The development of appropriate methods of analysis for surveillance of spatial health data is of critical importance to public health practitioners, yt surveillance for emerging outbreaks of disease in space and time is a relatively undeveloped arena of statistical methodology. Most of the vast literature in space-time surveillance has been developed for retrospective analyses of complete data sets. However, data in public health accumulate over time and sequential estimation of the model using all the data collected so far is a key concept to early detection of localized disease outbreaks. The impact derived from timely treatment and control measures can be dramatic, especially when monitoring maps of cancer disease-incidence, one of the leading causes of death worldwide. The goal of this application is to develop a statistical methodology for prospective spatio- temporal disease surveillance, with cancer surveillance being our primary focus. This technique will be used to detect areas of increased disease incidence as quickly as possible to reduce morbidity and mortality. The conditional predictive ordinate is a Bayesian diagnostic tool that detects unusual observations and it is proposed to extend its use to case event incidence. We will test these hypotheses in three specific aims.
In specific Aim 1 we will adapt the conditional predictive ordinate in a case event surveillance setting.
In specific Aim 2 the focus is the evaluation of computation approaches, and the implementation of the surveillance tools in an R package, a free statistical programming language available in many public health departments, and this will enable its use by public health practitioners.
In this project we will develop a novel model-based surveillance technique to monitor a map of cancer incidence over time. This technique will enable early detection of changes in disease risk helping to reduce undue morbidity and mortality. The implementation of the proposed technique in a user-friendly package within the R software environment will facilitate dissemination and use by public health practitioners
| Rotejanaprasert, Chawarat; Lawson, Andrew (2018) Bayesian prospective detection of small area health anomalies using Kullback-Leibler divergence. Stat Methods Med Res 27:1076-1087 |
| Rotejanaprasert, Chawarat; Lawson, Andrew; Bolick-Aldrich, Susan et al. (2016) Spatial Bayesian surveillance for small area case event data. Stat Methods Med Res 25:1101-17 |
