title: Spatio-temporal variation and prediction of ischemic heart disease hospitalizations in Shenzhen, China.
abstract: Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013-2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1,1). In addition, the combined analysis of the prediction of IHD hospital admissions and the general hospital distributions shows that Pingshan and Longgang might experience the most serious burden of IHD hospital services in the near future, although Futian would still have the greatest number and the highest incidence rate of hospital admissions for IHD.
authors.affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. wangwangyanxia@163.com.
authors.forename: Yanxia
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. qydu@whu.edu.cn.
authors.forename: Qingyun
authors.initials: Q
authors.lastname: Du
authors.affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. renfu@whu.edu.cn.
authors.forename: Fu
authors.initials: F
authors.lastname: Ren
authors.affiliation: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China. carlsl@126.com.
authors.forename: Shi
authors.initials: S
authors.lastname: Liang
authors.affiliation: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China. ldn308@163.com.
authors.forename: De-nan
authors.initials: DN
authors.lastname: Lin
authors.affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. tianqin@whu.edu.cn.
authors.forename: Qin
authors.initials: Q
authors.lastname: Tian
authors.affiliation: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China. chy@newhealth.com.cn.
authors.forename: Yan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China. jiajiali831@gmail.com.
authors.forename: Jia-jia
authors.initials: JJ
authors.lastname: Li
mesh_terms: D002681:China / Q000453:epidemiology; D005843:Geography; D006760:Hospitalization / Q000706:statistics & numerical data* / Q000639:trends*; D006801:Humans; D015994:Incidence; D008962:Models, Theoretical; D017202:Myocardial Ischemia / Q000175:diagnosis / Q000453:epidemiology*; D011446:Prospective Studies; D012189:Retrospective Studies; D012621:Seasons; D013997:Time Factors