DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2014 No. 114

Wenjuan Wang

Michelle Winner

Clara Burgert

Josh Colston

September 2014

This document was produced for review by the United States Agency for 

International Development.

Influence of Service Readiness on Use of 

Facility Delivery Care: 

A Study Linking Health Facility Data and 

Population Data in Haiti
 
Influence of Service Readiness on Use of Facility Delivery Care: 

A Study Linking Health Facility Data and Population Data in Haiti 

Wenjuan Wang 

Michelle Winner 

Clara Burgert 

Josh Colston 

ICF International 

Rockville, Maryland, USA 

September 2014 

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

Suite 500, Rockville, Maryland USA; telephone: +1 301–572–0398; fax: +1 301–572–0999; 

email: wenjuan.wang@icfi.com 
Acknowledgment: 

The authors would like to thank Fred Arnold and Paul Ametepi for their important input on the study 

design. We also thank Shireen Assaf for her helpful comments on the first draft of the paper. 

Editor: Sidney Moore 

Document Production: Yuan Cheng 

The DHS Working Papers series is an unreviewed prepublication series of papers reporting on research in 

progress that is based on Demographic and Health Surveys (DHS) data. This research is carried out with 

support provided by the United States Agency for International Development (USAID) through The DHS 

Program (#GPO–C–00–08–00008–00). 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: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 

301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Wang, Wenjuan, Michelle Winner, Clara Burgert, and Josh Colston. 2014. Influence of Service Readiness 

on Use of Facility Delivery Care: A Study Linking Health Facility Data and Population Data in Haiti. DHS 

Working Papers No. 114. Rockville, Maryland, USA: ICF International. 
ABSTRACT 

This study used a geospatial methodology to estimate the influence of service readiness at 

health facilities on women’s use of facility delivery care based on data from the 2012 Haiti 

Demographic and Health Survey (DHS) and the 2013 Haiti Service Provision Assessment (SPA) 

survey - a health facility census. 

By linking DHS clusters and SPA facilities with their GPS data, we measured health 

facilities’ service readiness to provide quality delivery care by the average and the highest 

readiness score of facilities within a 10 km buffer from the cluster. A facility’s readiness score was 

computed with principal component analysis using a wide range of indicators recommended by 

WHO. Multilevel logistic regressions showed that in rural areas, both average and highest levels 

of readiness were significantly associated with use of delivery service. However, in urban areas 

only the highest level of readiness was statistically significant. No association was found between 

the total number of health facilities offering delivery services and use of facility delivery. 

 

1 

1. INTRODUCTION 

Despite progress toward reaching the 5th Millennium Development Goal, Improved 

Maternal Health—reducing the maternal mortality ratio and achieving universal access 

reproductive health—Haiti is still one of the two countries outside of sub-Saharan Africa with the 

highest maternal mortality ratio—380 deaths per 100,000 live births (WHO 2014). Every year 

thousands of women in Haiti die from causes that can be prevented through access to 

comprehensive and skilled obstetric care during pregnancy, childbirth, and the postpartum period 

(Anderson et al. 2007; Kirigia et al. 2006). Use of maternal health services, especially facility 

delivery, remains low in Haiti. Only 36 percent of births take place in health facilities, according 

to the 2012 Haiti Demographic and Health Survey (DHS) (Cayemittes et al. 2013). Unless a 

woman delivers at a health facility, she is unlikely to have access to emergency obstetric care, 

which is considered the most important strategy for reducing maternal deaths (de Brouwere et al. 

1998; Graham et al. 2001). Therefore, increasing utilization of facility delivery is critical for Haiti 

to reduce maternal mortality. 

An extensive body of literature exits on factors that influence facility delivery (Exavery et 

al. 2014; Gabrysch and Campbell 2009; Kitui et al. 2013; Montagu et al. 2011; Moyer et al. 2013). 

The majority of studies have focused on the demand side, for example, the characteristics of 

women and their families. A few have looked at community-level factors such as community 

norms, media access, and the level of local development (Moyer and Mustafa 2013; Tey and Lai 

2013; Thind et al. 2008). The supply side—delivery care offered in health facilities—has received 

less attention (Gabrysch and Campbell 2009; Moyer et al. 2013). One of the main reasons for 

limited research on the effects of service provision is the lack of suitable data. The supply-side 

data typically come from health facilities. The supply information needs to be linked to data on 

individual women in order to explore the relationship between service provision and women’s use 

of delivery care. 

With the availability of geographic data from both household surveys and health facility 

surveys, it becomes possible to link population data and health facility data within a geographic 

information system (GIS). A few studies in sub-Saharan Africa have linked DHS data and facility 

census data to assess how distance to the closest facility affects women’s use of reproductive health 

services (Kyei et al. 2012; Lohela et al. 2012; Nesbitt et al. 2014). In Malawi and Zambia, by 
2 

linking DHS clusters and facilities (from the facility census), the authors found that, in Zambia, a 

longer straight-line distance from the DHS cluster to the closest facility offering emergency 

obstetric care significantly reduced the likelihood of facility delivery—by 65 percent for every 10 

kilometers increase in distance. However, such a relationship was not observed in Malawi (Lohela 

et al. 2012). Another study in Zambia with the same methodology but focusing on antenatal care 

found that distance to the closest facility had a significant effect on the content of antenatal care 

women received but had no effect on number of ANC visits and timing of the first visit (Kyei et 

al. 2012). In a rural setting in Ghana, Nesbitt et al. (2014) linked health facility census data and 

health and demographic surveillance data from about 600 villages and found a significant 

association between distance to the closest delivery facility and women’s likelihood of delivering 

in a health facility (Nesbitt et al. 2014). While these studies contributed to establishing a geospatial 

methodology to assess the relationship between service provision and use, linking DHS clusters 

and closest facilities is subject to certain levels of misclassification errors. Because DHS clusters’ 

coordinates are displaced before release; the closest facility identified based on the released 

geographic data may not actually be the nearest facility in reality (Skiles et al. 2013). 

While physical access is important, another key determinant of service utilization is the 

quality of care. Families may bypass the nearest health facility when quality is at stake (Choulagai 

et al. 2013; Kinney et al. 2010; Yaffee et al. 2012). In examining the effect of the quality of care 

on use of services, some studies looked at the quality of care from the user’s perspective (Karkee 

et al. 2014; Thind et al. 2008). While this is indicative, it is subject to respondent’s level of 

knowledge about the services provided at health facilities, which can be biased. Among the limited 

research based on linked population data and facility data, few looked at service provision in health 

facilities. The Zambia study measured level of care using an index that combined several process 

and structural aspects of antenatal care provided at facilities. Level of service provision at the 

closest health facility was found to be significantly associated with the content of antenatal care 

received (Kyei et al. 2012). In Nepal, when quality of care was measured solely in structural terms 

(e.g., infrastructure, availability of medicine, number of staff, etc.) a significant effect was also 

seen on the utilization of antenatal care and immunization services (Acharya and Cleland 2000). 

Because of Haiti’s mountainous terrain, physical accessibility remains one of the biggest 

barriers to health care utilization (Alexandre et al. 2005; Babalola 2014; Gage and Guirlene Calixte 

2006). In an effort to bring care closer to home and solve the issue of accessibility, Haiti, like most 
3 

developing countries, has implemented a hierarchical system of health care provision in which 

small facilities are located in villages or small communities and more equipped facilities are 

located in cities. However, there is a dearth of information on how prepared these facilities are to 

provide quality delivery care and how their service preparedness affects utilization. The 2013 

Service Provision Assessment (IHE and ICF International 2014) and the 2012 Demographic and 

Health Survey (Cayemittes et al. 2013) in Haiti provide an opportunity to link facilities and DHS 

clusters in order to explore the influence of service readiness on use of facility delivery care. 
4 

2. METHODS 

2.1 Setting 

The health system in Haiti is organized into three levels of care: primary, secondary, and 

tertiary. The primary level consists of three components: 1) community health centers and 

dispensaries located in a neighborhood or a communal ward; 2) health centers (with or without 

beds) located in a commune; and 3) communal referral hospitals located in the urban center of the 

district. The primary-level facilities are not mandated to provide delivery services. The secondarylevel facilities include departmental hospitals, which serve as reference health care facilities at the 

department level. The tertiary-level facilities refer to specialized and university health facilities, 

which serve as reference facilities at the national level. The secondary- and tertiary-level facilities 

provide inpatient services including delivery care. 

Health facilities in Haiti are managed by government entities, private entities, and entities 

that are a mixture of government and private. The majority of health facilities in Haiti are owned 

by the government. The private sector includes private for-profit and non-profit facilities. The forprofit facilities most often belong to a corporation involving individuals and investment capital 

through which physicians practice medicine. The non-profit facilities are administered primarily 

by associations, foundations, or religious groups. Another group of health facilities in Haiti is 

characterized by mixed management; these are private non-profit facilities that also receive 

subsidies or salaried regular staff from the government. 

