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

Drug overdose is the leading cause of death by injury in the United States.The 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 country.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 outbreak.The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic.Moreover,the geospatial distribution of the SUD epidemic is not homogeneous.The 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 policies.This 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 outbreaks.By analyzing the epidemic of SUD-related deaths,we also describe the epidemic behavior in areas with high incidence of cases.Finally,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.

A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies

Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus(DM)is a growing epidemic with global proportions.It is estimated that in 2019,463 million adults aged 20-79 years were living with DM.The 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 countries.Hence,new conceptual and methodological approaches to tackle the epidemic are long overdue.Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus.The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases.In this review,we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM.We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM.Finally,we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

Epidemiology for public health practice:The application of spatial epidemiology

Spatial epidemiology is the description and analysis of geographic patterns and variations in disease risk factors,morbidity and mortality with respect to their distributions associated with demographic,socioeconomic,environmental,health behavior,and genetic risk factors,and time-varying changes.In the Letter to Editor,we had a brief description of the practice for the mortality and the spacetime patterns of John Snow's map of cholera epidemic in London,United Kingdom in 1854.This map is one of the earliest public heath practices of developing and applying spatial epidemiology.In the early history,spatial epidemiology was predominantly applied in infectious disease and risk factor studies.However,since the recent decades,noncommunicable diseases have become the leading cause of death in both developing and developed countries,spatial epidemiology has been used in the study of noncommunicable disease.In the Letter,we addressed two examples that applied spatial epidemiology to cluster and identify stroke belt and diabetes belt across the states and counties in the United States.Similar to any other epidemiological study design and analysis approaches,spatial epidemiology has its limitations.We should keep in mind when applying spatial epidemiology in research and in public health practice.

Analysis of the Spatial Distribution Characteristics of Abnormal Bone Density in Male Long-Distance Runners from Different Regions in China

Objective: To investigate the occurrence pattern of abnormal bone density in male long-distance runners from several different regions of China, and provide a basis for elucidating the influences of geo-environmental differences on bone density. Methods: We employed a set of well-designed exclusion-inclusion criteria to recruit study subjects, in which compounding factors were managed and regional environmental traits were fully incorporated. WHO (World Health Organization) criteria for the diagnosis of osteoporosis were then used to examine the subjects to determine occurrence of abnormal bone density. The resulting data were analyzed using methods of spatial statistics, which included several approaches, such as spatial autocorrelation, hot spot analysis, and Geodetector Software analysis, to depict and analyze the spatial distribution of abnormal bone density in male athletes from different regions in China, thereby investigating the influences of geo-environmental factors (e.g., temperature, humidity, and altitude) on bone density. Results: A total of 685 subjects were effectively examined in this study, including 486 with normal bone density, 185 with osteopenia, and 14 with osteoporosis. Spatial distribution analysis revealed that the distribution of subjects with abnormal bone density overall exhibited a pattern indicating that the level of abnormal bone density in the eastern regions was higher than that in the western regions and that the levels of abnormal bone density in the southern and northern regions were higher than that in the middle regions. Spatial autocorrelation analysis revealed a Moran’s I = 0.136, Z-score = 1.114, and P value = 0.265 and indicated that the athletes with abnormal bone density were randomly distributed in each region. Hot spot analysis revealed that Tibet and Qinghai displayed distributions of cold spots. Geodetector Software analysis yielded a Q value for annual average temperature of 1.000 and a corresponding P value of 0.000, and the results revealed that temperature significantly affected bone density and that altitude, relative humidity, sunlight hours, and temperature variations displayed synergistic effects on bone density and could diminish the influences of temperature on bone density. Conclusion: Our data revealed that different regions displayed different distribution patterns of abnormal bone density such that the level in the eastern regions was higher than that in the western ones and that the levels in the southern and northern regions were higher than that in the middle regions;specifically, the provinces of Yunnan, Heilongjiang, Hainan, and Inner Mongolia had high rates of abnormal bone density, whereas Tibet and Qinghai had relatively good conditions of bone density. Our data suggested that suitable temperature changes and appropriate levels of temperature variations can decrease the occurrence rates of osteopenia and osteoporosis.

