The Constraints of Distance and Poverty on Institutional Deliveries in Pakistan: Evidence from Georeference-Linked Data Anrudh K. Jain, Zeba A. Sathar, and Minhaj ul Haque

While institutional deliveries in Pakistan have risen substantially over the last few years, the change has mainly occurred among the wealthy and those with access to services in urban areas. We assess the influence of economic and geographic access to health facilities on institutional deliveries by linking household survey data and georeferenced distance to facilities equipped to provide services for obstetric care in nine districts in Pakistan. Multilevel mixed-effect logistic regression analyses show that the net effect of an increase in distance to a facility by 1 kilometer is to decrease the odds of an institutional delivery by 3 percent. In contrast, household wealth and availability of at least basic emergency care within 10 kilometers substantially increase the odds of an institutional delivery. These effects are more pronounced in rural areas than in urban areas. Disadvantages faced by poor rural women can be minimized by upgrading existing facilities at district and subdistrict levels to provide comprehensive emergency care and by facilitating transportation of poor rural women directly to these facilities when they experience life-threatening complications of childbirth. (Studies in Family Planning 2015; 46[1]: 21–39)

T

he maternal mortality ratio (MMR) in developing countries has declined since the 1990s but remains high, especially in sub-Saharan Africa and South Asia. Millennium Development Goal 5 (MDG 5)—reducing the MMR by three-fourths between 1990 and 2015—remains elusive in most of these countries. The absence of periodic and direct estimates of the MMR from vital registration systems or from household surveys makes it difficult to assess the effects of interventions to reduce the MMR. International efforts to reduce the MMR have focused on increasing the proportion of births that take place in health facilities (institutional deliveries) or that are attended by skilled birth attendants (SBAs). The percent of deliveries attended by SBAs is one of the indicators included among the independent variables in models to estimate the MMR in developing countries (WHO 2014). However, there is

Anrudh K. Jain is Distinguished Scholar, Population Council, One Dag Hammarskjold Plaza, New York, NY 10017. Email: [email protected]. Zeba A. Sathar is Country Director, Population Council, Islamabad, Pakistan. Minhaj ul Haque is Independent Consultant, Islamabad. He was ­­­­Senior Program Manager at the Population Council’s Islamabad office when this study was undertaken. ©2015 The Population Council, Inc.

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Constraints of Distance and Poverty on Institutional Deliveries in Pakistan

a large overlap between births attended by SBAs and institutional deliveries, and the difference between the two is quite small in most developing countries. This reflects the lack of availability of skilled birth attendants in home deliveries in rural areas, which are instead attended by relatives or by traditional birth attendants (dais). Cross-country comparisons show a negative association between the MMR and each of these two indicators (Jain et al. 2013). However, the relationship between institutional deliveries and the MMR is not straightforward. Thaddeus and Maine (1994) articulated a threedelay framework to identify factors associated with a high MMR. The first refers to the delay in identifying life-threatening obstetric complications in home deliveries, the second to a delay in reaching a health facility, and the third to a delay in getting appropriate treatment of good quality after arriving at a health facility. Many women have to travel from facility to facility before reaching one that can provide comprehensive emergency obstetric care, and as a result some women arrive at the facility dead or die soon after. Thus, one of the foremost factors in saving women’s lives is the ability to access emergency obstetric care and receive treatment in time by a skilled provider. This requires that pregnant women experiencing life-threatening complications reach a facility that provides comprehensive (fully equipped) emergency obstetric care within a few hours, and that they are treated without delay. At the same time, much debate has taken place among the political and health care communities on which aspects of the health care system require change in order to reduce maternal mortality. The central question has been whether to prioritize the upgrading of existing facilities to provide comprehensive emergency obstetric care, or whether to place more emphasis on deliveries by skilled birth attendants and other community-based interventions (Thaddeus and Maine 1994; Lassi, Haider, and Bhutta 2010). We have been grappling with the same questions for more than a decade. Perhaps the answer lies in promoting both approaches. The small number of institutional deliveries that take place in developing countries may reflect a combination of factors, including limited availability of health facilities, poverty, cultural factors (e.g., women’s restricted mobility), and women not feeling the need to give birth in a health facility.1 Another factor that likely plays an important role in maternal health outcomes is the perceived or actual quality of available services for emergencies (Lindelow, Reinikka, and Svensson 2003; Hounton et al. 2008). The quality of comprehensive emergency obstetric care services at a health facility is likely to determine whether a mother survives even if she reaches a facility in time. An increase in facility-level deliveries overall may not reduce the MMR if pregnant women experiencing life-threatening complications are not treated in time to be saved. Furthermore, such an increase may not necessarily reflect improved maternal health outcomes for poorer women and those living far from urban centers. Our study focuses on the role of these factors in increasing or hindering the occurrence of institutional deliveries as a proxy for being able to access delivery care, especially during an obstetric emergency. Research on the association between geographic access to health facilities and institutional deliveries in general shows a negative association between the two. However, the indicators of geographic access in many studies are based on information about the nearest facility collected from the woman herself or from knowledgeable individuals in the community. For example, 1 Most women may not feel the need to give birth in a health facility, because a large proportion of births (about 85 percent) are classified as “normal,” having very low or close to zero risk of maternal death irrespective of place of delivery or whether they receive the assistance of a skilled birth attendant.

