Abstract
To evaluate the association between length of residence in an urban area and obesity among Peruvian rural-to-urban migrants. Cross-sectional database analysis of the migrant group from the PERU MIGRANT Study (2007). Exposure was length of urban residence, analysed as both a continuous (10-year units) and a categorical variable. Four skinfold site measurements (biceps, triceps, subscapular and suprailiac) were used to calculate body fat percentage and obesity (body fat percentage >25% males, >33% females). We used Poisson generalized linear models to estimate adjusted prevalence ratios and 95 % confidence intervals. Multicollinearity between age and length of urban residence was assessed using conditional numbers and correlation tests. A peri-urban shantytown in the south of Lima, Peru. Rural-to-urban migrants (n 526) living in Lima. Multivariable analyses showed that for each 10-year unit increase in residence in an urban area, rural-to-urban migrants had, on average, a 12 % (95 % CI 6, 18 %) higher prevalence of obesity. This association was also present when length of urban residence was analysed in categories. Sensitivity analyses, conducted with non-migrant groups, showed no evidence of an association between 10-year age units and obesity in rural (P=0·159) or urban populations (P=0·078). High correlation and a large conditional number between age and length of urban residence were found, suggesting a strong collinearity between both variables. Longer lengths of urban residence are related to increased obesity in rural-to-urban migrant populations; therefore, interventions to prevent obesity in urban areas may benefit from targeting migrant groups.
Highlights
In the analysis of the exposure as a categorical variable, both models used the
We showed that migrant groups living in an urban area for more than 30 years have a 39 % higher prevalence of obesity when compared with migrants living in an urban area for less than 20 years
Our study found a high degree of multicollinearity between the three mentioned timedependent variables: the mean variance inflation factor (VIF) found in Model C was above 10 and even four times greater than the one reported in another migrant study about obesity risk in the USA[18]
Summary
Study design The present study is a cross-sectional database analysis of the PERU MIGRANT Study. In the analysis of the exposure as a categorical variable, both models used the
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