 1.46 1.39 0.79 - 2.30 

3 or more 0.78 0.03 - 24.80 1.12 0.37 - 3.39 

Sex 

Male 1.00 1.00 

Female 1.05 0.45 - 2.45 0.91 0.56 - 1.48 

Mother’s education 

None 1.00 1.00 

Primary 0.67 0.14 - 3.32 1.91 0.97 - 3.72 

Secondary or higher 1.07 0.20 - 5.53 3.58** 1.40 - 9.12 

Wealth quintile 

Lowest 1.00 1.00 

Second 1.44 0.14 - 15.41 1.05 0.53 - 2.08 

Middle 0.97 0.13 - 7.45 0.60 0.29 - 1.27 

Fourth 0.35 0.07 - 1.80 1.06 0.50 - 2.27 

Highest 1.29 0.23 - 7.15 1.47 0.55 - 3.96 

Number of children 145 502 

Number of clusters 36 95 

***p<0.001 **p<0.01 *p<0.05 

3.6.2. Association between malaria parasitemia prevalence among children and availability of ITNs 

at health facilities providing malaria prevention and treatment services 

3.6.2.1 Availability if of ITNs at health facilities providing malaria prevention and treatment services 

We examined the 940 facilities in Malawi that offer malaria diagnosis and treatment services. Among these 

facilities, approximately 50 percent are health centers or maternity facilities, 34 percent are clinics, 12 

percent are hospitals, five percent are dispensaries and only 1 percent are health posts (Table 14). The type 

of facility varies widely between urban and rural areas. In rural areas, health centers/maternity facilities are 

the most prevalent type of facility with malaria diagnostic and treatment services (67 percent), while in the 

urban areas they represent only 10 percent of facilities. Conversely, 70 percent of urban facilities offering 

malaria diagnosis and treatment are clinics whereas only 18 percent of these facilities in rural settings are 

clinics. Health posts are only found in rural areas. Malaria diagnosis and treatment services in Malawi are 
58 

offered by a mix of public (48 percent), CHAM/faith-based (17 percent), Private (22 percent), NGO (6 

percent), and company facilities (7 percent). Government/public facilities are more common in rural areas, 

while private facilities are more common in urban areas. 

Table 14. Characteristics of health facilities in Malawi 

that provide malaria diagnosis and/or treatment 

services, by residence, Malawi SPA 2013-14 

Characteristics Urban Rural Total 

Type of facility 

Hospital 19.3 8.1 11.6 

Health centre/maternity 10.3 66.5 49.0 

Dispensary 0.8 6.2 4.5 

Clinic 69.6 17.9 33.9 

Health post 0.0 1.4 1.0 

Managing authority 

Public 23.0 59.1 47.9 

CHAM/other faith-based 7.7 21.5 17.2 

Private 47.8 10.1 21.8 

NGO 12.6 2.7 5.8 

Company 8.9 6.5 7.2 

Region 

North 13.8 18.3 16.9 

Central 41.1 35.6 37.3 

South 45.1 46.1 45.8 

Total number of facilities 292 648 940 

In order to evaluate the service environment for malaria prevention and treatment, availability of 

commodities relevant to malaria prevention, diagnosis and treatment were measured. For this analysis, ITNs 

at facilities offering malaria diagnosis and/or treatment was used as the facility-level variable of interest. 

Figure 25a-c show the percentage of facilities offering malaria diagnosis and/or treatment with ITNs 

available and observed, by residence, type of facility, and managing authority. In urban clusters, 19 percent 

of malaria diagnosis/treatment facilities had ITNs available and 50 percent of facilities in rural clusters. 

Health centers/maternity facilities and hospitals were much more likely to have ITN supplies (62 percent 

and 44 percent, respectively) than were dispensaries (20 percent), clinics (12 percent) or health posts (25 

percent). 
59 

Figure 25a. Percentage of malaria diagnosis/treatment facilities with ITNs available and observed, 

by type of facility, Malawi SPA 2013-14 

Figure 25b. Percentage of malaria diagnosis/treatment facilities with ITNs available and observed, 

by managing authority, Malawi SPA 2013-14 

44.4

61.5

19.6

11.6

25

0

20

40

60

80

100

Hospital Health

centre/maternity

Dispensary Clinic Health post

47.9

17.2 21.8

5.8 7.3

0

20

40

60

80

100

Public CHAM/faith-based Private NGO Company
60 

Figure 25c. Percentage of malaria diagnosis/treatment facilities with ITNs available and observed, 

by residence, Malawi SPA 2013-14 

3.6.2.2. Clusters’ access to facilities with ITNs 

We linked each of the clusters included in the 2012 Malawi MIS to all facilities within a 5 or 10 kilometer 

buffer and obtained a count of total facilities with ITNs available for every cluster. These counts were then 

use to divide clusters into three categories representing into low (no facility), medium (1-2 facilities), and 

high (3 or more facilities) access to malaria diagnosis and treatment facilities with ITNs available. This 

process was done separately for urban and rural clusters. Figure 26 shows the distribution of three levels of 

access to facilities with ITNs available among DHS clusters. While approximately half of all clusters fell 

within the medium access category for both urban and rural clusters, only 7 percent of urban clusters had 

low access compared to 33 percent of rural clusters. In contrast, 43 percent of urban clusters had high access 

to malaria diagnosis compared to only 13 percent of rural clusters. 

Figure 26. Distribution of access to facilities with ITNs available in Malawi clusters, by residence 

18.7

50.1

40.4

0

20

40

60

80

100

Urban Rural Total

15.9 21.9

45.5

59.4

38.6

18.8

Urban Rural

Low Medium High
61 

3.6.2.3. Malaria parasitemia prevalence among children and determinants 

The individual-level outcome of interest in this analysis is whether or not a child had a positive microscopy 

test for malaria. This outcome indicates infection with the malaria parasite but not necessarily clinical 

disease as asymptomatic children were tested. Current recommendations in Malawi call for universal 

coverage and use of ITNs by the entire population. The analysis of malaria parasitemia included 2,254 

children age 6-59 months who were tested for malaria parasites via microscopy as part of the 2012 Malawi 

MIS. Appendix Table A20 shows the background characteristics of these children. Malaria parasitemia 

prevalence varies in Malawi by urban-rural residence. Ten percent of urban children and 29 percent of rural 

children age 6-59 months tested positive for malaria parasites via microscopy (Appendix Table A21). 

Figure 27 shows the bivariate association between the level of access to facilities with ITN supplies and the 

percentage of children with a positive malaria microscopy test. In the urban areas, parasitemia prevalence 

is lower in children with greater access to facilities with ITN supplies. A quarter of children in clusters with 

low facility-level ITN availability tested positive for malaria, compared with 7 percent of children in 

clusters with medium ITN availability, and for 4 percent of children in clusters with high ITN availability. 

The difference in parasitemia prevalence in low ITN access clusters and medium access clusters is 

significant as is the difference between low and high access clusters. In rural areas there was no difference 

in parasitemia prevalence across levels of ITN availability in facilities (see Figure 27). 

Figure 27. Proportion of children age 6-59 months with a positive malaria microscopy test in 

Malawi, by level of access to facilities with ITNs available and residence 

Using multilevel models, we assessed how parasitemia prevalence (as measured by microscopy) in children 

age 6-59 months is associated with ITN availability at health facilities within the specified buffer of the 

cluster. We ran separate multilevel models for urban and rural areas. Table 15 presents odds ratios and 95% 

confidence intervals for the level of ITN availability, as well as for the covariates adjusted for in the models. 

In urban areas children with medium and high access to facilities with ITN availability had lower odds of 

testing positive for malaria parasites (OR=0.24; 95% CI=0.08–0.77 and OR=0.16; 95% CI=0.04–0.64, 

respectively). In rural areas, no significant association was found between the level of access to facilities 

with ITNs and the odds that a child had a positive malaria test, after controlling for background 

characteristics. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Urban Rural

Low Medium High
62 

Table 15. Results of multivariable logistic regressions of being malaria 

positive among children age 6-59 months in Malawi, by residence 

 Urban Rural 

Variables OR 95%CI OR 95%CI 

ITN supply available 

Low 1.00 1.00 

Medium 0.24* 0.08 - 0.77 1.28 0.65 - 2.55 

High 0.16** 0.04 - 0.64 0.78 0.32 - 1.93 

Household owns at least one ITN 

No 1.00 1.00 

Yes 0.26*** 0.12 - 0.54 0.78 0.59 - 1.03 

 

Age 

<1 1.00 1.00 

1-2 2.45 0.58 - 10.34 1.22 0.74 - 2.01 

2-3 6.24** 1.69 - 23.02 2.01** 1.22 - 3.30 

3-4 2.70 0.67 - 10.87 2.46*** 1.50 - 4.03 

4-5 3.65 0.96 - 13.87 2.50*** 1.52 - 4.11 

Sex 

Male 1.00 1.00 

Female 0.56 0.28 - 1.10 1.03 0.80 - 1.32 

Mother’s education 

None 1.00 1.00 

Primary 3.12* 1.02 - 9.56 1.00 0.75 - 1.33 

Wealth quintile 

Lowest 1.00 1.00 

Second 1.60 0.26 - 9.72 0.86 0.60 - 1.23 

Middle 2.73 0.49 - 15.09 1.00 0.69 - 1.45 

Fourth 1.96 0.48 - 8.03 0.67 0.45 - 1.00 

Highest 0.74 0.17 - 3.15 0.41** 0.22 - 0.75 

Region 

North 1.00 1.00 

Central 0.42 0.11 - 1.66 3.09** 1.32 - 7.22 

South 0.52 0.12 - 2.25 1.56 0.67 - 3.65 

Number of women 596 1,587 

Number of clusters 44 96 

***p<0.001 **p<0.01 *p<0.05 

Several background characteristics were also associated with odds of a positive malaria test. In both urban 

and rural settings, older children are more likely to test positive for malaria than are younger children. In 

rural settings, children from the wealthiest households were less likely to test positive for malaria than were 

children from the poorest households (OR = 0.41; 95%CI = 0.22-0.75). Also, in rural settings, children 

from the Central region had 3.1 times greater odds of malaria parasitemia than children from the North 

(95% CI = 1.32-7.22). In urban areas, children whose mothers completed at least primary school had 3.1 

times the odds of having a positive malaria test than children whose mothers did not complete primary 

school (OR = 1.0 – 9.6). Finally, urban children living in households owning at least one ITN were 

significantly had lower odds of testing positive for malaria than those living in households without ITNs 

(OR = 0.26; 95%CI = 0.12-0.54). 
63 

3.6.4. Association between IPTp2 coverage among pregnant women and service readiness for 

provision of IPTp services at health facilities 

3.6.4.1. Service readiness for provision of IPTp services at health facilities 

Among the 632 facilities in Malawi that offer ANC services, approximately 70 percent are health centers 

or maternity facilities, 16 percent are hospitals, 10 percent are clinics, three percent are dispensaries and 

less than 1 percent are health posts (Table 16). The type of facility varies widely between urban, and rural 

areas. In rural areas, health centers/maternity facilities are the most prevalent type of ANC facility (80 

percent), while in the urban areas they represent only 24 percent of ANC facilities. Conversely, 44 percent 

of urban facilities offering malaria diagnosis and treatment are hospitals whereas only 10 percent of these 

facilities in rural settings are hospitals. Similarly, 29 percent of ANC facilities in urban settings are clinics 

whereas only 6 percent of ANC facilities in rural areas are clinics. Health posts are only found in rural 

areas. ANC services in Malawi are offered by a mix of public (63 percent), CHAM/faith-based (24 percent), 

Private (7 percent), NGO (2 percent), and company facilities (5 percent). Government/public facilities are 

most common in both rural and urban settings, however, private facilities comprise a greater proportion of 

ANC facilities in urban settings than in rural settings (24 percent versus 3.3 percent, respectively). 