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Gage and Calixte (2006) used community-level data from key informants on proximity of health services to measure physical access in their analysis of health-service use in rural Haiti. A recent literature review suggests that some researchers have also used region and place of residence as a proxy for geographic distance (Gabrysch and Campbell 2009). While geographic distance when assessed for maternal health care is critical, several aspects of physical access have to be studied to get a fuller picture, including travel mode, terrain, availability and affordability of transport, and travel time. This literature review also concluded, “The increasing availability of georeferenced data provides a promising opportunity to link detailed health facility data with large-scale household data using a geographic information system (GIS)” (p. 16). Geographic access measured through georeferenced location of health services is clearly an advance over earlier measures of distance. It is also an important tool for ensuring that access is being measured as accurately as possible (Gabrysch and Campbell 2009). Although an increasing number of studies use GIS systems to measure distance to delivery care in Bangladesh (Chowdhury et al. 2006) and rural Zambia (Gabrysch et al. 2011), and distance to reproductive health in Malawi (Heard, Larsen, and Hozumi 2004), this method had not until now been applied in Pakistan. We use GIS technology to assess the effect of geographic distance on institutional deliveries in Pakistan. We also use economic ability to reduce travel time and facilitate travel as another aspect of access, especially the ability to arrange or use one’s own transport. The difference in geographic access to emergency care between the wealthy and poor is likely to be the major determinant of adverse maternal health outcomes among women in resource-constrained households, who face the disadvantages of both poverty and being located farther from emergency-care facilities (Pathak, Singh, and Subramanian 2010). Access in terms of geographic distance has a different meaning for the wealthy and poor, because it is linked with the availability of one’s own transport, the cost of public and paid transport, and the location of poorer settlements far from urban centers. Presumably, higher incomes offset greater distances to travel in rural settings, whereas in urban settings the poor may have to spend less on access because of the clustering of services in towns and cities. Not only are poorer women, especially those living in rural areas, less likely to have access to emergency care, they are also likely to have less knowledge of when and where to seek care. They are less likely to have their own transportation, a factor that could delay travel decisions and increase travel time to the facility.

PAKISTAN IN CONTEXT Pakistan, accounting for about 6 percent of the world’s maternal deaths, remains one of the top five countries contributing to the global burden of maternal mortality (Hogan et al. 2010). While the maternal mortality ratio has fallen from about 550 maternal deaths per 100,000 births in 1990 to 276 per 100,000 in 2010 (NIPS 2008), reaching the MDG 5 target of 140 per 100,000 by 2015 remains highly unlikely.2 Pakistan has made substantial progress in regard to two proxy indicators of the MMR. The percent of deliveries that took place at a health facility increased from 13 percent in 1990–91 to 48 percent in 2012–13. In addition, the percent of births assisted by a skilled provider increased from 39 percent in 2006–07 to 52 percent in 2012–13 (NIPS 2013). However, the difference between the two indicators remains around 4–5 percentage points, im2 The government of Pakistan assumed an MMR of 553 in 1990 and set the target of 140 for 2015.

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Constraints of Distance and Poverty on Institutional Deliveries in Pakistan

plying a lack of availability of SBAs for home deliveries, especially in rural areas.3 Despite this rise in both institutional deliveries and births attended by SBAs, there is concern that the women most in need of institutional deliveries in rural areas remain vulnerable to the risk of maternal death because of lack of access. For example, computations from the 2012–13 Pakistan Demographic and Health Survey (PDHS) indicate that only 27 percent of the poorest women living in rural areas have institutional deliveries, compared with 41 percent of the poorest urban women. In the 1990s, the issues related to maternal death and access to emergency care caught national attention when a study showed that women living within a 10-mile radius were arriving at a major urban hospital dead as a result of delays in seeking care and delays in arriving at a health facility (Jafarey and Korejo 1993). The public health system in Pakistan provides emergency obstetric care services mainly through tertiary district and teaching hospitals, and to some extent through second-tier tehsil (subdistrict) hospitals that are also located several kilometers from most rural settlements. First-tier basic health units and rural health centers located closer to remote and rural residents are currently not equipped to provide comprehensive or even basic emergency obstetric care. Private facilities also tend to cluster in and around urban areas, leaving facilities with fully functional 24/7 obstetric services very unevenly distributed across the country. Many interventions have been introduced in Pakistan to improve this situation (Jafarey et al. 2008). These interventions have largely focused on either improving emergency obstetric services or creating community-level awareness and demand for institutional deliveries. For example, a safe motherhood project implemented in Karachi in 1996–99 sought to raise awareness of danger signs and the importance of swift referral to a health facility equipped to handle obstetric emergencies. The project trained providers in counseling skills and established a referral system to link the community to a nearby hospital. An evaluation of the counseling provided to pregnant women and their spouses, which included the need to establish an emergency plan, revealed an improvement in knowledge of preventive measures during pregnancy—for example, the importance of discussing and receiving permission from family elders and/or husbands for referral to a hospital in the event of an obstetric emergency (Fikree, Jafarey, and Kureshy 1999). The Safe Motherhood Applied Research and Training Project (SMART), implemented in the D.G. Khan District from 2003 to 2005, tested the effect of community-based and health-system interventions. While obstetricians at district and subdistrict hospitals were given training in obstetric emergency care, more emphasis was placed on community-level interventions. These interventions focused primarily on training traditional birth attendants (TBAs) to recognize major obstetric complications and to know when to refer women to a health facility (Rashida and Miller 2006). Perinatal mortality, a proxy for maternal mortality, was substantially reduced in the short period of 18 months. Delays in arriving at health facilities were reduced by improving health systems and establishing better financial arrangements with local transporters to bring women to such facilities. Also, women’s health-care-seeking behaviors improved (Miller et al. 2012). The Pakistan Initiative for Mothers and Newborns Project (PAIMAN), established in ten districts in 2005–10, upgraded 31 health facilities and implemented interventions focused on community mobilization and behavior-change communication. As a result, the percent of institutional deliveries increased from 38 percent in 2005 to 50 percent in 2010. There was some 3 Data from the 2012–13 Pakistan Demographic and Health Survey (PDHS) indicate that about 8.5 percent of deliveries that occurred outside a health facility were attended by a skilled birth attendant, in comparison with 99 percent of those that occurred within a health facility.

