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Notice bibliographique
Résumé
Mary Gatineau and Shireen Mathrani from the National Obesity Observatory explore the relationship between ethnicity and obesity in the UK There is no straightforward relationship between obesity and ethnicity. Obesity prevalence varies substantially between ethnic groups in the UK and interpretation of data is difficult because of uncertainty about appropriate obesity thresholds and associated levels of health risk. In addition, health behaviours both across and within minority ethnic groups are influenced by a complex interplay of cultural, lifestyle and socioeconomic factors.1 Obesity prevalence The most current data on adult obesity by ethnic group are from the Health Survey for England (HSE) 2004. Findings suggest that compared to the general population, obesity prevalence is lower among men from black African, Indian, Pakistani and most markedly, Bangladeshi and Chinese communities. Among women, obesity prevalence appears to be higher for those from Black African, Black Caribbean and Pakistani groups than for women in the general population and lower for women from the Chinese ethnic group.2 The National Child Measurement Programme (NCMP) provides the most robust data on child obesity in the UK and includes a detailed breakdown by ethnic sub-group. Recent analysis by the National Obesity Observatory (NOO)i shows that in Reception class, obesity prevalence is especially high for boys and girls from Black African and Black other ethnic groups and boys from the Bangladeshi ethnic group.ii The pattern for girls in Year 6 is broadly similar to that of girls in Reception, while for boys in Year 6, obesity prevalence is significantly higher for all ethnic groups compared to White British, with boys of Bangladeshi ethnicity having the highest prevalence. The analysis also finds a trend of rising obesity prevalence for both boys and girls of Bangladeshi ethnicity, with no significant changes in any other ethnic groups.3 Figure 1 provides a summary of this rising trend for Bangladeshi children in Year 6 compared to all other ethnic groups combined. Obesity measures and thresholds There are a number of issues associated with the measurement of obesity and the thresholds used for minority ethnic groups in the UK. Different ethnic groups are associated with a range of different body shapes and different physiological responses to fat storage. Body mass index (BMI) is not always an accurate predictor of body fat or fat distribution in individuals. Research has shown that for the same level of BMI, people of African ethnicity appear likely to carry less fat and people of South Asian ethnicity more fat than the general population. This may have led to an overestimation of obesity among African and an underestimation among South Asian groups.4 South Asian and Chinese populations have been found to be at risk of chronic diseases and mortality at lower levels than European populations. Revised BMI thresholds and waist circumference measures have been recommended for these groups. NCMP findings demonstrate a very high prevalence of obesity among boys of Bangladeshi ethnicity. These findings are in contrast with the general perception that children from Black ethnic groups have the highest obesity prevalence. The high odds of children from Black groups being classified as obese may in fact be due to physical characteristics related to ethnicity and, in particular, height, which can lead to skewed BMI.3,iii Factors determining obesity riskiv Dietary patterns of minority ethnic groups are influenced by many factors including availability of food, level of income, health, food beliefs, religion, cultural patterns and customs.5 While many people from these groups have healthier eating patterns than the White population, less healthy diets are known to be of concern in some groups, in particular those of South Asian origin. Migration to the UK has a significant impact on dietary habits. …
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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 | 0,011 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle