Cost of hypertension illness and associated factors among patients attending hospitals in Southwest Shewa Zone, Oromia Regional State, Ethiopia
Bibliographic record
Abstract
Abstract Background Hypertension is a common vascular disease and the main risk factor for cardiovascular diseases. The impact of hypertension is on the rise in Ethiopia, so that, it is predictable that the cost of healthcare services will further increase in the future. We aimed to estimate the total cost of hypertension illness among patients attending hospitals in Southwest Shewa zone, Oromia Regional State, Ethiopia. Patients and methods Institution based cross-sectional study was conducted from July 1-30, 2018. All hypertensive patients who were on treatment and whose age was greater than eighteen years old were eligible for this study. The total cost of hypertension illness was estimated by summing up the direct and indirect costs. Bivariate and multivariate linear regression analysis was conducted to identify factors associated with hypertension costs of illness. Results Overall, the mean monthly total cost of hypertension illness was US $ 22.3 (95% CI, 21.3-23.3). Direct and indirect costs share 51% and 49% of the total cost, respectively. The mean total direct cost of hypertension illness per patient per month was US $11.39(95% CI, 10.6-12.1). Out of these, drugs accounted of a higher cost (31%) followed by food (25%). The mean total indirect cost per patient per month was US $10.89(95% CI, 10.4-11.4). Educational status, distance from hospital, the presence of companion and the stage of hypertension were predictors of the cost of illness of hypertension. Conclusion The cost of hypertension illness was very high when compared with the mean monthly income of the patients letting patients to catastrophic costs. Therefore, due attention should be given by the government to protect patients from financial hardships.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".