7
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      A spatial analysis of COVID-19 period prevalence in US counties through June 28, 2020: Where geography matters?

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          This study aims to understand how spatial structures, the interconnections between counties, matter in understanding COVID-19 period prevalence across the US.

          Methods

          We assemble a county-level dataset that contains COVID-19 confirmed cases through June 28, 2020 and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in COVID-19 period prevalence.

          Results

          The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, model fit is conspicuous in its heterogeneity across counties.

          Conclusions

          Spatial models can help partially explain the geographic disparities in COVID-19 period prevalence. These models reveal spatial variability in model fit including identifying regions of the country where fit is heterogeneous and worth closer attention in the immediate short term.

          Highlights

          • COVID-19 spatial clustering is shown along both coasts and in the Black Belt.

          • Aspatial models tend to overestimate case rates for Upper Great Plains counties.

          • Spatial models better fit counties with low COVID-19 case rates than aspatial ones.

          • Greater spatial heterogeneity in residual spans from Great Plains to Southwest Texas.

          Related collections

          Most cited references13

          • Record: found
          • Abstract: found
          • Article: not found

          GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

          During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Spatial epidemiology: an emerging (or re-emerging) discipline.

            Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Spatial Epidemiology: Current Approaches and Future Challenges

              Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.
                Bookmark

                Author and article information

                Contributors
                Role: Liberal Arts Professor of Sociology, Anthropology, Demography and Geography, Director, Graduate Program in Demography, Faculty Director, Graduate Programs in Applied Demography
                Role: Associate Professor
                Role: Clinical Assistant Professor
                Journal
                Ann Epidemiol
                Ann Epidemiol
                Annals of Epidemiology
                Elsevier Inc.
                1047-2797
                1873-2585
                28 July 2020
                28 July 2020
                Affiliations
                [1 ]Department of Sociology, University at Albany, State University of New York, 351 Arts & Sciences Building, 1400 Washington Ave, Albany, NY, USA 12222
                [2 ]Department of Sociology & Criminology The Pennsylvania State University, 211 Oswald Tower, University Park PA 16802-6211
                [3 ]Department of Orthopedic Surgery, College of Medicine, National Taiwan University, No.1, Jen Ai Rd. Section 1, Taipei, Taiwan
                Author notes
                []Corresponding author. Feinuo Sun, PhD Candidate, Department of Sociology, University at Albany, State University of New York, 351 Arts & Sciences Building, 1400 Washington Ave, Albany, NY, USA 12222 Phone number: (+1) 518-730-6522 fsun4@ 123456albany.edu
                Article
                S1047-2797(20)30274-X
                10.1016/j.annepidem.2020.07.014
                7386391
                32736059
                890a48db-89c7-41af-8e7a-13a43fdcaeb3
                © 2020 Elsevier Inc. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 11 May 2020
                : 3 July 2020
                : 21 July 2020
                Categories
                Article

                Public health
                covid-19,geographic disparities,spatial analysis,covid-19, coronavirus,ols, ordinary least squares,sac, spatial autoregressive combined,chrr, county health ranking and roadmaps,ihme, institute for health metrics and evaluation,hpsa, health professional shortage area,pca, principal component analysis

                Comments

                Comment on this article