Statistical monitoring of the hand, foot and mouth disease in China

Summary. In a period starting around 2007, the Hand, Foot, and Mouth Disease (HFMD) became wide-spreading in China,and the Chinese public health was seriously threatened. To prevent the outbreak of infectious diseases like HFMD, eﬀectivedisease surveillance systems would be especially helpful to give signals of disease outbreaks as early as possible. Statisticalprocess control (SPC) charts provide a major statistical tool in industrial quality control for detecting product defectives in atimely manner. In recent years, SPC charts have been used for disease surveillance. However, disease surveillance data oftenhave much more complicated structures, compared to the data collected from industrial production lines. Major challenges,includinglackofin-controldata,complexseasonaleﬀects,andspatio-temporalcorrelations,makethesurveillancedatadiﬃcultto handle. In this article, we propose a three-step procedure for analyzing disease surveillance data, and our procedure isdemonstrated using the HFMD data collected during 2008–2009 in China. Our method uses nonparametric longitudinal dataand time series analysis methods to eliminate the possible impact of seasonality and temporal correlation before the diseaseincidence data are sequentially monitored by a SPC chart. At both national and provincial levels, our proposed method caneﬀectively detect the increasing trend of disease incidence rate before the disease becomes wide-spreading.Key words: ARIMA model; Bootstrap; Disease surveillance and prevention; Early detection; Nonparametric longitudinaldata analysis; Statistical process control; Time series.1. IntroductionThe Hand, Foot, and Mouth Disease (HFMD) is a commoninfectiousdiseasecausedbyagroupofnon-polioenterovirusesthat belongs to the Human Enterovirus species A (HEV-A).Itsmainclinicalsymptomsincludeabriefprodromalfeverfol-lowedbypharyngitis,mouthulcersandrashonthehandsandfeet. In most cases, the disease is mild and self-limiting. How-ever, severe clinical complications with neurological symp-toms, such as meningitis, encephalitis and polio-like paralysis,may occur (cf., Wang et al., 2011). In 2009, the numberof HFMD cases reported in mainland China amounted to1,155,525, including 13,810 (or 1.2%) severe cases and 353(or 0.03%) deaths. The high case-fatality rate was attributedto reasons such as the rapid disease progression, late clinicaltreatment, and limited local medical capacity. It was alsofound that the case-fatality rate decreased considerably oncethe early clinical treatment was provided to patients withsevere symptoms. Therefore, eﬀective disease surveillanceand timely implementation of disease prevention and controlmeasures are critically important for minimizing the damageof HFMD (cf., World Health Organization, 2011). This articlepresents a case study of the HFMD data collected in Chinaduring 2008–2009. It will show that certain statistical meth-ods are helpful in early detection of the HFMD outbreaks.In the statistical literature, statistical process control(SPC) charts provide a major statistical tool for monitoringindustrial production lines and for controlling product qual-ity (cf., Qiu, 2014). In recent several years, SPC charts havebeen frequently used for disease surveillance and prevention(cf., Woodall, 2006). In this regard, some authors model thenumberofdiseaseincidentsbyaPoissondistributionandcon-struct likelihood-ratio-based control charts for disease moni-toring (Zhou and Lawson, 2008). Some people develop con-trol charts based on parametric time series modeling of thepossible temporal correlation in the observed data (Watier,Richardson, and Hubert, 1991). Some others propose multi-variate control charts to accommodate the spatial pattern ex-hibited by surveillance data (Jiang, Tsui, and Woodall, 2000;Rogerson and Yamada, 2004). However, disease surveillancedata often have much more complicated structures, comparedto the data collected from industrial production lines. Manyissues about the observed disease incidence data need to becarefully addressed before an SPC chart can be applied. Forexample,thePoissondistributionmodelandotherparametricdistribution models are rarely valid in practice. The diseaseincidenceoftenhasacomplicatedseasonaltrend,andthecon-ventional SPC chart cannot handle such a trend properly. Itis critically important to address all such issues properly inorder to eﬀectively detect the HFMD outbreaks.In this article, we propose a three-step procedure for de-tecting HFMD outbreaks, and this procedure is demonstratedusing the HFMD data collected by the Chinese CDC during2008–2009. Our proposed method consists of the followingthree steps:(i)