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Spatial epidemiology of diabetes: Methods and insights

World Journal of Diabetes 2021年第7期12卷 1042-1056页

作者： Diego F Cuadros Jingjing Li Godfrey Musuka Susanne F Awad Geography and Geographic Information Systems University of CincinnatiCincinnatiOH 45221United States Urban Health Collaborative Drexel UniversityPhiladelphiaPA 19104United States ICAP Columbia UniversityHarare 00000Zimbabwe Infectious disease Epidemiology Group Weill Cornell Medicine–QatarCornell UniversityDoha 24144Qatar World Health Organization Collaborating Centre for disease Epidemiology Analytics on HIV/AIDS Sexually Transmitted Infectionsand Viral HepatitisWeill Cornell Medicine–QatarCornell UniversityDoha 24144Qatar Department of Population Health Sciences Weill Cornell MedicineCornell UniversityNew YorkNY 10044United States

Diabetes mellitus(DM)is a growing epidemic with global *** is estimated that in 2019,463 million adults aged 20-79 years were living with *** latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades,which would have major implications for healthcare expenditures,particularly in developing ***,new conceptual and methodological approaches to tackle the epidemic are long *** epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency *** implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular *** this review,we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of *** reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of ***,we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

关键词： Diabetes mellitus Spatial epidemiology Risk factors Spatial statistics disease mapping

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Spatio-temporal clustering analysis of COVID-19 cases in Johor

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Infectious disease Modelling 2024年第2期9卷 387-396页

作者： Fong Ying Foo Nuzlinda Abdul Rahman Fauhatuz Zahroh Shaik Abdullah Nurul Syafiah Abd Naeeim School of Mathematical Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

At the end of the year 2019,a virus named SARS-CoV-2 induced the coronavirus disease,which is very contagious and quickly spread around the *** new infectious disease is called *** areas,such as the economy,social services,education,and healthcare system,have suffered grave consequences from the invasion of this deadly ***,a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious *** this research,the daily reported COVID-19 cases in 92 sub-districts in Johor state,Malaysia,as well as the population size associated to each sub-district,are used to study the propagation of COVID-19 disease across space and time in *** time frame of this research is about 190 days,which started from August 5,2021,until February 10,*** clustering technique known as spatio-temporal clustering,which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district *** results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre(Bandar Johor Bahru),and during the festive *** findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of *** the other hand,the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

关键词： disease mapping COVID-19 Hot-spot areas Sub-district level Spatio-temporal clustering Scan statistics

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Narrative minireview of the spatial epidemiology of substance use disorder in the United States:Who is at risk and where?

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World Journal of Clinical Cases 2023年第11期11卷 2374-2385页

作者： Diego F Cuadros Adam J Branscum Claudia M Moreno Neil J MacKinnon Digital Futures University of CincinnatiCincinnatiOH 45206United States Department of Biostatistics Oregon State UniversityCorvallisOR 97331United States Department of Physiology and Biophysics University of WashingtonSeattleWA 98195United States Department of Population Health Sciences Augusta UniversityAugustaGA 30912United States

Drug overdose is the leading cause of death by injury in the United *** incidence of substance use disorder(SUD)in the United States has increased steadily over the past two decades,becoming a major public health problem for the *** drivers of the SUD epidemic in the United States have changed over time,characterized by an initial heroin outbreak between 1970 and 1999,followed by a painkiller outbreak,and finally by an ongoing synthetic opioid *** nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug ***,the geospatial distribution of the SUD epidemic is not *** United States has specific locations where vulnerable communities at high risk of SUD are concentrated,reaffirming the multifactorial socioeconomic nature of this epidemic.A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation *** narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods,revealing some spatiotemporal patterns that have preceded the occurrence of *** analyzing the epidemic of SUD-related deaths,we also describe the epidemic behavior in areas with high incidence of ***,we describe public health interventions that can be effective for demographic groups,and we discuss future challenges in the study and control of the SUD epidemic in the country.

关键词： Substance use disorder Spatial epidemiology Risk factors Spatial statistics disease mapping

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Graph-based multivariate conditional autoregressive models

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Statistical Theory and Related Fields 2019年第2期3卷 158-169页

作者： Ye Liang Department of Statistics Oklahoma State UniversityStillwaterOklahoma 74078USA

The conditional autoregressive model is a routinely used statistical model for areal data thatarise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literatureand it has been shown that extending from the univariate case to the multivariate case is nottrivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modelling froman element-based perspective instead of the traditional vector-based perspective. We focus onthe joint adjacency structure of elements and discuss graphical structures for both the spatialand non-spatial domains. We assume that the graph for the spatial domain is generally knownand fixed while the graph for the non-spatial domain can be unknown and random. We proposea very general specification for the multivariate conditional modelling and then focus on threespecial cases, which are linked to well-known models in the literature. Bayesian inference forparameter learning and graph learning is provided for the focused cases, and finally, an examplewith public health data is illustrated.

关键词： Areal data disease mapping graphical model G-wishart distribution Markov random field reversible jump

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