1. INTRODUCTION
The COVID-19 pandemic has exacerbated existing racial and ethnic health and socioeconomic disparities in the United States. Notably, Black/African American, Latinx, and Indigenous populations have suffered disproportionate morbidity and mortality, as well as financial loss from subsequent economic disruption. These inequities are substantially due to existing systemic inequities that affect infectious disease transmission and recovery, including unequal access to medical care, suboptimal housing characteristics, and employment in essential services with minimal physical distancing. While the literature highlights the significant burden on communities of color in the United States as a result of the pandemic, there are few analyses to date that evaluate these disparities at higher resolution than the county level, and even fewer that disentangle cases originating in institutional congregate settings. Using stratified, higher-resolution data, we may be able to identify important community-level conditions that contribute to the clear and persistent disparities induced by the pandemic.
In particular, it has been widely recognized that older Americans, especially individuals living in nursing homes or assisted-living facilities, have faced significantly elevated risk of COVID-19 morbidity and mortality, especially early in the pandemic. Similarly, high case burdens have been observed in other institutional residential settings, such as prisons and homeless shelters. Models using total case and mortality rates without removing or controlling for these institutional settings may obfuscate trends or risk factors in community transmission. Since the racial and ethnic composition of institutional and non-institutional settings may differ, disparities may be better characterized using higher-resolution data. In addition, characteristics and risk factors associated with disease transmission and severity within institutional settings may not coincide with those driving COVID-19 transmission within non-institutional community settings. Efforts to disaggregate institutional and community outcomes would inform a more comprehensive understanding of health disparities in both institutional and non-institutional settings, in turn directing testing efforts and informing mitigation activities.
Analyses using highly resolved geospatial data provide the tools to identify specific, local factors that contribute to disease outcomes and hone targeted efforts to intervene and support communities. Such data are particularly useful for local public health departments, for whom local data from their community is more valuable than aggregated larger-scale trends. Local case data coupled with community-level sociodemographic data at the census-tract level can provide public health leaders with actionable and highly relevant local information and support pandemic response.
State public health departments have served a critical role during the pandemic in collecting and aggregating individual patient information across municipalities within the state and communicating relevant information back to local leaders. Due to patient privacy regulations, data on residential addresses associated with COVID-19 that would support generating census tract resolution case estimates and distinguishing institutional from non-institutional cases are not publicly available. Cross-sectoral partnerships and data sharing agreements between state public health agencies and academic researchers can support analyses that integrate protected public health data with community-level characteristics. This study reflects one such partnership between the Massachusetts Department of Public Health (MDPH) and academic researchers at Boston University School of Public Health to inform state and local interventions to mitigate COVID-19 risk and associated health disparities.
In this study, we geocoded residential home addresses (street, city/town, and zip code) of all individual COVID-19 cases confirmed via nucleic acid amplification tests (NAAT) from the first year of the COVID-19 pandemic in Massachusetts provided by MDPH. We then analyzed community-level sociodemographic and occupational predictors of outcomes at the census-tract level . The goals of this project were to: 1) estimate associations between community-level risk factors and COVID-19 cases and deaths by census tract in the state; 2) evaluate the sensitivity of these associations to the exclusion of institutional cases and deaths, given the relevance of institutional settings to larger-scale disease patterns; and 3) assess changes over time in these associations during the initial two Phases of the pandemic in Massachusetts.