Household food sources and diarrhoea incidence in poor urban communities, Accra Ghana
Bibliographic record
Abstract
Diarrhoeal diseases remain a significant cause of morbidity and mortality, particularly in poor urban communities in the Global South. Studies on food access and safety have however not considered the sources of discrete food categories and their propensity to harbour and transmit diarrhoeal disease pathogens in poor urban settings. We sought to contribute to knowledge on urban food environment and enteric infections by interrogating the sources and categories of common foods and their tendency to transmit diarrhoea in low-income communities in Accra. We modelled the likelihood of diarrhoea transmission through specific food categories sourced from home or out of home after controlling for alternate transmission pathways and barriers. We used structured interviews where households that participated in the study were selected through a multi-stage systematic sampling approach. We utilized data on 506 households from 3 low-income settlements in Accra. These settlements have socio-economic characteristics mimicking typical low-income communities in the Global South. The results showed that the incidence of diarrhoea in a household is explained by type and source of food, source of drinking water, wealth and the presence of children below five years in the household. Rice-based staples which were consumed by 94.5% of respondents in the week preceding the survey had a higher likelihood of transmitting diarrhoeal diseases when consumed out of home than when eaten at home. Sources of hand-served dumpling-type foods categorized as "staple balls" had a nuanced relationship with incidence of diarrhoea. These findings reinforce the need for due diligence in addressing peculiar needs of people in vulnerable conditions of food environment in poor urban settlements in order to reap a co-benefit of reduced incidence of diarrhoea while striving to achieve the global development goal on ending hunger.
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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 | 0.002 |
| 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".