Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

Abstract

AbstractThis study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people’s lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account.本研究旨在从时空角度研究苏格兰的社区因素与新冠肺炎发病率之间的关系。输出变量是苏格兰的新冠肺炎发病率。通过确定2020年至2021年期间的新冠肺炎病例峰值, 苏格兰新冠肺炎确诊病例可分为四个阶段。我们选择16个社区因素作为预测因子, 对新冠肺炎发病率进行建模。采用地理随机森林模型, 研究了新冠肺炎发病率主要决定因素的时空变化。空间分析表明, 在苏格兰南部, 宗教人士的比例与新冠肺炎发病率的关系最密切。在苏格兰北部, 颗粒物与新冠肺炎发病率的关系最紧密。此外, 在苏格兰东部和中部, 拥挤的家庭、流行病之前的紧急住院率、卫生和社会工作者分别与新冠肺炎发病率密切相关。一种可能的解释是, 预测因子和新冠肺炎发病率之间的关联可能受到当地环境(例如, 人们的生活方式)的影响, 而这种影响在苏格兰各地具有空间差异性。时间分析表明, 新冠肺炎发病率的主导因素在不同阶段有所不同, 这表明流行病政策应当考虑时空变化。Desde una perspectiva espaciotemporal, este estudio pretende investigar la asociación entre los factores a nivel de vecindario y la incidencia de COVID-19 en Escocia. La variable resultante es la incidencia del COVID-19 en Escocia. A partir de la identificación de los picos de oleada de casos de COVID-19, entre el 2020 y 2021, los casos confirmados de COVID-19 en Escocia pueden dividirse en cuatro fases. Para modelar la incidencia de COVID-19, dieciséis factores vecinales se escogieron como predictores. Se usaron modelos geográficos de bosque aleatorio para examinar la variación espaciotemporal de los principales determinantes de la incidencia del COVID-19. El análisis espacial indica que la proporción de gente religiosa es lo que más fuertemente se asocia con la incidencia de COVID-19 en el sur de Escocia, mientras que los materiales particulados son los más fuertemente asociados con la incidencia de COVID-19 en el norte de Escocia. Igualmente, el hacinamiento en los hogares, las tasas de ingreso a urgencias prepandémicas y los trabajadores de salubridad y sociales, son los factores que más fuertemente se asocian con la incidencia de COVID-19 en las partes oriental y central de Escocia, respectivamente. Una posible explicación para esto es que la asociación ente predictores y la incidencia de COVID-19 podría verse influida por el contexto local (e.g., los estilos de vida de la gente), que son variables espacialmente a través de Escocia. El análisis temporal indica que los factores dominantes asociados con la incidencia del COVID-19 también varían a través de la diferentes fases, sugiriendo que las políticas relacionadas con la pandemia deberían tener en cuenta las variaciones espaciotemporales.Key Words: COVID-19geographical random forest modelneighborhood factorsScotlandspatial-temporal pattern关键词：: 新冠肺炎地理随机森林模型社区因素苏格兰时空模式。Palabras clave:: COVID-19Escociafactores de vecindadmodelo geográfico de bosque aleatoriopatrón espaciotemporal AcknowledgmentsWe gratefully acknowledge support from the Scottish Funding Council and the DDI Data Platforms Innovation ProgrammeSupplemental MaterialSupplemental data for this article can be accessed on the publisher’s Web site at https://doi.org/10.1080/00330124.2023.2194363.Additional informationNotes on contributorsRuoyu WangRUOYU WANG is a Research Fellow in the Centre for Public Health, Queen’s University Belfast, Belfast, BT12 6BA, UK. E-mail: r.wang@qub.ac.uk. His research interests include healthy geography and public health.Tom ClemensTOM CLEMENS is a health geographer with interests in how the social and physical environment impacts health and well-being. E-mail: tom.clemens@ed.ac.uk.Margaret DouglasMARGARET DOUGLAS is a Consultant in Public Health with Public Health Scotland, and Honorary Clinical Senior Lecturer, University of Glasgow, Glasgow, G12 8QQ, UK. E-mail: margaret.douglas@doctors.org.uk. Her interests include health in all policies, health impact assessment, and links between place and health and economic policy and health.Markéta KellerMARKÉTA KELLER is a Healthcare Scientist in Epidemiology, Public Health Scotland, EH8 9AG, Scotland. E-mail: mkeller@ed.ac.uk. Her primary interest is in an epidemiology the interplay between medical and psychological health.Dan van der HorstDAN VAN DER HORST is a Professor of Energy, Environment and Society, University of Edinburgh, Edinburgh, EH9 3JW, UK. E-mail: dan.vanderhorst@ed.ac.uk. He studies why unsustainable development persists and how institutions and people can learn to use scarce resources in less wasteful, harmful, and unequal ways.