Table 16. Characteristics of health facilities 

in Malawi that provide ANC services, by 

residence, Malawi SPA 2013-14 

Characteristics Urban Rural Total 

Type of facility 

Hospital 44.4 9.9 16.2 

Health centre/maternity 24.4 80.8 70.5 

Dispensary 1.9 3.0 2.8 

Clinic 29.4 5.8 10.1 

Health post 0.0 0.4 0.4 

Managing authority 

Public 48.3 66.5 63.2 

CHAM/other faith-based 17.7 24.9 23.6 

Private 24.3 3.3 7.1 

NGO 4.4 1.0 1.6 

Company 5.3 4.3 4.5 

Zone 

North 16.0 19.2 18.6 

Central 40.8 36.3 37.1 

South 43.2 44.5 44.3 

Total number of facilities 116 516 632 

Facility’s service readiness for provision of IPTp was measured based on a number of components including 

the availability of IPTp guidelines, valid SP available and observed, self-report that IPTp services are 

offered as part of routine ANC, and ANC staff with recent IPTp training (see Appendix Table A22 for a 

detailed summary of indicators considered). Figures 28a-c indicate variations in readiness of health 

facilities for providing IPTp services. In urban clusters, 16 percent of ANC facilities were service ready for 

IPTp provision compared to 20 percent of facilities in rural clusters. Hospitals and health centers/maternity 

facilities were much more likely to be IPTp service ready (40 percent and 29 percent, respectively) than 

were dispensaries (12 percent), clinics (16 percent) or health posts (0 percent). 
64 

Figure 28a. Proportion of ANC facilities service ready for provision of IPTp, by type of facility, 

Malawi SPA 2013-14 

Figure 28b. Proportion of ANC facilities service ready for provision of IPTp, by managing 

authority, Malawi SPA 2013-14 

40.3

28.5

11.6

16.1

0.0

0

20

40

60

80

100

Hospital Health

centre/Maternity

Dispensary Clinic Health post

32.5

25.4

13.4

19.8 17.7

0

20

40

60

80

100

Government CHAM/faith-based Private NGO Company
65 

Figure 28c. Proportion of ANC facilities service ready for provision of IPTp, by residence, Malawi 

SPA 2013-14 

3.6.4.2. Access to facilities service -ready to provide IPTp in MIS clusters 

After each of the 140 clusters included in the 2012 Malawi MIS were linked to all ANC facilities within 

the specific buffer, we counted the number of facilities linked to each cluster with IPTp service readiness. 

These counts were then use to divide clusters into three categories representing low, medium and high 

access to IPTp service ready facilities. This process was done separately for urban and rural clusters with 

different cutoff values because the distribution of the number of facilities is very different between urban 

and rural clusters. In rural clusters Low, medium and high access were defined as zero, one and 2-4 facilities 

service ready for IPTp, respectively. In urban clusters low, medium, and high access were defined as 0-1, 

2-3, and 4-9 facilities, respectively. 

Figure 29 shows the distribution of low, medium and high access to ANC facilities service ready for IPTp 

provision among DHS clusters, by urban and rural residence. Approximately one third of urban clusters fell 

within the low access category (36 percent); thirty-nine percent had medium-level access and one quarter 

had high-level access to IPTp ready facilities. Over one half of rural clusters had low access (52 percent), 

32 percent had medium-level access and 16 percent had high level access to IPTp ready facilities. 

15.5

19.9

0

20

40

60

80

100

Urban Rural
66 

Figure 29. Percent distribution of Malawi clusters by access to IPTp services, according to 

residence 

3.6.4.3. IPTp2 coverage among pregnant women and determinants 

The individual-level outcome of interest in this analysis is whether or not an interviewed woman between 

the ages of 15 and 49 received two or more doses of SP for prevention of malaria during her last pregnancy 

that resulted in a live birth within two years of the survey. This intervention is recommended by Roll Back 

Malaria for all women in high malaria transmission settings. The analysis of IPTp2 included 990 women 

15-49 years of age who were interviewed and had a live birth in the two years before the interview in the 

2012 Malawi MIS. Appendix Table A23 shows the background characteristics of these women. IPTp2 

coverage does not vary significantly in Malawi by urban/rural setting; 56 percent of urban women and 53 

percent of rural women received the IPTp2 intervention (Appendix Table A24). 

Figure 30 shows the bivariate association between the level of access to facilities with IPTp service ready 

readiness and the percentage of women who received at least two doses of IPTp. In the urban areas, no 

significant association was seen between level of access to facilities service ready for IPTp provision and 

IPTp2 coverage. In rural settings, women in clusters with high access to IPTp service ready facilities were 

significantly less likely to have received two doses of SP during her last pregnancy compared to women in 

clusters with low access to IPTp services (16 percent vs. 51 percent, respectively). 

36.4

52.1

38.6

32.3

25

15.6

0%

20%

40%

60%

80%

100%

Urban Rural

Low Medium High
67 

Figure 30. Women’s use of IPTp2 in Malawi by level of access to ANC facilities service ready for 

IPTp provision, by residence 

Using multilevel models, we assessed how IPTp2 coverage in interviewed women with a live birth in the 

past two years is associated with IPTp service readiness at health facilities within the specified buffer of 

the cluster. We ran separate multilevel models for urban and rural areas. Table 17 presents odds ratios and 

95% confidence intervals for the level of IPTp service readiness, as well as for the covariates adjusted for 

in the models. In the multivariable model In rural areas the negative association between IPTp coverage 

and IPTp service readiness seen in the bivariate analysis becomes positive; women with high access to IPTp 

service ready facilities were more likely to have received the IPTp2 intervention than women in areas with 

low access (OR = 2.4; 95% CI=1.4-4.3). In urban areas, no significant association was found between the 

level of access to IPTp service ready facilities and the likelihood that a woman received IPTp2, after 

controlling for background characteristics. 

Only two other covariates were associated with odds of IPTp2. In urban clusters, women who reported 

having seen or heard messages about malaria in the past six months were more likely to have received the 

IPTp2 intervention then were those who had not be exposed to these messages (OR = 2.0; 95%CI = 1.0-

3.8). In rural settings, non-Catholic Christian women were more likely to have received the IPTp2 

intervention than were Catholics (OR = 1.9; 95%CI = 1.1-3.2). 

 

0

20

40

60

80

100

Urban Rural

Low Medium High
68 

Table 17. Results of multivariable logistic regressions of IPTp2 among women who 

had a live birth in the two years preceding the survey in Malawi, by residence 

 Urban Rural 

Variables OR 95% CI OR 95% CI 

Level of access to ANC facilities service 

ready for IPTp provision 

Low 1.00 1.00 

Medium 0.58 0.28 - 1.23 1.17 0.79 - 1.736

High 1.20 0.48 - 2.99 2.45** 1.39 - 4.301 

Age 

15-19 1.00 1.00 

20-24 0.93 0.32 - 2.73 1.41 0.77 - 2.600

25-29 0.98 0.31 - 3.09 0.97 0.49 - 1.907

30-34 1.01 0.30 - 3.44 1.23 0.59 - 2.559

35-39 1.58 0.35 - 7.14 0.96 0.43 - 2.110

40-49 0.19 0.01 - 2.82 1.17 0.49 - 3.046 

Parity 

1st birth 1.00 1.00 

2nd birth 1.41 0.69 - 2.86 1.06 0.70 - 1.607

3rd or higher 0.89 0.21 - 3.74 0.82 0.44 - 1.526 

Number of household members 

1-3 1.00 1.00 

4-5 0.90 0.41 - 2.00 1.21 0.72 - 2.034

6-7 0.85 0.33 - 2.18 1.62 0.90 - 2.933

8-9 0.78 0.23 - 2.65 1.90 0.88 - 4.094

10 or more 0.35 0.05 - 2.49 1.04 0.35 - 3.038 

Woman is literate 

No 1.00 1.00 

Yes 1.33 0.48 - 3.72 1.30 0.87 - 1.93 

Completed primary school 

No 1.00 1.00 

Yes 1.32 0.67 - 2.60 1.03 0.64 - 1.65 

Wealth quintile 

Lowest 1.00 1.00 

Second 1.95 0.25 - 15.12 1.34 0.82 - 2.17

Middle 0.92 0.16 - 5.38 1.36 0.83 - 2.25

Fourth 0.89 0.20 - 3.90 0.73 0.42 - 1.26

Highest 1.51 0.37 - 6.18 2.04 0.93 - 4.46 

Region 

Northern 1.00 1.00 

Central 0.39 0.15 - 1.04 0.70 0.41 - 1.21

Southern 1.29 0.50 - 3.38 0.78 0.46 - 1.33 

Woman’s religion 

Catholic 1.00 1.00 

CCAP 2.57 0.98 - 6.74 1.30 0.69 - 2.45

Anglican 0.81 0.19 - 3.42 1.04 0.24 - 4.48

Seventh Day Advent./ Baptist 1.59 0.52 - 4.86 1.07 0.46 - 2.49

Other Christian 0.97 0.40 - 2.40 1.90* 1.14 - 3.19

Muslim 1.47 0.52 - 4.21 1.49 0.83 - 2.69

No religion na 1.09 0.21 - 5.51

Other 0.56 0.07 - 4.31 0.89 0.42 - 1.84 

Woman has seen or heard messages 

about malaria in past six months

No 1.00 1.00 

Yes 1.99* 1.04 - 3.84 1.32 0.89 - 1.96 

Number of women 234 629 

Number of clusters 44 96 

***p<0.001 **p<0.01 *p<0.05 
69 

4. Discussion and Conclusions 

Using data from nationally representative household surveys and health facility censuses in Haiti and 

Malawi, this study developed a method of linking population data and health facilities data to examine the 

relationship between service environment and utilization for a number of key health services in Haiti and 

Malawi. In this section we discuss the key results for each health area, highlight benefits and constraints of 

the methodology, discuss the study limitations, and provide overall conclusions. 

