Poverty and Skilled Attendance at Delivery: A Study of Six. Developing Countries. MPH Capstone Paper. Lauralee Morris, MD, CCFP

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1 Poverty and Skilled Attendance at Delivery: A Study of Six Developing Countries MPH Capstone Paper Lauralee Morris, MD, CCFP Faculty Mentor: Michael J. McQuestion, Phd, MPH

2 ABSTRACT Objective: To examine several indicators of delivery care across a range of developing countries with a focus on the relative importance of poverty as a predictor of skilled attendance. Methods: This study is a secondary analysis of delivery care data from Demographic and Health Surveys (DHS) in Bangladesh, Bolivia, Egypt, Haiti, Indonesia and Zimbabwe. Several indicators of delivery care were examined with an emphasis on the proxy indicator for skilled attendance, Delivery with a Health Professional (DHP). Other indicators of delivery care describing the place of delivery (Facility DHP, Domiciliary DHP) and the type of attendant (doctor, nurse/midwife) were also calculated. Using bivariate and multivariate techniques, the following six predictors were examined for their association with DHP: maternal age, birth order, number of antenatal visits, maternal education, urban/rural residence, and wealth index. Findings: There was a wide range of DHP among the six countries (11.6% in Bangladesh, 72.7% in Zimbabwe). Facility DHP was greater than Domiciliary DHP in every country except Indonesia; only Indonesia and Zimbabwe had a higher proportion of deliveries attended by a nurse/midwife than by a doctor. In every country except Zimbabwe, poverty was the most important predictor of DHP. There were large disparities in the odds of delivering with a health professional for the richest women compared to the poorest women in multivariate analysis (adjusted OR=26.86 Bolivia, adjusted OR=3.50 Zimbabwe). Other predictors in the model explained a large part of the crude association between poverty and DHP, however the independent effect of poverty on DHP was large and statistically significant (P<0.001). Conclusion: In five out of six countries studied, poverty was the most important predictor of delivery care after controlling for maternal age, birth order, number of antenatal visits, maternal education, and urban/rural residence. Lauralee Morris 2

3 The estimated number of maternal deaths worldwide for the year 2000 was 529, The overwhelming majority of these deaths occurred in poor regions of the world with Africa and Asia each contributing almost half of all maternal deaths (251,000 and 253,000 respectively). 1 Sub-Saharan Africa alone accounted for 47% and South Central Asia contributed another 39%. 1 A comparison of maternal mortality indicators further demonstrates the inequalities between rich and poor countries; a woman in Zimbabwe for example has a 1 in 16- lifetime risk of suffering a maternal death, whereas a woman living in Canada faces only a 1 in 2,800 risk. 1 The maternal mortality ratio (MMR), the most commonly used indicator of maternal death, is 46 times greater in sub-saharan Africa than in developed countries. 1 In fact, of all the human development indicators, the MMR shows the greatest discrepancy between rich and poor countries. In addition to large disparities in maternal mortality between regions and countries, there are also large poor-rich differences within countries. Using data from the DHS, Graham and colleagues demonstrated that the poorest 20% of Indonesian women have a four fold increased risk of maternal death when compared with the richest 20% of women. 2 The most commonly used process indicator for maternal mortality, the percent of deliveries attended by a health professional (DHP), demonstrates even larger gaps between rich and poor. In a study of 44 countries, Gwatkin and colleagues demonstrated huge disparities in DHP between the richest quintile of women in a country and the poorest. 3 These sub-national differences were much larger than the cross-national differences in overall averages. In 2000, The United Nations General Assembly included both poverty and maternal health in its Millennium Development Goals (MDG) underscoring two priority areas for international public health. 4 To monitor progress towards the goal of improving maternal health, Lauralee Morris 3

4 two key indicators were recommended: the MMR and the percentage of women who receive skilled attendance at delivery. Skilled attendance is advocated as the most important intervention for the reduction of maternal mortality. Graham estimates that approximately 16-33% of all maternal deaths could be prevented by skilled attendance. 23 Although disparities within countries and between countries have highlighted the problem of poverty and maternal health, little work has been done to examine the relative importance of poverty as a predictor of skilled attendance. The aims of this study are 1) to examine several indicators of delivery care with a focus on the process indicator DHP and 2) to examine the association between the various predictors of delivery care and DHP, particularly the association between poverty and DHP. METHODS Sources of Data The DHS has conducted over 130 household surveys in about 70 countries. 5 Surveys are population based and nationally representative of the general population. Procedures and survey questions have been well tested and standardized to allow comparisons across countries and over time. Currently, the DHS is the best available source of international data on safe motherhood. 12 The countries included in this study were selected for geographic diversity, availability of data, and range of MMR (Tables 1 and 2). The countries represent different models of delivery care based on the conceptual framework developed by M. Koblinsky and colleagues. 6 Datasets for the six countries were downloaded from the Demographic and Health Survey website. 7 Each dataset was manipulated to produce a file that could be used for birth-based Lauralee Morris 4

