Trends in inequality in maternal and child health and health care in Uganda: Analysis of the Uganda demographic and health surveys

Background: Uganda has made great strides in improving maternal and child health. However, little is known about how this improvement has been distributed across different socioeconomic categories, and how the health inequalities have changed over time. This study analyses data from Demographic and Health Surveys (DHS) conducted in 2006, 2011, and 2016 in Uganda, to assess trends in inequality for a variety of mother and child health and health care indicators. Methods: The indicators studied are acknowledged as critical for monitoring and evaluating maternal and child health status. These include infant and child mortality, underweight status, stunting, and prevalence of diarrhea. Antenatal care, skilled birth attendance, delivery in health facilities, contraception prevalence, full immunization coverage, and medical treatment for child diarrhea and Acute Respiratory tract infections (ARI) are all health care indicators. Two metrics of inequity were used: the quintile ratio, which evaluates discrepancies between the wealthiest and poorest quintiles, and the concentration index, which utilizes data from all five quintiles. Results: The study found extraordinary, universal improvement in population averages in most of the indices, ranging from the poorest to the wealthiest groups, between rural and urban areas. However, significant socioeconomic and rural-urban disparities persist. Under-five mortality, malnutrition in children (Stunting and Underweight), the prevalence of anaemia, mothers with low Body Mass Index (BMI), and the prevalence of ARI were found to have worsening inequities. Healthcare utilization measures such as skilled birth attendants, facility delivery, contraceptive prevalence rate, child immunization, and Insecticide Treated Mosquito Net (ITN) usage were found to be significantly lowering disparity levels towards a perfect equity stance. Three healthcare utilization indicators, namely medical treatment for diarrhea, medical treatment for ARI, and medical treatment for fever, demonstrated a perfect equitable situation. Conclusion: Increased use of health services among the poor and rural populations leads to improved health status and, as a result, the elimination of disparities between the poor and the wealthy, rural and urban people. Recommendation: Intervention initiatives should prioritize the impoverished and rural communities while also considering the wealthier and urban groups.

