A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA.

Chawarat Rotejanaprasert,Andrew Lawson

doi:10.3390/ijerph15092042

Abstract

Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Although jittering by adding a small number from a uniform distribution to impose pseudo-continuity has been proposed, the approach can have a great influence on responses with small values. Thus we proposed an alternative to model the quantiles of relative risk for spatiotemporal areal health data within a Bayesian framework using the log-Laplace distribution. A simulation study was conducted to assess the performance of the proposed method and examine whether the model could robustly estimate quantiles of spatiotemporal count data. To perform a test with a real data example, we evaluated the potential application of clustering under the proposed log-Laplace and mean regression. The data were obtained from the total number of emergency room discharges for respiratory conditions, both infectious and non-infectious diseases, in the U.S. state of South Carolina in 2009. From both simulation and case studies, the proposed quantile modeling demonstrated potential for broad applicability in various areas of spatial health studies including anomaly detection.

Highlights

The ordinary mean regression has been a main analytic approach in epidemiological studies
We demonstrate with the case study of combined conditions, the proposed methods can be applied to individual or a group of diseases with shared etiology
In this study paper and we have proposed alternative model the between quantilesexceedence of spatiotemporal relative simulation case study, andan provided the to relationship probability and risk

Summary

Introduction

The ordinary mean regression has been a main analytic approach in epidemiological studies. It is usually assumed in the regression that the covariates have an effect only on the mean of the outcome distribution; other aspects, such as variance or skewness, are not usually considered. While this method offers attractive features such as computationally efficiency and convenience to interpret, it may not be effective in many cases. Quantile modeling has been developed to extend the mean regression to model conditional quantiles of the outcome variables [1,2]. The quantile regression technique may provide a more accurate explanation of functional changes than aiming solely on the mean

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