5 analysis (see Indicators of Delivery Care). Outcome variables and some predictor variables were constructed using standard variables coded by DHS 8. The six selected countries are Bangladesh (1999/2000), Bolivia (1998), Egypt (2000), Haiti (2000), Indonesia (1997), and Zimbabwe (1999). For each country, the most recent survey with publicly available data and a wealth index file was selected. Ever married women in Egypt and Indonesia were eligible for interview compared to the remaining countries where all women were eligible. DHS collects household and individual level data using a series of standardized questionnaires. The current study uses data from the Household Questionnaire 9 and the Woman s Questionnaire 10 ; both were examined for questions pertinent to this study. The Household Questionnaire collects background data on all household members, as well as characteristics of the household itself. It is used to identify women who are eligible for the individual Woman s Questionnaire. In addition to questions on fertility, family planning, child health, and HIV/AIDS, The Woman s Questionnaire also asks questions about women s utilization of maternal health services. With relevance to the current study, women were asked to identify the type of person/people who assisted them at all deliveries resulting in a live birth within the preceding 5 years. The women s responses were coded using seven categories of attendant: doctor, nurse/midwife, auxiliary midwife, Traditional Birth Attendant (TBA), relative/friend, other, no one. Interviewers probed for all persons who assisted at delivery. Women were also asked to identify the place of delivery. The response categories for place of delivery are: home (the woman s home or another s home), public sector (government hospital, health centre, health post or other public facility), private medical sector (private hospital/clinic, other private medical facility), other. Coding categories for place of delivery are Lauralee Morris 5

6 developed locally but the broad categories of home, public sector, private medical sector, or other, are maintained. Rather than capturing household wealth using data on income or consumption, DHS has developed a method of measuring household wealth using an assets approach. 11 For selected surveys, the head of the household is asked about ownership of a wide variety of consumer assets and amenities. Questions commonly asked in DHS surveys include type of flooring, water supply, sanitation facilities, as well as ownership of such items as a television, fridge and motor vehicle. Using this data each household is allocated into a poverty quintile. Thus, health indicators can be tracked for each quintile. Indicators of Delivery Care Delivery with a Health Professional (DHP) is the primary outcome measure. The numerator of DHP is the number of live births reported by a representative sample of women that were attended by a health professional in the 5 years preceding the survey; the denominator of DHP is the total number of live births in the same 5-year period. This leads to a birth-based estimate of DHP where all live births experienced by a sample of women are counted; thus, several births to the same woman may be included. 12 This is in contrast to a woman-based estimate where only one live birth per woman is included in the sample. A birth-based estimate is representative of all live births and provides a larger sample size than a woman-based estimate; however, a birth-based estimate may over-represent women who have multiple deliveries in the 5-year period. 12 DHS also employs birth-based estimates. A health professional is defined as a doctor, nurse or midwife. Where several attendants are reported, the type of attendant is attributed to the most qualified attendant. A doctor, nurse or midwife includes only medically trained and licensed individuals. TBA s, either trained or Lauralee Morris 6

7 untrained, are not included in this definition. Thus, DHP is a dichotomous variable, grouping doctors, nurses and midwives together as professional attendants and others as non-professional attendants. Additional indicators that describe the specific elements of skilled attendance in each country were also calculated. These indicators are 1) the type of professional in attendance (doctor versus nurse/midwife) expressed as a proportion of the total number of live births and 2) composite indicators that bring together the attendant and the place of delivery. These composite indicators are Facility DHP and Domiciliary DHP. 12 Facility DHP is the proportion of live births delivered by a health professional in a health facility expressed as a percentage of the total number of live births. Similarly, Domiciliary DHP is the proportion of live births delivered by a health professional in a home, as a percentage of the total number of live births. In all cases the reference period for the total number of live births is the five years preceding the survey. Background Characteristics Associated with DHP In addition to the wealth index, five other independent variables were chosen based on their theoretical association with DHP. The variables are woman s age at delivery, birth order, number of antenatal care visits, maternal education, place of residence (urban/rural) and wealth index. Many factors can affect a woman s use of delivery services. The three-delay model is a conceptual framework that focuses on the factors that influence a woman s decision to seek delivery care. 13 Some of the factors not measured by the variables chosen for this study are a woman s cultural and religious beliefs, decision-making power within her family and community, obstetrical complications in the current and previous pregnancies, previous experience with the health care system and the availability and quality of delivery care services. Although the choice of independent variables was limited by their availability for the six Lauralee Morris 7