4.1 Key Findings on the Association between Service Environment and Use of Health Services 

4.1.1. Family planning 

The results on family planning in Haiti indicate a strong association between the availability of methods 

choice and women’s use of modern contraceptive methods in rural and other urban areas. The presence of 

at least one facility offering a range of methods within a reasonable distance from the community was 

significantly associated with increased adoption of modern contraceptive methods. This finding is 

consistent with the findings of other studies, discussed earlier in this report (see section 1.2), even though 

different linking approaches were used in those studies. Providing a range of method choice is important to 

meet the needs of couples at different life stages and also to allow woman to switch methods if necessary, 

thereby increasing long-term use of contraception (Bruce 1990). However, choice of modern methods of 

contraception at health facilities is limited in Haiti—less than one-third of family planning facilities in the 

country provide three or more methods. The community’s access to facilities with multiple method choice 

is poor. In both urban and rural areas, about 17 percent of clusters do not have a facility within 10 km 

offering three or more contraceptive methods. These findings suggest family planning programs in Haiti 

need to focus on increasing method choice in both urban and rural areas. In rural areas, health centers 

without beds and dispensaries will need to be better resourced in contraceptive supplies because they are 

the two major types of facilities that provide FP services. 

4.1.2. Antenatal care 

Antenatal care services are widely available at health facilities (832 out of 905) in Haiti. As a result, all 

clusters irrespective of location have at least one ANC facility within 5 km or 10 km. However, the readiness 

in providing high-quality ANC is of concern; the study identified limited capacity to conduct urine and 

blood tests, little use of the ANC guidelines, and poor availability of tetanus vaccine. Our analysis showed 

that facilities’ service readiness is associated with women’s receipt of 4 or more ANC visits in rural and 

other urban areas, and with increased quality of care in rural areas. A study in Nepal also showed that the 

quality of health services in the health post within the community is significantly associated with women’s 

use of any antenatal care service (Acharya and Cleland 2000). As expected, our analyses showed that 

service readiness is an important determinant of women’s receipt of high quality ANC care. However, this 

association is only significant in urban areas; coverage of high quality care in other urban areas already 

reaches 70 percent. It is possible that other factors, for example, cost of services affect the quality of care 

received. A study in rural Zambia based on linked data from DHS and the National Health Facility Census 

also found that level of antenatal care services in the closest facility was associated with the increased 

quality of antenatal care that women received (Kyei, Campbell, and Gabrysch 2012). However, the authors 

acknowledged the potential misclassification of level of services, due to the difference in timing of the two 

surveys and imprecision in distance measures as a result of DHS cluster displacement. In rural areas, for 

both outcomes (service readiness and quality of care), the difference in the relationship between the service 

environment and the respective outcome was only significant when comparing high and low service 
70 

readiness. This could imply that a certain level or threshold of readiness is needed to see an impact on ANC 

outcomes. 

4.1.3. Facility delivery 

Access to normal delivery care and basic (BEmOC) and comprehensive (CEmOC) emergency care is 

important for reducing maternal mortality. In Haiti, less than one-third of health facilities mandated to 

provide delivery services provide BEmOC, which is far from the government’s goal of having 108 BEmOC 

facilities by 2015. We found that one-third of other urban clusters and half the rural clusters do not have 

any facility with BEmOC within the 5 km or 10 km buffer. Availability of CEmOC is even more limited. 

Nationwide, 36 facilities provide CEmOC and only 5 were located in rural areas where more than 150,000 

births occur every year. Our findings indicate a strong effect of having a BEmOC facility on facility delivery 

in the metropolitan area. However, the results in rural and urban areas seem to suggest that being able to 

access a facility providing normal delivery services (with or without BEmOC) is more important in rural 

and other urban areas. These results suggest that efforts and resources should focus on increasing access to 

normal delivery services in rural and other urban areas. In Haiti, only hospitals and health centers with beds 

are mandated to provide delivery services. Health centers and dispensaries are more available in rural areas 

but are not equipped to provide service delivery services. The government may change the mandate of 

lower-level facilities and provide essential resources including training health providers for delivery care 

to meet local needs. 

4.1.4. Care seeking for children’s diarrhea 

Diarrhea is one of the leading causes of under-five mortality in Haiti. Limited use of facility care for 

children’s diarrhea could be related to the quality of care at health facilities. Using a wide range of 

components identified by WHO that are essential for providing child curative care services, we assessed 

facilities’ readiness to provide the services. We found a significant positive association between the level 

of service readiness and care seeking for children’s diarrhea in the metropolitan area but not in rural and 

urban areas. Possibly because of limited health knowledge and/or cultural factors, caregivers of children in 

rural and urban areas may not consider diarrhea to be an illness of sufficient severity to need facility care, 

even when it is accessible. In the metropolitan area, however, people generally have more education and 

are more likely to have the economic means to afford facility care for children. Another possible reason for 

the differential use of facility care for children’s diarrhea may be that informal health services are more 

widely used for treatment of children’s diarrhea in less developed areas. This issue will be discussed in the 

limitation section. 

4.1.5. HIV testing 

In rural areas, living in a community with a facility within 10 km having HIV testing capacity was found 

to significantly increase the use of HIV testing services. However, in urban areas the availability of the 

items that support the quality of care are significantly associated with HIV testing. In the metropolitan area, 

no association is found between the HIV service environment and HIV testing. It should be noted that all 

clusters in the metropolitan area are linked to at least one facility with HIV testing capacity. In areas where 

HIV-related services are widely available, people are likely to travel further for these services because of 

the fear of social stigma. In rural areas, where half of the facilities lack HIV testing capacity, care seeking 

may be constrained to nearby facilities. 
71 

4.1.6. Malaria-related health services 

Service delivery at health facilities is considered important for malaria prevention and treatment. This study 

is the first we are aware of that links population data with health facility data to quantify the relationship 

between malaria-related population outcomes and the service environment. Despite the high malaria 

prevalence in rural areas of Malawi, over half of rural clusters do not have any facility within 10 km that is 

ready for provision of IPTp. As expected, high access to IPTp service-ready facilities is significantly 

associated with the IPTp2 coverage among pregnant women in rural areas. However, the association is not 

significant in urban areas. It is important to note that levels of service readiness are defined differently in 

urban and rural clusters because of the distribution of service-ready facilities. Future investigations of 

service readiness could use different methods of analysis such as population-adjusted measures of access. 

The association between ITN availability and malaria parasitemia is significant in urban areas but not in 

rural areas. Although this association was seen in multivariable models that controlled for household wealth 

and other socio-demographic variables, it is possible that other unmeasured factors are playing an important 

role. For example, well-provisioned facilities may be more common in clusters with good housing quality 

(screens, eaves, air-conditioning, etc.) in which children would be at low risk of malaria. We did not find 

an association between malaria diagnostic testing in children with fever and diagnostic capacity at health 

facilities in either rural or urban areas. This could be due to the relatively high access to facilities offering 

malaria diagnostic tests in both urban and rural areas. Almost 90% of all facilities had available RDTs, 

although fewer reported the other components of diagnostic capacity (trained personnel and possession of 

the proper guidelines). In practice, having RDTs available is likely sufficient for a child to be tested, 

regardless of training or protocols. Thus, the near universal availability of the RDTs could explain our 

results. 

4.2. Benefits of the Buffer Linkage Methodology 

The availability of GPS data of clusters and health facilities provides opportunities for linking households 

and health facilities to examine the relationship between service provision and use. Among the limited 

studies based on DHS and SPA surveys or other health facility surveys, most linked DHS clusters to the 

nearest health facility and analyzed how physical access to health facilities—i.e. distance to the nearest 

facility and/or the services provided at the health facility—affects population use of health services. This 

approach of using distance as a measurement of access to health care is prone to errors resulting from the 

geographical displacement of DHS clusters. The coordinates of DHS clusters are displaced up to 2 km in 

urban areas and 5 km in rural areas; therefore, the distance to the identified “nearest” facility may not be 

the distance wanted. Skiles et al. (2013) indicated that the distance to closest facility can be misclassified 

for 34 to 43 percent of clusters due to clusters displacement. 

When the identified “nearest” facility is not the closest in reality, associating its service provision with 

population use of services is problematic. In Haiti, we found that within the specific buffer, about 30 percent 

of clusters in rural and other urban areas were linked to more than one facility offering three or more family 

planning methods, and the majority of DHS clusters had more than one facility offering antenatal care 

services. In such circumstances, the displacement could cause one of these facilities to be identified as the 

closest facility, even though in reality it is not. Service provision in the identified facility therefore does not 

represent the service environment nearest to the community. Furthermore, even when the nearest facility is 

correctly identified, linking clusters to the nearest facility does not account for potential provider bypassing

because of other considerations such as cost and quality of care. Women may be willing to travel further 

for services if the perceived quality of care in the nearest facility is poor. 
72 

Instead of relating health services utilization to the service provision in a single facility (the nearest one), 

we consider it more important and reasonable to look at the average service provision of all health facilities 

within an accessible distance from the community; therefore, we measured the service environment 

comprised of all the facilities within a specified buffer. We were able to include all formal-sector health 

facilities that provide specific services to gauge the service environment because both the Haiti and Malawi 

SPA surveys are censuses. The use of a 10-kilometer buffer distance in rural areas and a 5-kilometer buffer 

distance in urban areas reduced misclassification error due to cluster displacement and ensured that all 

facilities within the maximum displacement radius around the real location of the cluster were captured 

(given that urban clusters were displaced up to 2 km and most rural clusters were displaced up to 5 km) 

(Burgert 2013). 

Depending on data available and indicators of interest, other methods can be applied to link household data 

and health facility data; these methods are discussed in Burgert (2014). It should be noted that the buffer 

linking method is not recommended in the absence of health facility censuses because such linkage based 

on facility sample data may result in substantial misclassification bias. Skiles et al. (2013) in their 

simulation analysis with Rwanda data showed that 50 to 60 percent of clusters were misclassified in terms 

of the number of linked family planning facilities, even using undisplaced GPS data on DHS clusters. When 

facility data come from a sample survey, linkage should only be made at the level for which the sample is 

representative, for example, at the regional level (Burgert 2014). 

4.3. Methodological Lessons Learned 

In the process of linking population data and health facility data for the purpose of analyzing multiple health 

services, some methodological lessons became evident. First, it is appropriate to stratify the analysis by 

urban and rural residence, and even further separate the metropolitan area from other urban areas. For 

example, in Haiti, as shown in the analysis for all health services examined, clusters in the metropolitan 

area were linked to a much greater number of health facilities than clusters in other urban and rural areas. 

Types of facilities and therefore service provision are different between them. In Malawi, similar findings 

were seen between urban and rural areas. 

Second, it is necessary to use different cutoff points for different locations to distinguish service 

environment among clusters. In Haiti, because clusters in the metropolitan area were linked to a much 

greater number of health facilities than clusters in other urban and rural areas, to measure service availability 

it was necessary to use different cutoff points to distinguish clusters in the metropolitan area. For example, 

in measuring the family planning service environment in terms of the availability of a range of contraceptive 

methods at health facilities, clusters in rural and other urban areas were categorized into three groups based 

on whether within the buffer, there was zero, one, or more than one facility providing three or more 

methods. However, in the metropolitan area all clusters were linked to one or more facilities, so a different 

cutoff—terciles of the number of facilities—was used to distinguish clusters. Similarly, different cutoffs 

were necessary in urban and rural clusters in Malawi. When using different cutoffs, the high-low difference 

is no long comparable between locations. 