2.2 Data 

Data used in this study come from the 2012 Haiti Demographic and Health Survey (HDHS) 

and the 2013 Haiti Service Provision Assessment (HSPA) survey (Cayemittes et al. 2013; IHE and 

ICF International 2014). The HDHS provides data on women’s use of facility-based delivery care 

as well as their socio-demographic characteristics. The HSPA provides information on the 

availability of delivery care at health facilities and facilities’ readiness to provide good-quality 

services. Geographic data collected in both surveys are used to link DHS clusters and SPA 

facilities. 

 
5 

HDHS data 

The 2012 HDHS is a population-based household survey that provides representative 

estimates for both 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 were selected with 

probability proportional to their population size from a national master sample frame. At the 

second stage, a systematic sample of households was drawn in each of the selected clusters. All 

women age 15-49 in the sampled households were eligible for individual interview. Of the 14,472 

women eligible for interview, 14,287 were successfully interviewed. 

The HDHS georeferenced the locations of the sampled clusters by 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 (Burgert et al. 2013). 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. Eight clusters could 

not be georeferenced and were classified as “missing” in the geographic dataset. 

This study used data on 5,515 women who had a live birth in the five years preceding the 

survey in 437 clusters with GPS data. Table 1 shows the background characteristics of these 

women. Around two-thirds of women had their most recent birth at age 20-34, while fewer than 

15 percent were younger than age 20 at the time of their most recent birth. For one-third of women, 

the most recent birth was their first child with one-third already having had three or more children 

at that time. Over 60 percent of women lived in rural areas, with 37 percent residing in the Ouest 

department, where the capital of Port-au-Prince is situated. Most of the women reported having 

completed primary education and 39 percent had secondary or higher education. 

 
6 

Table 1. Background characteristics of women who had a live birth in the five years preceding the 

survey, Haiti DHS 2012 

Background characteristics % Number of women 

Mother's age at birth 

<20 years 14.4 741 

20-34 67.5 3,479 

35-49 18.2 936 

Birth order 

1 33.0 1,702 

2-3 36.3 1,870 

4-5 16.5 852 

6+ 14.2 730 

Residence 

Urban 38.7 1,996 

Rural 61.3 3,159 

Department 

Ouest 36.5 1,884 

Sud-est 4.2 216 

Nord 9.8 503 

Nord-est 3.9 201 

Artibonite 15.0 773 

Centre 7.7 399 

Sud 7.3 377 

Grand'anse 3.7 189 

Nord-ouest 4.6 239 

Nippes 2.7 140 

Camps 4.5 234 

Mother's education 

None 19.2 991 

Primary 41.8 2,157 

Secondary or higher 38.9 2,007 

Wealth quintile 

Lowest 20.3 1,046 

Second 19.9 1,023 

Middle 21.7 1,119 

Fourth 21.7 1,118 

Highest 16.5 848 

 

Total 100.0 5,155 

 
7 

HSPA data 

The 2013 HSPA is a health facility census that provides data on availability and readiness 

to provide key health services in 907 public and private health facilities, from hospitals at the 

highest level to dispensaries at the lowest level. Data were collected from each facility through the 

use of a combination of survey instruments including the inventory questionnaire, the health 

provider interview questionnaire, observation of consultations, and provider and client exit 

interviews. The HSPA also georeferenced the locations of the health facilities using GPS receivers. 

Unlike the DHS data, facilities’ coordinates are not displaced. Figure 1 shows the location of DHS 

clusters (displaced) and SPA facilities. 

Figure 1. Geographic distribution of HDHS clusters and HSPA facilities 

 
8 

Data on 389 facilities that offer normal delivery services were used in this analysis. A 

facility’s preparedness to provide quality obstetric services was assessed primarily using the 

inventory questionnaire and the health provider interview questionnaire. The inventory 

questionnaire was administered to the most knowledgeable person for the obstetric services. Table 

2 shows the distribution of the facilities analyzed by type of facility, managing authority, and 

regional location. Around a quarter of facilities offering normal delivery were hospitals, with 

dispensaries making up a third of the total. Half of the facilities were managed by the government, 

while over 20 percent had a mixed managing authority. Over a quarter of facilities offering delivery 

services were situated in the Ouest department (26 percent)—where the capital, Port-au-Prince, is 

situated—the department with the fewest facilities was in Nippe (4 percent). 

Table 2. Sample distribution of health facilities providing delivery services, by background 

characteristics, Haiti SPA 2013 

Background characteristics Number of facilities % 

Type of facility 

Hospital 94 24.1 

Health center with beds 101 25.9 

Health center without beds 66 17.0 

Dispensary 128 33.0 

Managing authority 

Government 195 50.2 

NGO/private not-for-profit 47 12.1 

Private for-profit 65 16.7 

Mixed 82 21.1 

Department 

Ouest 102 26.2 

Sud-Est 35 9.0 

Nord 36 9.2 

Nord-Est 27 6.9 

Artibonite 53 13.6 

Centre 23 5.9 

Sud 24 6.2 

Grand-Anse 21 5.4 

Nord-Ouest 52 13.3 

Nippes 17 4.4 

 

Total 389 100.0 
9 

2.3 Definition of Key Variables 

The outcome variable of this analysis is dichotomous, indicating whether a woman used 

facility delivery care for the most recent birth in the five years preceding the survey. The key 

independent variable is facility readiness in providing good-quality delivery care. It was measured 

by a readiness score that was created with principal component analysis based on a set of service 

readiness indicators defined by WHO (WHO 2013). For each indicator—according to whether it 

met the criteria for availability—facilities were assigned a binary variable: 1-available, 0-

unavailable. A total of 37 readiness indicators were constructed; their definitions are presented in 

Table 31

. The readiness score was computed based on the first component resulting from the 

principal component analysis, which explained the largest proportion of the total variance. We 

used a factor loading cut-off of 0.4 to determine whether an indicator remained in the final score 

computation. The readiness score is a relative summary indicator of how ready a health facility is 

to provide good-quality delivery services. A higher score indicates better readiness and a lower 

score indicates poorer readiness compared with other facilities. 

Given the importance of basic and comprehensive emergency obstetric care (BEmOC and 

CEmOC) in reducing maternal mortality, we also looked at the availability of BEmOC and 

CEmOC at health facilities. According to the latest definition (WHO et al. 2009), a BEmOC 

facility should be 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 caesarean section and blood 

transfusion. 

 

1

 A few indicators were defined somewhat differently from the definitions shown in the SARA manual; this was done 

so they could be constructed using the available SPA data. For example, the SARA manual includes a laryngoscope 

among the list of equipment necessary for administering anesthesia; however, the HSPA questionnaire did not ask 

about the availability of laryngoscopes. In addition to the SARA indicators, “regular reviews of maternal or newborn 

deaths” was added to the list of readiness indicators. 
Table 3. Operational definitions of basic and comprehensive obstetric care indicators 

Indicator Operational definition Questionnaire Note Basic obstetric care Parenteral administration of antibiotics 

Respondent reports that intervention has been carried out at least once during the past 3 months by providers as part of their work in that facility 

Inventory 

Parenteral administration of oxytocic drug 

Respondent reports that intervention has been carried out at least once during the past 3 months by providers as part of their work in that facility 

Inventory 

Parenteral administration of anticonvulsants 

Respondent reports that intervention has been carried out at least once during the past 3 months by providers as part of their work in that facility 

Inventory 

Assisted vaginal delivery Respondent reports that intervention has been carried out at least once 

during the past 3 months by providers as part of their work in that facility 

Inventory 

Manual removal of placenta 

Respondent reports that intervention has been carried out at least once during the past 3 months by providers as part of their work in that facility 

Inventory 

Manual removal of retained products 

Respondent reports that intervention has been carried out at least once during the past 3 months by providers as part of their work in that facility 

Inventory 

Neonatal resuscitation Respondent reports that intervention has been carried out at least once 

during the past 3 months by providers as part of their work in that facility 

Inventory 

Guidelines for IMPAC Guidelines for Integrated Management of Pregnancy and Childbirth 

observed by interviewer in the service site 

Inventory 

Staff trained in IMPAC Respondents reports having received in-service training or training 

updates in Integrated Management of Pregnancy and Childbirth in the past 24 months 

Provider interview 

Emergency transportation 

Respondent reports that the facility has a functional ambulance or other vehicle for emergency transportation for clients stationed at and operating from that facility or has access one stationed at or operating from another facility and fuel is available for that vehicle on the day of interview 

Inventory 

Sterilization equipment Sterilization equipment observed by interviewer anywhere in the facility 

and reported functioning by respondent. Equipment may be electric (autoclave, dry heat sterilizer, boiler or steamer) or non-electric (autoclave or pot for boiling). If non-electric, facility must have a stove or cooker as a heat source. 