8 countries in this study, all the selected variables have previously been shown to be predictors of DHP. 12 Age Although age less than 20 and older than 35 is associated with increased maternal mortality, the pattern of association between age and DHP is expected to differ. Previous studies using multivariate techniques show that older women are more likely than younger women to use delivery services. 12,14 Elo has suggested a learning hypothesis to explain the association between age and DHP: women gain knowledge and experience with respect to health care services as they age and this informs their use of delivery care services. 14 In order to capture the effects of age, women were classified into 5-year intervals based on their age at delivery. Birth order There is a strong correlation between birth order and the utilization of maternal health services. First births are expected to receive the most delivery care with each successive birth receiving progressively less care. 12,15 The most commonly proposed explanation for birthorder effects is that women place less importance on delivery care services for higher parity pregnancies. 15 An alternative explanation is that having children in the house affects a woman s ability to access delivery care. 14 The variable for birth order was categorized 1, 2, 3, and 4+. Since DHS only collects data on live births, birth order is based on live births. Number of Antenatal Care Visits DHP has been found to increase with the number of antenatal visits. Curtis et al hypothesize two explanations for this observed relationship 1) the use of antenatal care reflects a woman s knowledge of delivery care and access to delivery services and 2) women who receive antenatal care will be made aware of the available services. 12 The variable was categorized into none, 1-3, and 4+ antenatal visits. Maternal Education Many health behaviors are highly correlated with maternal education including the use of delivery care services. Various causal pathways have been proposed to Lauralee Morris 8

9 explain the association between maternal education and health services use. It has been suggested that better educated women have more knowledge of modern health services, are better able to communicate with health care providers, have more decision making power within families, and finally, that their beliefs about disease causation influence their health seeking behavior. 14 The education variable in this study is expected to be strongly and positively associated with DHP. The variable was categorized into no education, primary, secondary and higher. Place of Residence Urban women receive more delivery care than do their rural counterparts. 16 Possible explanations for this observation include reduced access to delivery care in rural areas and differences in attitudes to medical services between urban women and rural women. Wealth Index Several studies using income and maternal education in multivariate analysis have shown an independent effect of wealth on the utilization of maternal health services. These same studies have found maternal education to be more important than wealth as a predictor of delivery care. 17,16,18 The DHS wealth index is only available for surveys done since the mid s. It is a relative measure of wealth used to compare health indicators by wealth quintile within countries; it cannot however be used for comparisons between countries. In this study, the wealth index is expected to have a strong and independent association with DHP. Statistical Analysis General descriptive analysis was performed to compare the characteristics of the individual women in each survey. Five indicators of delivery care were tabulated for each country: DHP, Facility DHP, Domiciliary DHP, the percent of doctors attending deliveries, the percent of nurse/midwives attending deliveries. Bivariate analysis of predictors versus DHP was performed and the results are shown graphically in Figures 1 through 6. Bivariate and Lauralee Morris 9

10 multivariate logistic regression techniques were used to determine crude and adjusted odds ratios and 95% confidence intervals (CI) for the association between the independent variables and DHP. The association between wealth index and DHP was examined for confounding and interaction; an alteration of greater than 10 % of the crude estimate between the crude estimate and the adjusted estimate was considered to be evidence of confounding. 19 All distributions, tabulations and regression models were weighted to control for the different complex sample designs used in each survey. Statistical analyses were performed using Stata SE for large datasets, version 8.2. Guidelines for the secondary analysis of DHS data was provided by The Safe Strategy Development Tool developed by the International Partnership for Skilled Attendance for Everyone (SAFE). 19 RESULTS Background Characteristics of women In every country except Bangladesh, the largest proportion of live births was to women aged 20-24; woman aged contributed a similar but smaller proportion (Table 3). In Bangladesh, very young women contributed the most live births with almost a third of all infants born to woman less than 19 years of age. Bangladesh was also the only country where women age were eligible for interview; in all the other countries, the age range was years (Table 1). This could partially explain the preponderance of deliveries in this age group but it is unlikely that there were enough deliveries in women younger than 15 to entirely explain the distribution. Lauralee Morris 10

11 Indonesia and Zimbabwe had the largest proportion of first order births, 32.8% for both countries. Haiti and Bolivia had the largest proportion of deliveries that were birth order 4 or greater (45.3% and 41.5 % respectively). Woman in Zimbabwe received the most antenatal care with 82.8% of all live births receiving four or more antenatal visits. In contrast, almost half of pregnant woman in Bangladesh and Egypt received no antenatal care. With the exception of Haiti and Indonesia, women tended to receive either no antenatal care or four or more visits. 47.4% of all live births in Bangladesh were to women with no education; Egypt and Haiti had similarly large proportions of deliveries to women with no education. Women delivering in Zimbabwe had the highest levels of education. Rural woman had the majority of live births in every country except Bolivia. In that country, only 43.5% of deliveries were to rural women; in contrast, 83.6% of deliveries in Bangladesh were to rural women. Delivery with a Health Professional and Composite Indicators The percent of deliveries with a health professional ranges widely from a minimum of 11.6% in Bangladesh to a maximum of 72.7% in Zimbabwe (Table 4). Haiti also has a low DHP at 22.0%. Indonesia, Bolivia and Egypt have similar levels of DHP at 49.1%, 55.6% and 60.9% respectively. The absolute difference between the country with the highest DHP (Zimbabwe) and the country with the lowest (Bangladesh) is 61.1 percentage points. In every country except Indonesia, Facility DHP is higher than Domiciliary DHP. Thus, in every country except Indonesia more professional deliveries are done in a health facility than in a home. In fact, in Bolivia, Haiti and Zimbabwe, nearly all professional deliveries take place Lauralee Morris 11