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reduction of the delay in the decision to seek care, but delays in arriving at a health facility and in provision of adequate care remained unchanged (Mahmood 2010). Analysis of facilities upgraded through PAIMAN showed that the incidence of complications as a percent of live births increased from 36 percent in 2007 to 47 percent in 2009. Furthermore, the case-fatality ratio declined from 927 maternal deaths per 100,000 complications in 2007 to 581 maternal deaths in 2009. Whereas all upgraded facilities were eligible to offer emergency obstetric care, nine district hospitals attended to about 60 percent of all obstetric complications, performing 90 percent of cesarean sections. About 90 percent of maternal deaths also occurred in these nine facilities (Jain et al. 2013). The authors interpreted these results to imply a reduction in the three delays and a reduction in community-level MMR. However, in the absence of a control group, it was not possible to attribute these changes to PAIMAN interventions. Information is not available on the effect of geographic access on the occurrence of institutional deliveries in Pakistan. Even information on the effect of factors like place of residence, women’s education, and household wealth is limited to the cross-tabulations included in reports from the PDHS. For example, computations from the 2012–13 PDHS indicate that the proportion of institutional deliveries increased with the household wealth index in both rural and urban areas: from 27 percent to 47 percent in rural areas, and from 41 percent to 94 percent in urban areas. A few studies also show that women living in remote and conservative settings face several other obstacles, including restricted mobility and autonomy during motherhood (Mumtaz et al. 2011). A well-established association also exists in Pakistan between women’s education, rural residence, and autonomy (Sathar and Kazi 2000). Regional and ethnic factors that affect women’s mobility and status are expected to exacerbate the disadvantages of geographic access and add additional obstacles preventing poor women in rural areas from leaving home (Sathar, Reichenbach, and Mahmood 2008). Our objective in what follows is to examine whether access in terms of distance to facilities influences institutional deliveries and thus maternal deaths, whether poorer women are at greater disadvantage because of lack of economic resources in addition to inequity in geographic access, and whether access or lack thereof to emergency obstetric services creates even more obstacles for women living in remote areas. By using innovative georeferenced data to measure geographic access to the nearest health facility and by linking such data with economic ranking of households and institutional deliveries, we will show how and why institutional deliveries are affected by geographic access and by the combination of economic status and geographic access in rural settings in Pakistan.

DATA This study uses linked data from health facilities and household surveys from nine districts in Pakistan. The global positioning system (GPS) was used to estimate geographic coordinates of all primary sampling units (PSUs) included in the household survey and all functioning health facilities included in the facility survey. These geographic coordinates were linked in ArcGIS, which enabled us to calculate the physical road distance from each PSU to each facility in a district and to relate this distance to the use of maternal health care services by women included in the household surveys. March 2015

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Constraints of Distance and Poverty on Institutional Deliveries in Pakistan

The household survey was conducted in 2005 by the National Institute of Population Studies (NIPS) in ten districts selected under the PAIMAN project. The sample frame was prepared using all villages listed in the 1998 Population Census. The sample was stratified into urban and rural PSUs, with the probability proportionate to the size of each district’s urban and rural populations. Each PSU was selected randomly within urban and rural strata, and approximately 20 households were selected within each PSU using systematic random sampling with a random start. All currently married women residing in the selected households were interviewed. Data on antenatal, natal, and postnatal care were collected under different sections of a precoded women’s questionnaire. To measure factors affecting women’s healthseeking behavior, information was also gathered on women’s sociodemographic characteristics and on wealth- and asset-related variables for each household. Population Council staff closely monitored data collection to ensure quality and made follow-up visits to identify the GPS location of all PSUs included in the household survey. A pioneering facility survey was carried out in 2008 to prepare GIS maps of all health facilities, ranging from large teaching and district headquarters hospitals in the public sector to registered small clinics of Hakims (traditional healers) and dispensers (paramedics trained to administer medication) in the private sector. A structured questionnaire, administered to those in charge of the facility to collect information on the type and level of available reproductive health care, included questions about staff, functional equipment, and amenities for the clients. Information was also collected about availability of family planning, child health, and obstetric care services. District census reports (PCO 1998) were used to obtain a list of villages with a population of at least 100 households. These villages were subsequently visited to document available health services. The field staff also visited those villages that had a population of less than 100 households but that reported having a local public- or private-sector facility. This approach ensured the documentation of all health facilities available in each district. GPS navigational devices determined the precise location (geographical coordinates) of every functional or nonfunctional facility. In addition, georeferenced digitized maps were prepared using Survey of Pakistan (SOP) 1:50,000 scale paper-based sheets. These maps show district, subdistrict, and Union Council (UC) boundaries, rivers, and main and small roads. Superimposing the GPS data (geographic coordinates) from the facility-based survey onto the SOP digitized maps produced a fully functional geographic information system (GIS) map permitting spatial and statistical analyses.

Linked Analytical Sample The linked dataset used in this analysis included nine districts in Pakistan.4 The analysis included 763 facilities that started offering obstetric-care services before 2005, to match the availability of services with the reference period for births included in this study.5 Out of these, 547 were equipped to provide only normal delivery services,6 91 were equipped to provide basic 4 One district was excluded because the household survey was conducted but the mapping/geolocation survey was not. 5 Data on facilities were collected in 2008, whereas the household survey was conducted in 2005. Facilities that started operating after 2005 cannot influence deliveries recorded in the household survey in 2005. 6 “Normal delivery” refers to the availability of assisted vaginal delivery in a health facility that does not have an operating room.

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emergency obstetric care,7 and the remaining 125 were equipped to provide comprehensive emergency obstetric care.8 Out of these, 92 facilities were classified as public facilities (18 with comprehensive emergency obstetric care, 18 with basic emergency obstetric care, and 56 with normal delivery services) and 671 as private facilities (107 with comprehensive emergency obstetric care, 73 with basic emergency obstetric care, and 491 with normal delivery services). From the nine districts in Pakistan, 8,305 women were included in the household survey. The eligible sample includes 4,435 women who gave birth in the three years prior to the survey. The remaining 3,870 women were excluded either because they did not have a delivery during the past three years (3,333 women) or because information was missing on one of the important variables (537 women).

DEFINITIONS AND MEASUREMENT The primary outcome variable of interest is the use of health facilities for delivering a child. This variable was constructed for women who gave birth in the three years preceding the household survey. One binary variable was constructed: delivery in any health facility was coded “1” for yes and “0” for no. Two independent variables of primary interest—geographic access and poverty—were measured as follows.

