The Burden and Socioeconomic Inequality in Catastrophic Out-of-pocket Health Expenditure in Post-Pandemic Nigeria
Bibliographic record
Abstract
Abstract Out-of-pocket (OOP) payments are the primary health financing mechanisms in Nigeria. This study—the first national analysis using post-COVID-19 data—examines the incidence and socioeconomic inequalities in catastrophic health expenditure (CHE) among Nigerian households using data from the nationally representative Nigeria General Household Survey 2023/2024. We estimated the proportion of households facing CHE using both household budget share (BS) and capacity-to-pay (CTP) approaches, with thresholds set at 10% and 40%, respectively. We assessed socioeconomic inequality in CHE incidence using the concentration index (CIX) and decomposed the CIX of CHE incidence using Wagstaff et al.’s (2003) approach. Our analysis show that households CHE incidence was 45.5% (95% CI: 43.5%–47.4%) and 43.1% (95% CI: 41.1%–45.0%) using the 10% BS and 40% CTP approaches, respectively. The Wagstaff-normalized CIX revealed pro-poor distributions of − 0.152 and − 0.178 for the BS and CTP approaches, respectively. Our decomposition analysis revealed that socioeconomic inequality in CHE was largely driven by female-headed households, larger households, households with elderly member(s), and rural residences. These findings indicate that OOP payments continue to impose a catastrophic financial burden on a substantial proportion of Nigerian households, particularly those with elderly individuals as well as female-headed, poor, large, and rural households. The intensity of these payments is also deeply impoverishing, highlighting the urgent need for targeted financial protection measures—such as the establishment of a comprehensive health insurance program for elderly Nigerians—to safeguard vulnerable populations and promote equitable access to healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".