Statistical Assessment of Neighborhood Socioeconomic Deprivation Environment in Spatial Epidemiologic Studies

Neighborhood socioeconomic deprivation has been associated with health behaviors and outcomes. However, neighborhood socioeconomic status has been measured inconsistently across studies. It remains unclear whether appropriate socioeconomic indicators vary over geographic areas and geographic levels. The aim of this study is to compare the composite socioeconomic index to six socioeconomic indicators reflecting different aspects of socioeconomic environment by both geographic areas and levels. Using 2000 U.S. Census data, we performed a multivariate common factor analysis to identify significant socioeconomic resources and constructed 12 composite indexes at the county, the census tract, and the block group levels across the nation and for three states, respectively. We assessed the agreement between composite indexes and single socioeconomic variables. The component of the composite index varied across geographic areas. At a specific geographic region, the component of the composite index was similar at the levels of census tracts and block groups but different from that at the county level. The percentage of population below federal poverty line was a significant contributor to the composite index, regardless of geographic areas and levels. Compared with non-component socioeconomic indicators, component variables were more agreeable to the composite index. Based on these findings, we conclude that a composite index is better as a measure of neighborhood socioeconomic deprivation than a single indicator, and it should be constructed on an area- and unit-specific basis to accurately identify and quantify small-area socioeconomic inequalities over a specific study region.

Spatial Distribution of <i>Anopheles sinensis</i>and Its Relationship with Natural Environmental Factors in Wanning County on Hainan Island, China

Anopheles sinensis is widely distributed in Wanning County, it is necessary to understand the spatial distribution characteristics of Anopheles sinensis in order to maintain the elimination of malaria in Wanning. During May and October 2009, we sampled adult mosquitoes at 36 villages within Wanning County on Hainan island, and collected meteorological and geographical data at each sampling site. We used these data to analyze the spatial distribution of adult Anopheles sinensis mosquitoes, and logistic regression analysis was applied to explore the association of the spatial distribution of Anopheles sinensis with the geographical and meteorological factors. We found that the density of Anopheles sinensis showed a significant positive spatial correlation. From May to October, on the whole, the high-density area was located in the central part of Wanning County. But each month there was a relatively high-density cluster, and their location and range were not exactly the same. From east to west, the density of Anopheles sinensis increased initially and then decreased, but from south to north, there were different trends in the periods of May to August and September to October. Logistic regression analysis showed that the main environmental factors related with the distribution of Anopheles sinensis were land use type, soil type, distance to road, air pressure and relative humidity. These analysis results showed that the distribution of Anopheles sinensis had obvious spatial heterogeneity in Wanning County, which was related with geographical and meteorological factors. The mechanism of these environmental factors on the distribution of Anopheles sinensis needs to be further studied.

Determining the spatial distribution of environmental and socio-economic suitability for human leptospirosis in the face of limited epidemiological data

Background:Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide.Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts.However,leptospirosis is often an underreported and misdiagnosed disease and consequently,spatial patterns of the disease remain unclear.In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe,we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them.Methods:We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019.Environmental and socioeconomic databases were obtained from satelite images and publicly available platforms on the web.Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge.We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants.Spatial similarity and correlations between index,clusters,and incidence rates were evaluated.Results:We were able to geocode 56.36%of the human leptospirosis cases reported in the national epidemiological database.The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas.Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions.Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants.The highest incidence rates overlapped areas with the highest suitability scores,the strength of association was low though(CSc r=0.21,P<0.001 and ESc r=0.19,P<0.001).Conclusions:We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge.The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available.Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan interventionmeasures.

Epidemiology of dengue virus infections in Nepal, 2006-2019

Background:Dengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the years.The main objective of this study was to explore the epidemiological patterns of dengue since its first outbreak(2006)to 2019 in Nepal.

High-risk spatiotemporal patterns of cutaneous leishmaniasis:a nationwide study in Iran from 2011 to 2020

Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.

Monitoring the diversity of malaria and dengue vector in Karachi:studying variation of genera and subgenera of mosquitoes under different ecological conditions

Introduction:Karachi,a city of unique terrain and moderate tropical climate,is home to several mosquito species.The geographical distribution and density of these species may vary within the city,owing to their interaction with an ever-increasing population and urban settings.As a consequence,the prevalence of vector-borne diseases is unpredictable within the geographical limits of Karachi city.In this spatiotemporal study,1,156 mosquito samples were collected from 50 study sites with unique ecological characteristics within the city and a taxonomical exercise was conducted to investigate different vector species thriving in different months and seasons of the year.The main genera of mosquitoes were identified and categorized using a pictorial key based on the standard guidelines of the Walter Reed Biosystematics Unit,substantiated with ancillary literature.Results:Three important genera were found in Karachi:Anopheles,Aedes and Culex.Important subgenera were subsequently identified,based on their susceptibility to major vector-borne diseases.January had the highest concentration of adult mosquitoes,as the colder weather conditions were suitable for breeding.May recorded the lowest number,owing to excessively hot weather when most of the breeding pockets had dried out;less vegetation(in pre-monsoon conditions)prevented mosquito growth.Conclusions:Slum areas showed an abundance of malaria and dengue vectors,owing to poor hygiene conditions caused by open sewage drains.Hence,a major precaution is to raise awareness among people about mosquito-borne diseases.The breeding habitats of these vectors should be studied using geospatial technologies to improve spatial and temporal coverage.