Abstract |
Introduction Evidence from low-income and middle-income countries suggests that migration status has an impact on health. However, little is known about the effect that migration status has on morbidity in sub-Saharan Africa. The aim of this study is to investigate the association between migration status and hypertension and diabetes and to assess whether the association was modified by demographic and socioeconomic characteristics.Methods A Quality ofLife survey conducted in 2015 collected data on migration status and morbidity from a sample of 28 007 adults in 508 administrative wards in Gauteng province (GP). Migration status was divided into three groups: non-migrant if born in Gauteng province, internal migrant if born in other South African provinces, and external migrant if born outside of South Africa. Diabetes and hypertension were defined based on self-reported clinical diagnosis. We applied a recently developed original, stepwise-multilevel logistic regression of discriminatory accuracy to investigate the association between migration status and hypertension and diabetes. Potential effect modification by age, sex, race, socioeconomic status (SES) and ward-level deprivation on the association between migration status and morbidities was tested.Results Migrants have lower prevalence of diabetes and hypertension. In multilevel models, migrants had lower odds of reporting hypertension than internal migrants (OR=0.86; 95\% CI 0.78 to 0.95) and external migrant (OR=0.60; 95\% CI 0.49 to 0.75). Being a migrant was also associated with lower diabetes prevalence than being an internal migrant (OR=0.84; 95\% CI 0.75 to 0.94) and external migrant (OR=0.53; 95\% CI 0.41 to 0.68). Age, race and SES were significant effect modifiers of the association between migration status and morbidities. There was also substantial residual between-ward variance in hypertension and diabetes with median OR of 1.61 and 1.24, respectively.Conclusions Migration status is associated with prevalence of two non-communicable conditions. The association was modified by age, race and SES. Ward-level effects also explain differences in association. |