12 in a health facility. In Indonesia, approximately 58% of all professional deliveries occur in the home (calculation not shown). In Bangladesh, Bolivia, Egypt and Haiti, doctors perform most professional deliveries. Indeed, in Bolivia and Egypt, delivery with a doctor constitutes the great majority of professional delivery. Only in Indonesia and Zimbabwe does the number of nurse/midwives attending deliveries exceed the number of physicians; in both countries nurse/midwifes attend about six times as many deliveries as physicians. Bivariate Analysis The results of bivariate analysis are shown in Figures 1 through 6. There appears to be no consistent relationship between age and DHP across all countries, however four of the countries (Bolivia, Egypt, Haiti, Indonesia) show declining use of DHP in older age categories. As expected, DHP is negatively associated with birth order. In every country, first order births have the largest proportion of DHP with progressive decreases in DHP with each successive birth. For all birth orders, women in Zimbabwe are more likely to deliver with a health professional than are women in other countries; women in Bangladesh, on the other hand, are the least likely to have professional attendance across all birth orders. In Bangladesh, only 5.6% of deliveries of birth order 4 or greater are delivered by a health professional. Haiti has the largest relative difference (ratio 3.46) between the lowest birth order group and the highest, with 37 % of first order births attended by a health professional and only 11% of births of order 4 or greater. In contrast, Zimbabwe has the greatest equality between birth order groups with first order births only 1.5 times more likely to receive DHP than birth order 4 or greater. Percent DHP is strongly and positively associated with the number of antenatal visits and maternal education. Women who received four or more antenatal visits were 2-6 times more Lauralee Morris 12

13 likely to deliver with a health professional than women who received no antenatal care. Similarly, women with a secondary education or higher were 2-7 times more likely to deliver with a health professional than women with no education. The greatest relative difference in DHP between the lowest and the highest education groups was in Haiti (ratio 6.90); the smallest relative differences were in Zimbabwe (ratio 1.98) and Egypt (ratio 2.03). The proportion of deliveries receiving DHP is larger in urban areas than in rural areas in every country. The largest inequality between urban and rural women is in Haiti where 49 % of urban women, but only 9 % of rural women receive DHP (ratio 5.36). Zimbabwe has the most equality between urban and rural groups with 90 % of urban women receiving DHP and 64 % of rural women (ratio 1.40). The most disadvantaged group is rural women in Bangladesh where only 7% of deliveries receive DHP. There are large differences within countries in the use of DHP by poverty quintile. Bolivia has the largest absolute difference in DHP between the poorest women and the richest women, with only 18 % of deliveries to the poorest women attended by a health professional compared to 98% of deliveries to Bolivia s richest women (absolute difference 80 percentage points); the smallest absolute difference exists in Zimbabwe (36 percentage points). The largest relative difference is in Haiti where the richest women are 22.3 times more likely to deliver with a health professional than are the poorest women (richest quintile DHP 67%, poorest quintile DHP 3%). Deliveries to women in the poorest quintiles in Haiti and Bangladesh are the most disadvantaged with only 3% receiving DHP. Poor rich disparities do not follow a linear gradient in every country. The richest quintiles in Bangladesh and Haiti have disproportionately higher levels of DHP when compared to the poorer quintiles. Lauralee Morris 13

14 Multivariate Analysis Multivariate analysis was done in order to determine the relative importance of the various predictors of DHP (Table 5a and 5b). The principle focus of the multivariate analysis is the impact of poverty on the use of DHP. In every country except Zimbabwe, the wealth index has the largest effect on DHP when compared with other predictors. The adjusted OR is highest in Bolivia (adjusted OR=26.86, P<0.001) and lowest in Zimbabwe (adjusted OR=3.50, P<0.001). This means that after controlling for age, birth order, antenatal care, maternal education and urban/rural residence, the odds of delivering with a health professional in Bolivia are 26 times greater for women in the richest quintile compared to women in the poorest quintile. In contrast, the odds of delivering with a health professional in Zimbabwe are only 3.5 times greater for the richest women compared to the poorest women. In Zimbabwe the largest effects are seen for maternal education; the odds of delivering with a health professional are 3.94 times greater for women with at least a post secondary education compared to women with no education In almost every country, wealth and DHP are associated across all quintiles in multivariate analysis; each successive quintile is significantly more likely to experiences DHP than the preceding one. The only exceptions to this occur in Bangladesh and Zimbabwe. In Bangladesh the second and third quintiles have the same odds of DHP as the poorest quintile (second quintile P=0.40, third quintile P=0.17); similarly in Zimbabwe the second quintile has the same odds as the poorest quintile (P=0.21). In every country and every wealth quintile, adjusting for the other predictors in the model results in an adjusted OR that is smaller in magnitude than the crude OR. In Bolivia, Egypt and Indonesia, all crude odds ratios are statistically significant at the level and Lauralee Morris 14

