Bayesian and Frequentist Comparison for Epidemiologists: A Non Mathematical Application on Logistic Regressions

A full Bayesian multilevel approach for modeling interaction effects in single-vehicle crashes

Interaction effects constitute crucial crash attributes that can be classified into two distinct categories: spatiotemporal interactions and factor interactions. These interactions are rarely addressed systematically in modeling the severity of single-vehicle (SV) crashes. This study focuses on uncovering these crash attributes by designing a full Bayesian spatiotemporal interaction multilevel logit (STIML-logit) approach with heterogeneity in means and variances (HMV). Meanwhile, a nested Gaussian conditional autoregressive (CAR) structure is proposed to fit the spatiotemporal interaction component and its effectiveness is verified by calibrating four different interaction patterns. A standard multilevel logit (with and without HMV), a multilevel logit with HMV, and a spatiotemporal multilevel logit with HMV are constructed for comparison. Risk factors are decomposed into traffic environment factors (group level) and individual crash factors (case level) to construct a multilevel structure and to capture possible interactions between risk factors from different levels (cross-level factor interactions). We perform regression modeling utilizing SV crash cases covering 96 major urban roads in Shandong, China. The modeling results underscore several significant findings: (1) the STIML-logit with HMV demonstrates the best regression performance, suggesting that systematically dealing with the interaction effects and the HMV is a trustworthy modeling perspective; (2) crash models with the nested CAR outperform those with the traditional CAR and the result is supported by all the spatiotemporal statistical functions, highlighting the potential advantages of the nested structure; (3) all the environment factors maintain significant interactions with the case factors, highlighting that the contribution of the environment factors to crash injuries is not constant but is rather influenced by the specific case-related crash factors. The study introduces a promising regression architecture for modeling crash injuries and revealing subtle crash attributes.

Bayesian multilevel analysis of determinants of acute respiratory infection in children under the age of five years in Ethiopia

BACKGROUND

Acute respiratory tract infection (ARI) is one of the leading causes of illness and mortality in children under the age of five worldwide. Pneumonia, which is caused by a respiratory tract infection, kills about 1.9 million children under the age of 5 years around the world. The majority of these deaths occur in underdeveloped countries. According to the 2016 Ethiopia Demographic and Health Survey (EDHS), the prevalence rate of ARI in Ethiopia was 7%. Prevalence is defined as the number of infectious diseases present at a given period in relation to the total number of children under the age of five who have been exposed to ARI. The goal of this study was to determine the risk factors for acute respiratory infection among children under the age of five in Ethiopia.

METHODS

To provide representative samples of the population, a community-based cross-sectional sampling scheme was designed. Bayesian multilevel approach was employed to assess factors associated with the prevalence of ARI among children under age five in Ethiopia. The data was collected from 10,641 children under the age of 5 years out of which 9918 children were considered in this study.

RESULTS

The ARI prevalence rate in children under the age of 5 years was assessed to be 8.4%, somewhat higher than the country's anticipated prevalence rate. Children whose mothers did not have a high level of education had the highest prevalence of ARI. The key health, environmental, and nutritional factors influencing the proportion of children with ARI differed by area. Tigray (15.3%) and Oromia (14.4%) had the highest prevalence of ARI, while Benishangul Gumuz had the lowest prevalence (2.6%). The use of vitamin A was investigated, and the results revealed that roughly 43.1% of those who received vitamin A had the lowest prevalence of ARI (7.7%) as compared to those who did not receive vitamin A. Diarrhea affected 11.1% of children under the age of five, with the highest frequency of ARI (24.6%) and the highest prevalence of ARI reported in children whose drinking water source was unprotected/unimproved (9.4%).

CONCLUSIONS

The prevalence of ARI among children under the age of 5 years was found to be strongly affected by the child's age, household wealth index, mother's educational level, vitamin A supplement, history of diarrhea, maternal work, stunting, and drinking water source. The study also found that the incidence of ARI varies significantly between and within Ethiopian areas. When intending to improve the health status of Ethiopian children, those predictive variables should be taken into consideration.

Impact of air pollution on cause-specific mortality in Korea: Results from Bayesian Model Averaging and Principle Component Regression approaches

Health effects related to air pollution are a major global concern. Related studies based on reliable exposure assessment methods would potentially enable policy makers to propose appropriate environmental management policies. In this study, integrated Bayesian Model Averaging (BMA) and Principle Component Regression (PCR) were adopted to assess the severity of air pollution impacts on mortality related to circulatory, respiratory and skin diseases in 25 districts of Seoul, South Korea for the years 2005-2015. These methods were consistent in determining the best regression models and most important pollutants related to mortality in those highly susceptible to poor air quality. Specifically, the results demonstrated that pneumonia was highly associated with air pollution, with a large determination coefficient (BMA: 0.46, PCR: 0.51) and high model's posterior probability (0.47). The most reliable prediction model for pneumonia was indicated by the lowest Bayesian Information Criterion. Among the pollutants, particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) was associated with serious health risks on evaluation, with the highest posterior inclusion probabilities (range, 80.20 to 100.00%) and significantly positive correlation coefficients (range, 0.14 to 0.34, p < 0.05). In addition, excessive PM₁₀ concentration (approximately 2.54 times the threshold) and a continuous increase in mortality due to respiratory diseases (approximately 1.50-fold in 10 years) were also exhibited. Overall, the results of this study suggest that currently, socio-environmental policies and international collaboration to mitigate health effects of air pollution is necessary in Seoul, Korea. Moreover, consideration of uncertainty of the regression model, which was verified in this research, will facilitate further application of this approach and enable optimal prediction of interactions between human and environmental factors.