The geographical distribution of disease risk is known to vary over time and the space-time (ST) analysis of disease maps is now a frequent focus of analysis with Epidemiology or Public Health. One important aspect of the risk profile that can change over time is the existence of underlying (latent) components of risk that can yield common disease outcomes. Conventional risk models usually examine the spatial structure by modeling the mean level of risk. This can lead to quite sophisticated Bayesian hierarchical models. These models can account for clustering of risk and smooth out noise from disease data. However they are not designed to find out about underlying latent components of risk. Our proposal has four components that address latent structure for ST analysis of disease risk: The development of spatio-temporal Poisson mixture models. It is planned to extend spatial mixture models into the spatio-temporal disease mapping context. The primary concern will be to extend the spatial Poisson mixture hidden component model to the spatio-temporal domain. This extension will allow the use of spatially- and temporally- structured weights with a random number of latent components. In this way we expect to be able to produce a flexible modeling strategy for spatio-temporal disease incidence data. Development and evaluation of a mean mixture model approach to latent structure. It is planned to change the approach to examine the use of mean mixtures in a ST context. In this approach, we will focus on temporally-varying underlying components with spatial structure confined to the weights that are available for each region. This separation leads to interesting issues concerning continuity of the components and the appropriate design of prior distributions, including the use of multivariate CAR prior distributions for weights. The development of Dirichlet process mixture models for the spatio-temporal domain. It is proposed that Dirichlet process models for univariate geo-referenced disease incidence data be extended for the situation where spatio-temporal data is observed. The extension will be focused on the use of time-referenced basis functions and the idea of latent spatio-temporal risk profiles will be considered. Comparative evaluation and software development there is a need to make a comparison of the above methods in application to real and simulated data scenarios. This will be achieved by application of the methods to credible temporally-varying simulated risk component scenarios as well as readily available real ST datasets for childhood outcomes (asthma, birth anomalies and birth weight).There is also a need for flexible software to be made available that can allow researchers and public health workers to be able to use advanced modeling approaches, with their ability to flexibly build appropriate descriptions of the observed disease data.
This aim will be achieved by the development of software within the R and WinBUGS programming environment.

Public Health Relevance

Underlying structure in disease map evolution may be hidden but its estimation could be potentially useful in predicting future disease outcomes. Our methods address the estimation of these components and can help to establish hitherto unseen etiological factors or effects that are important.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21HL088654-02
Application #
7879453
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Wolz, Michael
Project Start
2009-07-01
Project End
2012-04-30
Budget Start
2010-05-01
Budget End
2012-04-30
Support Year
2
Fiscal Year
2010
Total Cost
$221,250
Indirect Cost
Name
Medical University of South Carolina
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
183710748
City
Charleston
State
SC
Country
United States
Zip Code
29425
Lawson, Andrew B; Choi, Jungsoon; Zhang, Jiajia (2014) Prior choice in discrete latent modeling of spatially referenced cancer survival. Stat Methods Med Res 23:183-200
Hossain, M M; Lawson, A B; Cai, B et al. (2014) Space-Time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues. Environmetrics 25:84-96
Hossain, Md Monir; Lawson, Andrew B; Cai, Bo et al. (2013) Space-time stick-breaking processes for small area disease cluster estimation. Environ Ecol Stat 20:91-107
Cai, Bo; Lawson, Andrew B; Hossain, Monir et al. (2013) Bayesian semiparametric model with spatially-temporally varying coefficients selection. Stat Med 32:3670-85
Cai, Bo; Lawson, Andrew B; Hossain, Md Monir et al. (2012) Bayesian latent structure models with space-time-dependent covariates. Stat Modelling 12:145-164
Lawson, Andrew B (2012) Bayesian point event modeling in spatial and environmental epidemiology. Stat Methods Med Res 21:509-29
Choi, Jungsoon; Lawson, Andrew B; Cai, Bo et al. (2012) A Bayesian latent model with spatio-temporally varying coefficients in low birth weight incidence data. Stat Methods Med Res 21:445-56
Lawson, Andrew B; Choi, Jungsoon; Cai, Bo et al. (2012) Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data. J Agric Biol Environ Stat 17:417-441
Choi, Jungsoon; Lawson, Andrew B; Cai, Bo et al. (2011) Evaluation of Bayesian spatio-temporal latent models in small area health data. Environmetrics 22:1008-1022
Kirby, Russell S; Liu, Jihong; Lawson, Andrew B et al. (2011) Spatio-temporal patterning of small area low birth weight incidence and its correlates: a latent spatial structure approach. Spat Spatiotemporal Epidemiol 2:265-71

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