Porous safety net: catastrophic health expenditure and its determinants among insured households in Togo
Bibliographic record
Abstract
BACKGROUND: In Togo, about half of health care costs are paid at the point of service, which reduces access to health care and exposes households to catastrophic health expenditure (CHE). To address this situation, the Togolese government introduced a National Health Insurance Scheme (NHIS) in 2011. This insurance currently covers only employees and retirees of the State as well as their dependents, although plans for extension exist. This study is the first attempt to examine the extent to which Togo's NHIS protects its members financially against the consequences of ill-health. METHODS: Data was obtained from a cross-sectional representative households' survey involving 1180 insured households that had reported illness in the household in the 4 weeks preceding the survey or hospitalization in the 12 months preceding the survey. The incidence and intensity of CHE were measured by the catastrophic health payment method. A logistic regression was used to analyse determinants of CHE. RESULTS: The results indicate that the proportion of insured households with CHE varies widely between 3.94% and 75.60%, depending on the method and the threshold used. At the 40% threshold, health care cost represents 60.95% of insured households' total monthly non-food expenditure. This study showed that the socioeconomic status, the type of health facility used, hospitalization and household size were the highest predictors of CHE. Whatever the chosen threshold, care in referral and district hospitals significantly increases the likelihood of CHE. In addition, the proportion of households facing CHE is higher in the lowest income groups. The behaviour of health care providers, poor quality of care and long waiting time were the main factors leading to CHE. CONCLUSION: A sizable proportion of insured households face CHE, suggesting gaps in the coverage. To limit the impoverishment of insured households with low income, policies for free or heavily subsidized hospital services should be considered. The results call for an equitable health insurance scheme, which is affordable for all insured households.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".