15 remain so after adjustment. In Bangladesh, a statistically significant crude OR for the third quintile becomes insignificant after adjustment. In Haiti and Zimbabwe all significant odds ratios remain significant after adjustment, however the level of significance changes for some quintiles. Thus, other predictors of DHP explain a large part of the crude association between wealth and DHP, however the independent effect of poverty on DHP remains large and highly significant in most cases (P<0.001). Further analysis (not shown) reveals that the main confounders of the association between wealth and DHP are maternal education, urban/rural residence and antenatal care. Maternal education consistently confounds the relationship in all countries and at all levels of the wealth hierarchy, thus part of the crude association between poverty and DHP is explained by the fact that wealthier women are better educated. Urban/rural residence, which was expected to have the same effect on the association between poverty and DHP as maternal education, only acts as a confounder in the two richest quintiles in five out of six countries. The exception to this is Bolivia where urban/rural residence acts as a confounder at all levels of wealth. In the remaining five countries, urban/rural residence is not a confounder in the lowest three quintiles, meaning that place of residence does not influence the odds of DHP for poor women in these countries The pattern of confounding for the number of antenatal care visits varies considerably. The confounding effects of the number of antenatal visits is mixed across countries. Antenatal care is a mediator of the association between poverty and DHP rather than a true confounder; it does not offer an alternative explanation for the observed relationship but does act as an intermediary that influences the association between the two variables. 12 Evidence of an interaction between the wealth index and the main confounder in the model, maternal Lauralee Morris 15

16 education was sought. The interaction was only significant in Indonesia (P<0.001) and therefore it was not included in the final model. Besides the confounding effect of various predictors on the main association of interest, poverty versus DHP, each variable also has its own association with the main outcome variable. There is no consistent pattern of association between age and DHP in the crude odds ratios for age, however there is a trend for older women to have lower odds of DHP in some countries. Unlike the other predictors in the model, adjustment generally strengthens the association between age and DHP and reverses negative trends in the crude estimates. Thus, after controlling for birth order, number of antenatal care visits, maternal education, urban/rural residence, and wealth index, the odds of DHP for women aged forty and older are 2-4 times higher than the odds for women aged 19 and younger. In general, the odds of DHP are lowest for the youngest women with a progressive increase in the odds with increasing age category. Further analysis (not shown) shows that birth order largely explains the crude relationship between age and DHP. Older women will generally have higher order births and it is for this reason that they receive less DHP rather than because of their age. In all countries, birth order has a negative association with DHP in both crude and adjusted models. After adjustment, the crude OR for birth order decreases in size but remains significant. Antenatal care and maternal education and are strongly and positively associated with DHP in both crude and adjusted estimates and generally show a consistent pattern across all six countries. In general, crude odds ratios decrease in magnitude after adjustment but remain statistically significant. In the multivariate model, the odds of DHP are 2-6 times greater for women with secondary education or higher than for women who have no education; similarly, Lauralee Morris 16

17 women who receive at least four antenatal visits have 2-12 times the odds of DHP as women who have no antenatal care. In every country, women living in urban areas receive more DHP than women in rural areas with an adjusted Odds Ratios of between 1.45 and The greatest disparity exists in Indonesia, where urban women receive 2.33 times more DHP than rural women. Urban women in Bolivia, Egypt and Haiti all receive about 1.5 times more DHP than their rural counterparts. DISCUSSION Almost 20 years have passed since the article, Where is the M in MCH? first drew international attention to the problem of maternal mortality. 20 Two years after the publication of this seminal article, the Safe Motherhood Initiative was launched at the 1987 Safe Motherhood Conference in Nairobi, Kenya. Since then numerous international conferences have included safe motherhood in their purview, most notably the World Summit for Children in 1990, the International Conference on Population in 1994, the Fourth World Conference on Women in Beijing in 1995 and the 1997 Technical Consultation on Safe Motherhood in Colombo, Sri Lanka. 1 Recently, the United Nations has included maternal health in its Millennium Declaration. 4 Despite the international attention it has received in the last 20 years, the problem of high maternal mortality persists in much of the developing world. The two most important recommendations to come out of the 1987 Nairobi conference focused on antenatal risk screening and the use of TBA s for low risk deliveries. By the time of the 1997 conference in Colombo, it appeared that neither of these strategies was effective in reducing the number of maternal deaths. 21, 22 Attention shifted to maternal mortality as an issue of health infrastructure with the key recommendation of ensuring skilled attendance at every Lauralee Morris 17

