Epidemiologic transition and the double burden of disease in Ghana: What do we know at the neighborhood level?
Bibliographic record
Abstract
Many developing countries including Ghana are currently experiencing dual disease burdens emerging from an unprecedented risk overlap that drive their epidemiological transitions. Yet, siloed and disintegrated approaches continue to take precedence in health research and policy programs that drive competition for limited resources to address competing health problems. The objective of this study was to offer empirical evidence in support of a cogent argument for an integrated framework for the study and management of infectious and chronic health conditions in Ghana. We did so by examining the prevalence, determinants, and neighborhoods trajectories of the double burden of disease using data from a cross-sectional neighborhood-based study in Ghana. We fitted multinomial multilevel multivariate models to a sample of 1377 individual surveys and the results presented as odds ratios. Findings show that amidst a rising burden of NCDs, infectious diseases remain the most common health condition and participants in deprived neighborhoods were significantly more likely to report poor health outcomes. Risk factors such as tobacco and alcohol consumption were significantly associated with NCDs and infectious diseases and respondents who reported being diagnosed with NCDs and infectious diseases in the past year were likely to engage in leisure time physical activities and eat healthy. Based on our findings, we recommend health reforms in Ghana and argue for the design and implementation of an integrated framework for the study and management of the double burden of disease in Ghana and similar developing country settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".