Geographic Access to Services Physical distances between PSUs and health facilities were measured using the network analyst extension of the ArcGIS package. Georeferenced facility and PSU location data were combined with the road/path layer to create a dataset using “Object Destination Cost Matrix” of ArcGIS. Preferences were defined/declared using the PSU locations as “objects,” facilities as “destinations,” and the road/path layer as the “cost” (distance in meters). This resulted in new integrated road-network matrix data, where each facility is a row/case, the name of each PSU is a column, and the cells contain the distance in meters. Thus, distance was estimated from any PSU to any facility within a district. The “Object Destination Cost Matrix” procedure can measure a “road distance” only where the road layer is available. The network analyst procedure failed in cases where either or both the PSU and the facility were located away from a road. In these cases, we first measured the “as the crow flies” distance from a PSU (or a health facility) to the nearest road and added it to the road distance described above.9 As a final check on this calculation, we physically verified the procedure on drawing maps. The distance-based explanatory variables were constructed from the road-network matrix data where information about the nature of services offered in a facility is available. 7 Basic emergency obstetric care is the availability of parenteral oxytocics, antibiotics, and anticonvulsants; assisted deliveries; manual extraction of the placenta; and removal of retained products. 8 Comprehensive emergency obstetric care is basic emergency obstetric care plus the capability of performing cesarean sections and blood transfusions. 9 The distance variable is unlikely to be affected by the characteristics of the terrain (e.g., a river) between the PSU and the health facility because this distance largely reflects the road distance rather than the shortest distance between two points as indicated by “as the crow flies.”

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Constraints of Distance and Poverty on Institutional Deliveries in Pakistan

We constructed two variables from distance files: the physical distance in kilometers between a PSU and the nearest health facility (public or private) that provides at least normal delivery services, and the highest level of delivery care available within 10 km of the PSU.10 We have no direct measure of the quality of services offered by these facilities. However, we did collect information about availability of equipment and commodities at each facility and classified them into three groups according to their ability to offer normal, basic, and comprehensive emergency obstetric care services. The highest level of delivery care available within 10 km of the PSU variable was coded in four categories: none, normal, basic, and comprehensive. These indicators may underestimate the effect of physical access on the likelihood of an institutional delivery, because they reflect the geographic distances within a district. In practice, women, especially those living in the border PSUs, may go to a facility outside the district where they reside.

Poverty A wealth index was calculated for each household in rural and urban areas separately by the first component extracted from running a factor analysis on 25 household amenities and consumer durable goods, construction material used in the dwelling, persons per sleeping room, energy sources, and ownership of agricultural land. This procedure is similar to that used by Filmer and Pritchett (1999), who constructed the wealth index as a proxy for relative poverty by running the factor analysis on permanent income-related variables to extract values of principal components. Households were divided into four quartile categories indicating the relative wealth of each household: lowest, lower middle, upper middle, and highest.

Measurement of Other Indicators The use of facilities for childbirth is also inhibited by Pakistani women’s restricted mobility and autonomy, which reflect the influence of factors such as patriarchy, conservative religion, and other cultural factors. In the absence of any direct indicator of mobility and autonomy, we used women’s education as a proxy for cultural barriers such as restricted mobility and limited empowerment.11 Although restricted mobility adversely affects girls’ education, as women’s education increases so do their mobility and autonomy. In addition to reflecting many positive aspects for the respondent, education can also be used as a proxy for mobility and autonomy. To examine the effect of cultural factors on institutional deliveries, we included the educational level of the respondent and her husband (coded as “0” for none, “1” for primary, and “2” for secondary and higher), whether she watched television, and whether she was engaged in paid work. We measured demand or “felt need for institutional delivery” by a binary variable based on whether women reported experiencing any complications during the last pregnancy (coded “0” for no and “1” for yes). It was anticipated that families would try to take a woman to a 10 Actual distance was calculated in meters, i.e. with three decimals of accuracy in kilometers. This variable was transformed in integers by rounding to the nearest integer. Thus, 1 kilometer refers to a distance between 0.500 and 1.499 kilometers. To minimize the effect of skewedness in this variable, we grouped any distance from 35.500 to maximum and coded it as 36 kilometers. However, we used the actual distance in creating the second variable, which is based on services available within 9.999 kilometers. 11 Although we have good and direct indicators of geographic and economic barriers to use of services, we do not have direct indicators of cultural (e.g., restricted mobility) and other (availability of transportation) barriers. We also do not have any direct indicators of the quality of the services available at these facilities. The lack of these data constrains generalizability of results.

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health facility for delivery if she experienced complications during her pregnancy. We also used women’s age in a single year as another covariate.

Statistical Analysis Exploratory analysis was first conducted through bivariate analysis to identify the relationships between geographic access, poverty, and the extent of institutional deliveries. Because the sample of women is nested within a PSU that is nested within a district, multilevel mixed-effect logistic regression analyses were conducted to estimate the independent effects of access and poverty on the use of health facilities for deliveries. Our model takes the general form:

Ln [pij /(1 – pij)] = xij β + wjν + γj (1)

where Ln [pij /(1 – pij)] is the logit in which pij is the probability that woman i in community j delivered in a health facility; xij and wj are vectors of individual-level and community-level characteristics; β and ν are vectors of estimated parameter coefficients; and γj is an unexplained residual term at the community level. The variance of γj or the community effect measures the extent to which women within the same community are similar to women from other communities in regard to place of delivery. The community effect is expected to be reduced as additional variables at the community and individual levels are included in the fixed part of the model. We included one cluster-level variable—district with nine categories—in the fixed part of the model, and a PSU with 346 categories was included as the random component.12 The basic models estimated the unadjusted effects of access, poverty, and other covariates on the odds of having an institutional delivery. Two models were then constructed in order to estimate the adjusted or net effects of access, poverty, and other covariates on the odds of having an institutional delivery. Model 1 included a geographic access variable measured in kilometers. Model 2 included access to the highest level of care available within 10 km of the PSU. Both models also included poverty and other covariates in addition to the two clustering variables. We also ran these models separately for rural and urban areas, but show the results of Model 1 only. All statistical analysis was carried out by using STATA version 11.2. We used the “xtlogit” command in STATA for multilevel analysis.

RESULTS Descriptive Findings Profile of Respondents

The average age of women included in this study is 28.5 years. As shown in Table 1, a large majority of the women (84 percent) live in rural areas, had no education (77 percent), were 12 The sample of women is nested within PSUs, which in turn are nested within each district. We used district variables as fixed effects and PSU as random effect. The standard deviation for the random component (PSU effect) decreased from 1.146 to 1.017 after adding the district as the clustering variable in the fixed part of the model (not shown). The PSU effect further decreased to 0.694 after all the covariates were included in Model 2. All coefficients remained significantly higher than zero. Similarly, the value of intra-class correlation decreased from 0.285 to 0.128 but remained significantly higher than zero, indicating that variables included in these models do not explain all the variance between PSUs.