Third, a 5 kilometer buffer may not be appropriate for defining the service environment for clusters in the 

metropolitan area. Because of the high density of health facilities and DHS clusters in the metropolitan 

area, using a 5 km buffer may result in adjacent clusters linking to (more or less) the same group of facilities; 

therefore, there is limited variation across metropolitan clusters in terms of the service environment. For 

example, in the assessment of ANC services, we found much less variation in the ANC service readiness 

among clusters in the metropolitan area compared with clusters in rural and other urban areas. Similar 

results were found regarding the service environment in providing curative child care: readiness scores in 
73 

the metropolitan area were largely homogenous while the interquartile range was wider in other urban and 

rural clusters. 

Moreover, the populations in metropolitan areas are likely to have more transportation options available 

and can more easily access health services that are geographically further from their home. Therefore, the 

service environment defined with a 5 km buffer may not represent a meaningful service environment for 

the communities in this area. This study included the metropolitan area for comparison purposes. The 

findings indicate that the buffer linkage between DHS clusters and SPA facilities may not be appropriate 

in areas with a high density of both health facilities and population. More precise measurements of the 

service environment are needed for such areas. 

4.4. Study Limitations 

One important limitation of this study is the temporal gap between the outcome variables and the service 

variables. Facility data reflect the “current” service environment at the time of the Haiti 2013 SPA and the 

Malawi 2013-14 SPA, while outcomes may be measured over a time period preceding the household 

surveys in the two countries. For example, in Malawi, IPTp2 is measured during a 2-year period preceding 

the 2012 Malawi MIS; and in Haiti, antenatal care and facility delivery variables cover use of services for 

the last birth in a 5-year period preceding the 2012 Haiti DHS. Associating service provision and use data 

could be problematic if the service environment changed substantially over the time period. 

For malaria, there is also the potential for biases due to seasonality. Malaria parasitemia is seasonal, 

therefore is possible that commodity supplies vary with the malaria season. If malaria outcomes and service 

environment are measured in different seasons—for example, one in high-prevalence season and one in 

low-prevalence season—bias may be introduced in examining their association. In Malawi, however, 

malaria transmission is holoendemic so seasonal variation in commodity supplies at health facility should 

be limited, and seasonal biases in Malawi should be minimal. Moreover, IPTp data are collected over a 

two-year lag so seasonality should not affect the measurement. 

Another important limitation is the potential confounding effect of the use of informal health services or 

other modes of service delivery. SPA data only reflect services provided at formal health care facilities. 

Informal sectors may also be used for health care. For example, the majority of condom users in Haiti 

obtained condoms from the informal sector (shops, friends or relatives); therefore it is necessary to exclude 

condom from the analysis. Diarrhea treatment in Haiti also relies heavily on the informal health sector. 

Among children who received treatment for a recent episode of diarrhea, 20 percent received treatment 

from mobile clinics, shops, and traditional healers, which are not captured in the SPA survey. Use of the 

informal health sector is usually more common in the least developed areas, which could partially explain 

the nonsignificant association between the level of service readiness at health facilities and care seeking for 

children’s diarrhea in rural and other urban areas. Use of informal health services is less of concern for 

antenatal care, delivery care, and HIV testing. The reported sources for these services are mostly formal 

health facilities. 

People may use other modes of service delivery in addition to health facilities. For example, the Malawi 

National Malaria Control Program’s ITN policy promotes free distribution of ITNs for children attending 

their first Expanded Program on Immunization (EPI) visit, among other modes of distribution. Availability 

of ITNs at health facilities represents potential access to a very effective malaria prevention intervention. 

However, ITNs are also distributed via national campaigns every two or three years, which could mitigate 

the effects of facility-level ITN availability on malaria prevalence. National distribution campaigns are 
74 

designed to provide equitable ITN access to the population but the availability of ITNs is not equal across 

health facilities from different clusters, which could influence malaria prevalence. 

4.5. Conclusions 

This report linked population survey data and health facility census data using a buffer-linkage method. As 

demonstrated, this linkage method can be applied in several health areas to examine how the service 

environment affects health service utilization when the available data sources are a household survey and 

a health facility census that were conducted around the same time period. This method has been found to 

be useful in settings with low density of health facilities. For most health services, a significant association 

is found in rural areas (and other urban areas in Haiti) between access to or readiness of service provision 

and utilization of health services. The relationship does not hold however in the Haiti metropolitan area 

because more precise measurements of the health service environment are needed for areas with high 

density of health facilities and population. 
75 

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79 

Appendix 

Table A1. Percentage of family planning facilities in Haiti that provide specific family 

planning methods1, by residence, Haiti SPA 2013 

Methods Metropolitan Other urban Rural Total 

Pill 68.3 82.0 77.6 77.0 

Injectables 69.1 81.3 77.8 77.1 

Male condom 74.8 88.7 80.3 81.1 

Implants 11.4 41.9 15.3 19.9 

Female condom 2.4 6.0 6.4 5.7 

IUD 7.3 5.3 0.6 2.6 

Emergency contraception 8.9 8.6 3.9 5.7 

Standard days method 14.6 19.9 12.0 14.0 

Male sterilization 4.1 12.0 2.5 4.6 

Female sterilization 5.7 17.3 2.7 6.1 

Provide three or more methods 

(excluding condom) 27.6 50.5 23.1 29.3 

Total number of facilities 123 150 483 756 

1 Facility provides, prescribes, or counsels clients on family planning methods and facility has valid 

products observed on-site if method is pill, injectables, implants, male condom, female condom, and 

emergency contraception 

 
80 

Table A2. Percent distribution of married women age 15-49 in Haiti, by selected family planning 

(FP) characteristics and background characteristics, according to residence 

 Metropolitan Other urban Rural 

Characteristics % N % N % N 

Current use of modern contraception* 

No 78.5 1519 69.6 846 70.3 2689

Yes 21.5 416 30.4 370 29.7 1137 

Access to facilities with 3+ modern methods 

Low 39.9 773 17.1 209 16.6 635

Medium 32.2 624 40.5 493 29.2 1119

High 27.8 539 42.3 515 54.2 2072 

Exposure to FP messages 

No 39.6 766 47.3 575 59.6 2281

Yes 60.4 1169 52.7 641 40.4 1545 

Contact with FP providers 

No 78.9 1527 70.3 855 68.4 2617

Yes 21.1 408 29.7 361 31.6 1209 

Mother’s age at birth 

15-24 21.1 409 16.9 205 21.3 814

25-34 41.6 804 43.9 534 36.2 1385

35-44 28.2 546 29.3 356 28.9 1105

45-49 9.1 176 9.9 121 13.6 522 

Number of living children 

0-1 48.2 933 39.4 479 26.2 1004

2-3 35.6 689 38.1 463 35.4 1355

4-5 12.8 247 16.5 200 20.9 801

6+ (3.4) 66 6.1 74 17.4 666 

Mother’s education 

None 10.1 196 11.0 134 34.0 1301

Primary 31.7 613 36.0 437 44.4 1700

Secondary or higher 58.2 1126 53.1 645 21.6 825 

Occupation 

Not employed 36.3 702 28.1 342 32.6 1249

Agriculture 4.4 85 4.8 59 13.0 497

Professional 52.6 1017 62.4 759 52.8 2021

Other 6.8 131 4.7 57 1.6 60 

Wealth quintile 

Lowest 0.0 0 0.0 0 35.2 1345

Second 0.3 5 0.5 6 34.0 1299

Middle 11.3 218 22.8 278 20.5 786

Fourth 37.8 732 40.0 486 8.2 312

Highest 50.6 979 36.6 446 2.2 83 

Department 

Aire metropolitaine/Reste-Ouest 100.0 1935 7.7 93 20.4 780

Sud-est 0.0 0 4.9 59 6.1 234

Nord 0.0 0 28.2 342 10.0 384

Nord-est 0.0 0 8.8 107 4.3 165

Artibonite 0.0 0 22.6 275 21.8 835

Centre 0.0 0 6.8 82 10.5 402

Sud 0.0 0 6.2 75 10.4 399

Grand’anse 0.0 0 5.0 61 5.1 194

Nord-Ouest 0.0 0 7.7 94 6.4 243

Nippes 0.0 0 2.2 27 5.0 190 

Religion 

Catholics 42.0 814 43.7 532 41.8 1598

Protestant 50.3 974 50.6 615 47.3 1811

Vaudousant/other 7.6 148 5.7 70 10.9 417 

Total 100.0 1935 100.0 1216 100.0 3826

* Condom is excluded 
81 

Table A3. Percentage of married women age 15-49 in Haiti who are currently 

using a modern contraceptive method*, by selected family planning (FP) 

characteristics, background characteristics, and residence, Haiti DHS 2012 

Characteristics Metropolitan Other urban Rural 

Access to facilities with 3+ modern methods

Low 22.7 22.7 20.2 

Medium 19.7 33.0 26.2 

High 21.8 31.1 34.5 

Exposure to FP messages 

No 23.3 29.3 28.2 

Yes 20.3 31.4 32.0 

Contact with FP providers 

No 18.5 25.7 24.2 

Yes 32.7 41.6 41.6 

Mother’s age at birth 

15-24 25.6 33.6 25.4 

25-34 26.8 32.8 35.6 

35-44 14.0 28.7 32.1 

45-49 10.9 19.5 15.7 

Number of living children 

0-1 17.2 24.3 18.9 

2-3 28.0 36.7 35.4 

4-5 21.3 30.7 34.8 

6+ 13.8 30.4 28.4 

Mother’s education 

None 17.1 32.8 28.2 

Primary 22.1 33.1 31.4 

Secondary or higher 21.9 28.1 28.6 

Occupation 

Not employed 21.5 27.4 26.2 

Agriculture 34.6 26.8 28.7 

Professional 21.8 31.3 32.2 

Other 10.8 41.1 26.7 

Wealth quintile*

Lowest * * 29.2 

Second * * 28.2 

Middle 25.4 37.6 33.6 

Fourth 26.6 32.1 31.5 

Highest 16.7 24.0 18.4 

Department 

Aire metropolitaine/Reste-Ouest 21.5 15.9 25.2 

Sud-est na 34.3 21.7 

Nord na 35.7 39.4 

Nord-est na 33.3 28.6 

Artibonite na 24.5 26.0 

Centre na 47.9 37.7 

Sud na 28.1 32.8 

Grand’anse na 34.6 28.8 

Nord-Ouest na 20.5 28.1 

Nippes na 32.5 35.2 

Religion 

Catholics 19.6 33.8 33.0 

Protestant 22.3 28.1 26.4 

Vaudousant/other 26.4 25.0 31.6 

Total 21.5 30.4 29.7 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that 

a figure is based on fewer than 25 unweighted cases and has been suppressed. 

