Examining the disparities: A cross-sectional study of socio-economic factors and food insecurity in Togo
Bibliographic record
Abstract
BACKGROUND: Despite many interventions, Togo continues to have one of the highest rates of poverty and food insecurity in the sub-Saharan African region. Currently there is no systematic analysis of the factors associated with household food-insecurity in this country. This study aimed at exploring the factors associated with food insecurity in Togo. METHODS: This was a cross-sectional study that used data from five waves (2014 to 2018) of the Gallup World Poll (GWP) for Togo. Sample size included 4754 participants, aged 15 and above. Food insecurity was measured using the Food Insecurity Experience Scale (FIES) questionnaire as per the Food and Agricultural Organization (FAO) guidelines. Our outcome variable was food insecurity, categorized as: 1) food secure (FIES score = 0-3), moderately food insecure (FIES score = 4-6), and severely food insecure (FIES score = 7-8). We did descriptive and multinomial regressions to analyze data using Stata version 16. RESULTS: Between 2014 and 2018, the percentage of severe food insecurity fluctuated-42.81% in 2014, 37.79% in 2015, 38.98% in 2016, 45.41% in 2017, and 33.84% in 2018. Whereas that of moderate food insecurity increased from 23.55% to 27.33% except for 2016 and 2017 where the percentage increased to 32.33% and 27.46% respectively. In the logistic regression analysis, we found that respondents with lower than elementary education had a higher relative risk ratio of moderate (RRR = 1.45,95%CI = 1.22-1.72) and severe (RRR = 1.72, 95%CI = 1.46-2.02) food insecurity compared to those with secondary and higher education. Rural respondents had higher RRR of severe food insecurity (RRR = 1.37, 95%CI = 1.16-1.62) compared to those who lived in the urban areas. Compared with those in the richest wealth quintile, respondents in the poorest wealth quintile had 2.21 times higher RRR of moderate (RRR = 2.21, 95%CI = 1.69-2.87) and 3.58 times higher RRR of severe (RRR = 3.58, 95%CI = 2.81-4.55) food insecurity. CONCLUSION: About two-thirds of participants experienced some level of food insecurity in 2018. Lower levels of education, rural residency and poorer household wealth index areas were associated with a higher risk of food insecurity. National food security programs should focus on promoting education and improving socioeconomic condition of people especially in rural areas.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".