Malaria Prevalence and Macroeconomic Output in Ghana, 1990 to 2019
Bibliographic record
Abstract
In Ghana, malaria remains the number 1 reason for outpatient department visits, making it a major public health problem. Thus, there could be significant lost productivity days as a result of malaria morbidity and mortality, which could negatively affect economic output at the macrolevel. Nonetheless, there is a dearth of empirical evidence of the effect of malaria on macroeconomic output in Ghana. This study therefore aims to provide the foremost empirical evidence regarding the effect of malaria prevalence on macroeconomic output in Ghana using a time series design with data spanning the period 1990 to 2019. Gross Domestic Product (GDP), serving as a proxy for macroeconomic output, is the dependent variable, while the prevalence of malaria (overall, among only males and among only females) serves as the main independent variable. The Ordinary Least Square (OLS) regression is used as the baseline estimation technique and the Instrumental Variable Two-Stage Least Square (IV2SLS) regression is employed as the robustness check estimator due to its ability to deal with endogeneity. The IV2SLS regression results show that a percentage increase in the overall prevalence of malaria is associated with a 1.16% decrease in macroeconomic output at 1% significance level. We also find that the effect of malaria in males on macroeconomic output is slightly higher relative to females. The findings from the OLS regression are not qualitatively different from the IV2SLS regression estimates. There is therefore the need to strengthen efforts such as quality case management, larval source management, mass distribution of long-lasting insecticide-treated bed nets, social behavior change, surveillance (both epidemiological and entomological), intermittent preventive treatment of malaria in pregnancy, research among others, which are important toward eliminating malaria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".