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KENFIN-EDURA: Explaining non-communicable disease-related behaviour in the context of urbanization, family and wealth


Kanerva, Noora (2022), KENFIN-EDURA: Explaining non-communicable disease-related behaviour in the context of urbanization, family and wealth, Dryad, Dataset,



The prevalence of non-communicable diseases is increasing in lower-middle-income countries as these countries transition to unhealthy lifestyles. The transition is mostly predominant in urban areas. We assessed the association between wealth and obesity in two sub-counties in Nairobi City County, Kenya, in the context of family and poverty.


A total of 149 households, response rate of 93%, participated, 72 from Embakasi and 77 from Langata. Most of the participants residing in Embakasi belonged to the lower income and education groups whereas participants residing in Langata belonged to the higher income and education groups. About 30% of the pre-adolescent participants in Langata were with at least overweight, whereas the respective number in Embakasi was only 6% (p<0.001). In contrast, the prevalence of adults (mostly mothers) with overweight and obesity was high (65%) and similar in the two study areas. Wealth (b = 0.01; SE 0.0; p=0.003) and income (b = 0.29; SE 0.11; p=0.009) predicted higher BMI z-score in pre-adolescents.


In Nairobi, pre-adolescent overweight was already highly prevalent in the middle-income area, while the proportion of women with overweight/obesity was high also in the low-income area. These results suggest that a lifestyle promoting obesity is prevalent even in lower income areas  in urban Kenya, and this is a strong justification for promoting healthy lifestyles across all socio-economic classes.


This cross-sectional study was conducted among of 9-14 years old pre-adolescents and their guardians living in low- (Embakasi) and middle-income (Langata) sub-counties. The sociodemographic characteristics were collected using a validated questionnaire. Weight, height, mid-upper arm circumference, and waist circumference were measured using standard approved protocols.  Socioeconomic characteristics of the residential sites were accessed using Wealth Index, created by using Principal Component Analysis.  Statistical analyses were done by analysis of variance (continuous variables, comparison of areas) and with logistic and linear regression models.


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