Bayesian Spatio-Temporal Modeling of Hyperlipidemia Risk in IRAN; A Repeated Cross- Sectional Analysis

Shayesteh  Alinia; Kamyar  Mansouri; Shahram  Arsang-Jang

doi:10.18502/jbe.v10i2.17640

Shayesteh Alinia Student Research Committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Kamyar Mansouri Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Shahram Arsang-Jang Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.

DOI: https://doi.org/10.18502/jbe.v10i2.17640

Keywords: Random effect model; Hierarchical Model; Clustering; Epidemiology; Hyperlipidemi

Abstract

Introduction: The incidence of hyperlipidemia in Iran is on a consistent rise, potentially contributing significantly to increased susceptibility to cardiovascular diseases and other health complications linked to elevated blood lipid levels. This study employs hierarchical Bayesian model to assess the heightened lipid risk on a broader scale across Iran's provinces.

Methods: This study included individuals diagnosed with hyperlipidemia from all provinces of Iran in 2019. The primary focus of the investigation included essential variables such as the mean age, gender distribution, and the documented incidence of hyperlipidemia cases in each province. Population data, stratified by province, age group, and gender, were sourced from the Iranian Statistics Center database. The analysis employed the Besag-York-Mollié (BYM) model, with parameter estimation executed through the Hamiltonian Monte Carlo method.

Results: In this investigation, the prevalence and spatial distribution of hyperlipidemia were explored within a diverse population of 1,609,538 patients across various regions in Iran. The relative risk of hyperlipidemia surpassed 1 in 16% of Iranian provinces (Posterior probability [PP] > 0.8), with a calculated 95% Confidence interval (CI) of 0.304 to 0.879. The overall prevalence of hyperlipidemia was determined to be 0.815. Significant heterogeneity in hyperlipidemia was identified among different provinces, with Tehran exhibiting the highest relative risk (RR=1.701; 95% CrI: 1.69, 1.713). Notably, gender (RR=1.008; CI: 1.007, 1.009 for males and RR=1.005;CI: 1.003, 1.007 for females) and age were not found to have a statistically significant effect on the relative risk of the disease.

Conclusion: In conclusion, the study sheds light on the spatial dynamics of hyperlipidemia in Iran. 16% of provinces displayed a heightened relative risk, emphasizing the need for targeted public health strategies.