Economic burden of malaria on rural households in Gwanda district, Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Malaria is a serious public health problem in sub-Saharan Africa and is a leading cause of morbidity and mortality. AIM: To estimate the economic burden of malaria in rural households. SETTING: The study was conducted in Gwanda district of Matabeleland South in Zimbabwe. A total of five malarious wards and all their households were selected for the study frame, out of which 80 households were chosen using clinic records. METHODS: A retrospective analysis of secondary data and a cross-sectional household survey were conducted to estimate the household economic burden of malaria. Eighty households from five rural wards were identified from the health facility malaria registers and followed up. A household was eligible for inclusion if there had been at least one reported malaria case during the period of 2013-2015. Interviewer administered questionnaires were used to collect household data on economic costs of malaria. RESULTS: Our findings showed that households spent an average of $3.22 and $56.60 for managing an uncomplicated and a complicated malaria episode respectively. A household lost an average of eight productive working days per each malaria episode resulting in an average loss of 24% of the monthly household income. An estimated 35%, mostly poorer households suffered catastrophic health expenditures. CONCLUSION: Malaria imposes significant economic burdens particularly on the poorer and vulnerable households. Although there are no user fees at rural clinics, households incur other costs to manage a malaria patient. These costs are far worse for complicated cases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".