An empirical analysis of the effects of schistosomiasis and lymphatic filariasis on macroeconomic output in Ghana
Bibliographic record
Abstract
Background Schistosomiasis and Lymphatic Filariasis (LF) are endemic in Ghana. These diseases cause significant morbidity and disability which can adversely affect the participation of affected persons and their families in economic activities, resulting in reduced economic output at the macrolevel. This study, therefore aims to provide the first empirical evidence of the effects of these diseases on economic output at the macrolevel in Ghana. Methods The study uses time series data on Ghana collected from secondary sources over the period, 1990–2019. Gross Domestic Product (GDP) is used to proxy macroeconomic output (i.e., dependent variable) and the main independent variables are the point prevalence of schistosomiasis and LF. The Ordinary Least Square (OLS) and the Instrumental Variable Two-Stage Least Square (IV2SLS) regressions are employed as estimation techniques. Results Using the IV2SLS regression, a percentage increase in the overall prevalence of schistosomiasis as well as the prevalence of schistosomiasis among males and females are found to be associated with a 1.36 %, 1.30 % and 1.39 % fall in macroeconomic output respectively, at the 1 % level of significance. Similarly, a percentage increase in the overall prevalence of LF as well as the prevalence of LF among males and females are found to be associated with a 0.37 %, 0.37 % and 0.38 % fall in macroeconomic output respectively, at the 1 % level of significance. Results from the OLS regression are not qualitatively different. Conclusion There is the need to strengthen efforts towards fighting schistosomiasis and LF in Ghana in order to reduce their associated economic losses.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".