18 delivery. Despite confusion over the definition of skilled attendance and debate over its postulated link with maternal mortality, ensuring skilled attendance at every delivery is now promoted as the most important intervention in the effort to reduce high rates of maternal death. 12 Skilled attendance or the process through which a woman is provided with adequate care during labor, delivery and the early post-partum period 23 is now the priority of many international agencies involved in safe motherhood; it forms the basis of the most commonly used process indicator of delivery care, DHP. Skilled attendance requires two main components 1) a skilled attendant who is trained to proficiency in the skills needed to manage normal (uncomplicated) pregnancy, childbirth and the immediate postnatal period, and in the identification, management and referral of complications in women and newborns 24 and 2) an enabling environment that includes the necessary equipment, drugs, health infrastructure and a referral systems. 25 Graham has conceived more broadly of skilled attendance to include the policy and political environment, socio-cultural influences and health system financing. 23 Although the proxy indicator DHP gives us information about a key component of skilled attendance, the skilled attendant, there are several important problems with the indicator. DHP groups together doctors, nurses and midwives based on their professional designation alone; however, not every attendant who satisfies the professional designation will have the necessary skill set to satisfy the requirements of skilled attendance. 25 Problems with a women s reporting of professional attendance will also affect DHP. For example, it is not certain that a woman is able to recall or accurately identify her attendants. 12 In addition, since women are asked to report all attendants, even attendants who have a very brief and perhaps superficial involvement in the delivery may be reported. 23 An important limitation of the measurement of Lauralee Morris 18

19 DHP using DHS data is that only live births are included and thus estimates of DHP will miss a number of stillbirth deliveries. 5 DHP does not provide information about the enabling environment in which the skilled attendant works. Similarly, although providing information about the place of delivery, the composite indicators Facility DHP and Domiciliary DHP do not specify whether an environment satisfies the definition of an enabling environment. Institutional delivery does not ensure an enabling environment. For example, a delivery in a health facility that lacks the drugs necessary to treat post-partum hemorrhage does not meet the requirements of skilled attendance; however, a home delivery could conceivably be equipped with the necessary drugs, equipment, and referral systems for skilled attendance. The concept of skilled attendance is limited in its potential to prevent maternal deaths that occur outside of labor, delivery and the immediate post-partum period. Skilled attendance cannot reasonably be expected to prevent maternal deaths that occur antenatally (septic abortion, ectopic pregnancy, ante partum eclampsia and hemorrhage) or post-natally (post-natal sepsis). Measurement problems have plagued The Safe Motherhood Initiative and resulted in the use of DHP and other process indicators. 26 Because maternal death is a relatively rare event, very large sample sizes are required to produce a precise estimate of the MMR. Stanton and colleagues has shown that sampling errors associated with maternal mortality ratios are too large for this indicator to be used for monitoring trends. 27 In addition to problems with precision, maternal mortality measurements have long reference periods (7-10 years) making them impractical for program monitoring. Besides the question of the validity of DHP as a proxy indicator for skilled attendance, there is also the larger question of how skilled attendance relates to maternal mortality. There is Lauralee Morris 19

20 currently no evidence for a causal link between skilled attendance and reduction in maternal mortality, however there is ecological, historical and clinical evidence that supports the association. Ecological analysis from developing countries shows countries that have a higher proportion of skilled attendance also tend to have lower MMR. 23 Historically, the decline in maternal mortality in Western countries occurred concomitant with the rise in professional attendance. 28 Lastly, clinical trials have proven that there are very effective interventions at the individual level that reduce the number of maternal deaths. In addition to the challenges of measuring skilled attendance, measuring wealth is also difficult. The wealth index uses the approach of measuring relative wealth, rather than absolute wealth; thus, it is country specific and not suitable for cross-country comparisons. 29 It measures household wealth based on the assets and amenities reported in the household questionnaire using a method called Principle Components Analysis (PCA). 11 PCA finds patterns among groups of variables and assumes that these assets correlate with underlying wealth. Data on assets and amenities are combined to produce a household s position on a wealth scale. A study from Guatemala demonstrated that the wealth index performed well in comparison to measures of expenditure, a more traditional measure of wealth. 11 The strength of the DHS wealth index is that it allows economic status to be measured inexpensively using data already available from the DHS. Four models of delivery care have been identified based on where the delivery takes place and who attends the delivery. 6 In model 1 the woman delivers in the home with the assistance of a TBA, family member or other layperson. In model 2, birth also occurs in the home but with a professional birth attendant. Ideally, both attendants are supported by a referral hospital and a functioning health system. In model three and four, women deliver in a health Lauralee Morris 20

