The syndemics of food and water insecurities on emotional distress and overall wellbeing in Ghana: Findings from a cross-sectional study
Bibliographic record
Abstract
• The study employed a robust and reliable global wellbeing measure, with questions adapted to reflect the lived experiences of the study context. • The study concurrently assesses the associations and interactions between household food and water insecurities on health outcomes. • The study employed multilevel mixed effects generalized linear and logistics models to analyze emotional distress and wellbeing. Water and food security are essential to health and wellbeing. Although globally, progress has been made in improving access to safe drinking water and adequate amounts of healthy and nutritious diets, insecurities remain, resulting in major public health concerns. Furthermore, we know little about the syndemics of living with both water and food insecurities. This study examines the relationship between water and food insecurities, as well as their interaction effects on emotional distress and overall wellbeing. Using Ghana as a case study, we conducted a cross-sectional household survey (n = 1,036) using a multi-stage sampling technique and employed multilevel mixed effects generalized linear and logistics models (meglm and melogit) to analyze the outcome variables. Participants subjective wellbeing was measured using a modified global wellbeing measure that follows a multidimensional approach. Emotional distress was measured using the General Health Questionnaire (GHQ-20) which assesses several aspects of emotional distress including predisposition to depression, anxiety, and social impairment. We found that medium water insecure (aOR=1.79, p ≤ 0.05) and severe food insecure (aOR=2.05, p ≤ 0.05) households had higher likelihood of reporting emotional distress compared to households that did not experience either water or food insecurities, respectively. In addition to the main effects, there were significant interaction effects between experiencing medium water insecurity and severe food insecurity on emotional distress. Similarly, there were significant interaction effects between experiencing medium water insecurity and severe food insecurity as well as experiencing severe water insecurity and severe food insecurity on subjective wellbeing compared to households that were both water and food secure, respectively. In addition to water and food insecurities at the household level, other significant predictors of emotional distress and wellbeing included income adequacy, housing security and poverty. Conceptualizing, measuring, and tracking the syndemics of food and water insecurities on emotional distress and overall wellbeing provides useful insight into the need for and efficacy of public health and global development interventions.
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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 | 0.004 |
| 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 | 0.001 |
| 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".