Understanding Unmet Healthcare Needs in Nigeria: Implications for Universal Health Coverage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Many individuals in low- and middle-income countries with healthcare needs do not access the necessary, often lifesaving healthcare services. Existing universal health coverage (UHC) indicators do not account for a portion of the population with unmet healthcare needs. Objective: To estimate the prevalence, wealth-related inequality, and determinants of unmet healthcare needs in Nigeria using data from the nationally-representative Nigeria Living Standards Survey, 2018-2019. Methods: We analyzed data from a cross-sectional sample of 116 320 Nigerians from 22 110 households selected using multi-stage probability sampling. The outcome variable was self-reported unmet healthcare needs. We conducted concentration index (CIX) analyzes to assess wealth-related inequalities and performed multilevel logistic regression analysis to identify the determinants of unmet healthcare needs at the individual, household, and community levels. Results: The prevalence of unmet healthcare needs was 5.2% (95% CI: 5.0-5.5), representing about 11 million Nigerians (95% CI: 10.5-11.5 million). The most common reasons were high costs (unaffordability) and the perception that the illness or injury was not serious. Wagstaff-normalized CIX for unmet healthcare needs was pro-poor: -0.09730 for the general population and -0.10878 for those with chronic illnesses. Significant determinants of unmet healthcare needs include age (AOR: 0.99, 95% CI: 0.99-1.00), chronic illness (AOR: 8.73, 95% CI: 7.99-9.55), single-person households (AOR: 1.55, 95% CI: 1.20-2.02), poorest quintile households (AOR: 1.45, 95% CI: 1.19-1.78), and mildly (AOR: 1.17, 95% CI: 1.01-1.36) or moderately food-insecure households (AOR: 1.30, 95% CI: 1.11-1.51). Conclusion: A significant proportion of Nigerians, particularly the very poor, chronically ill, those living alone, or food insecure, have unmet healthcare needs. This highlights the necessity for targeted interventions to ensure vulnerable populations can access essential healthcare services. To progress toward UHC, the Nigerian health system must address critical issues related to healthcare accessibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it