The role of universal health coverage in secondary prevention: A case study of Ghana’s National Health Insurance Scheme and early-onset hypertension
Bibliographic record
Abstract
Launched in 2003, Ghana’s National Health Insurance Scheme (NHIS) was a move towards Universal Health Coverage. There is a dearth of studies that have since investigated the effect of the scheme on non-communicable diseases (NCDs) like hypertension. While a major cause of mortality and morbidity, hypertension remains mostly undiagnosed in Ghana. Secondary prevention comprising early detection and prompt treatment is, hence, important in reducing disease burden. This study assessed the association between active NHIS membership and the likelihood of having early-onset hypertension detected and treated. A cross-sectional analysis of the 2014 Ghana Demographic and Health Survey (male dataset) was conducted. Unadjusted analysis used binary logistic regression with active NHIS membership as the independent variable and detection of early-onset hypertension as the dependent variable. Early-onset hypertension was defined as the onset of hypertension at 55 years or younger. Covariates for the adjusted regression models were age, BMI, smoking status, place of residence, wealth, and education level. The association between membership and treatment was also assessed. Unadjusted and adjusted results showed that the odds of early-onset hypertension being detected in participants with active NHIS membership were respectively 2.4 (95 % CI:1.56 – 3.59, p = 0.000) and 2.2 (95 % CI 1.43 – 3.24, p = 0.000) that of those without active membership. There was no significant association between membership and treatment. This study suggests that NHIS membership may play a beneficial role in the secondary prevention of NCDs in Ghana. Further research is, nevertheless, needed to understand how membership, NCDs, and other contextual factors are interrelated. • UHC in Ghana may be associated with increased detection of hypertension. • The association between UHC and treatment and control of hypertension is inconclusive. • UHC may play a role in the secondary prevention of non-communicable diseases in sub-Saharan Africa.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".