The effect of per capita income on the prevalence of schistosomiasis in selected African countries: a panel study
Bibliographic record
Abstract
BACKGROUND: Schistosomiasis is one of the parasitic diseases of poverty caused by larval forms of trematode worms. Individuals get infected upon contact with water infected by these larvae through skin penetration. Thus, poor people without access to basic water and sanitation services, among others are more likely to contract the disease. The greatest burden of the disease is found in Africa where over 200 million people require preventive treatment. Given that Africa has experienced economic growth in recent times, albeit with some declines, this study investigated the effect of per capita income on the prevalence of schistosomiasis in selected countries on the continent. METHODS: The study employed panel data on 35 African countries over the period 2002-2019. The data were obtained from the Global Burden of Diseases Study (GBD) 2021 database and the World Bank's World Development Indicators (WB's WDI) database. Prevalence of schistosomiasis i) among males and females (overall), ii) among males and iii) among females were used as the baseline dependent variables. Three other indicators of schistosomiasis were used for robustness purposes. The growth rate of Gross Domestic Product (GDP) per capita (per capita income) was used as the main independent variable. The dynamic panel system Generalised Method of Moments (GMM) regression was employed as the empirical estimation technique. RESULTS: In the baseline, the study found a negative significant effect of per capita income on the prevalence of schistosomiasis (overall prevalence of schistosomiasis (β = -0.021, p < 0.05), prevalence of schistosomiasis among males (β = -0.04, p < 0.01) and prevalence of schistosomiasis among females (β = -0.04, p < 0.01)). The findings were not qualitatively different when the three other indicators of schistosomiasis were used. CONCLUSION: Enhancing per capita income on the African continent remains critical towards the fight against schistosomiasis. Therefore, measures such as educational and skills development, technological advancement, among others, that can enhance per capita income should be deepened by governments, firms (including financial institutions) and other stakeholders.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".