Huang X, Lambert S, Lau C, Soares Magalhaes R J, Marquess J, Rajmokan M, Milinovich G, Hu W
School of Public Health and Social Work,Institute of Health and Biomedical Innovation Queensland University of Technology,Brisbane,Australia.
UQ Child Health Research Centre, The University of Queensland,Brisbane,Australia.
Epidemiol Infect. 2017 Apr;145(6):1221-1230. doi: 10.1017/S0950268816003289. Epub 2017 Jan 16.
Pertussis epidemics have displayed substantial spatial heterogeneity in countries with high socioeconomic conditions and high vaccine coverage. This study aims to investigate the relationship between pertussis risk and socio-environmental factors on the spatio-temporal variation underlying pertussis infection. We obtained daily case numbers of pertussis notifications from Queensland Health, Australia by postal area, for the period January 2006 to December 2012. A Bayesian spatio-temporal model was used to quantify the relationship between monthly pertussis incidence and socio-environmental factors. The socio-environmental factors included monthly mean minimum temperature (MIT), monthly mean vapour pressure (VAP), Queensland school calendar pattern (SCP), and socioeconomic index for area (SEIFA). An increase in pertussis incidence was observed from 2006 to 2010 and a slight decrease from 2011 to 2012. Spatial analyses showed pertussis incidence across Queensland postal area to be low and more spatially homogeneous during 2006-2008; incidence was higher and more spatially heterogeneous after 2009. The results also showed that the average decrease in monthly pertussis incidence was 3·1% [95% credible interval (CrI) 1·3-4·8] for each 1 °C increase in monthly MIT, while average increase in monthly pertussis incidences were 6·2% (95% CrI 0·4-12·4) and 2% (95% CrI 1-3) for SCP periods and for each 10-unit increase in SEIFA, respectively. This study demonstrated that pertussis transmission is significantly associated with MIT, SEIFA, and SCP. Mapping derived from this work highlights the potential for future investigation and areas for focusing future control strategies.
在社会经济条件良好且疫苗接种覆盖率高的国家,百日咳疫情呈现出显著的空间异质性。本研究旨在探讨百日咳风险与社会环境因素之间的关系,以及这些因素对百日咳感染时空变异的影响。我们获取了澳大利亚昆士兰州卫生部门按邮政区域统计的2006年1月至2012年12月期间百日咳报告的每日病例数。采用贝叶斯时空模型来量化每月百日咳发病率与社会环境因素之间的关系。社会环境因素包括月平均最低气温(MIT)、月平均水汽压(VAP)、昆士兰州学校日历模式(SCP)以及地区社会经济指数(SEIFA)。2006年至2010年百日咳发病率上升,2011年至2012年略有下降。空间分析表明,2006 - 2008年昆士兰州各邮政区域的百日咳发病率较低且空间分布较为均匀;2009年后发病率较高且空间异质性更大。结果还显示,每月MIT每升高1℃,百日咳月发病率平均下降3.1% [95%可信区间(CrI)1.3 - 4.8],而SCP期间以及SEIFA每增加10个单位,百日咳月发病率平均分别上升6.2%(95% CrI 0.4 - 12.4)和2%(95% CrI 1 - 3)。本研究表明,百日咳传播与MIT、SEIFA和SCP显著相关。这项工作得出的地图突出了未来调查的潜力以及未来控制策略的重点区域。