Local modeling

A Structured Additive Modeling of Diabetes and Hypertension in Northeast India

This article was originally published here

PLoS One. 2022 Jan 13;17(1):e0262560. doi: 10.1371/journal.pone.0262560. eCollection 2022.


BACKGROUND: Several factors are associated with the risk of diabetes and hypertension. In India, they even vary a lot from district to district. Therefore, diabetes and hypertension control strategies must appropriately address local risk factors and consider the specific causes of diabetes and hypertension prevalence at the sub-population level and in specific settings. specific contexts. This article examines the demographic and socioeconomic risk factors as well as the spatial disparity of diabetes and hypertension among adults aged 15–49 years in northeastern India.

METHODS: The study used data from the Indian Demographic Health Survey, which was conducted across the country between 2015 and 2016. All men and women aged 15 to 49 were tested for diabetes and hypertension in the survey. A Bayesian geo-additive model was used to determine risk factors for diabetes and hypertension.

RESULTS: The prevalence rates of diabetes and hypertension in northeast India were 6.38% and 16.21%, respectively. Prevalence was higher among men, urban residents, and those who were widowed/divorced/separated. The functional relationship between household wealth index and diabetes and hypertension was found to be an inverted U-shape. As household wealth status increased, its effect on diabetes also increased. However, interestingly, the opposite was observed in the case of hypertension, i.e. as household wealth status increased, its effect on hypertension decreased. Unstructured spatial variation for diabetes was mainly due to unobserved risk factors present in one district that were unrelated to neighboring districts, while for hypertension the structured spatial variation was due to unobserved factors that were related to neighboring districts.

CONCLUSION: Diabetes and hypertension control measures must take into account local and non-local factors that contribute to spatial heterogeneity. More emphasis should be placed on efforts to assess district-specific factors in the prevalence of diabetes within a region.

PMID:35025967 | DOI:10.1371/journal.pone.0262560