Editorial
Special Issue on Spatial Methods for Health Policy Research
Statistical Methods in Medical Research 2014, Vol. 23(2) 117–118 ! The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0962280212447031 smm.sagepub.com
Spatial biostatistics combines techniques from geography, epidemiology, public health, and statistics to explore spatial patterns in health outcomes, to better understand the health needs of communities, and to provide guidance in allocating health resources. These methods are playing an increasingly important role in health policy planning and decision making. To date, spatial analytic methods have been used to assess geographic variation in cancer incidence,1–4 infant mortality,5 outpatient medical expenditures,6 and physician prescribing patterns.7 Researchers have also used spatial analyses to evaluate access to health resources, such as emergency departments,8 primary care services,9 and palliative care.10 By discerning spatial patterns in health outcomes, policy makers and public health officials can design cost-effective interventions that address the specific needs of communities. This special issue of Statistical Methods in Medical Research includes five papers describing novel approaches to spatial data analysis and its application to public health and health policy research. The papers were originally presented as part of an invited session entitled ‘‘Spatial Methods for Health Policy Research’’ held in October, 2011, at the 9th annual International Conference on Health Policy Statistics (ICHPS) in Cleveland, Ohio. The papers cover a wide range of methodological topics, including multivariate modeling, survival analysis, missing data methods, spatial convolution models, and strategies for prior elicitation. In the first paper, Neelon et al. develop a bivariate probit model for the joint spatial analysis of areal-referenced binary data. They use their model to explore geographic variation in rates of preterm birth and low birth weight, and discuss the health policy implications of their findings. In paper 2, MacNab explores identifiability issues that can arise when choosing a prior distribution for spatial random effects. Using traffic fatality data, she highlights structural differences among various prior distributions and proposes a generalized weighted convolution model that can yield improved inferences in disease mapping applications. In the third paper, Reich et al. tackle the common problem of missing data in spatial analysis. In the spatial setting, missingness arises not only in the response variables and covariates but also in the geographic locations of the observations. The authors develop a hierarchical Bayesian model that accounts for uncertainty in the geographic locations, and apply their model to a study examining the effect of air pollution on adverse birth outcomes. In paper 4, Congdon considers the case in which health data are collected on a large geographic scale, but inferences at a more refined scale are of policy interest. He proposes a discrete process convolution model for the analysis of such data and uses the approach to estimate neighborhood-level chronic disease prevalence when less-discriminant information from healthprovider agencies is all that is available. And finally, in many applications, covariate effects vary across geographic regions. Often, these spatially varying effects can be modeled parsimoniously using a finite mixture representation. In the final paper, Lawson et al. describe a finite-mixture accelerated failure time (AFT) model for the spatial analysis of prostate cancer survival. They describe various strategies for modeling the mixing weights and discuss advantages and limitations of each approach.
Downloaded from smm.sagepub.com at NORTHERN ARIZONA UNIVERSITY on June 4, 2015
118
Statistical Methods in Medical Research 23(2)
These papers highlight new approaches to spatial data analysis with special emphasis on their application to public health and health policy research. We hope readers—both statisticians and health professionals—will find these papers applicable to their own research. Brian Neelon Department of Biostatistics and Bioinformatics Duke University Medical Center, Durham, NC, USA Andrew B Lawson Division of Biostatistics & Epidemiology, College of Medicine Medical University of South Carolina, Charleston, SC, USA References 1. Liang S, Carlin BP and Gelfand AE. Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information. Ann of App Stat 2009; 3: 943–962. 2. Mather FJ, Chen WV, Morgan LH, et al. Hierarchical modeling and other spatial analysis in prostate cancer incidence data. Am J Prev Med 2006; 30(2S): S88–S100. 3. Zhou H, Lawson AB, Hebert J, et al. A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer. Stat Med 2008; 27: 1468–1489. 4. Zhang J and Lawson A. Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J Appl Statistics 2011; 38: 591–603. 5. Banerjee S, Wall MM and Carlin BP. Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 2003; 4: 123–142.
6. Moscone F and Knapp M. Exploring the spatial pattern of mental health expenditure. J Mental Health Pol and Econ 2005; 8: 205–217. 7. Cheng CL, Chen Y-C, Liu T-M, et al. Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug prescribing pattern in Taiwan. BMC Public Health 2011; 11: 380. 8. Neelon B, Ghosh P and Loebs PF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J Roy Stat Soc 2012; 175(4): 1–25. 9. Mobley LR, Root E, Anselin L, et al. Spatial analysis of elderly access to primary care services. Int J Health Geog 2006; 5: 19. 10. Cinnamon J, Schuurman N and Crooks VA. A method to determine spatial access to specialized palliative care services using GIS. BMC Health Serv Res 2008; 8: 140.