Inventory 

(Continued...)

10 
Table 3. – Continued

Indicator Operational definition Questionnaire Note 

Examination light Functioning spotlight source that can be used for patient examination 

observed by interviewer in delivery area and reported functioning by respondent 

Inventory 

Delivery pack Delivery pack (or all of the following: cord clamp, episiotomy scissors, 

scissors or blade to cut cord, suture material and needle holder) observed by interviewer in delivery area 

Inventory 

Suction apparatus (mucus extractor) 

Suction bulb or suction apparatus with catheter observed by interviewer in delivery area and reported functioning by respondent 

Inventory 

Manual vacuum extractor Manual vacuum extractor observed by interviewer in delivery area and 

reported functioning by respondent 

Inventory 

Vacuum aspirator or D&C kit 

Vacuum aspirator or D&C kit observed by interviewer in delivery area and reported functioning by respondent 

Inventory 

Newborn bag & mask Newborn bag & mask observed by interviewer in delivery area and 

reported functioning by respondent 

Inventory 

Delivery bed Delivery bed observed by interviewer in delivery area Inventory 

Partograph Blank partographs observed by interviewer in delivery area Inventory Gloves Disposable latex gloves observed by interviewer in delivery area Inventory The SARA manual specifies that 

they be sterile. The SPA questionnaire does not. 

Antibiotic eye ointment for newborns 

Tetracycline or other antibiotic eye ointment for newborn observed by interviewer in delivery area or in the main location in the facility where medicines and commodities are routinely stored 

Inventory 

Injectable uterotonic Oxytocin or other injectable uterotonic observed by interviewer in delivery 

area or in the main location in the facility where medicines and commodities are routinely stored 

Inventory The SARA manual specifies 

oxytocin, but the SPA questionnaire allows "other injectable uterotonic" 

Injectable antibiotics Injectable antibiotic (gentamycin, Benzathine Benzylpenicillin, Ceftriaxone, 

ampicillin or metronidazole injection) observed by interviewer in delivery area or in the main location in the facility where medicines and commodities are routinely stored 

Inventory The SARA manual specifies 

Procaine Benzylpenicillin, but the SPA questionnaire asks about Benzathine Benzylpenicillin 

Injectable magnesium sulphate 

Injectable magnesium sulphate observed by interviewer in delivery area or in the main location in the facility where medicines and commodities are routinely stored 

Inventory 

(Continued...)

11 
Table 3. – Continued

Indicator Operational definition Questionnaire Note 

Skin disinfectant Skin disinfectant observed by interviewer in delivery area or in the main 

location in the facility where medicines and commodities are routinely stored 

Inventory 

Intravenous solution with infusion set observed by interviewer in delivery area or in the main location in the facility where medicines and commodities are routinely stored 

Intravenous solution (plasma expanders) with infusion set (Normal saline or Ringers Lactate, or Dextrose 5%) observed by interviewer in delivery area or in the main location in the facility where medicines and commodities are routinely stored 

Inventory 

Regular reviews of maternal or newborn deaths 

Respondent reports that facility participates in regular reviews of maternal or newborn deaths of 'near misses' 

N/A Indicator not in SARA 

Comprehensive obstetric care 

Inventory 

Caesarean section services 

Respondent reports that the facility offers cesarean section Inventory 

Blood transfusion Respondent reports that the facility offers blood transfusion services Inventory Guidelines for CEmOC adapted for Haiti 

Guidelines for comprehensive emergency obstetric care observed by interviewer in the delivery area 

Inventory 

Staff member providing delivery trained in CEmOC 

Respondents reports having received in-service training or training updates in Comprehensive Emergency Obstetric Care in the past 24 months 

Provider interview 

Anesthesia equipment Anesthesia equipment (all of the following: anesthesia machine, tubings 

and connectors to connect to the endotracheal tube, stylet, pediatric and adult resuscitator bag and mask, oropharyangeal airway, endotracheal tubes, Magill's forceps) observed by interviewer in the delivery area and reported functioning by respondent 

Inventory The SARA manual includes a 

laryngoscope in the list of equipment, but the SPA does not ask about this 

Incubator Incubator observed by interviewer in delivery area and reported 

functioning by respondent 

Inventory 

(Continued...)

12 
Table 3. – Continued

Indicator Operational definition Questionnaire Note 

Blood typing Blood group tests (Anti-A, Anti-B and Anti-D reagents) and centrifuge for 

CSF microscopy observed by interviewer in facility and reported functioning by respondent 

Inventory The SARA manual specifies that the 

facility must have ABO and Rhesus blood group test, but the SPA asks about Anti-A, B and D reagents 

Cross matching test Blood group tests (Anti-A, Anti-B and Anti-D reagents), centrifuge for CSF 

microscopy, Coomb's reagent and incubator observed by interviewer in facility and reported functioning by respondent 

Inventory The SARA manual says "grouping 

sera", but the SPA questionnaire specifies "Coomb's reagent" 

Blood supply sufficiency Respondent reports that the facility offers blood transfusion services and 

has not run out of blood for more than one day at any time during the past 3 months 

Inventory 

Blood supply safety Respondent reports that the facility offers blood transfusion services and 

either obtains blood only from national or regional blood banks or obtains blood from other sources but screened for HIV, syphilis, hepatitis B and hepatitis C 

Inventory 

13 
14 

2.4 Linking Clusters and Health Facilities 

The steps to link DHS clusters and SPA facilities are illustrated in Figure 2. First, within a 

GIS2

, a matrix was created with the direct distance measurement from every DHS cluster location 

to every health facility. Second, the facility-level data on service provision (i.e., availability of 

BEmOC and CEmOC, readiness scores) were linked to each cluster in the “long” table. Finally, 

the facility data were summarized to the cluster level to measure cluster access to health services. 

The distances were operationalized, creating three groups of facilities within a 5-, 10-, and 15-

kilometer buffer distance from a cluster. For BEmOC and CEmOC, the aggregation indicator at 

the cluster level was whether there was a BEmOC or CEmOC facility within the buffer. For service 

readiness, we constructed two measurements at the cluster level: the average readiness score of 

the facilities within the specified distance from the cluster and the highest score among the 

facilities within the buffer. 

2.5 Analysis 

In the descriptive analysis we describe 1) the levels of individual service readiness 

indicators and 2) the availability of BEmOC and CEmOC at health facilities that offer normal 

delivery services. As indicated earlier, access to health services was assessed at the DHS cluster 

level. We estimated the percentages of DHS clusters linked to a BEmOC or CEmOC facility within 

the buffer. Given that the readiness score is a relative measurement, we divided the average and 

the highest scores of facilities in the specific buffer of the cluster into low-, medium-, and highlevel groups based on the score terciles for all facilities. DHS clusters’ access to different levels of 

service readiness was assessed for all three buffers: 5 km, 10 km, and 15 km. We stratified the 

analysis by urban and rural residence because of the differences in access to health services 

between urban and rural areas. 

Multilevel (individual- and cluster-level) logistic regression models were used to 

investigate the association between service readiness and women’s use of facility-based delivery 

care. The outcome was whether a woman had her most recent birth at a health facility. The key 

predictor was service readiness measured at the cluster level, with two continuous variables: the 

average readiness score of facilities in a specified buffer and the highest readiness score within the 

 

2

 “Near Table” tool in ArcInfo (ESRI, Redlands, CA) 
15 

buffer. We fitted two models for each buffer: one estimating the effect of the average readiness 

score and the other estimating the effect of the highest readiness score. The multivariate analysis 

was stratified by urban and rural, and conducted for all three buffers: 5 km, 10 km, and 15 km. As 

an indicator of service availability, the number of facilities offering delivery care within the buffer 

could potentially affect women’s use of facility delivery services; therefore it was controlled for 

in the regression. Other variables adjusted for included women’s age at birth, birth order, mother’s 

education, household wealth quintile, number of antenatal care visits, and region (department). 

Figure 2. Illustration of linking DHS clusters and SPA facilities 

 
16 

3. RESULTS 

3.1 Facilities’ Readiness in Providing Delivery Services 

Among the 389 facilities offering delivery services in Haiti, the majority are dispensaries 

(128) followed by health centers with beds (101) and hospital (94) (Table 4). The availability of 

basic obstetric care items on the day of the survey varied widely by type of facility, with hospitals 

generally having many of the items and dispensaries having few. Some items were commonly 

available across facility types, such as suction apparatus (81.5 percent) and gloves (94.1 percent); 

while other items were rare such as manual vacuum extractor (10.0 percent) (Table 4). Only 22.6 

percent of facilities providing delivery services also provided caesarean section services; this 

includes a majority of hospitals. With the exception of having a staff member providing delivery 

who was trained in CEmOC, the availability of comprehensive obstetric care items did not exist 

in dispensaries or in health centers without beds. Hospitals were more likely to have the 

comprehensive obstetric care items but cross matching test was present in only 10 percent. Blood 

transfusion was available in 73.4 percent of hospitals. 

