Understanding the Connections between Neighborhood Environments and the Burden of Infectious and Non-communicable Diseases in Ghana
Bibliographic record
Abstract
Amidst the persistent burden of infectious diseases (i.e., malaria, HIV) across many developing country settings, there is now the added burgeoning burden of non-communicable diseases [NCDs (i.e., cancer, and diabetes)]. This concurrence, described as the double burden of disease (DBD), presents a fundamental public health challenge to governments and relevant stakeholders situated in the global south. The DBD phenomenon has triggered a wave of research interests and policy debates with a focus on behavioral change interventions to the neglect of population-level determinants like the environment. The DBD further appears to have emerged at a time when the healthcare system is not well positioned to effectively respond to population health needs. This dissertation contributes to these gaps by examining the relationship between neighborhood environments and the burden of NCDs and infectious diseases across three cities (Accra, Tamale, and Wa) in Ghana using a sample of 1386 surveys that were collected between September – December 2021. I further examined whether NCD health outcomes differed by gender spatially using multilevel analytic techniques in Stata 14.2 software. Results show that about 1 in 4 people reported at least one NCD health condition and nearly 1 in 5 reported suffering the DBD. The findings further revealed spatial variations of NCD and infectious disease outcomes as respondents from relatively deprived neighborhoods were significantly more likely to report poor health outcomes. Men who spend time outside their residential neighborhood in a typical week were more likely to report being diagnosed with NCDs compared with their counterparts who spend the entire week in their residential neighborhoods. The results demonstrate the need to move beyond individualistic risk factors to discussing important population-level determinants of NCDs like the environment in the fight against NCDs in the developing world. Moreover, the static view of the role of place on health is too reductionist and there is the need to account for other spaces people frequent through a gendered lens. Finally, the emerging epidemiologic transition in Ghana and other developing countries calls for a retooling of the healthcare system to effectively respond to the changing health needs of the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".