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Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States.
medRxiv. 2020 Sep 11M

Abstract

BACKGROUND

Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed.

METHODS

We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state.

RESULTS

In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5.

CONCLUSIONS

Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.

Authors+Show Affiliations

Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina (Brian Neelon, Fedelis Mutiso); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore (Noel T Mueller); Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore (Noel T Mueller); Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston (John L Pearce); Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Sara E Benjamin-Neelon).Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina (Brian Neelon, Fedelis Mutiso); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore (Noel T Mueller); Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore (Noel T Mueller); Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston (John L Pearce); Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Sara E Benjamin-Neelon).Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina (Brian Neelon, Fedelis Mutiso); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore (Noel T Mueller); Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore (Noel T Mueller); Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston (John L Pearce); Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Sara E Benjamin-Neelon).Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina (Brian Neelon, Fedelis Mutiso); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore (Noel T Mueller); Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore (Noel T Mueller); Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston (John L Pearce); Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Sara E Benjamin-Neelon).Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina (Brian Neelon, Fedelis Mutiso); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore (Noel T Mueller); Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore (Noel T Mueller); Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston (John L Pearce); Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Sara E Benjamin-Neelon).

Pub Type(s)

Preprint

Language

eng

PubMed ID

32935111

Citation

Neelon, Brian, et al. "Spatial and Temporal Trends in Social Vulnerability and COVID-19 Incidence and Death Rates in the United States." MedRxiv : the Preprint Server for Health Sciences, 2020.
Neelon B, Mutiso F, Mueller NT, et al. Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States. medRxiv. 2020.
Neelon, B., Mutiso, F., Mueller, N. T., Pearce, J. L., & Benjamin-Neelon, S. E. (2020). Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States. MedRxiv : the Preprint Server for Health Sciences. https://doi.org/10.1101/2020.09.09.20191643
Neelon B, et al. Spatial and Temporal Trends in Social Vulnerability and COVID-19 Incidence and Death Rates in the United States. medRxiv. 2020 Sep 11; PubMed PMID: 32935111.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States. AU - Neelon,Brian, AU - Mutiso,Fedelis, AU - Mueller,Noel T, AU - Pearce,John L, AU - Benjamin-Neelon,Sara E, Y1 - 2020/09/11/ PY - 2020/9/16/entrez PY - 2020/9/17/pubmed PY - 2020/9/17/medline JF - medRxiv : the preprint server for health sciences JO - medRxiv N2 - BACKGROUND: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. METHODS: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. RESULTS: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. CONCLUSIONS: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds. UR - https://www.unboundmedicine.com/medline/citation/32935111/Spatial_and_temporal_trends_in_social_vulnerability_and_COVID_19_incidence_and_death_rates_in_the_United_States_ L2 - https://doi.org/10.1101/2020.09.09.20191643 DB - PRIME DP - Unbound Medicine ER -
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