Factors Influencing Expenditure on Unhealthy Foods Among the Urban Poor in Malaysia
Bibliographic record
Abstract
Consumption of unhealthy foods possesses harmful effects on health, thereby increasing the disease burden. If low-income people, especially those living in urban areas, do not make efforts to reduce their consumption on unhealthy foods, the health‒economic costs borne by them will rise. To date, there is a growing number of Malaysian studies that examine factors affecting consumption expenditure on unhealthy foods, but none has paid attention to the urban poor. The objective of this study is to narrow this research gap. A seemingly unrelated regressions model was utilised to estimate the effects of sociodemographic and health factors on expenditure of oil and fats, processed foods, sugar-sweetened beverages and alcoholic drinks. Income and household size were positively associated with expenditure on unhealthy foods. Individuals who were between 61 and 70 years old had higher expenditure on unhealthy foods than their younger counterparts. The Chinese spent less on certain unhealthy foods compared to the Malays. Being employed, having tertiary-level education, being married and living with chronic diseases increased spending on unhealthy foods. These findings suggest that the Malaysian government should consider increasing the tax on sugar-sweetened beverages and using health campaigns to educate the urban poor about the risks of unhealthy foods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".