* Condom users are excluded from the analysis
82 

Table A4. Percentage of ANC facilities in Haiti that have specific items available, by type of 

facility, Haiti SPA 2013 

 Hospital 

Health center 

with beds 

Health center 

without beds Dispensary Total 

Domain 1: Staff and Training 

National ANC guidelines 

available 21.6 31.2 16.4 32.1 25.6 

At least one trained staff* 77.5 59.2 49.0 26.8 45.0 

Domain 2: Equipment 

Blood pressure apparatus 93.1 93.6 94.8 95.5 94.7 

Domain 3: Diagnostics 

Urine protein testing 11.8 8.8 8.2 2.4 6.4 

Iron testing 23.5 23.2 15.2 2.7 12.4 

Domain 4: Medicines and 

commodities 

Iron tablets 68.6 72.8 68.8 69.6 69.7 

Folic acid tablets 73.5 70.4 65.8 55.7 63.3 

Tetanus vaccine 28.4 24.8 23.0 32.5 27.8 

Total number of facilities 102 125 269 336 832 

 *At least one staff member providing ANC was trained in ANC in the past two years 

 
83 

Table A5. Percent distribution of women in Haiti who had a live birth in the five years preceding 

the survey by selected ANC characteristics and background characteristics, according to 

residence, Haiti DHS 2012 and Haiti SPA 2013 

 Metropolitan Other Urban Rural 

Characteristics % N % N % N 

Service readiness for ANC1 

Low 29.9 364 35.6 295 37.6 1081 

Medium 42.7 519 40.2 333 29.6 853 

High 27.3 332 24.2 200 32.8 945 

4 or more ANC visits 

No 22.9 273 20.5 167 40.0 1150 

Yes 77.1 921 79.5 650 60.0 1726 

Quality of ANC care2 

Low 39.1 442 29.0 229 45.5 1158 

High 60.9 689 71.0 560 54.5 1384 

Mother’s age at birth 

<20 yrs 15.3 186 13.8 115 13.7 396 

20-34 69.4 843 71.9 596 65.3 1879 

35-49 15.2 185 14.3 118 21.0 604 

Birth order 

1 42.3 514 39.6 328 26.8 773 

2-3 37.7 458 37.9 314 35.1 1010 

4-5 13.6 166 14.3 119 18.4 530 

6+ 6.4 78 8.1 67 19.7 566 

Mother’s education 

None 8.5 103 8.8 73 26.8 772 

Primary 32.1 390 34.5 286 48.1 1384 

Secondary or higher 59.4 721 56.7 470 25.1 722 

Occupation 

Not employed 42.3 514 34.5 286 38.3 1102 

Agriculture 5.3 65 3.3 28 11.0 315 

Professional 45.8 556 57.0 473 48.9 1406 

Other 6.5 79 5.1 43 1.9 55 

Wealth quintile3 

Lowest na na na na 36.4 1046 

Second na na na na 34.4 991 

Middle na na na na 19.7 567 

Low-Middle 14.2 173 26.4 219 na na 

Fourth 43.2 524 40.2 333 7.7 222 

Highest 42.6 517 33.4 277 1.8 52 

Department 

Aire metropolitaine/ 

Reste-Ouest 100.0 1214 8.2 68 20.9 601 

Sud-est 0.0 0 4.5 37 6.2 179 

Nord 0.0 0 28.7 238 9.2 265 

Nord-est 0.0 0 9.2 77 4.3 124 

Artibonite 0.0 0 20.6 171 20.9 602 

Centre 0.0 0 8.0 66 11.6 333 

Sud 0.0 0 6.4 53 11.2 324 

Grand’anse 0.0 0 5.4 44 5.0 145 

Nord-Ouest 0.0 0 6.8 57 6.3 182 

Nippes 0.0 0 2.2 18 4.2 122 

(Continued)
84 

Table A5. − Continued

 Metropolitan Other Urban Rural 

Characteristics % N % N % N 

Religion 

Catholics 40.2 488 41.0 340 41.1 1184 

Protestant 50.0 607 50.5 418 47.6 1370 

Vaudousant/other 9.8 119 8.5 71 11.3 324 

Exposure to Media 

Less than once a week 13.5 164 20.5 170 43.3 1246 

Weekly or more 86.5 1050 79.5 659 56.7 1632 

Total 24.7 1214 16.8 829 58.5 2878 

1 Within appropriate buffer; 2 High quality ANC is defined as having any 6 of the following 8 items: weight measured, 

blood pressure measured, urine sample taken, blood sample taken, iron supplementation, tetanus vaccine given, 

informed of signs of complications, intestinal parasites treatment given during pregnancy; 3 First 3 quintiles 

combined for metropolitan and other urban 

 
85 

Table A6. Percentage of women in Haiti who had a live birth in the five years preceding 

the survey and had four or more ANC visits at the last birth, by ANC service provision 

environment, women’s background characteristics, and residence, Haiti DHS 2012 and 

Haiti SPA 2013 

Characteristics Metropolitan Other Urban Rural 

Service readiness for ANC1

Low 80.9 78.9 55.7 

Medium 74.4 77.5 60.6 

High 77.2 84.0 64.4 

Quality of ANC care2

Low 68.3 65.8 53.8 

High 92.3 91.0 79.8 

Mother’s age at birth 

<20 63.2 71.0 52.8 

20-34 80.5 81.7 62.8 

35-49 75.7 76.7 56.0 

Birth Order

1 82.7 84.2 66.4 

2-3 75.9 79.5 62.7 

4-5 75.3 70.7 56.7 

6+ 52.1 72.8 49.8 

Mother’s education 

None 61.6 57.5 47.8 

Primary 68.4 68.7 58.2 

Secondary or higher 84.1 89.8 76.6 

Occupation 

Not employed 75.9 79.1 57.2 

Agriculture 80.8 72.9 52.8 

Professional 77.8 80.7 64.3 

Other 77.5 73.6 49.2 

Wealth quintile3

Lowest na na 50.8 

Second na na 56.1 

Middle na na 72.1 

Low-Middle 42.8 65.3 na 

Fourth 27.6 78.1 85.8 

Highest 11.5 92.5 79.9 

Department 

Aire metropolitaine/Reste-Ouest 77.1 73.8 51.6 

Sud-est 0.0 80.0 57.2 

Nord 0.0 79.2 58.4 

Nord-est 0.0 82.9 78.2 

Artibonite 0.0 80.1 58.7 

Centre 0.0 79.2 65.7 

Sud 0.0 77.9 67.3 

Grand’anse 0.0 74.4 55.4 

Nord-Ouest 0.0 85.4 61.5 

Nippes 0.0 84.3 65.5 

Religion 

Catholics 77.6 80.8 58.7 

Protestant 81.2 81.5 61.4 

Vaudousant/other 52.9 61.9 58.8 

Exposure to Media 

Less than once a week 62.9 66.9 49.8 

Weekly or more 79.3 82.8 67.8 

Total 77.1 79.5 60.0 

1 Within appropriate buffer; 2 High quality ANC is defined as having any 6 of the following 8 items: weight 

measured, blood pressure measured, urine sample taken, blood sample taken, iron supplementation, 

tetanus vaccine given, informed of signs of complications, intestinal parasites treatment given during 

pregnancy;

 3 First 3 quintiles combined for metropolitan and other urban
86 

Table A7. Percentage of women in Haiti who had a live birth in the five years preceding the 

survey and received high quality ANC, by ANC service provision environment, women’s 

background characteristics, and residence, Haiti DHS 2012 and Haiti SPA 2013 

Characteristics Metropolitan Other Urban Rural 

ANC facility service readiness1

Low 56.5 71.9 50.1

Medium 58.2 69.7 53.3

High 69.8 71.8 60.0

4 or more ANC visits 

No 27.5 39.0 34.3

Yes 67.7 77.0 63.9

Mother’s age at birth 

<20 45.5 63.4 52.9

20-34 63.2 72.0 55.7

35-49 66.3 73.6 51.4

Birth order

1 59.7 73.3 58.3

2-3 57.4 73.6 56.4

4-5 73.9 59.7 53.5

6+ 63.3 66.2 45.8

Mother’s education 

None 58.6 60.1 45.6

Primary 62.8 60.6 52.4

Secondary or higher 60.2 78.3 66.1

Occupation 

Not employed 56.6 66.8 50.6

Agriculture 74.3 60.7 50.2

Professional 64.5 74.4 58.5

Other 52.6 68.2 45.3

Wealth quintile3

Lowest na na 48.9

Second na na 50.1

Middle na na 60.4

Low-Middle 54.6 57.6 na

Fourth 63.0 71.2 75.9

Highest 60.7 80.5 72.5

Department 

Aire metropolitaine/Reste-Ouest 60.9 58.8 44.0

Sud-est 0.0 54.8 43.8

Nord 0.0 75.7 68.1

Nord-est 0.0 69.1 58.2

Artibonite 0.0 68.9 50.2

Centre 0.0 64.0 60.6

Sud 0.0 79.5 61.9

Grand’anse 0.0 72.8 56.0

Nord-Ouest 0.0 80.4 56.5

Nippes 0.0 84.1 65.4

Religion 

Catholics 62.8 72.2 55.6

Protestant 62.0 72.5 54.5

Vaudousant/other 45.9 55.1 50.2

Exposure to Media 

Less than once a week 58.5 60.9 48.5

Weekly or more 61.2 73.5 58.6

Total 60.9 71.0 54.5 

1 Within appropriate buffer; 2 High quality ANC is defined as having any 6 of the following 8 items: Weight measured, 

blood pressure measured, urine sample taken, blood sample taken, iron supplementation, tetanus vaccine given, 

informed of signs of complications, intestinal parasites treatment given during pregnancy; 3 First 3 quintiles combined 

for metropolitan and other urban 
87 

Table A8. Among facilities in Haiti that offer normal delivery care, percentage that offer specific 

basic (BEmOC) and comprehensive (CEmOC) emergency care services, by residence, managing 