21 facility with a professional birth attendant. A model three facility is a basic obstetric facility without surgical or blood transfusion services; model four delivery is usually a tertiary care setting with both surgical and blood transfusion capabilities. 6 In countries where most women deliver in a model one setting, rates of maternal mortality are high. 6 In this study, Bangladesh and Haiti both have model one delivery care for most women with correspondingly low proportions of DHP (11.6% and 22%). Although both countries have high maternal mortality ratios (380 deaths/100,000 live births for Bangladesh and 680 deaths/100,000 live births for Haiti), they are not the highest among the six countries selected for this study (Table 2). Marjorie Koblinsky and Oona Campbell have examined delivery care in Bolivia, Egypt, Indonesia and Zimbabwe in a series of case studies aimed at identifying program elements associated with declines in maternal mortality. 30 Indonesia is a rare example of a country moving from model 1 to a model 2 system of delivery care. The pattern of findings displayed in Table 4 is consistent with this transition. Indonesia is the only country where more professional delivery occurs in the home than in a health facility (Facility DHP 20.6% versus Domiciliary DHP 28.6%) and one of the two countries where more professional attendants are midwives or nurses rather than doctors (42.1% versus 7.0%). These percentages reflect Indonesia s strong village based midwife program. 31 Bolivia, Egypt and Zimbabwe are all examples of countries moving from a model 1 to a mix of model 3 and 4 systems. 30 In contrast to Indonesia, the trend in these countries is to move deliveries out of the home and into health facilities. 30 Specific safe motherhood interventions differ between countries, however, as does the role of government. 30 Lauralee Morris 21

22 Bolivia is a country that has aggressively intervened to reduce its maternal mortality rates. 32 In 1996, it instituted a national health insurance plan, the National Maternal and Child Health Insurance (MCHI), to cover pregnancy, labor and delivery care, including Caesarian Sections and other obstetrical emergencies, post-partum care and newborn care. 32 Two years later, the Basic Health Insurance plan (BHI), was introduced to cover additional services including post-abortion care and patient transfer in the case of obstetrical emergency. Insurance plans cover delivery care by a doctor in a health facility. 32 This study calculated DHP from the DHS survey done shortly after the institution of the health insurance plans. The large proportion of Facility DHP (53.0%) compared to Domiciliary DHP (2.6%) is consistent with the emphasis on promoting delivery in a health facility. Egypt is similar to Bolivia in that most professional deliveries occur in a facility rather than in a home (Facility DHP 48.2% versus Domiciliary DHP 12.7%) and doctors out number nurse /midwifes as professional attendants (53.5 % of deliveries attended by a doctor, 7.4% of deliveries attended by a nurse/midwife). Unlike Bolivia however, the transition from model 1 to model 3/ 4 in Egypt seems to have evolved from changes in the health care system that were not specifically aimed at safe motherhood. 33 In the 60 s and 70 s medical schools were expanded at the same time that midwifery schools were closed; schools for dayas (TBA s) had already been closed in the 1950 s. 33 Although midwifes have enjoyed a program of re-licensing started in 1995, they still attend far fewer births than do physicians. 33 Zimbabwe is unique among the six countries in this study; it is the only country where nurse/midwifes attend most of the facility deliveries. Almost all professional deliveries (72.2%) are facility based and nurse/midwifes attend 6 times more deliveries than do physicians (nurse/midwife 61.2%, doctor 11.6 %). Zimbabwe also has the highest DHP (72.7%) among the Lauralee Morris 22

23 six countries studied, substantially higher than that of Egypt, the country with the second largest proportion of women delivering with a health professional (60.9%). After independence in 1980, the government of Zimbabwe improved health services by building health care facilities, increasing the number of trained staff, building roads and communications infrastructure, and removing financial barriers to access. 34 These measures resulted in the high levels of DHP seen in this study and previous DHS surveys. 34 It is an apparent paradox then, that of the six countries in this study, Zimbabwe has the highest MMR. Estimates from the DHS show a doubling of maternal deaths between 1994 and The most likely explanation for the apparent contradiction between Zimbabwe s DHP and MMR is the effect of Zimbabwe s HIV/AIDS epidemic on maternal mortality. UNAIDS estimates that at the end of 2003, almost 25% of Zimbabwe s adult population between the ages of 15 and 49 were infected with HIV/AIDS. 35 The direct sisterhood method of measuring MMR used by DHS employs a time of death definition rather than a cause of death definition. 36 Thus it captures deaths while pregnant or within 42 days of delivery, excluding accidental deaths. It includes both deaths from direct maternal causes such as obstructed labor and post-partum hemorrhage, and deaths form indirect causes such as malaria, tuberculosis and other illnesses associated with HIV infection. Thus, large proportions of women delivering with a health professional does not preclude high maternal mortality ratios, especially in settings where HIV/AIDS is prevalent. This study once again illustrates the large differences in delivery care between countries. More interesting however are the large differentials in delivery care between different subgroups within a country, particularly the poor-rich gap. Four countries (Haiti, Indonesia, Egypt and Bolivia) have sub national rich-poor differentials that are similar to or exceed the overall national differences. Disparities in subgroups defined by maternal age, birth order, maternal Lauralee Morris 23

