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Missing the vulnerable-Inequalities in social protection in 13 sub-Saharan African countries: Analysis of population-based surveys.

David ChipantaSilas Amo-AgyeiLucas HertzogAhmad Reza HosseinpoorMichael SmithCaitlin MahoneyJuan Gonzalo Jaramillo MeijaOlivia KeiserJanne Estill
Published in: PLOS global public health (2024)
We assessed socioeconomic inequalities in social protection coverage among the public, men and women living with the human immunodeficiency virus (MLHIV, WLHIV), and adolescent girls and young women (AGYW). We used population-based data from Cameroon, Côte d'Ivoire, Ethiopia, Eswatini, Kenya, Lesotho, Malawi, Namibia, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe. We constructed concentration curves (CC) and computed concentration indices (CIX) for each country and population group. A CC represents the cumulative percentage of social protection coverage plotted on the y-axis against the cumulative proportion of the population-ranked by socioeconomic status from the poorest to the richest-on the x-axis. The CIX quantifies the concentration of social protection coverage among the poor or the rich. The sample size ranged from 10,197 in Eswatini to 29,577 in Tanzania. Social protection coverage among the public varied from 5.2% (95% Confidence Interval 4.5%-6.0%) in Ethiopia to 39.9% (37.0%-42.8%) in Eswatini. It ranged from 6.9% (5.7%-8.4%) MLHIV in Zambia to 45.0% (41.2-49.0) among WLHIV in Namibia. Among AGYW, it varied from 4.4% (3.6-5.3) in Ethiopia to 44.6% (40.8-48.5) in Eswatini. Socioeconomic inequalities in social protection coverage favored the poor in 11/13 countries surveyed. It favored the rich in Cameroon and was undefined in Côte d'Ivoire. The CIX in these 11 countries ranged from -0.080 (p = 0.002) among the public in Malawi to -0.372 (p< 0.001) among WLHIV in Zimbabwe. In 8 of these 11 countries, ≥15% of people from the poorest households reported receiving social protection. Only in countries with higher levels of social protection coverage did most people from the poorest households achieve high coverage. Social protection coverage was low and favored the poor. Pro-poor social protection is insufficient to reach the poor. Research is required to reach the poorest households with social protection in Africa.
Keyphrases
  • healthcare
  • mental health
  • human immunodeficiency virus
  • hepatitis c virus
  • machine learning
  • hiv infected
  • antiretroviral therapy
  • electronic health record
  • big data
  • data analysis
  • health insurance
  • hiv testing