Table 4. Among facilities offering delivery services, percentage with the indicated items available 

 Hospital 

Health 

center with 

beds 

Health 

center 

without 

beds Dispensary Total 

Basic obstetric care 

Parenteral administration of antibiotics 90.4 66.3 41.1 29.7 55.8 

Parenteral administration of oxytocic drug 98.9 85.1 57.7 38.3 68.4 

Parenteral administration of anticonvulsants 71.3 36.6 9.1 7.8 30.8 

Assisted vaginal delivery 94.7 88.1 68.1 50.8 74.0 

Manual removal of placenta 70.2 64.4 41.1 30.5 50.6 

Manual removal of retained products 72.3 54.5 45.5 27.3 48.3 

Neonatal resuscitation 66.0 55.4 30.2 19.6 41.9 

Guidelines for IMPAC 23.4 24.8 18.3 22.6 22.6 

Staff trained in IMPAC 58.5 42.6 28.9 11.7 33.9 

Emergency transportation 47.9 22.8 9.1 0.0 19.0 

Sterilization equipment 92.6 71.3 47.0 23.5 56.5 

Examination light 52.1 41.6 31.7 23.4 36.5 

Delivery pack 94.7 85.1 80.4 63.3 79.4 

Suction apparatus 92.6 93.1 86.4 61.8 81.5 

Manual vacuum extractor 28.7 8.9 3.0 0.8 10.0 

(Continued...) 
17 

Table 4. – Continued

 Hospital 

Health 

center with 

beds 

Health 

center 

without 

beds Dispensary Total 

Vacuum aspirator or D&C kit 35.1 22.8 21.1 4.7 19.5 

Newborn bag & mask 75.5 40.6 27.2 7.8 35.9 

Delivery bed 97.9 98.0 94.0 86.7 93.6 

Partograph 40.4 32.7 28.7 3.9 24.4 

Gloves 92.6 93.1 95.3 87.5 91.5 

Antibiotic eye ointment for newborns 79.8 68.3 72.6 53.1 66.8 

Injectable uterotonic 79.8 56.4 48.3 36.0 53.9 

Injectable antibiotics 96.8 87.1 80.4 52.3 76.8 

Injectable magnesium sulphate 85.1 57.4 48.3 21.9 50.8 

Skin disinfectant 75.5 64.4 63.4 56.2 64.2 

Intravenous solution with infusion set 84.0 76.2 72.8 67.1 74.5 

Regular reviews of maternal or newborn 

deaths 44.7 22.8 12.1 5.5 20.5 

Comprehensive obstetric care 

Caesarean section services 83.0 9.9 0.0 0.0 22.6 

Blood transfusion 73.4 12.9 0.0 0.0 21.0 

Guidelines for CEmOC adapted for Haiti 20.2 2.0 0.0 0.0 5.4 

Staff member providing delivery trained in 

CEmOC 47.9 39.6 27.4 7.8 29.0 

Anesthesia equipment 29.8 3.0 0.0 0.0 8.0 

Incubator 35.1 3.0 0.0 0.0 9.2 

Blood typing 68.1 5.9 0.0 0.0 18.0 

Cross matching test 10.6 0.0 0.0 0.0 2.6 

Blood supply sufficiency 30.9 4.0 0.0 0.0 8.5 

Blood supply safety 62.8 5.0 0.0 0.0 16.4 

 

Number of facilities providing delivery 

services 94 101 66 128 389 

Table 5 shows the availability of comprehensive obstetric care items in the 88 facilities 

providing caesarean section services. Of these, less than half had staff trained in CEmOC in the 

last two years and less than one-fourth had available guidelines for CEmOC adapted in Haiti (Table 

5). Blood group test and centrifuge along with blood supply safety were available in the majority 

of facilities (79.5 percent and 72.7 percent, respectively) (Table 5). 
18 

Table 5. Among facilities providing caesarean section services, percentage with the indicated items 

available 

 % 

Guidelines for CEmOC adapted in Haiti 23.9 

Staff trained in CEmOC in last 2 years 46.6 

Anesthesia equipment 35.2 

Incubator 40.9 

Blood group tests and centrifuge 79.5 

Cross match testing 11.4 

Blood supply sufficiency 37.5 

Blood supply safety 72.7 

Number of facilities providing caesarean section services* 88 

* Including 78 hospitals and 10 health centers with beds 

Figure 3 shows the availability of basic and comprehensive emergency obstetric care at 

health facilities by type of facility. Overall, availability of BEmOC and CEmOC was limited at 

health facilities in Haiti (18 percent and 9 percent, respectively). Higher-level facilities—hospitals 

and health centers with beds—were more likely to provide BEmOC and CEmOC than lower-level 

health facilities including health centers without beds and dispensaries. Very few health centers 

without beds and dispensaries provided BEmOC and none provided CEmOC. By managing 

authority, the percentage providing BEmOC and CEmOC was the highest among NGO/private 

not-for-profit facilities (Figure 4). Despite government facilities being the major providers of 

delivery care, only 20 percent were BEmOC facilities and 10 percent were CEmOC facilities. 

Facilities with a mixed managing authority had the lowest emergency obstetric care capacity. 

 
19 

Figure 3. Percentage of facilities providing BEmOC and CEmOC, by type of facility

Figure 4. Percentage of facilities providing BEmOC and CEmOC, by managing authority 

45

19

2

6

18

32

6

9

Hospital Health center with

beds

Health center without

beds

Dispensary Total

BEmOC CEmOC

20

26

15

9 10

13

9

6

Government/public NGO/ Private not for profit Private for profit Mixed

BEmOC CEmOC
20 

3.2 DHS Clusters’ Access to Bemoc and Cemoc Facilities 

DHS clusters located near any health facility is dependent on population and facility 

density, with urban areas generally having greater access than rural areas. DHS clusters in urban 

areas are likely to be located near a BEmOC facility, with 86 percent of clusters linked to a 

BEmOC facility within 5 km (Figure 5). Rural clusters, on the other hand, are much less likely to 

be located near a BEmOC facility, with only 55 percent within 10 km of a BEmOC facility. In 

other words, 45 percent of rural clusters do not have access to a BEmOC facility within 10 km. 

Overall, the percentage of DHS clusters near a CEmOC facility is lower than the percentage near 

a BEmOC facility, with only 38 percent of DHS clusters having access to a CEmOC facility within 

5 km (71 percent urban and 14 percent rural) (Figure 6). 

Figure 5. Percentage of DHS clusters having a BEmOC facility within the specified distance, by 

residence 

 

86

20

48

91

55

70

96

79

86

Urban Rural Total

5km 10km 15km
21 

Figure 6. Percentage of DHS clusters having a CEmOC facility within the specified distance, by 

residence 

3.3 DHS Clusters’ Access to Different Levels of Service Readiness 

Figures 7 and 8 show the percent distribution of DHS clusters by various levels of readiness 

in providing basic and comprehensive obstetric care services. Figure 7 shows the levels of average 

score of facilities providing the services within a given distance of the DHS cluster while Figure 

6 shows the levels of readiness score of the facility with the highest score. The average score for 

facilities located near the majority of DHS clusters in urban areas is high while the majority of 

rural clusters have facilities with scores that average to a medium level. Figure 8, however, shows 

that when looking only at the highest score for a facility many more urban and rural clusters have 

access to facilities with high scores. For example, more than half of rural clusters are within 5 km 

of a facility with a high-level score and 79 percent are within 10 km of such a facility. In urban 

areas more than 90 percent of clusters have access to a facility with a high-level readiness score. 

 

71

14

38

85

38

57

88

62

73

Urban Rural Total

5km 10km 15km
22 

Figure 7. Percent distribution of DHS clusters by levels of readiness in providing basic and 

comprehensive obstetric care services at facilities within the specified distance, urban and rural 

areas 

Figure 8. Percent distribution of DHS clusters by levels of readiness in providing basic and 

comprehensive obstetric care services at the facility with the highest readiness score within the 

specified distance, urban and rural 

2 2 0

27

14

6

26 28 33

39

54 68

72 70 67

34 32 26

5km 10km 15km 5km 10km 15km

Low Medium High

Urban Rural

18

5 4 7 2

31

14

5

93 95 98

51

79

95

5km 10km 15km 5km 10km 15km

Low Medium High

Urban Rural
23 

3.4 Utilization of Facility-Based Delivery and Determinants 

Of the 5,155 women for whom data are available, 40 percent delivered their most recent 

birth at a health facility (Table 6). As expected, younger women, women with fewer children, 

women with more education, and women in wealthier households were more likely to deliver at a 

heath facility. A substantial difference in facility delivery is observed between urban and rural 

areas. Women in urban areas were more than twice as likely to give births at health facilities as 

their counterparts in rural areas. There was also great variation among regions (departments), with 

delivery at a health facility ranging from 19 percent in Grand Anse to 48 percent in Ouest. Facility 

delivery was also associated with a greater number of antenatal care visits. 

