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Social Inequalities in Maternal Mortality Among the Provinces of Ecuador

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Social Inequalities in Maternal Mortality Among the Provinces of Ecuador Pan American Journal Original research of Public Health Social inequalities in maternal mortality among the provinces of Ecuador Antonio Sanhueza,1 Jakeline Calle Roldán,2 Paulina Ríos-Quituizaca,3 Maria Cecilia Acuña,1 and Isabel Espinosa1 Suggested citation Sanhueza A, Calle Roldán J, Ríos-Quituizaca P, Acuña MC, Espinosa I. Social inequalities in maternal mortality among the provinces of Ecuador. Rev Panam Salud Publica. 2017;41:e97. ABSTRACT Objective. This study set out to describe the association between the maternal mortality ratio (MMR) estimates and a set of socioeconomic indicators and compute the MMR inequali- ties among the provinces of Ecuador. Methods. A cross-sectional ecological study was conducted, using data for 2014 from the country’s 24 provinces. The MMR estimate was calculated for each province, as well as the association and its strength between MMR and specific socioeconomic indicators. For the indi- cators that were found to be significantly associated with MMR, inequality measurements were computed. Results. Despite a relatively low MMR for Ecuador overall, ratios differed substantially among the provinces. Five socioeconomic indicators proved to be statistically significantly associated with MMR: total fertility rate, the percentage of indigenous population, the percent- age of households with children who do not attend school, gross domestic product, and the percentage of houses with electrical service. Of these five, only three had MMR inequalities that were significant: total fertility rate, gross domestic product, and the percentage of households with electricity. Conclusions. This study supports research arguing that national averages can be mislead- ing, as they often hide differences among subgroups at the local level. The findings also suggest that MMR is significantly associated with some socioeconomic indicators, including ones linked with significant health outcome inequalities. In order to reduce health inequities, it is crucial that countries look beyond national averages and identify the subgroups being left behind, explore the particular social determinants that generate these health inequalities, and examine the specific barriers and other factors affecting the subgroups most vulnerable to maternal health inequalities. Keywords Maternal mortality; health inequalities; social determinants of health; health equity; Ecuador; Latin America. The year 2015 marked the end of the health and education for millions of peo- advances at the national level, insufficient Millennium Development Goal (MDG) ple. In terms of maternal mortality, Ecua- progress was made at the subnational era. For Ecuador, that period had been one dor was one of the countries of Latin level, with thousands of people still liv- of economic growth (1), improvements in America and the Caribbean that experi- ing in poverty and thousands of women water quality and in sanitation, and an enced the steepest reductions in the ma- losing their lives due to preventable expansion of social services, including ternal mortality ratio. For the country as a pregnancy-related causes (3). whole, the ratio decreased from 185 ma- Today, Ecuador and other countries 1 Pan American Health Organization, Washington, D.C., United States of America. Send correspon- ternal deaths per 100 000 live births in face a new global health agenda that dence to Antonio Sanhueza, [email protected] 1990 to 64 deaths per 100 000 live births in prioritizes universal health and equity, 2 Ministry of Public Health of Ecuador, Quito, Ecuador. 3 Facultad de Ciencias Médicas, Universidad 2015, a reduction of 65.4% (2). Despite including through the Sustainable De- Central del Ecuador, Quito, Ecuador. these impressive economic and social velopment Goals (SDGs) and the new Rev Panam Salud Publica 41, 2017 1 Original research Sanhueza et al. • Social inequalities in maternal mortality in Ecuador Global Strategy for Women’s, Children’s was considered a proxy for poverty. On socioeconomic indicator, and the fifth and Adolescents’ Health (4). In order for the other hand, the relationship between quintile (Group 5) represents the most these nations to take on these ambitious fertility and social factors helps to put advantaged provinces for the indicator. targets, they have to start looking be- the fertility rate into a larger, overall The AD and the RR indicate the gap be- yond national averages and identify the context, including identifying the most tween the most disadvantaged and the subgroups that are being left behind. It vulnerable sectors. most advantaged groups. The AD and is also important that the countries start The analyses included the MMR esti- the RR were computed by, respectively, exploring the specific barriers and other mate for each province, which was calcu- subtracting and dividing the MMR in conditions affecting these subgroups, lated by dividing the number of maternal each of these two groups. Higher values and identify the mechanisms