Associations among food security, BMI, diet diversity and food consumption patterns of women in rural Kenya
Notice bibliographique
Résumé
Enhancing food security is one of the United Nations Sustainable Development Goals. Improved agricultural production is an approach to addressing food insecurity. However, these efforts can result in significant changes that are associated with increased risk of non-communicable chronic diseases. Previous research with women in Central Kenya found that the number of years a woman-farmer was engaged in dairy development was associated with greater food security and energy (kJ) intake. No studies have examined food security and the nutrition transition among rural farm women involved in dairy development initiatives. A cross-sectional survey was conducted in 2009 and again in 2017. Chain referral sampling was used in 2009 to recruit 111 women in five groups based on how long they had been a dairy group member (non-members and members from one to more than 10 years). In 2017, 20 women were randomly selected from 85 of the dairy-group member households surveyed in 2009 and a comparison group of 20 women was randomly selected from a list that was generated by referral from the participants. In person one-on-one interviews were conducted with a translator. Demographic, household food security, and dietary intake were collected in both years. In 2009 women's height and weight data were collected for BMI calculation. Associations among food security, BMI, dietary intake, and year of data collection were examined. Demographic characteristics were comparable between 2009 and 2017 and between food secure and food insecure households. In both years, the majority of households were categorized as not food insecure (67% and 75%; 2009 and 2017, respectively). In 2009 almost half the women (49.4%) had BMIs in the overweight and obese category. Food secure women had a significantly higher median BMI (26.5) compared with food insecure women (24.0). Diet diversity was low (<5) for all women and there was little evidence for a classic "western diet" of the nutrition transition. There was evidence of globalization in food availability and greater purchasing capacity that was demonstrated by the high proportion of women that consumed sugar, refined fats and oils, refined maize and wheat flours, and bread. Household food security was associated with the consumption of sugar, cabbage, wheat flour and bread. These findings support a unique rural nutrition transition, with food security, characterized by low dietary diversity, access to refined basic foods and high unhealthy BMI. Governments and non-governmental organizations need to support nutrition-sensitive agriculture and public health programs to curb the crisis of overweight, undernourishment and chronic diseases.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».