Uganda is a landlocked country in East Africa with a total land area of 241,559 square kilometers and a population of 44.3 million people [13]. Uganda is classified as a low-income country by the World Bank, with a low human development index (HDI of 0.544), ranking 159th out of 189 countries [13]. Up to 21.4% of the population lives in poverty, with less than US$ 1 per person per day [14]. Overall health expenditure per capita is $ 43, and total health expenditure accounts for 6.5% of GDP [15]. By the year 2019, infant mortality and under-five mortality were rated at 33 and 46 per 1000 live births respectively; and maternal mortality ratio was at 375 deaths per 100,000 live births [16]. The dependent variable in this study was Inequality levels; measured through quintile ratios and concentration indices. Quintile ratio provides information on the disparities between the wealthy and the poor [17]. It allows for comparisons of health status or health-care utilization between the richest and poorest quintiles. The Concentration Index on the other hand, is a relative measure of inequality that reveals how concentrated a health indicator is among the disadvantaged or advantaged [17]. It uses data from all five wealth quintiles to provide a complete picture by quantifying the degree of inequalities in the population. Explanatory variables were grouped into two categories: Maternal and child health variables, such as infant mortality rate, under-five mortality rate, and child underweight, Child stunting, prevalence of anemia, prevalence of fever, prevalence of Acute Respiratory tract Infections (ARIs), prevalence of diarrhea; in children under five years of age, and Mother’s low Body Mass Index (BMI). The other category includes variables of healthcare service use, such as antenatal care, skilled birth attendance, delivery in health facilities, full immunization coverage, medical treatment for child diarrhea, contraceptive prevalence rate, deliveries in government-owned health facilities, medical treatment for ARIs, medical treatment for fever in children under the age of five, and use of ITN. This study makes use of data from three Uganda Demographic and Health Surveys (UDHS), which were conducted in 2006, 2011, and 2016 by a state specialized unit, Uganda Bureau of Statistics (UBOS). The UDHS uses a stratified two-stage cluster sampling procedure. In the first stage, clusters are selected from sampling frames using the most recent census. Households are selected from each cluster at the second stage. The UDHS captures information in such areas as: women’s and children’s demographic and socioeconomic characteristics, household characteristics, maternal and child health status parameters, and maternal and child healthcare service parameters using questionnaires. It also involves conducting height and weight measurements of children and women, testing for anemia, malaria and Vitamin A deficiency [11, 18, 19]. Inclusion criteria: All women age 15–49 and who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In one-third of the sampled households, all men age 15–54, including both usual residents and visitors who stayed in the household the night before the interview, were eligible for individual interviews. In the subsample of households selected for the male survey, anaemia testing was performed among eligible women age 15–49 and men age 15–54 who consented to being tested and among children age 6–59 months whose parents or guardians consented. In the same subsample, blood samples were collected from children age 6–59 months whose parents or guardians consented to malaria testing with rapid diagnostic test (RDT) kits. Height and weight measurements were recorded for children age 0–59 months, women age 15–49, and men age 15–54. In 2006, there were 8,531 women and 2,503 men interviewed; in 2011, there were 8,674 women and 2,295 men interviewed; and in 2016, there were 18,506 women and 5,336 males interviewed. Up to 10,173 children under five years (2,687 children in 2006, 2,350 children in 2011 and 5,136 children in 2016) participated in the nutrition assessment exercise [11, 18, 19]. The surveys excluded households in institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools [11, 18, 19]. This study adopted a structural theory to understanding health inequalities [20]. The structural theory suggests that differences in social groups’ socioeconomic circumstances, such as income, wealth, power, environment, and access, explain differences in health outcomes [21]. This argument is supported by evidence that health inequalities have decreased when structural inequalities have decreased [22] and that community health has improved when more resources have been provided [23], and, most convincingly, that the people with the most resources in any society are always the healthiest, regardless of their behaviors [24]. Even when a health issue is obviously linked to a genetic mutation, mortality disparities by socioeconomic class are large [25]. Indicators analyzed are in two categories: Maternal and child health outcome indicators, such as infant mortality rate, under-five mortality rate, and child underweight, Child stunting, prevalence of anemia, prevalence of fever, prevalence of Acute Respiratory Tract Infections (ARIs), prevalence of diarrhea; in children under five years of age, and Mother’s low BMI. This study considered children with a Z-score less than minus two standard deviations (SD) from the median of the WHO reference population for height-for-age (Stunting) and weight-for-age (Underweight) [26; 27]. The UDHS collected data on children’s nutritional status by measuring the height and weight of all children under the age of five in a subsample of one in every three families chosen for the survey. Weighing was done with a lightweight electronic SECA scale designed and built under the supervision of UNICEF. Shorr Productions designed a measuring board that was used to take height measurements. Children under the age of 24 months were measured laying down (recumbent length) on the board, while older children were measured standing tall [19]. The nutritional status of children was determined using WHO’s new growth guidelines published in 2006 [27]. A BMI of 18.5 was utilized in this study to identify thinness or acute malnutrition in women aged 15 to 49 [28]. BMI is calculated by dividing one’s weight in kilograms by one’s height in meters squared (kg/m2). The body mass index (BMI) is used to determine whether a person is lean or obese. The height and weight of women aged 15 to 49 were measured in one out of every three UDHS homes [18]. In this study, anemia was defined as a haemoglobin level in children less than 11 g/dl [29]. Blood samples were collected for anaemia testing from eligible women and men who consented to be examined, as well as from all children aged 6–59 months who had permission from their parents or guardians. A drop of blood was taken from the prick site (a finger prick or a heel prick in the case of children age 6–11 months) into a microcuvette, and haemoglobin analysis was performed on-site using a battery-powered portable HemoCue analyzer [19]. Malaria testing was only done on children aged 6 to 59 months; no adults were screened. A drop of blood was tested immediately using the SD Bioline Pf/Pv RDT, which is a qualitative test for the detection of histidine-rich protein II (HRP-II) antigen of Plasmodium falciparum (Pf) and/or Plasmodium vivax (Pv) in human whole blood, using the same finger (or heel) prick used for anaemia testing [19]. Plasmodium falciparum is the most common Plasmodium species in Uganda. Inequality was measured using two different methods: First, we considered quintile ratios. The ratio indicator compares health status or health-care utilization between the richest and poorest quintiles. To some extent, this indicator provides information on the disparities between the wealthy and the poor. However, it is based solely on data from the two wealthiest quintiles and ignores the remaining three quintiles between the top and bottom, and hence cannot provide a comprehensive picture of inequality over the entire population [30]. The second indicator is the Concentration Index; which is a relative measure of inequality that reveals how concentrated a health indicator is among the disadvantaged or advantaged [17; 30]. Its size represents the degree of inequality. The concentration index calculates the degree of economic inequality by utilizing data from all five quintiles. As a result, it is a synthesis of inequality throughout the entire population [17]. The concentration index has a range of -1 to + 1. Traditionally, if the health status measure is a “bad” in the sense that it depicts poor health, the index takes a negative value, suggesting that the poorest segments of the population bear the largest burden of poor health. If the health status measure is a “good,“ in the sense that it indicates a positive feature of health, the index takes a positive value, suggesting that the poor are significantly less healthy. In the absence of inequities, the concentration index has a value of zero. The concentration index (C) is calculated in a spreadsheet program from grouped data using the following formula [17]. Where Pt is the cumulative percent of the sample ranked by wealth status in group t, Lt is the corresponding Lorenz curve ordinate, and T is the total number of wealth groups, which is five in this analysis [17].

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