generating deaths by the total number of live births of AD and of RR indicate greater inequal- current health inequalities. reported in the year 2014, expressed as ity in maternal mortality. To help Ecuador prepare for these the number of maternal deaths per The MSII and PRSII were computed tasks, this study had two objectives: (1) 100 000 live births. A 95% confidence in- through regression model fitting. In or- to describe the association between ma- terval (CI) for the MMR in each province der to fit the models, the complete data ternal mortality and a set of socioeco- was also computed. set of the provinces was first ordered by nomic indicators and, (2) based on those First, an exploratory data analysis was socioeconomic indicator status, from the socioeconomic indicators, to compute carried out in order to ascertain the MMR most disadvantaged to the most the inequalities in maternal mortality and the selected socioeconomic indica- advantaged. among the provinces of Ecuador, using tors in Ecuador. Then, a study of MMR The MSII is based on the estimated data for the year 2014. inequality was conducted for each of the slope parameter by fitting a linear regres- selected socioeconomic indicators by sion model that considers the MMR as MATERIALS AND METHODS taking two steps: (1) study the associa- the dependent variable and the Ridit (the tion and its strength between MMR and cumulative relative position of each For this research, a cross-sectional eco- each of the socioeconomic indicators and province with respect to the socioeco- logical study was conducted in 2014, us- (2) for the socioeconomic indicators that nomic indicator, which ranges between ing data from the 24 provinces of were found to be significantly associated 0 and 1) as the independent variable. The Ecuador. with MMR, compute the MMR inequal- weighted least squares method was used The variables considered in this study ity measurements. in this case. were one heath indicator, nine socioeco- A weighted least squares regression The MSII was computed as MSII = b nomic indicators, and one demographic model was used to study the association (RiditMin - RiditMax), where b is the esti- indicator. between MMR and the socioeconomic mated slope parameter computed by fit- The one heath indicator was the mater- indicators. The weights were the number ting the linear regression model. nal mortality ratio (MMR). of live births in each province. The The RiditMin and the RiditMax are, respec- The nine socioeconomic indicators strength of the association was assessed tively, the observed minimum and maxi- were: (1) total fertility rate; (2) percent- with the Pearson correlation coefficient. mum Ridit values. The theoretical age of indigenous population; (3) per- The bootstrap method was used by con- minimum and maximum Ridit values are centage of households with children who sidering 2 000 resamples in order to esti- 0 and 1, so that MSII = -b. do not attend school; (4) gross domestic mate the Pearson correlation and its The 95% CI for the MSII was computed product (GDP) per capita; (5) average bias-corrected 95% CI. as [bU (RiditMin - RiditMax), bL (RiditMin - Rid- household income; (6) percentage of Simple and complex measures of in- itMax)], where bL and bU are, respectively, poverty with unsatisfied basic needs; (7) equality were computed to explore the the lower and upper limits computed percentage of households with inade- magnitude of MMR inequality for each from the 95% CI for the slope parameter. quate services (e.g., no connection to a socioeconomic indicator. The simple In order to compute the PRSII, the piped water supply or to the sewer sys- measures included computing the abso- Poisson regression model was fitted by tem or a septic tank); (8) percentage of lute difference (AD) and the relative ratio considering the number of maternal households with electricity; and (9) aver- (RR) (8). The complex measures included deaths as the response variable and the age number of persons per bedroom. the modified slope index of inequality Ridit as the independent variable. The one demographic indicator was (MSII) proposed by Bacallao in 2007 (9) The formula for the PRSII was PRSII = live births. (which is an absolute measure of in- exp{b (RiditMin - RiditMax)}, where exp is the The indicators used in this study were equality) and the Poisson relative slope exponential function and b is the estimated derived from three key information index of inequality (PRSII) (which is a slope parameter computed by fitting sources (5–7) on socioeconomic condi- relative measure proposed for this work the Poisson regression model. RiditMin tions in Ecuador and its provinces. that utilizes the Poisson regression and RiditMax are, respectively, the observed Some of the associations between the model) (10). We computed 95% CIs for minimum and maximum Ridit values. socioeconomic indicators and the MMR all those inequality measurements. The theoretical minimum and maxi- are intuitive, but others may not be. For In order to compute the AD and the mum Ridit values are 0 and 1, so that the instance, the average number of people RR, quintiles of provinces for each of the PRSII = exp{-b}.
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