authority, and type of facility, Haiti SPA 2013 

 Residence Managing Authority Type of Facility

Services Total

Metropolitan Other 

urban Rural 

Government/ 

Public 

NGO/ 

Private 

notforprofit Private 

forprofit 

Mission/ 

Faithbased Hospital 

Health 

center 

with 

bed 

BEmOC 

Antibiotics 94.9 78.0 68.9 75.0 88.9 88.2 60.6 90.4 66.3 77.9

Uterotonic 

drugs/oxytoxics 94.9 95.1 86.5 96.4 96.3 90.2 78.8 98.9 85.1 91.8

Anticonvulsants 48.7 67.1 40.5 64.3 63.0 43.1 33.3 71.3 36.6 53.3

Manual removal of 

placenta 59.0 79.3 58.1 79.8 63.0 52.9 60.6 70.2 64.4 67.2

Assisted vaginal delivery 89.7 92.7 90.5 94.0 88.9 90.2 87.9 94.7 88.1 91.3

Removal of retained 

products 64.1 75.6 48.6 79.8 66.7 52.9 33.3 72.3 54.5 63.1

Total BEmOC 30.8 46.3 14.9 44.0 37.0 13.7 21.2 44.7 18.8 31.3 

CEmOC 

Blood transfusions 61.5 50.0 23.0 38.1 51.9 54.9 24.2 73.4 12.9 42.1

Caesarean section 71.8 52.4 23.0 42.9 55.6 54.9 27.3 83.0 9.9 45.1

Total CEmOC 20.5 28.0 6.8 22.6 22.2 11.8 15.2 31.9 5.9 18.5 

Total number of facilities 

offering normal delivery 

care 39 82 74 84 27 51 33 94 101 195 

 
88 

Table A9. Percent distribution of women in Haiti who had a live birth in the 

five years preceding the survey, by ANC and delivery care characteristics 

and women’s background characteristics, according to residence, Haiti 

DHS 2012 and Haiti SPA 2013 

 Metropolitan 

Other 

Urban Rural 

Characteristics % N % N % N 

Facility delivery 

No 39.6 480 40.8 338 75.9 2,183 

Yes 60.4 734 59.2 491 24.1 695 

Availability of Basic Emergency 

Obstetric Care Services1 

None 1.0 12 12.1 101 18.0 519 

Any facility with normal delivery 

services but no BEmOC 2.7 32 23.2 193 34.3 988 

Any BEmOC 96.4 1,170 64.6 535 47.6 1,371 

Availability of Comprehensive 

Emergency Obstetric Care Services1 

None 1.0 12 12.1 101 18.0 519 

Any facility with normal delivery 

services but no BEmOC 18.6 226 33.0 273 50.6 1,455 

Any BEmOC 80.5 977 54.9 455 31.4 904 

Number of ANC visits 

None 6.8 83 4.8 40 11.7 336 

1 2.2 26 3.0 25 4.6 133 

2-3 13.5 164 12.3 102 23.7 681 

4+ 75.9 921 78.4 650 60.0 1,726 

Don’t know/missing 1.6 19 1.4 12 0.1 3 

Mother’s age at birth 

<20 15.3 186 13.8 115 13.7 396 

20-34 69.4 843 71.9 596 65.3 1,879 

35-49 15.2 185 14.3 118 21.0 604 

Child’s birth order 

1 42.3 514 39.6 328 26.8 773 

2-3 37.7 458 37.9 314 35.1 1,010 

4-5 13.6 166 14.3 119 18.4 530 

6+ 6.4 78 8.1 67 19.7 566 

Mother’s education 

None 8.5 103 8.8 73 26.8 772 

Primary 32.1 390 34.5 286 48.1 1,384 

Secondary or higher 59.4 721 56.7 470 25.1 722 

Wealth quintile 

Lowest 0.0 0 0.0 0 36.4 1,046 

Second 0.3 4 0.7 6 34.4 991 

Middle 13.9 168 25.7 213 19.7 567 

Fourth 43.2 524 40.2 333 7.7 222 

Highest 42.6 517 33.4 277 1.8 52 

Total 100.0 1,214 100.0 829 100.0 2,878 

1 Availability of health facilities with delivery services is assessed within 10 kilometers for 

rural clusters and 5 kilometers for metropolitan and other urban clusters. 
89 

Table A10. Percentage of women in Haiti who had a live birth in the five years 

preceding the survey and delivered the most recent birth in a facility, by availability of 

obstetric care services (BEmOC and CEmOC), number of ANC visits, women’s 

background characteristics, and residence, Haiti DHS 2012 and Haiti SPA 2013 

Characteristics Metropolitan Other Urban Rural 

Availability of Basic Emergency Obstetric 

Care Services1 

No BEmOC (41.4) 

No facilities with normal delivery services 36.7 7.8 

At least one facility with normal delivery but no 

EmOC 52.5 28.5 

BEmOC 61.2 65.9 27.1 

Availability of Comprehensive Emergency 

Obstetric Care Services1 

No CEmOC 59.6 

None 36.7 7.8 

Normal Delivery (no CEmOC) 57 27.2 

CEmOC 60.7 65.6 28.7 

Total 60.4 59.2 24.1 

Number of ANC visits 

0 24.5 (24.3) 4 

1 * (28.3) 17.1 

2-3 45.6 37.8 15.3 

4+ 66.5 65.8 32 

Don’t know/missing * * * 

Mother’s age at birth 

<20 59 57.6 30.3 

20-34 61.2 59.9 25.8 

35-49 58.2 57.5 14.9 

Birth order 

1 71.8 72.7 42.3 

2-3 58.3 54.6 23.7 

4-5 45 45.5 14.3 

6+ (30.8) 39.2 9.4 

Mother’s education 

None 21 35.8 10.1 

Primary 49.3 43.3 21.4 

Secondary or higher 72.1 72.5 44.5 

Wealth quintile 

Lowest * * 9.7 

Second * * 21.3 

Middle 41.2 40.8 38.7 

Fourth 51.1 53.9 56.8 

Highest 76.2 81.1 73.5 

Total 60.4 59.2 24.1 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is 

based on fewer than 25 unweighted cases and has been suppressed. 

1 Availability is assessed within 10 kilometers for rural clusters and 5 kilometers for metropolitan and other 

urban clusters. Since nearly all metropolitan clusters have at least one facility with normal delivery 

services, a combined “No EmOC” category is used which combines “None” with “Any facility with normal 

delivery services but no EmOC.” 

 
90 

Table A11. Percentage of facilities with selected items to support child curative care services among 

facilities in Haiti that offer curative care for children under five, by residence, type of facility, and managing 

authority, Haiti SPA 2013 

Residence Managing Authority Type of Facility 

Total

Components of the SARA 

Readiness Score 

Metropolitan Other 

urban Rural 

Government/ 

Public 

NGO/ 

Private 

not-forprofit 

Private 

forprofit 

Mission/ 

Faithbased Hospital 

Health 

center 

with 

bed 

Health 

center 

without 

bed 

Dispensary 

Domain 1: Staff and Training 

Guidelines for IMCI 18.0 35.1 36.5 42.0 24.5 20.6 36.5 28.6 32.5 22.5 42.6 33.0

Guidelines for growth 

monitoring 16.7 38.2 27.8 27.6 32.9 20.1 32.6 27.6 33.3 21.9 30.5 27.8

Staff trained in IMCI 36.0 41.8 31.1 38.3 37.1 26.3 32.1 51.4 40.5 33.7 26.8 34.0

Staff trained in growth 

monitoring 48.0 53.2 37.9 47.0 47.5 26.3 48.3 57.1 46.0 45.2 35.1 42.7 

Domain 2: Equipment 

Child and infant scale 52.0 63.6 46.5 52.2 51.7 43.2 55.6 59.0 57.1 51.8 45.2 50.8

Length/height measuring 

equipment 45.3 76.8 63.8 70.5 51.7 46.9 75.8 75.2 63.5 53.3 66.8 63.1

Thermometer 95.3 96.4 95.9 95.2 96.5 94.9 97.8 94.3 98.4 96.7 94.8 95.9

Stethoscope 100.0 98.8 98.1 97.0 99.3 99.5 100.0 99.0 100.0 100.0 96.8 98.6

Growth chart 32.0 50.9 45.6 49.5 42.0 31.4 50.0 41.0 51.6 38.1 47.2 44.2 

Domain 3: Diagnostics 

Hemoglobin testing 45.3 53.9 22.7 25.2 39.1 41.2 32.5 64.8 55.6 38.5 10.4 32.8

General stool microscopy 42.0 51.5 21.6 23.4 33.5 39.6 33.7 59.0 50.0 39.3 9.2 31.0

Malaria diagnostic capacity 55.3 66.7 30.2 33.4 48.9 51.5 41.0 75.2 63.5 52.6 15.3 41.7 

Domain 4: Medicines and 

commodities 

Oral rehydration solution 74.0 90.3 92.3 90.1 86.0 84.0 93.3 83.8 88.1 85.2 93.1 88.7

Amoxicillin syrup/suspension 82.0 86.7 73.4 74.8 74.8 81.5 80.3 86.7 81.7 80.7 70.6 77.5

Co-trimoxazole 

syrup/suspension 58.7 81.2 64.0 66.7 65.1 62.9 70.8 75.2 65.1 68.2 62.9 66.4

Paracetamol 87.3 95.2 85.4 84.4 90.2 88.7 90.4 91.4 91.3 90.4 83.0 87.6

Vitamin A capsules 41.3 58.9 58.2 63.7 51.8 41.8 57.4 46.7 44.4 46.8 68.6 55.4

Me-/albendazole 

capsules/tablets 88.0 91.5 88.2 88.9 86.7 86.6 92.7 87.6 90.5 89.3 88.2 88.8

Zinc sulphate tablets or syrup 44.0 61.9 64.8 70.6 54.6 44.3 64.1 50.5 65.1 49.4 70.7 60.5 

Mean Total Readiness Score 44.9 59.3 49.0 52.6 46.5 45.2 55.3 55.6 59.2 48.8 46.5 50.3

Total number of facilities 150 165 533 333 143 194 178 105 126 270 347 848 

 
91 

Table A12. Percent distribution of children under age five years in Haiti with reported 

diarrhea in the two weeks preceding the interview, by service readiness of facilities 

for treating childhood illness, whether care was sought for a public or private 

source, background characteristics of child, and background characteristics of 

mother, according to residence, Haiti DHS 2012 and Haiti SPA 2013 

 Metropolitan Other Urban Rural 

Characteristics % N % N % N 

Service readiness for 

treating childhood 

illness1 

Low 29.4 89 42.0 83 33.5 253 

Medium 36.7 110 28.9 57 34.6 262 

High 33.9 102 29.1 57 31.9 242 

Care was sought from a 

public or private source 

for diarrhea 

No 56.8 171 63.8 126 68.5 518 

Yes 43.2 130 36.2 71 31.5 239 

Age of child 

<1 29.4 88 30.2 60 31.4 238 

1-2 32.7 98 31.3 62 33.7 255 

2-3 18.4 55 19.1 38 17.3 131 

3-4 13.9 42 11.0 22 9.8 74 

4-5 5.7 17 8.4 17 7.8 59 

Sex of child 

Female 53.2 160 52.4 103 47.1 356 

Male 46.8 141 47.6 94 52.9 400 

Mother’s age 

<20 8.0 24 9.5 19 10.1 76 

20-34 79.6 240 69.9 138 66.9 507 

35-49 12.4 37 20.5 41 23.0 174 

Mother’s education 

None 8.3 25 7.4 15 25.2 191 

Primary 41.4 125 46.1 91 48.8 369 

Secondary or higher 50.3 152 46.5 92 26.0 197 

Wealth quintile 

Lowest 0.0 0 0.0 0 34.5 261 

Second 0.7 2 1.3 2 40.3 305 

Middle 21.1 64 37.8 75 16.6 126 

Fourth 43.5 131 40.3 80 7.3 55 

Highest 34.7 104 20.6 41 1.3 10 

Total 100.0 301 100.0 197 100.0 757 

1 To evaluate the service environment for curative child care, a readiness score was generated at the 

health facility level based on four domains of facility readiness: staff and training, equipment, diagnostic 

capacity, and the availability of medicines and commodities (see Appendix Table 1 for a detailed 

summary of the components of the readiness score). DHS clusters were then linked with the service 

readiness scores for facilities within a 5 or 10 kilometer buffer, and an overall readiness score was 

generated for each cluster based on the median score of all facilities linked to that cluster. These 

scores were divided into terciles, which we refer to as low, medium, and high readiness. 