Table 6. Percentage of women who delivered the most recent birth at a health facility by background 

characteristics, Haiti DHS 2012 

Background characteristics % Number of women 

Mother's age at birth 

< 20 years 43.8 741 

20-34 41.2 3,479 

35-49 30.0 936 

Birth order 

1 58.3 1,702 

2-3 38.3 1,870 

4-5 25.7 852 

6+ 15.2 730 

Residence 

Urban 59.9 1,996 

Rural 26.7 3,159 

Department 

Ouest 48.1 1,884 

Sud-est 24.3 216 

Nord 43.2 503 

Nord-est 41.1 201 

Artibonite 33.3 773 

Centre 28.4 399 

Sud 37.5 377 

Grand'anse 19.2 189 

Nord-ouest 28.9 239 

Nippes 31.1 140 

Camps 50.7 234 

(Continued...) 
24 

Table 6. – Continued

Background characteristics % Number of women 

Mother's education 

None 14.5 991 

Primary 30.2 2,157 

Secondary or higher 62.0 2,007 

Antenatal care visits 

None 9.6 484 

1 24.3 191 

2-3 23.5 978 

4+ 48.9 3,468 

Don't know/missing 60.8 34 

Wealth quintile 

Lowest 9.7 1,046 

Second 21.6 1,023 

Middle 41.4 1,119 

Fourth 53.2 1,118 

Highest 77.6 848 

 

Total 39.5 5,155 

Using multilevel models, we assessed how women’s utilization of facility delivery is 

associated with service readiness at health facilities within a specified buffer of the cluster. We ran 

two multilevel models for each of the three buffers separately for urban and rural areas. The 

description of the results focuses on the 10-km buffer (Tables 7 and 8). Results for the other two 

buffers can be found in the appendices (Appendix Tables A and B). 

 
25 

Table 7. Results of multivariate regression of use of facility delivery on the average readiness score, 

Haiti 2012-2013 

 Urban Rural 

Variables OR 95% CI OR 95% CI 

Average readiness score of facilities within 

10 km 

1.463 0.896 - 2.388 1.780*** 1.391 - 2.276 

Number of facilities offering delivery care 

within 10 km 

1.002 0.985 - 1.019 1.026 0.998 - 1.055 

Department (ref.=Ouest) 

Sud-est 1.417 0.465 - 4.311 1.280 0.677 - 2.420 

Nord 1.602 0.793 - 3.236 1.340 0.811 - 2.213 

Nord-est 1.687 0.661 - 4.309 2.485* 1.226 - 5.040 

Artibonite 1.523 0.701 - 3.312 1.473 0.971 - 2.234 

Centre 1.514 0.567 - 4.045 1.763* 1.035 - 3.004 

Sud 1.111 0.387 - 3.188 1.583 0.955 - 2.624 

Grand'anse 0.780 0.262 - 2.319 1.008 0.478 - 2.122 

Nord-ouest 0.930 0.349 - 2.482 1.405 0.765 - 2.581 

Nippes 2.281 0.471 - 11.032 1.495 0.751 - 2.973 

Camps 1.320 0.785 - 2.220 1.255 0.466 - 3.377 

Wealth quintile (ref.=highest) 

Lowest 0.201*** 0.115 - 0.352 

Second 0.260** 0.100 - 0.672 0.371*** 0.220 - 0.626 

Middle 0.353*** 0.245 - 0.509 0.674 0.411 - 1.104 

Fourth 0.415*** 0.316 - 0.545 0.965 0.591 - 1.574 

Education (ref.=none) 

Primary 1.381 0.916 - 2.082 1.507** 1.107 - 2.052 

Secondary or higher 2.358*** 1.542 - 3.607 2.306*** 1.632 - 3.259 

Birth order (ref.=1) 

2-3 0.418*** 0.319 - 0.548 0.293*** 0.226 - 0.380 

4-5 0.359*** 0.239 - 0.539 0.130*** 0.087 - 0.193 

6+ 0.248*** 0.141 - 0.436 0.086*** 0.052 - 0.142 

Antenatal care visits (ref.=none) 

1 2.292* 1.065 - 4.929 4.326*** 2.062 - 9.075 

2-3 2.039** 1.193 - 3.485 3.792*** 2.114 - 6.803 

4+ 3.974*** 2.457 - 6.429 5.954*** 3.413 - 10.387

 

Mother's age at birth 1.047*** 1.024 - 1.070 1.072*** 1.050 - 1.095 

 

Observations (unweighted) 1,954 3,304 

Number of DHS clusters 175 257 

*p<0.05 **p<0.01 ***p<0.001 

 
26 

Table 7 presents odds ratios and 95% confidence intervals for the average readiness score 

as well as for the covariates adjusted for in the models. All of the urban clusters (175) and most of 

the rural clusters (257 of 262) were linked to a health facility offering delivery care within 10 km 

of the cluster. In rural areas the average readiness score of the facilities is positively and 

significantly associated with women’s use of facility delivery care after controlling for the number 

of facilities available within the distance and women’s characteristics. The odds ratio is 1.78, 

which means that an increase of one unit in the average score among these facilities corresponds 

to a 78 percent increase in women’s odds of going to a facility for delivery. In urban areas, 

however, the positive relationship between service readiness score and women’s use of facility 

delivery was not observed. Among the factors controlled for, the total number of facilities offering 

delivery care within the buffer was not significantly associated with use of facility delivery. We 

also found no significant associations between utilization and the number of certain types of 

facilities, for example, hospitals or health centers with beds (data not shown). Women’s individual 

characteristics and number of antenatal care visits were found to be associated with utilization of 

facility delivery, in the expected direction. 

Looking at the highest readiness score among the facilities within 10 km of the cluster, we 

found it is significantly associated with women’s likelihood of delivering at a health facility in 

both urban and rural areas (Table 8). Women’s odds of giving birth at health facilities increases 

by 36 percent in urban areas and 53 percent in rural areas with every unit of increase in the highest 

score among the facilities within 10 km, after controlling for other covariates. 

 
27 

Table 8. Results of multivariate regression of use of facility delivery on the highest readiness score, 

Haiti 2012-2013 

 Urban Rural 

Variables OR 95% CI OR 95% CI 

Highest readiness score among facilities 

within 10 km 

1.363* 1.007 - 1.843 1.527*** 1.293 - 1.804 

Number of facilities offering delivery care 

within 10 km 

0.994 0.974 - 1.014 1.000 0.970 - 1.031 

Departement (ref.=Ouest) 

Sud-est 1.315 0.436 - 3.967 1.139 0.610 - 2.126 

Nord 1.229 0.595 - 2.540 1.265 0.769 - 2.080 

Nord-est 1.291 0.519 - 3.214 2.564** 1.272 - 5.168 

Artibonite 1.166 0.545 - 2.495 1.231 0.818 - 1.852 

Centre 1.480 0.557 - 3.932 1.843* 1.088 - 3.121 

Sud 1.135 0.404 - 3.190 1.781* 1.085 - 2.924 

Grand'anse 0.608 0.198 - 1.865 1.009 0.482 - 2.114 

Nord-ouest 0.634 0.244 - 1.646 1.121 0.620 - 2.028 

Nippes 1.845 0.390 - 8.738 1.433 0.723 - 2.839 

Camps 1.325 0.791 - 2.222 1.450 0.547 - 3.839 

Wealth quintile (ref.=highest) 

Lowest NA NA 0.200*** 0.115 - 0.350 

Second 0.263** 0.102 - 0.680 0.354*** 0.210 - 0.595 

Middle 0.360*** 0.250 - 0.519 0.627 0.382 - 1.027 

Fourth 0.417*** 0.318 - 0.548 0.941 0.577 - 1.534 

Education (ref.=none) 

Primary 1.387 0.920 - 2.091 1.530** 1.124 - 2.083 

Secondary or higher 2.367*** 1.548 - 3.621 2.285*** 1.617 - 3.228 

Birth order (ref.=1) 

2-3 0.417*** 0.318 - 0.548 0.291*** 0.224 - 0.377 

4-5 0.358*** 0.238 - 0.537 0.127*** 0.086 - 0.189 

6+ 0.242*** 0.138 - 0.426 0.083*** 0.050 - 0.137 

Antenatal care visits (ref.=none) 

1 2.287* 1.064 - 4.914 4.311*** 2.057 - 9.033 

2-3 2.051** 1.200 - 3.505 3.786*** 2.110 - 6.792 

4+ 3.954*** 2.444 - 6.396 5.976*** 3.426 - 10.424 

 

Mother's age at birth 1.047*** 1.024 - 1.070 1.074*** 1.052 - 1.097 

 

Observations (unweighted) 1,954 3,304 

Number of DHS clusters 175 257 

*p<0.05 **p<0.01 ***p<0.001 
28 

4. DISCUSSION AND CONCLUSION 

This study estimated the influence of service readiness on women’s use of facility delivery 

care by linking facility census data and household survey data in a geographic information system 

(GIS). 

