Drought in Ethiopia: A Population Health Equity Approach to Build Resilience for the Agro-Pastoralist Community
Bibliographic record
Abstract
BACKGROUND: A devastating drought is ravaging Africa, with Ethiopia being the worst-hit country. Ethiopia’s economy is predominantly reliant on rain-fed farming and livestock. The agriculture sector contributes up to 85% of the country’s livelihoods. The drought has threatened agro-economy and health of over 15 million agro-pastoralist population who herd the largest livestock in Africa. Some governments announced its commitment in the UN to extend support for the drought-affected people. The Sendai framework for Disaster Risk Reduction prioritizes proactive rather than reactive relief response that can promote health resilience. Applying population health matrices can serve the purpose by exploring the determinants of health, their impacts on the differential health outcomes for population sub-groups and to improve the overall health of the population by addressing the health inequity. OBJECTIVE: This study aims to identify the critical population health outcomes, underlying determinants, and the leverage points for actions that can guide effective policies and interventions for building health resilience for the vulnerable agro-pastoralist population in Ethiopia. METHODS: Two researchers searched nine academic and grey literature bibliographic databases for drought literature and related health interventions. We used the PRISMA checklist to synthesize data and Hamilton tools to evaluate individual study quality. We analyzed data employing disaster vulnerability and WHO’s social determinants of health and health equity frameworks. Socioeconomic, political and cultural backgrounds are examined to identify policy and leverage points for effective population health interventions. RESULTS: Health issues are diverse that revolve around the major determinants of health such as food security, infrastructure, health systems, disaster preparedness, household productivity-income, livestock dependence and access to the market economy. These determinants are further affected by socioeconomic, political and cultural contexts. Despite dire vulnerability and health inequity, some potentials evolved from recent public health field practices as the leverage points for policy actions and interventions. CONCLUSION: The recommended interventions can be implemented through an interdisciplinary population health approach to get the maximum impacts on health resilience. Evidence gathered from the worst drought niche in Africa can be useful to tackle similar droughts induced health issues in other parts of the continent. Future intervention research on the ground can generate robust evidence for action to build health resilience.
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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 | 0.039 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".