Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China

Yan Jia; Qing Xu; Yuchen Zhu; Chunyu Li; Chang Qi; Kaili She; Tingxuan Liu; Ying Zhang; Xiujun Li

doi:10.1186/s12889-023-16350-y

Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China

BMC Public Health. 2023 Jul 25;23(1):1422. doi: 10.1186/s12889-023-16350-y.

Authors

Yan Jia¹, Qing Xu², Yuchen Zhu¹, Chunyu Li¹, Chang Qi¹, Kaili She¹, Tingxuan Liu¹, Ying Zhang³, Xiujun Li⁴

Affiliations

¹ Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
² Institute of Immunization and Preventive Management, Shandong Center for Disease Control and Prevention, Jinan, 250014, China.
³ Faculty of Medicine and Health, School of Public Health, University of Sydney, Camperdown, NSW, 2006, Australia.
⁴ Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China. xjli@sdu.edu.cn.

Abstract

Background: Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles.

Methods: Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared.

Results: Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2-Q4) were 0.79 (0.69-0.91), 0.54 (0.44-0.65), and 0.48 (0.38-0.61), respectively; RR (95%CI) for higher relative humidity (Q2-Q4) were 0.76 (0.66-0.88), 0.56 (0.47-0.67), and 0.49 (0.38-0.63), respectively; RR (95%CI) for higher wind velocity (Q2-Q4) were 1.43 (1.25-1.64), 1.85 (1.57-2.18), 2.00 (1.59-2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different.

Conclusions: Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China.

Keywords: Measles; Meteorological factors; Regional relative risks; Spatiotemporal Bayesian model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
China / epidemiology
Humans
Incidence
Measles* / epidemiology
Measles* / prevention & control
Meteorological Concepts*
Temperature