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Background Improved access to and quality obstetric care in health facilities reduces maternal and neonatal morbidity and mortality. We examined spatial patterns, within-country wealth-related inequalities and predictors of inequality in skilled birth attendance and caesarean deliveries in sub-Saharan Africa. Methods We analysed the most recent Demographic and Health Survey data from 25 sub-Saharan African countries. We used the concentration index to measure within-country wealth-related inequality in skilled birth attendance and caesarean section. We fitted a multilevel Poisson regression model to identify predictors of inequality in having skilled attendant at birth and caesarean section. Results The rate of skilled birth attendance ranged from 24.3% in Chad to 96.7% in South Africa. The overall coverage of caesarean delivery was 5.4% (95% CI 5.2% to 5.6%), ranging from 1.4% in Chad to 24.2% in South Africa. The overall wealth-related absolute inequality in having a skilled attendant at birth was extremely high, with a difference of 46.2 percentage points between the poorest quintile (44.4%) and the richest quintile (90.6%). In 10 out of 25 countries, the caesarean section rate was less than 1% among the poorest quintile, but the rate was more than 15% among the richest quintile in nine countries. Four or more antenatal care contacts, improved maternal education, higher household wealth status and frequently listening to the radio increased the rates of having skilled attendant at birth and caesarean section. Women who reside in rural areas and those who have to travel long distances to access health facilities were less likely to have skilled attendant at birth or caesarean section. Conclusions There were significant within-country wealth-related inequalities in having skilled attendant at birth and caesarean delivery. Efforts to improve access to birth at the facility should begin in areas with low coverage and directly consider the needs and experiences of vulnerable populations.
We used the most recent Demographic and Health Surveys (DHS) collected from 25 sub-Saharan African countries. The DHS programme uses standardised methods to ensure uniformity of data collected across time and countries. We included all DHS that were conducted from 2013 to 2020. Countries are expected to adopt the full standard model questionnaire, but they can add questions of particular interest. However, questions in the model can be deleted if they are irrelevant for a specific country. The DHS uses standard sampling methods and design across all countries. The sampling methods and design have been described elsewhere.19 The study population includes all women of reproductive age (15–49 years) who had at least one live birth during the 5 years preceding the respective surveys. Only the most recent live birth was included in this analysis to reduce recall bias. We examined two primary outcomes: birth assisted by skilled attendant and delivery by CS. SBA was defined as whether the delivery took place in the presence of qualified personnel: a doctor, nurse, midwife, auxiliary midwife or other cadres that each country individually considers as skilled delivery attendants. Data on assistance at birth in the survey questionnaires were collected through answers to the question ‘Who assisted with the delivery of (NAME OF THE CHILD)? Information on caesarean sections are based on women’s self-reported answer to the question: ‘Was (NAME OF THE CHILD) delivered by caesarean, that is, did they cut your belly open to take the baby out?” We also assessed disparities in place of delivery and type of facility (private vs public). Place of delivery was defined as—birth at home that includes the respondent’s home or another non-institutional setting or birth at a health facility (institutional delivery), which may include public health facilities or the private medical sector. Public sector deliveries are those occurring in publicly funded, government health facilities. Private sector births are those occurring in facilities outside the public sector, and can be further divided into two categories: private-for-profit facilities and private not for profit facilities. We used the WHO Commission on Social Determinants of Health framework to explain predictors of inequality in the use of SBA and CS.15 We used household wealth index and education levels to explain socioeconomic position of women. The wealth index was constructed using principal components analysis based on ownership of selected household assets such as television (TV), radio, refrigerator and vehicle; materials used for housing construction; and access to sanitation facilities and clean water. Households were ranked into quintiles from the poorest (Q1) to richest (Q5) depending on their level of wealth. We categorised mothers’ education levels as (no education, primary, secondary or higher). We determined accessibility to health facilities based on the distance to the facility, and ability to afford treatment costs. We considered the distance to a health facility and lack of money for treatment as barriers to accessing health services and categorised—as a big problem or not a big problem. We include exposure to media, which was categorised based on the frequency of reading newspapers, listening to the radio and watching TV as not at all, less than once a week, and once a week or more. We also included the use of antenatal care (ANC) that was categorised as three or fewer contacts, and four or more contacts. Type of place of residence were categorised as urban or rural. Lastly, maternal factors such as age (15–24, 25–29, 30–34) and parity (1–6) were also included in the analysis. We used concentration index (CCI) to estimate wealth-related within-country inequalities in SBA and CS. The CCI ranges between −1 and +1; an index of 0 indicate equality in having SBA or CS. A positive values of CCI indicate a pro-rich coverage of SBA or CS. In contrast, a negative index implies an uneven concentration of SBA among the poor.20 The DHS uses a stratified, two-stage, random sampling design in all countries. Sample weights are included in the DHS to translate unbalanced sampling into national representative data. We used generalised latent linear and mixed model that adjusted for country, clusters and sampling weights to fit multilevel Poisson regression. We specified a three-level model to examine predictors of inequality in SBA and CS. For the first outcome (SBA) models—at level 1, we adjusted for women and household factors (181 191 women); at level 2, we adjusted for clustering (14 643 clusters) and at level 3, we adjusted for a country (25 countries). For the CS models—level 1 included 1 80 837 women; level 2 had 14 643 clusters and level 3 covered 25 countries. Results are presented with adjusted risk ratio (RR) and statistical significance was declared when the p value was <0.05. Analyses were conducted using Stata V.14.2 and IBM Statistical Package for Social Sciences (SPSS) V.25.0. We generated maps using ArcGIS software V.10.7.1. No patients or the public were directly involved in the design, conduct, reporting or dissemination plans of this research.
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