Households, the omitted level in contextual analysis: disentangling the relative influence of households and districts on the variation of BMI about two decades in Indonesia

作者：Vaezghasemi Masoud; Ng Nawi; Eriksson Malin; Subramanian S V

来源：International Journal for Equity in Health, 2016, 15(1): 102.

DOI：10.1186/s12939-016-0388-7

• Body mass index
• Multilevel modelling
• Omitted level
• Contextual effect
• Households
• Indonesian family life survey

Background: Most of the research investigating the effect of social context on individual health outcomes has interpreted context in terms of the residential environment. In these studies, individuals are nested within their neighbourhoods or communities, disregarding the intermediate household level that lies between individuals and their residential environment. Households are an important determinant of health yet they are rarely included at the contextual level in research examining association between body mass index (BMI) and the social determinants of health. In this study, our main aim was to provide a methodological demonstration of multilevel analysis, which disentangles the simultaneous effects of households and districts as well as their associated predictors on BMI over time.
Methods: Using both two- and three-level multilevel analysis, we utilized data from all four cross-sections of the Indonesian Family life Survey (IFLS) 1993 to 2007-8.
Results: We found that: (i) the variation in BMI attributable to districts decreased from 4.3 % in 1993 to 1.5 % in 1997-98, and remained constant until 2007-08, while there was an alarming increase in the variation of BMI attributable to households, from 10 % in 2000 to 15 % in 2007-08; (ii) ignoring the household level did not change the relative variance contribution of districts on BMI, but ignoring the district level resulted in overestimation of household effects, and (iii) households' characteristics (socioeconomic status, size, and place of residence) did not attenuate the variation of BMI at the household-level.
Conclusions: Estimating the relative importance of multiple social settings allows us to better understand and unpack the variation in clustered or hieratical data in order to make valid and robust inferences. Our findings will help direct investment of limited public health resources to the appropriate context in order to reduce health risk (variation in BMI) and promote population health.

• 出版日期2016-7-7