Catastrophic health payments in Ghana post-National Health Insurance Scheme implementation: an analysis of service-specific health expenditures
Bibliographic record
Abstract
BACKGROUND: Ghana implemented several health reforms in the 1970s and 1990s. Still, several access barriers persist, including high out-of-pocket (OOP) spending, which led to the implementation of the National Health Insurance Scheme (NHIS) in 2003 to achieve Universal Health Coverage and lower OOP spending. This study evaluates the incidence and intensity of catastrophic health expenditure (CHE) among Ghanaian households post-NHIS, considering OOP health spending on different health services. METHODS: Data came from the Ghana Living Standards Surveys rounds 6 (2012/2013) and 7 (2016/2017) and the Annual Household Income and Expenditure Survey 2022/2023. Key variables were OOP spending on three health service categories (medical products, outpatient and inpatient) and total expenditure. The incidence and intensity of CHE for various health service categories were calculated using service-specific thresholds. A household incurs CHE for each service when OOP health spending as a share of total expenditure exceeds the service-specific threshold. RESULTS: Overall, at the 10% threshold, CHE headcount for total OOP health spending increased from 1.26% (95% CI 1.11% to 1.44%) to 11.45% (95% CI 10.86% to 12.07%) between 2012 and 2023. CHE gaps were also substantial for overall and service-specific OOP health spending. Medical supplies account for a large share of total OOP health spending, with CHE headcount rising from 1.34% (95% CI 1.18% to 1.53%) to 12.24% (95% CI 11.64% to 12.89%) between 2012 and 2023 at the 10% original threshold. Although the results were mixed, rural, northern and low-income households experienced substantial financial burdens. At the 20% threshold, the CHE headcount for inpatient services increased from 0.84% (95% CI 0.64% to 1.10%) to 4.38% (95% CI 3.83% to 4.99%) for northern dwellers between 2012 and 2023. DISCUSSION/CONCLUSIONS: Despite NHIS coverage, high-cost services like medical supplies, hospital stays and frequently used outpatient services substantially drive CHE in Ghana, particularly for underserved populations. Addressing them requires prioritised policy interventions to expand NHIS coverage for essential services and improve financial protection, especially for rural and low-income households.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".