The Haitian government considers hospitals and health centers to be the major providers 

of delivery services; dispensaries and health centers without beds are not mandated to provide 

delivery services (MSPP 2000). Nevertheless, our results show that half of the facilities offering 

delivery services are dispensaries and health centers without beds. These lower-level facilities are 

poorly prepared to provide delivery services. Not only is there almost no capacity to provide 

emergency obstetric care these lower-level facilities also lack essential equipment and supplies for 

normal delivery care. Only 9 percent of health centers without beds and none of the dispensaries 

have functional emergency transportation. As a result, there is high risk of death for mother and 

newborn when an obstetrical emergency occurs. 

Access to basic and comprehensive emergency care is key to reducing maternal mortality 

(WHO 2014; Wildman and Bouvier-Colle 2004). One of the goals of the 2013-2016 Haiti health 

strategic plan is to provide BEmOC in 108 facilities by 2015 (MSPP 2012). Our results indicate 

progress has been slow in meeting this goal. When the 2013 HSPA was implemented only 70 

facilities provided BEmOC and among hospitals where half of the births occur only 45 percent 

provide BEmOC. Availability of CEmOC is even more limited—offered at less than 10 percent of 

facilities nationwide. Given that BEmOC facilities (i.e., hospitals and health centers) are located 

primarily in urban areas, most urban clusters are within 5 km of a BEmOC facility. In rural areas, 

however, access to BEmOC facilities is more limited: only one in five DHS clusters in rural areas 

is located within 5 km of a BEmOC facility. More program effort is needed to expand BEmOC 

access in rural areas where 60 percent of the Haitian population resides. 

Delivery at a health facility can reduce maternal mortality only if women are assisted by a 

skilled birth attendant (SBA) who is capable of managing common life-threatening obstetric 

complications (Harvey et al. 2007). However, less than half of the facilities offering caesarean 

section services have staff trained in CEmOC in last two years. Guidelines for CEmOC were not 

commonly available in service areas. 
29 

Our study found that better service environment in health facilities within a 10-km radius 

is significantly associated with greater probability of women delivering at a health facility. This 

finding agrees with that of previous research that service provision is a strong determinant of 

delivery service use (Karkee et al. 2014; Moyer and Mustafa 2013; Stekelenburg et al. 2004). As 

indicated in the introduction, because of methodological constraints in linking population data and 

health service data, most previous studies were limited to measuring service provision from the 

client’s perspective. Several recent studies took advantage of geographic data to associate health 

facilities and DHS clusters. These studies focused primarily on distance to the closest facility or 

the service in the closest facility; however, this approach can be problematic because DHS cluster 

locations have been displaced. Gabrysch et al. (2011) linked facility data and population data and 

examined the influence of level of care at the closest facility on use of delivery care. The authors 

found that an increase in service provision—measured by the availability of BEmOC and CEmOC 

functions—was associated with 26 percent higher odds of facility delivery. Our study improved 

on this methodology: instead of looking at the closest facility—where estimates may be subject to 

misclassification errors—our analysis measured the effect of a service environment within a 

reasonable distance. 

Our findings indicate that both the average level of service readiness and the highest level 

of service readiness can affect the use of delivery care. In rural areas, both average and highest 

levels of readiness are significantly associate with use of delivery service. However, in urban areas 

only the highest level of readiness was statistically significant. In urban areas where there are more 

facilities, more accessible transportation, and more financial resources, women may be able to 

choose to deliver at facilities with the highest quality of care. In rural areas, the average level of 

quality of care in an area can substantially increase usage of delivery care and the effect size is 

stronger than the highest level of readiness. However, it should be noted that the data available in 

this study did not allow for linking directly to the facility used by the woman for delivery; it only 

allows for understanding the service environment where the woman lives. 

It is interesting that no association is found between the total number of health facilities 

offering delivery services and use of facility delivery. We also found no significant associations 

between utilization and the number of certain types of facilities, for example, hospitals or health 

centers with beds. All DHS clusters are located within 10 km of a facility offering delivery 

services. The findings appear to suggest increasing number of facilities in the area does not 
30 

improve service utilization, after controlling for service readiness and other cofounders. The 

number of facilities is just one aspect of access to delivery services. Financial resources and 

cultural factors can also hinder women from going to a facility for delivery. We used 10 km to 

define the service environment because of the displacement of DHS clusters. It is possible that 

physical access to health facilities within 10 km is still challenging for some families, especially 

in mountainous rural areas. Increasing the density of health facilities does not necessarily improve 

utilization if the facilities are not reachable. 

Service provision measures used in existing research included facility infrastructure, 

obstetric equipment, number of staff, availability of drugs, provision of maternal and child health, 

and provision of BEmOC and CEmOC. In addition to these aspects, our study also captured a wide 

range of other items identified by WHO that are essential for providing quality delivery services. 

Signal functions considered in our study have been performed during the last three months; 

however, the difference in time between the two surveys may limit the association if quality of 

care changed in a location over the five-year period of the deliveries studied. 

In addition to the methodological improvement of measuring the service environment, 

some of other strengths of our study lie in the use of facility census data and nationally 

representative household data, which together have led to more generalizable results. Additionally, 

the use of observation during facility data collection increases the robustness of the readiness 

indicators and thus the accuracy of the relationship between service provision and delivery service 

utilization. Also, controlling for demographic confounding factors and the number of facilities in 

a service environment strengthens the findings on the association between service readiness and 

delivery care seeking. However, there are some limitations to this study. First, cause-effect 

association cannot be established because of the descriptive nature of the survey. Second, the 

facilities data were collected a year after the household data. Third, it would be helpful to include 

indicators reflecting the actual process of service delivery, for example, indicators on providers’ 

adherence to standards of care. However, this information was not available from the 2013 HSPA 

in Haiti. 

The use of a 10-kilometer buffer to create the service environment reduces 

misclassification errors from DHS GPS displacement. Linking all facilities within a 10-kilometer 

buffer creates a service environment that permits analysis of a woman’s use of a facility for 
31 

delivery, without directly linking her to the facility she used, by linking her to all the facilities she 

was likely to have used. However, the straight-line buffer approach does not take into account the 

mountainous terrain or the impassibility of roads during the rainy season, which may limit a 

woman’s access to a linked facility. Nesbitt et al. (2014) compared six different measures of spatial 

access and found that the straight-line linkage yields results similar to other geospatial algorithms 

in a developing-country setting (Nesbitt et al. 2014). 

This study shows that service readiness at health facilities has a strong influence on the use 

of these services by woman living in the facilities service environment. Most facilities in rural 

Haiti are poorly equipped and not yet ready to provide quality delivery services. Our results suggest 

that efforts and resources should focus on improving the service environment in rural areas. This 

may require changing the actual mandate of lower-level facilities to account for the local reality 

and health needs. 

Further research is needed to examine the factors impeding use of delivery care services in 

urban areas. Although health facilities in urban area are better equipped and more able to provide 

quality delivery services, 40 percent of women still deliver at home. Cost of care was the most 

frequently cited barrier to care seeking by women, according to the results of the 2012 Haiti DHS. 

Other social and cultural factors may also play a role. 
32 

REFERENCES 

Acharya, L.B., and J. Cleland. 2000. "Maternal and Child Health Services in Rural Nepal: Does 

Access or Quality Matter More?" Health Policy Plan 15(2): 223-39. 

Alexandre, P.K., G. Saint-Jean, L. Crandall, and E. Fevrin. 2005. "Prenatal Care Utilization in 

Rural Areas and Urban Areas of Haiti." Revista Panamericana de Salud Pública 18(2): 84-

92. 

Anderson, F.W., S.U. Morton, S. Naik, and B. Gebrian. 2007. "Maternal Mortality and the 

Consequences on Infant and Child Survival in Rural Haiti." Maternal and Child Health 

Journal 11(4): 395-401. 

Babalola, S.O. 2014. "Factors Associated with Use of Maternal Health Services in Haiti: A 

Multilevel Analysis." Revista Panamericana de Salud Pública 36(1): 1-9. 

Burgert, C.R., J. Colston, T. Roy, and B. Zachary. 2013. Geographic Displacement Procedure and 

Georeferenced Data Release Policy for the Demographic and Health Surveys. DHS Spatial 

Analysis Reports No. 7. Calverton, Maryland, USA: ICF International. 

Cayemittes, M., M.F. Busangu, J. de Dieu Bizimana, B. Barrère, B. Sévère, V. Cayemittes, and E. 

Charles. 2013. Enquête Mortalité, Morbidité et Utilisation des Services, Haïti, 2012. 