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Constraints of Distance and Poverty on Institutional Deliveries in Pakistan

TABLE 1  Crude effect of selected indicators of access and women’s characteristics on occurrence of institutional deliveries, nine districts in Pakistan Percent Place of delivery Crude distribution Variable of women Home Health facility odds ratio (N) 4,435 4,435 4,435 Total (percent) 100.0 68.3 31.7 Distance from PSU to nearest facility providing at least   normal delivery services 0.93*** Access to highest level of delivery care within 10 km of PSU No facility 21.1 79.5 20.5 1.00 Normal delivery services 28.2 72.9 27.1 1.57* Basic emergency obstetric care services 14.1 66.7 33.3 2.99*** Comprehensive emergency obstetric care services 36.6 59.0 41.0 3.93*** Household wealth Lowest 20.0 82.7 17.3 1.00 Lower middle 24.4 76.8 23.2 1.55*** Upper middle 27.0 69.0 31.0 2.36*** Highest 28.6 50.5 49.5 5.16*** Women’s education None 76.8 75.4 24.6 1.00 Primary 10.2 53.2 46.8 2.31*** Secondary or higher 12.9 38.4 61.6 4.87*** Watched television No 64.5 75.5 24.5 1.00 Yes 35.5 55.4 44.6 2.25*** Engaged in paid work No 65.6 65.2 34.8 1.00 Yes 34.4 74.2 25.8 0.70*** Experienced complications during last pregnancy No 41.3 73.6 26.4 1.00 Yes 58.7 64.6 35.4 1.65*** Residence Rural 83.7 72.5 27.5 1.00 Urban 16.3 47.1 52.9 4.14*** *Significant at p < .05; **p < .01; ***p < .001. PSU = Primary sampling unit. NOTE: Odds ratios were estimated after adjusting for clustering effect of PSUs and districts in multilevel models.

not engaged in paid work (66 percent), and did not watch television (65 percent). While the number of observations was equally distributed among the four wealth quartiles, the distribution of the weighted sample is slightly skewed toward upper quartiles. Access to Health Facilities Varies by Place of Residence and Economic Status

The average distance from a PSU to the nearest health facility that offers at least normal delivery care is 7 km, 21 percent of women have no access to a health facility within 10 km, and only 37 percent of women have access to a facility offering comprehensive emergency obstetric care within that radius. Access to health facilities increases with household wealth quartile. Poor women have little access to health facilities offering normal, basic, or comprehensive delivery care. The average distance to the nearest facility increases from 5 km among the wealthiest households to 10 km among the poorest households (Figure 1). Similarly, 38 percent of the poorest women have no access to a health facility within 10 km of the PSU of their residence, and only 26 percent have access to a facility offering comprehensive emergency obstetric care Studies in Family Planning 46(1)

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FIGURE 1 Average distance (km) to nearest health facility, by household wealth and location of primary sampling unit, nine districts in Pakistan 16

Kilometers

12

Level of household wealth 11.9

Lowest 10.3 8.2

8

6.6

Lower middle 7.1

5.8

5.8

Upper middle 5.0

Highest

4 1.6 1.3 1.6 1.1 0

Rural

Urban Residence

Total

within the same distance of the PSU. In comparison, 12 percent of women in the richest households have no access to a health facility within 10 km of the PSU, and 46 percent have access to a facility offering comprehensive care (Figure 2). Women in rural areas have poorer access to health facilities than those in urban areas. For example, the average distance from a PSU in rural areas to the nearest health facility offering at least normal delivery services is about 8 km, in comparison to a little over 1 km in urban areas (not shown). About 25 percent of women in rural areas have no access to a health facility within 10 km of the PSU, and 27 percent have access to a facility offering comprehensive emergency obstetric care services within 10 km. In comparison, all women in urban areas have FIGURE 2 Percent distribution of highest level of delivery care available within 10 km of primary sampling unit, by urban–rural residence and household wealth, nine districts in Pakistan 100

Percent

80 60 40 20 0

27.1 15.3

36.6

14.8 85.1

32.5

14.2

8.4 6.5

21.0

Rural

Urban

Total

11.5

Level of delivery care 38.7

30.3

Lowest

22.8

45.7

Comprehensive Basic

15.1

32.8 38.0

25.1

32.9

21.7

28.2

Residence

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25.5

15.9

15.0 26.9

Normal None

12.4

Lower Upper Highest middle middle Household wealth

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access to a health facility within 10 km of the PSU of their residence, and 85 percent have access to a health facility offering comprehensive emergency obstetric care services within 10 km (Figure 2). The negative relationship between household wealth and access is limited to rural areas. Women in urban areas have similar access to a health facility irrespective of their wealth status (Figure 1). Poor women in rural areas have to travel a longer distance to a health facility than wealthy women. Use of Health Facilities Varies by Geographic and Economic Access

The study found that about 68 percent of deliveries occurred at home, 7 percent at district and subdistrict hospitals, 22 percent at private hospitals and clinics, and 3 percent in other public-sector facilities. An overwhelming majority of deliveries (91 percent) were reported as normal. The remaining 9 percent required medical intervention (4 percent were reported as assisted vaginal deliveries and 5 percent as requiring a cesarean section). Most of the deliveries that required medical intervention (92 percent) occurred in a health facility. About 59 percent of women reported experiencing some complications during the last pregnancy. The nature and severity of these complications are unknown. However, 10 percent of women who experienced complications during pregnancy reported requiring medical intervention, compared with 7 percent who did not experience complications (data not shown). These comparisons indicate that it is difficult to predict the likelihood of a life-threatening complication well in advance. Some women develop complications during childbirth and require medical intervention at that time. Table 1 shows the results of bivariate analysis indicating crude effects of geographic access, poverty, and other covariates on place of delivery. Proximity to a facility that provides at least normal delivery care has a substantial effect on the odds of having an institutional delivery. The crude odds ratio shown in Table 1 indicates a 7 percent decline in the odds of institutional delivery for each increase of 1 km in distance to the nearest facility providing at least normal delivery services. The proportion of institutional deliveries increases from 21 percent among women with no access to a health facility within 10 km to 41 percent among women with access to a health facility offering comprehensive emergency obstetric care services, and from 17 percent to 50 percent with increasing household wealth status (Table 1). The proportion of women delivering in a health facility increases from 25 percent to 62 percent with improvements in their education. The proportion of institutional deliveries among those who watched television was higher (45 percent) than among those who did not (25 percent). However, only 26 percent of women who reported engaging in paid work delivered in a health facility, compared with 35 percent of those who did not. The level of institutional deliveries among women who experienced complications during the last pregnancy was higher (35 percent) than among those who did not (26 percent). About 28 percent of rural women had an institutional delivery in comparison with 53 percent of women in urban areas. The crude odds ratios measuring the effect of each of these factors on institutional delivery were all statistically significant (Table 1). However, not all women who reported complications during pregnancy were able to seek treatment. Approximately 29 percent cited poverty and 9 percent cited distance to a facility as reasons for not seeking treatment for pregnancy complications (data not shown). Studies in Family Planning 46(1)