 
92 

Table A13. Percentage of children in Haiti with diarrhea in the two weeks 

preceding the survey for whom care was sought from a public or private 

source, by level of facility readiness (service environment) for treating 

childhood illness, child’s background characteristics, mother’s background 

characteristics, and residence, Haiti DHS 2012 and Haiti SPA 2013 

Characteristics Metropolitan 

Other 

Urban Rural 

Service readiness for treating 

childhood illness1 

Low 28.5 34.7 28.4 

Medium 39.9 38.6 29.8 

High 59.5 35.8 36.8 

Child’s age 

<1 53.7 35.5 30.4 

1-2 53.8 34.9 30.7 

2-3 (37) (39.7) 34.0 

3-4 (11.6) (26.5) 28.1 

4-5 * * 38.6 

Sex of child 

Female 44.8 36.3 33.7 

Male 41.3 36.0 29.7 

Mother’s age 

<20 * * 37.5 

20-34 44.2 38.0 29.4 

35-49 * (30.5) 35.2 

Mother’s education 

None * * 27.8 

Primary 38.9 31.6 31.9 

Secondary or higher 47.9 40.1 34.5 

Wealth quintile 

Lowest * * 29.8 

Second * * 27.9 

Middle 40.8 31.8 34.9 

Fourth 41.0 29.9 (50.7) 

Highest 48.3 (53.8) * 

Total 43.2 36.2 31.5 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates 

that a figure is based on fewer than 25 unweighted cases and has been suppressed. 

1 To evaluate the service environment for curative child care, a readiness score was 

generated at the health facility level based on four domains of facility readiness: staff and 

training, equipment, diagnostic capacity, and the availability of medicines and commodities 

(see Appendix Table 1 for a detailed summary of the components of the readiness score). 

DHS clusters were then linked with the service readiness scores for facilities within a 5 or 

10 kilometer buffer, and an overall readiness score was generated for each cluster based 

on the median score of all facilities linked to that cluster. These scores were divided into 

terciles, which we refer to as low, medium, and high readiness. 

Note: the N is total denominator - not the number for whom care was sought 

 
93 

Table A14. Percentage of facilities providing HIV counseling or testing services in Haiti that have 

HIV testing capacity and other items to support quality HIV testing and counseling services, by 

residence, Haiti SPA 2013 

HIV services Metropolitan Other Urban Rural Total 

HIV testing capacity1 71.3 78.4 50.5 64.1 

Items to support quality HIV CT services 

HIV testing and counseling guideline 27.1 41.0 25.0 30.2 

Staff trained in HIV counseling and testing2 45.0 45.3 28.2 37.7 

Visual and auditory privacy 73.6 69.8 75.0 73.1 

Condoms 48.1 65.5 64.2 60.2 

All items to support quality HIV CT services 8.5 10.8 6.6 8.3 

Total number of facilities 129 139 212 480 

1 Facility had HIV rapid diagnostic test kits or ELISA test with ELISA washer, ELISA reader, incubator, specific assay kit 2 Facility had at least one interviewed staff member providing HIV testing services who reported receiving in-service

training in some aspects of HIV testing and counseling during the 24 months preceding the survey 

 
94 

Table A15. Percent distribution of women age 15-49 in Haiti, by level of facility service readiness (service 

environment) for HIV testing, whether woman had an HIV test in the 12 months preceding the survey, and 

women’s background characteristics, according to residence, Haiti DHS 2012 and Haiti SPA 2013 

 Metropolitan Other urban Rural 

Characteristics % N % N % N 

Service readiness for HIV testing 

Low 34.1 1417 11.9 325 12.9 852 

Medium 37.4 1555 52.0 1427 57.7 3815 

High 28.5 1183 36.1 991 29.4 1942 

Had HIV test in last 12 months 

No 69.5 2889 68.6 1881 80.7 5332 

Yes 30.5 1265 31.4 862 19.3 1276 

Women’s age 

15-24 43.1 1790 43.7 1200 43.6 2883 

25-34 31.9 1325 31.1 853 27.1 1790 

35-44 18.2 755 18.3 501 19.6 1298 

45-49 6.9 285 6.9 189 9.6 637 

Education 

None 6.4 267 6.9 189 23.5 1551 

Primary 26.4 1097 29.7 815 45.3 2991 

Secondary or higher 67.2 2790 63.4 1740 31.3 2066 

Occupation 

Not employed 51.0 2119 49.1 1346 46.2 3051 

Agriculture 4.4 184 3.5 95 9.8 651 

Professional 38.1 1583 43.1 1182 42.3 2796 

Other 6.4 268 4.4 120 1.7 110 

Wealth quintile 

Lowest 0.0 0 0.0 0 32.4 2143 

Second 0.2 6 0.4 10 33.4 2208 

Middle 9.1 380 19.0 522 22.8 1504 

Fourth 36.2 1503 38.4 1053 8.3 546 

Highest 54.5 2266 42.2 1159 3.1 206 

Marital status 

Never married 39.0 1621 43.6 1196 33.9 2239 

Currently married 51.0 2120 47.3 1298 59.3 3921 

formerly married 10.0 414 9.1 249 6.8 448 

Department 

Aire metropolitaine/Reste-Ouest 100.0 4155 7.3 199 19.2 1265 

Sud-est 0.0 0 4.5 125 6.8 448 

Nord 0.0 0 27.9 765 9.9 657 

Nord-est 0.0 0 8.4 230 4.3 285 

Artibonite 0.0 0 23.2 637 21.2 1403 

Centre 0.0 0 6.3 174 10.0 661 

Sud 0.0 0 7.0 192 11.9 785 

Grand’anse 0.0 0 4.9 133 5.0 329 

Nord-Ouest 0.0 0 8.4 231 6.7 439 

Nippes 0.0 0 2.1 57 5.1 336 

Religion 

Catholics 36.1 1501 41.6 1141 39.6 2614 

Protestant 56.6 2351 53.0 1455 51.2 3382 

Vaudousant/other 7.3 303 5.4 147 9.3 612 

Had a birth in past year 

No 91.4 3799 92.1 2525 85.6 5657 

Yes 8.6 356 7.9 218 14.4 951 

Total 100.0 4155 100.0 2743 100.0 6608 
95 

Table A16. Percentage of women age 15-49 in Haiti who were tested for HIV 

in the 12 months preceding the survey, by level of service readiness 

(service environment) for HIV testing and background characteristics, Haiti 

DHS 2012 and Haiti SPA 2013 

Characteristics Metropolitan Other urban Rural 

Service readiness for HIV 

testing 

Low 30.3 24.3 11.3 

Medium 29.7 30.3 19.5 

High 31.7 35.3 22.4 

Women’s age 

15-24 23.8 25.1 17.6 

25-34 41.2 42.7 28.4 

35-44 31.3 31.7 15.8 

45-49 20.2 19.5 8.5 

Education 

None 24.0 16.4 11.8 

Primary 24.6 25.2 17.1 

Secondary or higher 33.4 36.0 28.1 

Occupation 

Not employed 27.4 26.1 17.0 

Agriculture 40.4 32.3 14.5 

Professional 33.6 38.0 22.7 

Other 29.7 26.6 24.4 

Wealth quintile 

Lowest na na 13.5 

Second na na 17.5 

Middle na na na 

Low-Middle 29.1 25.8 22.6 

Fourth 27.3 29.1 31.8 

Highest 32.8 36.2 40.7 

Marital status 

Never in union 17.5 23.1 11.9 

Currently married 40.8 40.0 23.7 

Formerly married 28.3 26.7 18.1 

Department 

Aire metropolitaine/ 

Reste-Ouest 30.5 28.6 17.5 

Sud-est na 25.7 15.6 

Nord na 36.3 21.8 

Nord-est na 24.7 19.9 

Artibonite na 28.1 17.0 

Centre na 29.0 17.9 

Sud na 35.4 25.3 

Grand’anse na 34.6 17.8 

Nord-Ouest na 32.0 19.6 

Nippes na 37.1 25.0 

Religion 

Catholics 34.9 32.5 20.0 

Protestant 27.7 30.0 19.0 

Vaudousant/other 29.5 37.2 17.6 

Had a birth in the past year 

No 26.7 28.1 14.8 

Yes 71.0 69.8 45.9 

Total 30.5 31.4 19.3 

 
96 

Table A17. Percentage of facilities in Malawi with specific indicators of malaria diagnostic 

capacity, by type of facility and residence, Malawi SPA 2013-14 

Type of Facility Residence 

Indicators Hospital Total 

Health 

centre/ 

maternity

Dispensary Clinic Health 

post Urban Rural 

Facility offers malaria rdt 98.2 95.3 77.1 77.4 42.4 89.1 88.1 88.4 

Facility offers malaria lab test 61.7 5.9 0.0 10.9 0.0 28.0 7.8 13.7 

Facility offers any malaria test 

(lab or rdt) 99.1 95.3 77.1 78.5 42.4 90.6 88.1 88.9 

Staff trained in RDT 77.1 57.3 36.6 35.5 11.1 51.1 51.1 51.1 

Staff trained in malaria 

diagnosis (RDT or 

microscopy) 78.9 61.8 41.5 40.0 22.6 55.6 55.2 55.3 

Facility has RDT protocol 82.6 66.9 54.3 45.8 42.4 56.8 62.8 61.0 

Diagnostic Capacity1 72.6 39.5 18.3 17.9 11.1 34.0 35.5 35.0 

Total 107 459 45 293 10 265 649 915 

1 Composite of RDT or microscopy equipment, staff trained in malaria diagnosis, and RDT protocol available in the 

facility. 

2 Diagnostic capacity, malaria treatment guidelines, first-line medicines, and personnel recently trained in malaria 

diagnosis and/or treatment available. 