Calverton, Maryland, USA: MSPP, IHE and ICF International. 

Choulagai, B., S. Onta, N. Subedi, S. Mehata, G.P. Bhandari, A. Poudyal, B. Shrestha, M. Mathai, 

M. Petzold, and A. Krettek. 2013. "Barriers to Using Skilled Birth Attendants' Services in 

Mid- and Far-western Nepal: A Cross-Sectional Study." BMC International Health and 

Human Rights 13: 49. 

de Brouwere, V., R. Tonglet, and W. van Lerberghe. 1998. "Strategies for Reducing Maternal 

Mortality in Developing Countries: What Can We Learn from the History of The 

Industrialized West?" Trop Med Int Health 3(10): 771-82. 

Exavery, A., A.M. Kante, M. Njozi, K. Tani, H.V. Doctor, A. Hingora, and J.F. Phillips. 2014. 

"Access to Institutional Delivery Care and Reasons for Home Delivery in Three Districts 

of Tanzania." International Journal for Equity in Health 13: 48. 
33 

Gabrysch, S., and O.M.R. Campbell. 2009. "Still Too Far to Walk: Literature Review of the 

Determinants of Delivery Service Use." BMC Pregnancy and Childbirth 9: 34. 

Gabrysch, S., S. Cousens, J. Cox, and O.M.R. Campbell. 2011. "The Influence of Distance and 

Level of Care on Delivery Place in Rural Zambia: A Study of Linked National Data in a 

Geographic Information System." PLoS Medicine 8(1): e1000394. 

Gage, A.J., and M. Guirlene Calixte. 2006. "Effects of the Physical Accessibility of Maternal 

Health Services on Their Use in Rural Haiti." Population Studies (Camb) 60(3): 271-88. 

Graham, W.J., J.S. Bell, and C.H.W. Bullough. 2001. "Can Skilled Attendance at Delivery Reduce 

Maternal Mortality in Developing Countries?" In Studies in Health Service Organization 

and Policy, edited by V.D. Brouwere, V. Lerberghe, and B.I.P. Antwerp. Antwerp, 

Belgium: TIG Press. 

Harvey, S.A., Y.C. Blandon, A. McCaw-Binns, I. Sandino, L. Urbina, C. Rodriguez, I. Gomez, P. 

Ayabaca, S. Djibrina, and Nicaraguan Maternal and Neonatal Health Quality Improvement 

Group. 2007. "Are Skilled Birth Attendants Really Skilled? A Measurement Method, Some 

Disturbing Results and a Potential Way Forward." Bulletin of the World Health 

Organization 85(10): 783-90. 

Institut Haïtien de l’Enfance (IHE), and ICF International. 2014. Évaluation de Prestation des 

Services de Soins de Santé, Haïti, 2013. Rockville, Maryland, USA: IHE and ICF 

International. 

Karkee, R., A.H. Lee, and P.K. Pokharel. 2014. "Women's Perception of Quality of Maternity 

Services: A Longitudinal Survey in Nepal." BMC Pregnancy and Childbirth 14: 45. 

Kinney, M.V., K.J. Kerber, R.E. Black, B. Cohen, F. Nkrumah, H. Coovadia, P.M. Nampala, J.E. 

Lawn, Science in Action: Saving the Lives of Africa's Mothers, Newborns, and Children 

Working Group, H. Axelson, A.M. Bergh, M. Chopra, R. Diab, I. Friberg, O. Odubanjo, 

N. Walker, and E. Weissman. 2010. "Sub-Saharan Africa's Mothers, Newborns, and 

Children: Where and Why Do They Die?" PLoS Medicine 7(6): e1000294. 

Kirigia, J.M., D. Oluwole, G.M. Mwabu, D. Gatwiri, and L.H. Kainyu. 2006. "Effects of Maternal 

Mortality on Gross Domestic Product (GDP) in the WHO African Region." African 

Journal of Health Sciences 13(1-2): 86-95. 
34 

Kitui, J., S. Lewis, and G. Davey. 2013. "Factors Influencing Place of Delivery for Women in 

Kenya: An Analysis of the Kenya Demographic and Health Survey, 2008/2009." BMC 

Pregnancy and Childbirth 13: 40. 

Kyei, N.N., O.M.R. Campbell, and S. Gabrysch. 2012. "The Influence of Distance and Level of 

Service Provision on Antenatal Care Use in Rural Zambia." PLoS ONE 7(10): e46475. 

Lohela, T.J., O.M.R Campbell, and S. Gabrysch. 2012. "Distance to Care, Facility Delivery and 

Early Neonatal Mortality in Malawi and Zambia." PLoS ONE 7(12): e52110. 

Ministère de la Santé Publique et de la Population (MSPP). 2000. Représentation du Système de 

Santé. Port-au-Prince, Haïti: MSPP. 

Ministère de la Santé Publique et de la Population (MSPP). 2012. Plan Stratégique National de 

Santé de la Reproduction et de la Planification Familiale 2013-2016. Port-au-Prince, Haiti: 

MSPP. 

Montagu, D., G. Yamey, A. Visconti, A. Harding, and J. Yoong. 2011. "Where Do Poor Women 

in Developing Countries Give Birth? A Multi-country Analysis of Demographic and 

Health Survey Data." PLoS ONE 6(2): e17155. 

Moyer, C.A., P. Dako-Gyeke, and R.M. Adanu. 2013. "Facility-Based Delivery and Maternal and 

Early Neonatal Mortality in Sub-Saharan Africa: A Regional Review of the Literature." 

African Journal of Reproductive Health 17(3): 30-43. 

Moyer, C.A., and A. Mustafa. 2013. "Drivers and Deterrents of Facility Delivery in Sub-Saharan 

Africa: A Systematic Review." Reproductive Health 10: 40. 

Nesbitt, R.C., S. Gabrysch, A. Laub, S. Soremekun, A. Manu, B.R. Kirkwood, S. Amenga-Etego, 

K. Wiru, B. Hofle, and C. Grundy. 2014. "Methods to Measure Potential Spatial Access to 

Delivery Care in Low- and Middle-income Countries: A Case Study in Rural Ghana." 

International Journal of Health Geographics 13: 25. 

Skiles, M.P., C.R. Burgert, S.L. Curtis, and J. Spencer. 2013. "Geographically Linking Population 

and Facility Surveys: Methodological Considerations." Population Health Metrics 11(1): 

14. 
35 

Stekelenburg, J., S. Kyanamina, M. Mukelabai, I. Wolffers, and J. van Roosmalen. 2004. "Waiting 

Too Long: Low Use of Maternal Health Services in Kalabo, Zambia." Tropical Medicine 

and International Health 9(3): 390-8. 

Tey, N.P., and S.L. Lai. 2013. "Correlates of and Barriers to the Utilization of Health Services for 

Delivery in South Asia and Sub-Saharan Africa." The Scientific World Journal 2013: 

423403. 

Thind, A., A. Mohani, K. Banerjee, and F. Hagigi. 2008. "Where to Deliver? Analysis of Choice 

of Delivery Location from a National Survey in India." BMC Public Health 8: 29. 

World Health Organization (WHO). 2013. Service Availability and Readiness Assessment (SARA): 

An Annual Monitoring System for Service Delivery. Implementation Guide. Geneva, 

Switzerland: WHO. 

World Health Organization (WHO). 2014. Trends in Maternal Mortality: 1990-2013. Estimates 

by WHO, UNICEF, UNFPA, the World Bank and the United Nations Population Division. 

Geneva, Switzerland: WHO. 

WHO, UNFPA, UNICEF, and AMDD. 2009. Monitoring Emergency Obstetric Care: A 

Handbook. Geneva, Switzerland: WHO. 

Wildman, K., and M.H. Bouvier-Colle. 2004. "Maternal Mortality as an Indicator of Obstetric 

Care in Europe." BJOG 111(2): 164-9. 