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Multilevel Regression Analysis Use of Health Facilities for Deliveries Increases with Increase in Geographic Access

Table 2 shows the adjusted odds ratios according to two models indicating the net or independent effects of each of the two indicators of geographic access on the occurrence of institutional deliveries. Model 1 shows the net effect of distance to the nearest facility providing at TABLE 2  Adjusted effects of indicators of access and women’s characteristics on occurrence of institutional deliveries, according to four multilevel logistic models, nine districts in Pakistan Adjusted odds ratios Model 1 Model 2 Model 1A Model 1B Variable Total Total Rural Urban PSU Level Distance from PSU to nearest facility providing at least   normal delivery services 0.97** 0.98* 1.00 Access to highest level of delivery care within 10 km of PSU No facility 1.00 Normal delivery services 1.14 Basic emergency obstetric care services 1.79** Comprehensive emergency obstetric care services 1.72** Individual-level factors Household wealth Lowest 1.00 1.00 1.00 1.00 Lower middle 1.33* 1.36* 1.44* 0.96 Upper middle 1.80*** 1.83*** 1.85*** 1.51 Highest 3.13*** 3.19*** 3.28*** 2.50** Women’s education None 1.00 1.00 1.00 1.00 Primary 1.50** 1.50** 1.63** 1.29 Secondary or higher 2.62*** 2.61*** 2.18*** 4.52*** Experienced complications during last pregnancy No 1.00 1.00 1.00 1.00 Yes 1.72*** 1.72*** 1.70*** 1.77** Clustering Residence Rural 1.00 1.00 Urban 2.44*** 2.30*** District 1 1.00 1.00 1.00 1.00 2 1.08 0.83 1.09 2.00 3 1.30 1.01 1.25 2.14 4 1.41 1.07 0.95 4.61* 5 1.46 1.27 1.31 3.70 6 2.57*** 2.31*** 2.40*** — 7 2.14** 1.79* 2.23** 3.53 8 2.29** 1.79* 2.22** 3.98 ** *** 9 2.77 2.31 2.28** 7.40** Random component sigma PSU 0.710 0.694 0.694 0.653 rho 0.133 0.128 0.128 0.115 Regression statistics   Log likelihood –2,313.07 –2,310.69 –1,862.87 –440.51 Number of PSUs 346 346 269 77 Number of women 4,424 4,424 3,667 757 *Significant at p < .05; **p < .01; ***p < .001.  — = No urban women in this district. PSU = Primary sampling unit. sigma = standard deviation. Rho = intraclass correlation. NOTES: Access indicator included in Models 1, 1A, and 1B is different from that included in Model 2. Odds ratios are adjusted for variables shown in the table for each model. Other variables included in the models are women’s employment, exposure to television, age, and husband’s education. None of these variables showed any significant effect.

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least normal delivery care, and Model 2 shows the independent effect of access to the highest level of delivery care available within 10 km of the PSU of residence. The odds of institutional deliveries decrease by 3 percent with an increase of 1 km in distance, and this net effect of proximity to a facility providing at least normal delivery remains statistically significant after controlling for the effect of poverty, education, and other covariates13 (AOR = 0.97 Model 1). However, this net effect of proximity remained statistically significant only among women living in rural as opposed to urban areas (Models 1A and 1B). The odds of having an institutional delivery among women with access only to normal delivery care was not different statistically from those who did not have access to any facility within 10 km. However, having access to either basic (AOR = 1.79) or comprehensive (AOR = 1.72) emergency obstetric care increased women’s odds of institutional deliveries in comparison with those having no access to a health facility (Model 2). Access to at least basic emergency obstetric care not only increases the odds of institutional deliveries but is also important for receiving treatment for pregnancy-related complications. Poverty Poses Additional Risk Even after Controlling for Differences in Geographic Access

The results of the multilevel regression analysis confirm the effects observed in bivariate analyses. The adjusted odds ratios shown in Table 2 are smaller than the crude odds ratios shown in Table 1, but almost all adjusted odds ratios remain statistically significant in both models. The level of household wealth, education, urban residence, experience of complications, and urban residence significantly increase the odds of an institutional delivery in both models. For example, women from the wealthiest households are three times more likely to deliver in a health facility than those from the poorest households. This effect of household wealth is independent of the effects of geographic access and other covariates included in Models 1 and 2. Women having at least secondary-level education are 2.6 times more likely to deliver in a health facility that those having no education. Women in urban areas are 2.4 times more likely to deliver in a health facility than those in rural areas. Women who experience complications during pregnancy are also 1.7 times more likely to deliver in a health facility than those who do not experience any complications (Models 1 and 2). However, none of the other covariates—women’s employment, exposure to television, age, and husband’s education—shows any significant association with the likelihood of having an institutional delivery (data not shown). We estimated the net effects of geographic access and other covariates on the odds of institutional deliveries separately for rural and urban areas (Models 1A and 1B). Because 84 percent of women live in rural areas, the effects of these covariates in rural areas are quite similar to those observed for the total sample. However, geographic distance in urban areas has no effect on the likelihood of an institutional delivery. The effect of both household wealth and women’s education is limited. Only the most educated women and those living in the wealthiest households are more likely than other women to have an institutional delivery. 13 Similar results were obtained when we used log transformation of geographic distance. The odds of institutional deliveries were estimated to decrease by 11 percent with doubling of distance from the PSU of residence to a health facility providing at least normal delivery services. This effect was net of other covariates (data not shown).