 
97 

Table A18. Percentage of children under five in Malawi who had fever in the 

two weeks preceding the mother’s interview, by whether the child used an 

ITN the previous night, background characteristics of the child, background 

characteristics of the mother, and residence, Malawi MIS 2012 

 Urban Rural 

Characteristics % N % N 

Slept under an ITN the previous night 

No 50.0 40 44.6 299 

Yes 50.0 40 55.4 372 

Age of child 

<1 19.3 15 15.4 104 

1-2 22.0 17 26.4 177 

2-3 24.8 20 23.3 156 

3-4 18.4 15 19.1 128 

4-5 15.5 12 15.8 106 

Sex of child 

Male 51.8 41 48.1 323 

Female 48.2 38 51.9 348 

Birth order 

1 64.5 51 58.6 393 

2 32.6 26 36.0 241 

3 or more 3.0 2 5.4 36 

Mother’s education 

None 9.4 7 24.4 163 

Primary 57.8 46 66.5 446 

Secondary or greater 32.8 26 9.2 61 

At least a primary school education 

No 9.4 7 24.4 163 

Yes 90.6 72 75.6 507 

Wealth quintile 

Lowest 7.9 6 26.7 179 

Second 4.3 3 25.6 172 

Middle 7.3 6 23.2 155 

Fourth 25.7 20 17.5 117 

Highest 54.7 43 7.0 47 

Region 

Northern 11.1 9 10.7 72 

Central 64.0 51 42.1 282 

Southern 24.9 20 47.2 317 

Total 79 671 

 
98 

Table A19. Percentage of children under five in Malawi with recent fever who had 

a finger/heel stick, by whether the child slept under an ITN the night before the 

interview, background characteristics of the child, background characteristics of 

the mother, and residence, Malawi MIS 2012 

Characteristics Urban Rural 

Child slept under an ITN last night 

No 34.8 20.4 

Yes 34.3 18.6 

Age of child 

<1 23.8 22.7 

1-2 45.4 27.4 

2-3 42.5 15.9 

3-4 29.3 10.4 

4-5 26.2 18.9 

Sex of child 

Male 30.9 21.5 

Female 38.4 17.5 

Birth order 

1st child 38.1 17.1 

2nd child 26.3 22.0 

3 or more 47.2 27.5 

Mother’s education 

None 28.0 14.1 

Primary 29.5 19.6 

Secondary or higher 45.4 32.3 

Mother has at least a primary school education

No 28.0 14.1 

Yes 35.2 21.1 

Wealth quintile 

Lowest 45.8 20.0 

Second 35.0 19.5 

Middle 41.1 11.6 

Fourth 24.6 21.6 

Highest 36.7 37.2 

Region 

Northern 32.4 20.5 

Central 29.1 16.1 

Southern 49.5 22.1 

Total 34.5 19.4 

Total number of children 79 671 

 
99 

Table A20. Percent distribution of children age 6-59 months in 

Malawi, by whether household owns at least one ITN, background 

characteristics of child, and background characteristics of 

mother, according to residence, Malawi MIS 2012 

 Urban Rural 

Characteristics % N % N 

Household owns at least one ITN 

No 38.3 111 33.4 656 

Yes 61.7 179 66.6 1,307 

Age of child 

6-11 months 11.9 34 10.2 200 

1-2 years 20.8 60 23.9 469 

2-3 years 22.2 64 22.1 434 

3-4 years 22.4 65 22.1 434 

4-5 years 22.8 66 21.7 425 

Sex of child 

Male 47.1 137 46.1 905 

Female 52.9 154 53.9 1,058 

Mother has at least primary education 

No 17.0 49 31.8 624 

Yes 83.0 241 68.2 1,339 

Wealth quintile 

Lowest 4.5 13 24.4 478 

Second 4.8 14 25.7 505 

Middle 5.5 16 22.2 435 

Fourth 17.6 51 19.2 376 

Highest 67.8 197 8.6 169 

Region 

Northern 10.9 32 14.3 312 

Central 45.0 131 42.9 953 

Southern 44.1 128 43.8 988 

Total 12.9 291 87.1 1963 

 
100 

Table A21. Percentage of children age 6-59 months in Malawi who 

tested positive for malaria via microscopy, by whether household owns 

at least one ITN, background characteristics of child, background 

characteristics of mother, and residence, Malawi MIS 2012 

Variables Urban Rural 

Household owns at least one ITN 

No 14.3 32.3 

Yes 6.7 27.6 

Age of child 

6-11 months 5.9 19.4 

1-2 years 7.1 22.1 

2-3 years 16.1 31.8 

3-4 years 6.6 32.6 

4-5 years 10.3 35.4 

Sex of child 

Male 10.7 28.5 

Female 8.6 29.7 

Mother has at least primary education

No 6.6 32.2 

Yes 10.2 27.7 

Wealth quintile 

Lowest 13.9 37.0 

Second 14.7 29.8 

Middle 22.9 30.1 

Fourth 20.9 23.5 

Highest 4.8 15.2 

Region 

Northern 12.1 20.1 

Central 11.2 36.4 

Southern 7.4 25.2 

Total 9.6 29.2 

 
101 

Table A22. Percentage of ANC facilities in Malawi that had specific items related to malaria 

diagnosis, prevention, and treatment available, by type of facility and residence, Malawi SPA 

2013-14 

 Type of Facility Residence 

Items Hospital Total

Health 

centre/ 

maternity

Dispensary ClinicHealth 

post Urban Rural 

IPTp guidelines observed at facility 56.5 49.0 52.7 35.7 100.0 43.1 50.6 49.2 

Valid SP for IPTp available and 

observed at facility 94.2 97.2 81.4 95.2 50.0 91.3 96.9 95.9 

IPTp services offered as part of 

routine ANC 99.1 98.7 100.0 98.4 100.0 99.2 98.7 98.8 

ANC staff trained in IPTp 60.2 40.1 29.5 27.3 0.0 60.1 37.5 41.6 

Service ready for provision of IPTp 

services1 40.3 28.5 11.6 16.1 0.0 15.5 19.9 18.5 

Total number of facilities 103 445 18 64 2 116 516 632 

1 An ANC facility is considered service ready for provision of IPTp services if it has valid SP available and observed, 

IPTp guidelines available, at least one staff member with in-service training or training updates on IPTp within the past 

24 months 

 
102 

Table A23. Percent distribution of women age 15-49 in Malawi who 

had a live birth in the two years preceding the survey, by background 

characteristics, according to residence, Malawi MIS 2012 

 Urban Rural 

Characteristics % N % N 

Age 

15-19 10.2 13 10.6 92 

20-24 34.7 43 28.9 250 

25-29 25.4 31 25.1 218 

30-34 19.6 24 19.2 167 

35-39 8.4 10 11.2 97 

40-44 0.8 1 4.2 36 

45-49 0.9 1 0.9 8 

Parity 

1st birth 50.8 62 39.4 341 

2nd birth 41.4 51 49.4 428 

3rd or higher 7.8 10 11.2 97 

Number of household members 

1-3 26.4 32 21.4 186 

4-5 40.5 50 39.5 342 

6-7 23.4 29 27.2 236 

8-9 7.1 9 9.1 79 

10 or more 2.6 3 2.8 24 

Completed primary school 

No 45.3 56 80.8 700 

Yes 54.7 67 19.2 167 

Woman is literate 

No 15.0 18 39.8 345 

Yes 85.0 104 60.2 522 

Wealth quintile 

Lowest 5.1 26.5 229 

Second 3.7 6 25.7 223 

Middle 6.8 5 22.2 192 

Fourth 17.3 8 18.2 157 

Highest 67.1 21 7.5 65 

Region 

Northern 11.9 15 13.6 118 

Central 46.8 57 41.6 361 

Southern 41.3 51 44.8 388 

Woman’s religion 

Catholic 18.3 22 17.5 152 

CCAP 21.6 26 10.8 94 

Anglican 4.1 5 1.3 11 

Seventh Day Advent./ Baptist 10.7 13 5.8 50 

Other Christian 26.9 33 33.4 289 

Muslim 14.8 18 20.9 181 

No religion 1.7 14 

Other 3.7 5 8.7 75 

(Continued) 
103 

Table A23. − Continued

 Urban Rural 

Characteristics % N % N 

Woman’s ethnicity 

Chewa 28.2 35 40.2 348 

Tumbuka 7.2 9 11.7 101 

Lomwe 16.4 20 13.7 119 

Tonga 1.8 2 1.6 14 

Yao 21.6 26 19.8 171 

Sena 1.0 1 4.0 34 

Nkhonde 2.0 2 1.0 9 

Ngoni 16.9 21 4.9 42 

Other 5.0 6 3.2 28 

Woman has seen or heard messages about 

malaria in past six months 

No 67.6 81 72.5 581 

Yes 32.4 39 27.5 221 

Total 123 867 

 
104 

Table A24. Percentage of women age 15-49 in Malawi who had a live 

birth in the two years preceding the survey and who took at least two 

doses of SP for prevention of malaria at least once during ANC, by 

background characteristics and residence, Malawi MIS 2012 

 Urban Rural 

Characteristics % N % N 

Age 

15-19 50.8 13 43.5 92 

20-24 53.8 43 57.3 250 

25-29 55.5 31 49.9 218 

30-34 60.4 24 56.3 167 

35-39 58.9 10 50.7 97 

40-44 0.0 1 50.0 36 

45-49 100.0 1 66.3 8 

Parity 

1st birth 58.5 62 52.1 341 

2nd birth 55.5 51 55.6 428 

3rd or higher 37.9 10 43.3 97 

Number of household members 

1-3 58.0 32 47.4 186 

4-5 54.1 50 53.8 342 

6-7 53.0 29 56.9 236 

8-9 61.2 9 50.3 79 

10 or more 65.0 3 49.5 24 

Completed primary school 

No 48.8 56 51.7 700 

Yes 61.3 67 57.7 167 

Woman is literate 

No 38.6 18 47.6 345 

Yes 58.6 104 56.3 522 

Wealth quintile 

Lowest 44.5 46.8 229 

Second 55.2 6 56.0 223 

Middle 35.6 5 58.2 192 

Fourth 52.9 8 43.3 157 

Highest 59.3 21 70.9 65 

Region 

Northern 67.1 15 60.9 118 

Central 44.2 57 50.8 361 

Southern 65.3 51 52.3 388 

Woman’s religion 

Catholic 51.8 22 46.3 152 

CCAP 67.2 26 48.3 94 

Anglican 57.5 5 47.5 11 

Seventh Day Advent./ Baptist 59.0 13 51.8 50 

Other Christian 47.2 33 63.1 289 

Muslim 61.7 18 50.2 181 

No religion 44.7 14 

Other 32.4 5 41.6 75 

(Continued) 
105 

Table A24. − Continued

 Urban Rural 

Characteristics % N % N 

Woman’s ethnicity 

Chewa 46.1 35 48.8 348 

Tumbuka 65.7 9 63.2 101 

Lomwe 63.2 20 56.4 119 

Tonga 71.3 2 70.4 14 

Yao 58.7 26 50.0 171 

Sena 25.5 1 69.7 34 

Nkhonde 53.4 2 29.9 9 

Ngoni 50.8 21 54.1 42 

Other 74.8 6 44.0 28 

Woman has seen or heard messages about 

malaria in past six months 

No 51.3 81 51.3 581 

Yes 66.0 39 57.6 221 

Total 55.6 123 52.8 867