Urbanization and Health in Developing Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Future population growth will take place predominantly in cities of the developing world. The impact of urbanization on health is discussed controversially. We review recent research on urban-rural and intra-urban health differences in developing countries and investigate whether a health advantage was found for urban areas. METHODS: We systematically searched the databases JSTOR, PubMed, ScienceDirect and SSRN for studies that compare health status in urban and rural areas. The studies had to examine selected World Health Organization health indicators. RESULTS: Eleven studies of the association between urbanization and the selected health indicators in developing countries met our selection criteria. Urbanization was associated with a lower risk of undernutrition but a higher risk of overweight in children. A lower total fertility rate and lower odds of giving birth were found for urban areas. The association between urbanization and life expectancy was positive but insignificant. Common risk factors for chronic diseases were more prevalent in urban areas. Urban-rural differences in mortality from communicable diseases depended on the disease studied. CONCLUSION: Several health outcomes were correlated with urbanization in developing countries. Urbanization may improve some health problems developing countries face and worsen others. Therefore, urbanization itself should not be embraced as a solution to health problems but should be accompanied by an informed and reactive health policy.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".