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Study Limitations This study has several limitations. First, we estimated geographic access in terms of the distance to health facilities available within the district. However, some women, especially those in border areas, may go to a facility outside the district. The lack of this information may bias the effect of geographic access on institutional delivery. Second, we do not have information on the availability and cost of transportation and time taken to reach a health facility. Third, we do not have direct information on the quality of care provided at these facilities. In its absence we divided facilities into three groups depending on ability to provide services for normal delivery, basic emergency obstetric care, and comprehensive emergency obstetric care. However, we do not know whether these services were available 24/7. Fourth, we do not have a direct measure of women’s mobility and autonomy, which may restrict their freedom to go outside the home for childbirth. Instead, we have used their education as a proxy for their greater mobility and autonomy. Fifth, we do not have information on the nature and severity of complications experienced during pregnancy. Future studies need to collect direct measures of these factors to better understand the effects of access, poverty, and other factors on institutional deliveries.

DISCUSSION Our analysis focused on the links between geographic access and economic status of households and the likelihood of women having an institutional delivery. Linking a household survey with a facility survey in a geographic information system (GIS) allowed us first to calculate the exact road distance between a health facility and a PSU, which in turn allowed us to more accurately estimate the effect of geographic proximity to obstetric health facilities on the use of those facilities. Our results indicate that poor women in Pakistan have limited access to obstetric care within 10 km of the PSU of their residence. We have demonstrated that each of the two indicators of geographic access significantly decreases the odds of an institutional delivery. For example, an increase of 1 km in the distance to a facility is associated with a net decrease of 3 percent in the odds of an institutional delivery. This effect of proximity is independent of the effects of household wealth and other individual characteristics. Moreover, having access to a facility providing basic or emergency obstetric care within 10 km has a strong effect on the likelihood of an institutional delivery. This result makes sense because the treatment of obstetric complications would require access to at least this level of care. Each of the covariates—household wealth, women’s education, and experience of complications during pregnancy—independently increases the odds of an institutional delivery. Because 84 percent of women live in rural areas, the results shown for the total sample apply equally to those living in rural areas. However, geographic distance has no effect on the odds of institutional deliveries in urban areas. Moreover, the effects of household wealth and women’s education in urban areas are limited to the highest categories of wealth and education. Perhaps once the disadvantage of lack of geographic proximity is eliminated, the effects of economic access and other cultural factors also diminish. Using women’s education as a proxy indicator of cultural barriers such as restricted mobility and limited empowerment confirms that women having no education continue to be disadvantaged even after adjusting for the effects of restricted geographic and economic access March 2015

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to health facilities. This supports the idea that women who are disadvantaged economically and geographically are also likely to have the disadvantages of limited mobility, autonomy, and possibly information sources to make critical decisions about delivery care. Our analysis shows that experiencing pregnancy complications increases the odds of an institutional delivery after controlling for all other covariates. However, we also note that the vast majority (about 68 percent) of women who experienced complications during pregnancy did not deliver in a health facility, but survived to be included in this study. Moreover, the percent of women who reported having had an assisted vaginal delivery or a cesarean section does not differ substantially by whether or not they reported having complications during pregnancy, indicating that the likelihood of life-threatening complications is difficult to predict well in advance of childbirth. In the absence of information about women who died during childbirth, we have used the occurrence of institutional delivery as a proxy indicator of maternal mortality because of its presumed negative relationship with the MMR. While reducing the MMR does not require that all women deliver in a health facility, about 15 percent of women who experience obstetric complications will require medical interventions. The probability of a maternal death even among this 15 percent, at the current level of MMR of 276 maternal deaths per 100,000 births, is about 1.84 percent [= (276÷15,000) × 100]. Data presented elsewhere have shown that the case-fatality ratio, or maternal deaths as percent of complications, among women delivering in facilities upgraded during the PAIMAN project, has decreased from 0.93 percent in 2007 to 0.58 percent in 2009 (Jain et al. 2013). Clearly, the case-fatality ratio among women delivering outside these and similar public or private facilities must be much higher than for those who delivered in these upgraded facilities. These comparisons also imply that the MMR can be reduced substantially if a majority of the 15 percent of women requiring medical interventions are able to reach an appropriate health facility in time to be treated. Hence a rise in the percent of institutional deliveries may not always reduce the MMR. The MMR is likely to decrease only if women who experience life-threatening complications around the time of childbirth are able to reach an appropriate health facility and receive timely, good-quality treatment. However, the likelihood of having a complication requiring medical intervention is difficult to predict well in advance. Some women develop these complications during childbirth, and it is essential to find ways to transport them to the right facility in time to be saved. The effect of poverty appears to be stronger than that of geographic proximity, and the effect of geographic proximity is limited in rural areas. Poor women in rural Pakistan are at a higher risk of dying during childbirth because they continue to suffer from the triple disadvantage of limited geographic access, inability to pay for transportation and care, and restricted mobility and autonomy. Minimizing the effect of these disadvantages to save poor women’s lives is essential if Pakistan is to achieve further reductions in maternal mortality. One clear policy option is to focus on continued fertility decline, which would contribute to a further reduction in the MMR (Jain 2011). Nevertheless, a major reduction in the MMR would require making motherhood safer. The strategies to reduce the MMR by making motherhood safer include improvements in availability and quality of comprehensive emergency obstetric services, placement of skilled birth attendants or midwives in rural communities, and strengthening of referral systems between rural communities and health facilities that provide comprehensive emergency obstetric care services. Earlier interventions have used one of these Studies in Family Planning 46(1)

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strategies at a time. However, improvements in any one of these three strategies is unlikely to be sufficient. For example, even if trained midwives are deployed in all rural communities, this strategy alone is unlikely to reduce the MMR substantially, because trained midwives cannot perform cesarean sections to manage life-threatening complications when they arise at home. Whereas trained midwives can perform normal deliveries and also reduce the delay in referring women to a health facility that provides comprehensive emergency obstetric care, women still have to reach these facilities to be saved. Furthermore, women have to be treated successfully once they arrive. A reduction in the MMR would therefore depend on an increase in the proportion of women with pregnancy-related complications who are able to reach comprehensive emergency obstetric care facilities in time and survive once they arrive. The government needs to take clear policy steps to ensure access to basic and comprehensive emergency obstetric care during and around the time of childbirth for every woman, but especially for those who experience life-threatening complications. In particular, the focus of resources and efforts has to be directed at those most likely to need this care—the poor and rural women of Pakistan. We cannot become complacent with the rise in facility-level deliveries, because it masks these long-standing differentials in access.