24 education and urban/rural residence identify women who are particularly vulnerable to neglect with respect to delivery care services. Young women, high parity women, those who are poorly educated and rural dwellers are all vulnerable. Despite large poor-rich differentials in DHP in all countries, some countries have more equality between rich and poor quintiles when compared with others. Zimbabwe in particular has the smallest relative and absolute difference in DHP between the richest quintile and the poorest quintile. Zimbabwe also has the smallest relative difference between the highest categories and the lowest categories for birth order, maternal education and urban/rural residence. It should be noted however that Zimbabwe s economic and political fortunes have changed dramatically since 1999, the year of the DHS survey. Political unrest, reductions in government health expenditures and the expanding HIV epidemic have undoubtedly affected the equality of delivery care in Zimbabwe. Haiti appears to have the least equality, with the largest relative difference in DHP between the richest quintile and the poorest quintile, as well as the largest relative differences for birth order, maternal education and urban/rural residence. The observed associations between age, birth order, maternal education, antenatal care, urban/rural residence and wealth quintile are consistent with previous studies. 12 A significant finding of this study is that the wealth index was the strongest predictor of DHP after controlling for all the other variables in the model, including maternal education. Previous multivariate analyses have found education to be more important than wealth as a predictor of delivery care. 16, 18 Maternal education was the main confounder of the association between poverty and DHP across the entire wealth hierarchy, thus, the lower use of DHP by women in all wealth quintiles is partly due to lack of education. Urban/rural residence was less important and only confounded the relationship between poverty and DHP in the richest quintiles. It would seem that poor urban Lauralee Morris 24

25 women face the same difficulty obtaining DHP as poor rural women. Rich women are able to access delivery services regardless of their place of residence. Given the high costs of health infrastructure and delivery care services, it is not surprising that poverty is an important predictor of DHP; however, the causal pathways whereby wealth affects DHP is complex and will differ greatly based on individual, local and national circumstances. 17 This study examined several distal socio-economic determinants of delivery care. Other distal determinants of delivery care that could disproportionately affect the poor are national economic crises, disruption of health services from wars and natural disasters, and the effect of pandemics such as HIV/AIDS. Proximate determinants that mediate the effects of wealth on the use of delivery care services are also important. Factors such as absent or inadequate health insurance, the high opportunity costs of health care and lack of transportation will affect poor women s use of delivery care services. Kunst 17 emphasizes the complex interplay of factors that lead to the under-utilization of delivery care by poor women; however, he suggests that experimental or observational studies focusing on a few factors that are amenable to intervention may be the best way forward for future research. CONCLUSIONS There are large disparities in DHP between countries and between subgroups within countries. There are particularly large differentials in delivery care between wealth quintiles, although some countries demonstrate more equality between quintiles when compared with others. DHP is highly dependent on national context. In addition to DHP, delivery care indicators that describe the place of delivery and the type of attendant are useful in describing the elements of skilled attendance for individual countries. In five out of six countries studied, Lauralee Morris 25

26 poverty was the most important predictor of delivery care after controlling for maternal age, birth order, number of antenatal visits, maternal education, and urban/rural residence. Lauralee Morris 26

27 Table 1 Description of the Demographic and Health Surveys (DHS) included in this study Country Years of Fieldwork Implementing Organization Number of Households Interviewed Number of Women Interviewed Eligibility Criteria of Women s Interview Bangladesh National Institute of Population, Research and Training 9,854 10,544 All women, Bolivia 1998 Instituto Nacional de Estadística 12,109 11,187 All women, Egypt 2000 Ministry of Health and Population, National Population Council 16,957 15,573 Ever married women, Haiti 2000 Institut Haitien de l Enfance 9,595 10,159 All women, Indonesia 1997 Central Bureau of Statistics, National Family Planning Coordinating Board, Ministry of Health 34,255 28,810 Ever married women 15-49, who usually lived in the household Zimbabwe 1999 Central Statistical Office 6, All women, Table 2. Countries by Region and Maternal Mortality Ratio. Country and Region Maternal Mortality Ratio (per 100,000 live births) Source and Year Sub-Saharan Africa Zimbabwe Direct Sisterhood Method Northern Africa Egypt 84 Reproductive Age Mortality Study 2000 South Central Asia Bangladesh 380 Household Survey Southeastern Asia Indonesia Modeled estimate Latin America and the Caribbean Bolivia Modeled estimate Haiti Direct Sisterhood Method

28 Table 3. Percent distribution of live births and the number* of live births by country and woman s background characteristics Background Characteristic Bangladesh Bolivia Egypt Haiti Indonesia Zimbabwe Age at Delivery % N % N % N % N % N % N <= Total , Birth Order Total , Antenatal care None Total , Woman s Education No education Primary Secondary , Residence Rural Urban Total , Poverty Quintile Poorest Second Third Fourth Richest Total , *The number of live births (N) is weighted for the sample design of each survey