Dietary patterns among adults in three low-income urban communities in Accra, Ghana
Bibliographic record
Abstract
OBJECTIVE: Dietary patterns describe the dietary behaviour and habits of individuals. Unhealthy dietary patterns provide individuals with limited nutrients while increasing the risk of nutrition-related diseases. Unhealthy dietary patterns are high in urban areas, especially among low-income urban residents. This study examined dietary patterns in three low-income urban communities in Accra, Ghana, between 2011 and 2013. METHODS: This study used Wave 2 and 3 data from the Urban Health and Poverty Survey (EDULINK 2011 and 2013). The sample size was 960 in 2011 and 782 in 2013. Dietary pattern was examined using factor analysis and the NOVA food classification system. Summary statistics were computed for sociodemographic characteristics and diet frequency and pattern. Differences in dietary behaviours between 2011 and 2013 were also estimated. Three logistic regression models were computed to determine the predictors of dietary patterns. RESULTS: The frequency of consumption of animal-source foods (ASF) and fruits was higher in 2013 compared with 2011. The intake of processed culinary ingredients (NOVA Group 2), processed foods (NOVA Group 3) and ultra-processed foods (NOVA Group 4) was higher in 2013 versus 2011. In 2013, 29% consumed ultra-processed foods compared to 21% in 2011. Three dietary patterns (rice-based, snack-based, and staple and stew/soup) were identified. About two out of every five participants consumed the food items in the rice (43%) and staple and sauce patterns (40%). The proportion of participants who consumed the food items in the snack pattern was 35% in 2011 but 41% in 2013. Respondents aged 25-34 and those with higher education often consumed the snack-based and rice-based dietary patterns. In 2013, participants in Ussher Town had a higher probability of consuming food items in the snack pattern than those living in Agbogbloshie. CONCLUSIONS: This study found that between 2011 and 2013, more participants consumed ASFs, fruits, and processed foods. A complex interplay of personal and socio-cultural factors influenced dietary intake. The findings of this study mirror global changes in diet and food systems, with important implications for the primary and secondary prevention of NCDs. Health promotion programs at the community level are needed to address the increasing levels of processed food consumption.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".