CONCLUSION Poor rural women in Pakistan continue to suffer from the triple disadvantage of limited geographic access, inability to pay for transportation and care, and restricted mobility and autonomy reflected by no or little education. These disadvantages can be minimized by upgrading existing facilities at district and subdistrict levels to provide comprehensive emergency care and by expediting the transport of poor rural women to these facilities when life-threatening childbirth complications occur. The income and residential inequities in institutional deliveries and their potential impact on the MMR can be minimized by reducing the distance between place of residence and health facilities providing quality comprehensive emergency obstetric care. This can be done by opening new facilities that provide comprehensive emergency obstetric care, which would be an expensive proposition. Moreover, it may be difficult to maintain the quality at these facilities. Alternatively, the focus could be first on improving the quality of comprehensive emergency obstetric care services available at the existing district- and subdistrict-level hospitals. There is growing evidence that women have either been declared dead on arrival, when they actually did arrive alive, or have been referred elsewhere much farther away, which greatly increases their risk of dying (Mir et al. 2014). These hospitals need to be fully equipped and staffed. There is also a need to improve quality of care—having trained staff present 24/7, improving behavior toward poor clients, and improving communication skills—in order to treat poor women in a timely fashion once they reach these facilities. These improvements would assure women and their families that if they can reach these facilities, they will receive roundthe-clock service, including essential care such as blood transfusions and anesthesia, without delay. As mentioned elsewhere, a woman-level data-collection system must be set up at these facilities to monitor the progress made in attracting women with complications to these facilities and monitoring the effect of quality of care on maternal mortality (Jain et al. 2013). March 2015

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These improvements at the facility level, while essential, will not eliminate the problem of limited economic access to care among poor rural women. For this, the government has to find better ways to transport poor rural women with obstetric complications from their homes to facilities having comprehensive emergency obstetric care in time to be saved. In addition, ways must be found to reduce unnecessary delays and to better identify major obstetric complications when they occur at home. This can be done by training community-level workers and/or by placing additional SBAs in communities and providing them with a list of facilities equipped to provide emergency obstetric care. Communities also need to form partnerships with local transportation owners—a strategy adopted successfully in a safe motherhood project in Pakistan (Rashida and Miller 2006) and in North Western Nigeria (Shehu, Ikeh, and Kuna 1997) that contributed to a reduction in maternal mortality. Delays in reaching these fully functional facilities should be reduced by linking rural communities and lower-level health facilities to higher-level facilities through the use of public/private ambulances.

REFERENCES Campbell, Oona M.R. and Wendy J. Graham. 2006. “Strategies for reducing maternal mortality: Getting on with what works,” The Lancet 368(9543): 1284–1299. Chowdhury, Mahbub Elahii et al. 2006. ”Equity in use of home-based or facility-based skilled obstetric care in rural Bangladesh: An observational study,” The Lancet 367(9507): 327–332. Fikree, Fariyal F., Sadiqua N. Jafarey, and Nazo Kureshy. 1999. “Final report: Assessing the effectiveness of a safe motherhood information, education and communication counselling strategy.” Karachi, Pakistan: Aga Khan University, Department of Community Health Sciences. Filmer, Deon and Lant Pritchett. 1999. “The effect of household wealth on educational attainment: Evidence from 35 countries,” Population and Development Review 25(1): 85–120. Gabrysch, Sabine and Oona M.R. Campbell. 2009. “Still too far to walk: Literature review of the determinants of delivery service use,” BMC Pregnancy Childbirth 9: 34. www.biomedcentral.com/1471-2393/9/34. Gabrysch, Sabine, Simon Cousens Jonathan Cox, and Oona M.R. Cambell. 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 Med 8(1): e1000394. Gage, Anastasia J. and Marie Guirlène Calixte. 2006. “Effects of the physical accessibility of maternal health services on their use in rural Haiti,” Population Studies 60(3): 271–288. Heard, Nathan J., Ulla Larsen, and Dairiku Hozumi. 2004. “Investigating access to reproductive health services using GIS: Proximity to services and the use of modern contraceptives in Malawi,” African Journal of Reproductive Health 8(2): 164–179. Hogan, Margaret C., Kyle J. Foreman, Mohsen Naghavi, et al. 2010. “Maternal mortality for 181 countries, 1980-2008: A systematic analysis of progress towards Millennium Development Goal 5,” The Lancet 375(9726): 1609–1623. Hounton, Sennen, Glyn Chapman, and Joris Menten, et al. 2008. “Accessibility and utilisation of delivery care within a skilled care initiative in rural Burkina Faso,” Tropical Medicine & International Health 13(Suppl. 1): 44–52. Jafarey, Sadiqua N., Imtiaz Kamal, Asma Fozia Qureshi, and Fariyal Fikree. 2008. ”Safe motherhood in Pakistan,” International Journal of Gynecology and Obstetrics 102(2): 179–185. Jafarey, Sadiqua N. and R. Korejo. 1993. “Mothers brought dead: An enquiry into causes of delay,” Social Science & Medicine 36(3): 371–372. Jain, Anrudh K. 2011. “Measuring the effect of fertility decline on the maternal mortality ratio,” Studies in Family Planning 42(4): 247–260.

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ACKNOWLEDGMENTS The PAIMAN project was funded by the United States Agency for Development (USAID) through a cooperative agreement with the JSI Research and Training Institute. The authors would like to acknowledge USAID and JSI for the data used in this analysis. Rehan Niazi and Tauqeer Izhar provided computing support for the extensive analysis and particularly the linking of GIS facility and household-level data. Ali Mir provided valuable comments on earlier versions of the article. The authors take full responsibility for the analysis and the interpretation of results.

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