hierarchical model

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. Thise model play a pivotal role in spatial modeling, adeptly handling uncertainties arising from diverse spatial data sources.

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. Utilizing a direct approach, disease prevalence and expected case numbers were calculated. The analysis employed the Besag-York-Mollié (BYM) model, with parameter estimation executed through the Hamiltonian Monte Carlo method.

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 > 0.8), with a calculated 95% confidence interval 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.

In conclusion, this investigation employed hierarchical Bayesian models to evaluate the prevalence and spatial distribution of hyperlipidemia across the provinces of Iran. The analysis unveiled a significant escalation in the relative risk of hyperlipidemia in 16% of Iranian provinces, underscoring the spatial heterogeneity in disease prevalence. This study contributes invaluable insights into the spatial dynamics of hyperlipidemia in Iran, establishing a groundwork for the formulation of targeted public health strategies.

Nowadays, one of the major public health problems among children is growth failure. It can be characterized in terms of either inadequate growth or the inability to maintain growth..
The main objective of this study was to examine the effects of some factors on growth failure among a sample of infants less than two years old..
The present longitudinal archival study relied on data gathered from health files from February 2007 to July 2010 for 1,358 children under two years of age, selected from eight health centers in the east and northeast parts of Tehran, Iran. In the present study, growth failure refers to at least a 50 g decrease in an infant’s weight as recorded at each attendance in comparison to the previous measurement. The impacts of risk indicators were assessed using the Bayesian hierarchical logistic regression modeling technique..
The highest and lowest percentage of growth failure was 5.8% and 0.1%, respectively, in the eleventh and the first month after birth. The obtained results from the Bayesian hierarchical modeling revealed that diarrhea (95% credible interval (CrI): 0.70 - 3.31), discontinuation of breastfeeding (95% CrI: 0.77 - 5.96), and respiratory infections (95% CrI: 2.07 - 4.61) were significant risk factors for growth failure. The random term at the child level was significant (95% CrI: 0.74 - 7.82), while the variation in centers was extremely small (95% CrI: 0.004 - 4.22)..
It was noted that a relatively high prevalence of growth failure was observed in the study sample. For minimizing the impact of significant risk factors on growth failure, the early detection of growth failure and its risk indicators is of great importance. In addition, when the focus of the analysis is on the different nested sources of variability and the data has a hierarchical structure, using a hierarchical modeling approach is recommended to achieve more accurate results..