Yaffee, A.Q., L.K. Whiteside, R.A. Oteng, P.M. Carter, P. Donkor, S.D. Rominski, M.E. Kruk, 

and R.M. Cunningham. 2012. "Bypassing Proximal Health Care Facilities for Acute Care: 

A Survey of Patients in a Ghanaian Accident and Emergency Centre." Tropical Medicine 

and International Health 17(6): 775-81. 
APPENDIX 

Appendix Table A. Results of multivariate regression of use of facility delivery on the average readiness score: 5 km and 15 km, Haiti 2012-2013 

Variables 

5 km 15 km 

Urban Rural Urban Rural 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Average readiness score of facilities within 5 km 

1.558** 1.176 - 2.064 1.228* 1.027 - 1.469 

Number of facilities offering delivery care within 5 km 

0.993 0.972 - 1.014 1.034 0.938 - 1.141 

Average readiness score of facilities within 15 km 

 3.060** 1.390 - 6.737 2.428*** 1.618 - 3.644

Number of facilities offering delivery care within 15 km 

 0.994 0.978 - 1.010 1.003 0.988 - 1.018

Department (ref.=Ouest) Sud-est 1.089 0.402 - 2.948 0.904 0.449 - 1.822 1.953 0.597 - 6.385 1.606 0.826 - 3.123Nord 1.294 0.743 - 2.255 0.938 0.516 - 1.706 1.814 0.843 - 3.903 1.507 0.908 - 2.502Nord-est 1.100 0.497 - 2.433 1.448 0.640 - 3.278 2.160 0.787 - 5.931 3.031** 1.472 - 6.241Artibonite 0.976 0.533 - 1.788 1.100 0.677 - 1.786 1.537 0.668 - 3.535 1.415 0.936 - 2.139

Centre 1.215 0.515 - 2.866 1.663 0.885 - 3.125 1.281 0.459 - 3.574 1.607 0.935 - 2.761

Sud 0.845 0.336 - 2.122 2.409** 1.256 - 4.620 0.705 0.223 - 2.231 1.269 0.748 - 2.152

Grand'anse 0.445 0.160 - 1.233 0.475 0.152 - 1.481 0.704 0.228 - 2.171 1.079 0.512 - 2.275Nord-ouest 0.681 0.289 - 1.602 0.847 0.440 - 1.632 1.168 0.425 - 3.215 1.834 0.969 - 3.473

Nippes 1.444 0.332 - 6.278 1.377 0.604 - 3.141 2.327 0.488 - 11.099 1.534 0.768 - 3.063Camps 1.318 0.785 - 2.213 1.181 0.455 - 3.069 1.412 0.845 - 2.358 1.550 0.576 - 4.170Wealth quintile (ref.=highest) Lowest NA NA 0.195*** 0.107 - 0.356 NA NA 0.184*** 0.105 - 0.322Second 0.273** 0.105 - 0.708 0.382*** 0.222 - 0.660 0.255** 0.098 - 0.658 0.342*** 0.203 - 0.577

Middle 0.364*** 0.251 - 0.528 0.699 0.418 - 1.169 0.355*** 0.246 - 0.511 0.641 0.391 - 1.053

Fourth 0.431*** 0.327 - 0.569 1.035 0.616 - 1.738 0.419*** 0.319 - 0.550 0.969 0.594 - 1.580

(Continued...)

36 
Appendix Table A. – Continued

Variables 

5 km 15 km 

Urban Rural Urban Rural 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Education (ref.=none) Primary 1.403 0.924 - 2.131 1.371 0.968 - 1.942 1.365 0.907 - 2.056 1.501** 1.105 - 2.038Secondary or higher 2.488*** 1.616 - 3.832 2.178*** 1.479 - 3.208 2.305*** 1.508 - 3.523 2.326*** 1.651 - 3.278

Birth order (ref.=1) 2-3 0.400*** 0.304 - 0.528 0.334*** 0.248 - 0.450 0.417*** 0.318 - 0.547 0.295*** 0.228 - 0.383

4-5 0.336*** 0.222 - 0.507 0.160*** 0.101 - 0.252 0.353*** 0.235 - 0.530 0.129*** 0.087 - 0.1926+ 0.250*** 0.141 - 0.443 0.090*** 0.050 - 0.161 0.244*** 0.139 - 0.429 0.085*** 0.051 - 0.141Antenatal care visits (ref.=none) 1 2.562* 1.175 - 5.588 5.184*** 2.259 - 11.893 2.375* 1.107 - 5.096 3.890*** 1.891 - 8.0042-3 2.236** 1.291 - 3.874 4.080*** 2.095 - 7.945 2.072** 1.214 - 3.537 3.514*** 2.009 - 6.1484+ 4.369*** 2.666 - 7.161 6.495*** 3.437 - 12.273 3.987*** 2.468 - 6.441 5.572*** 3.278 - 9.470Mother's age at birth 1.049*** 1.026 - 1.072 1.066*** 1.041 - 1.092 1.047*** 1.024 - 1.070 1.072*** 1.050 - 1.095

Observations (unweighted) 1,934 2,214 1,954 3,383 Number of DHS clusters 173 172 175 262 

*p<0.05 **p<0.01 ***p<0.001 

37 
Appendix B. Results of multivariate regression of use of facility delivery on the highest readiness score: 5 km and 15 km, Haiti 2012-2013 

Variables

5 km 15 km 

Urban Rural Urban Rural 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Highest readiness score among facilities within 5 km 

1.271 0.999 - 1.618 1.098 0.934 - 1.291 

Number of facilities offering delivery care within 5 km 

0.991 0.969 - 1.015 1.016 0.912 - 1.132 

Highest readiness score among facilities within 15 km 

 2.067*** 1.384 - 3.089 1.784*** 1.392 - 2.287

Number of facilities offering delivery care within 15 km 

 0.985 0.968 - 1.002 0.997 0.982 - 1.013

Department (ref.=Ouest) Sud-est 1.207 0.437 - 3.329 0.825 0.405 - 1.683 1.596 0.512 - 4.980 1.367 0.718 - 2.604Nord 1.249 0.710 - 2.198 0.899 0.488 - 1.656 1.071 0.504 - 2.278 1.243 0.753 - 2.052Nord-est 1.255 0.560 - 2.811 1.459 0.637 - 3.343 1.291 0.505 - 3.301 2.911** 1.419 - 5.972

Artibonite 1.048 0.569 - 1.931 1.075 0.657 - 1.757 0.879 0.383 - 2.017 0.981 0.642 - 1.498Centre 1.586 0.660 - 3.808 1.728 0.913 - 3.269 1.231 0.444 - 3.415 1.752* 1.027 - 2.987Sud 1.112 0.445 - 2.775 2.512** 1.298 - 4.861 0.871 0.294 - 2.580 1.514 0.912 - 2.515Grand'anse 0.617 0.228 - 1.668 0.445 0.141 - 1.407 0.461 0.146 - 1.458 0.975 0.462 - 2.055Nord-ouest 0.648 0.272 - 1.542 0.812 0.418 - 1.578 0.658 0.258 - 1.677 1.299 0.713 - 2.366Nippes 1.628 0.360 - 7.355 1.344 0.583 - 3.100 1.327 0.277 - 6.364 1.314 0.655 - 2.635Camps 1.317 0.778 - 2.231 1.239 0.469 - 3.270 1.369 0.825 - 2.272 1.513 0.564 - 4.055Wealth quintile (ref.=highest) Lowest NA NA 0.192*** 0.105 - 0.351 NA NA 0.186*** 0.106 - 0.325Second 0.269** 0.103 - 0.701 0.376*** 0.217 - 0.649 0.262** 0.102 - 0.677 0.331*** 0.197 - 0.558

Middle 0.363*** 0.250 - 0.528 0.693 0.414 - 1.160 0.365*** 0.253 - 0.525 0.605* 0.369 - 0.993Fourth 0.433*** 0.328 - 0.571 1.037 0.617 - 1.745 0.420*** 0.320 - 0.551 0.957 0.587 - 1.560Education (ref.=none) Primary 1.419 0.934 - 2.156 1.372 0.968 - 1.945 1.353 0.899 - 2.038 1.473* 1.084 - 2.001Secondary or higher 2.526*** 1.639 - 3.892 2.183*** 1.481 - 3.219 2.307*** 1.510 - 3.525 2.197*** 1.558 - 3.097

(Continued...)

38 
Appendix Table B. – Continued

Variables

5 km 15 km 

Urban Rural Urban Rural 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Birth order (ref.=1) 2-3 0.400*** 0.304 - 0.527 0.333*** 0.247 - 0.448 0.419*** 0.320 - 0.550 0.293*** 0.226 - 0.380

4-5 0.334*** 0.221 - 0.504 0.158*** 0.100 - 0.250 0.357*** 0.238 - 0.536 0.127*** 0.086 - 0.189

6+ 0.247*** 0.139 - 0.437 0.089*** 0.049 - 0.160 0.246*** 0.140 - 0.433 0.082*** 0.050 - 0.136

Antenatal care visits (ref.=none) 1 2.529* 1.159 - 5.516 5.223*** 2.277 - 11.980 2.341* 1.092 - 5.021 3.871*** 1.881 - 7.9662-3 2.193** 1.266 - 3.798 4.077*** 2.093 - 7.940 2.036** 1.193 - 3.473 3.448*** 1.970 - 6.033

4+ 4.304*** 2.627 - 7.053 6.532*** 3.457 - 12.342 3.928*** 2.433 - 6.341 5.520*** 3.247 - 9.384

Mother's age at birth 1.049*** 1.026 - 1.072 1.066*** 1.041 - 1.092 1.046*** 1.024 - 1.069 1.073*** 1.051 - 1.096Observations (unweighted) 1,934 2,214 1,954 3,383 Number of DHS clusters 173 172 175 262 

*p<0.05 **p<0.01 ***p<0.001 

39