TY - JOUR
T1 - Infant mortality and social environment in Georgia
T2 - An application of hotspot detection and prioritization
AU - Yang, Tse Chuan
AU - McManus, Brian
N1 - Funding Information:
Acknowledgments This study was completed under the auspices of the NSF Project (0307010) on digital governance and surveillance hotspot geoinformatics of detection and multicriteria prioritization. We acknowledge the support of Penn State’s Social Science Research Institute for the continued support of Dr. Yang’s position. Additional support has been provided by the Population Research Institute (PRI) at Penn State, which receives core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24-HD41025). We thank the anonymous reviewers’ for their suggestions. We also thank Drs. G.P. Patil, Wayne Myers, and Sharadchandra Joshi for their assistance and insight.
PY - 2010
Y1 - 2010
N2 - Recent years have witnessed the growth of new information technologies and their applications to various disciplines. The goal of this paper is to demonstrate how the two innovative methods, upper level set scan (ULS) hotspot detection and the multicriteria prioritization scheme, facilitate population health and break new ground in public health surveillance. It is believed that the social environment (i.e. social conditions and social capital) is one of the determinants of human health. Using infant health data and 10 additional indicators of social environment in the 159 counties of Georgia, ULS identified 52 counties that are in double jeopardy (high infant mortality and a high rate of low infant birth weight). The multicriteria ranking scheme suggested that there was no conspicuous spatial cluster of ranking orders, which improved the traditional decision making by visual geographic cluster. Both hotspot detection and ranking methods provided an empirical basis for re-allocating limited resources and several policy implications could be drawn from these analytic results.
AB - Recent years have witnessed the growth of new information technologies and their applications to various disciplines. The goal of this paper is to demonstrate how the two innovative methods, upper level set scan (ULS) hotspot detection and the multicriteria prioritization scheme, facilitate population health and break new ground in public health surveillance. It is believed that the social environment (i.e. social conditions and social capital) is one of the determinants of human health. Using infant health data and 10 additional indicators of social environment in the 159 counties of Georgia, ULS identified 52 counties that are in double jeopardy (high infant mortality and a high rate of low infant birth weight). The multicriteria ranking scheme suggested that there was no conspicuous spatial cluster of ranking orders, which improved the traditional decision making by visual geographic cluster. Both hotspot detection and ranking methods provided an empirical basis for re-allocating limited resources and several policy implications could be drawn from these analytic results.
KW - Hotspot detection
KW - Infant mortality
KW - Multicriteria prioritization
KW - Social capital
UR - http://www.scopus.com/inward/record.url?scp=78650524360&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650524360&partnerID=8YFLogxK
U2 - 10.1007/s10651-010-0166-4
DO - 10.1007/s10651-010-0166-4
M3 - Article
AN - SCOPUS:78650524360
SN - 1352-8505
VL - 17
SP - 455
EP - 471
JO - Environmental and Ecological Statistics
JF - Environmental and Ecological Statistics
IS - 4
ER -