Downloaded from smm.sagepub.com at NORTHERN ARIZONA UNIVERSITY on June 4, 2015
Special Issue on Spatial Methods for Health Policy Research
Statistical Methods in Medical Research 2014, Vol. 23(2) 117–118 ! The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0962280212447031 smm.sagepub.com
Spatial biostatistics combines techniques from geography, epidemiology, public health, and statistics to explore spatial patterns in health outcomes, to better understand the health needs of communities, and to provide guidance in allocating health resources. These methods are playing an increasingly important role in health policy planning and decision making. To date, spatial analytic methods have been used to assess geographic variation in cancer incidence,1–4 infant mortality,5 outpatient medical expenditures,6 and physician prescribing patterns.7 Researchers have also used spatial analyses to evaluate access to health resources, such as emergency departments,8 primary care services,9 and palliative care.10 By discerning spatial patterns in health outcomes, policy makers and public health officials can design cost-effective interventions that address the specific needs of communities. This special issue of Statistical Methods in Medical Research includes five papers describing novel approaches to spatial data analysis and its application to public health and health policy research. The papers were originally presented as part of an invited session entitled ‘‘Spatial Methods for Health Policy Research’’ held in October, 2011, at the 9th annual International Conference on Health Policy Statistics (ICHPS) in Cleveland, Ohio. The papers cover a wide range of methodological topics, including multivariate modeling, survival analysis, missing data methods, spatial convolution models, and strategies for prior elicitation. In the first paper, Neelon et al. develop a bivariate probit model for the joint spatial analysis of areal-referenced binary data. They use their model to explore geographic variation in rates of preterm birth and low birth weight, and discuss the health policy implications of their findings. In paper 2, MacNab explores identifiability issues that can arise when choosing a prior distribution for spatial random effects. Using traffic fatality data, she highlights structural differences among various prior distributions and proposes a generalized weighted convolution model that can yield improved inferences in disease mapping applications. In the third paper, Reich et al. tackle the common problem of missing data in spatial analysis. In the spatial setting, missingness arises not only in the response variables and covariates but also in the geographic locations of the observations. The authors develop a hierarchical Bayesian model that accounts for uncertainty in the geographic locations, and apply their model to a study examining the effect of air pollution on adverse birth outcomes. In paper 4, Congdon considers the case in which health data are collected on a large geographic scale, but inferences at a more refined scale are of policy interest. He proposes a discrete process convolution model for the analysis of such data and uses the approach to estimate neighborhood-level chronic disease prevalence when less-discriminant information from healthprovider agencies is all that is available. And finally, in many applications, covariate effects vary across geographic regions. Often, these spatially varying effects can be modeled parsimoniously using a finite mixture representation. In the final paper, Lawson et al. describe a finite-mixture accelerated failure time (AFT) model for the spatial analysis of prostate cancer survival. They describe various strategies for modeling the mixing weights and discuss advantages and limitations of each approach.
Downloaded from smm.sagepub.com at NORTHERN ARIZONA UNIVERSITY on June 4, 2015
118
Statistical Methods in Medical Research 23(2)
These papers highlight new approaches to spatial data analysis with special emphasis on their application to public health and health policy research. We hope readers—both statisticians and health professionals—will find these papers applicable to their own research. Brian Neelon Department of Biostatistics and Bioinformatics Duke University Medical Center, Durham, NC, USA Andrew B Lawson Division of Biostatistics & Epidemiology, College of Medicine Medical University of South Carolina, Charleston, SC, USA References 1. Liang S, Carlin BP and Gelfand AE. Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information. Ann of App Stat 2009; 3: 943–962. 2. Mather FJ, Chen WV, Morgan LH, et al. Hierarchical modeling and other spatial analysis in prostate cancer incidence data. Am J Prev Med 2006; 30(2S): S88–S100. 3. Zhou H, Lawson AB, Hebert J, et al. A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer. Stat Med 2008; 27: 1468–1489. 4. Zhang J and Lawson A. Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J Appl Statistics 2011; 38: 591–603. 5. Banerjee S, Wall MM and Carlin BP. Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 2003; 4: 123–142.
6. Moscone F and Knapp M. Exploring the spatial pattern of mental health expenditure. J Mental Health Pol and Econ 2005; 8: 205–217. 7. Cheng CL, Chen Y-C, Liu T-M, et al. Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug prescribing pattern in Taiwan. BMC Public Health 2011; 11: 380. 8. Neelon B, Ghosh P and Loebs PF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J Roy Stat Soc 2012; 175(4): 1–25. 9. Mobley LR, Root E, Anselin L, et al. Spatial analysis of elderly access to primary care services. Int J Health Geog 2006; 5: 19. 10. Cinnamon J, Schuurman N and Crooks VA. A method to determine spatial access to specialized palliative care services using GIS. BMC Health Serv Res 2008; 8: 140.
Downloaded from smm.sagepub.com at NORTHERN ARIZONA UNIVERSITY on June 4, 2015
