A Comprehensive Analysis of COVID-19 Transmission and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

Bhowmik, Tanmoy; Tirtha, Sudipta Dey; Iraganaboina, Naveen Chandra; Eluru, Naveen

doi:10.1101/2020.08.03.20164137

Cited by 5 publications

(5 citation statements)

References 23 publications

(42 reference statements)

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“…transmission. [11][12][13][14] Our ndings also align with another study that pointed out health disparities may play a more important role in COVID-19 transmission than government interventions (such as SAH order) and community-level compliance to such interventions. [13] Our study had some limitations.…”

Section: Discussionsupporting

confidence: 84%

Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

Chang

Tang

Hatef

et al. 2020

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BackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1st 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

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Section: Discussionsupporting

confidence: 84%

Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

Chang

Tang

Hatef

et al. 2020

Preprint

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“…Coronavirus disease 2019 (COVID-19) pandemic, as of August 20 th , has spread to 188 countries with a reported 23.1 million cases and 802 thousand fatalities [ 1 ]. The pandemic has affected the mental and physical health of people across the world significantly taxing the social, health and economic systems [ 2 , 3 ]. Among the various countries affected, United States has reported the highest number of confirmed cases (5.5 million) and deaths (173 thousand) in the world [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes

et al. 2021

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Background Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Methods and findings We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. Conclusions The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).

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“…All the response variables considered in the study are continuous in nature and thus, linear regression framework would be an appropriate choice for analyzing the data. However, in our data, every county is considered 21 times and a simple linear regression method is not appropriate for such repeated measures 8 . Therefore we adopted a linear mixed approach (Autoregressive moving average -ARMA model structure, see 8 for details) to accommodate for the influence of repeated observations at a county.…”

Section: Empirical Analysismentioning

confidence: 99%

“…A significant amount of research has been conducted on understanding COVID-19 transmission (see 8,9 for details). However, only a limited amount of research has examined detailed hospitalization data 10–19 , particularly from a public health planning perspective 13,14,16,19 .…”

Section: Introductionmentioning

confidence: 99%

“…In our study, we employ a robust modeling framework to analyze the hospitalization and ICU usage demand at a county level. Specifically, we adopt a mixed linear modelling approach 8 to account for the repeated observations at the county level (multiple weeks of data). Further, the model estimation exercise is carried out using a comprehensive list of county level independent variables including a) COVID-19 transmission related factors; b) mobility trends; c) health indicators; d) demographics; e) spatial factors and f) temporal factors.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand

Bhowmik

Eluru

2021

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Background: As of February 19, 2021, our review yielded a small number of studies that investigated high resolution hospitalization demand data from a public health planning perspective. The earlier studies compiled were conducted early in the pandemic and do not include any analysis of the hospitalization trends in the last 3 months when the US experienced a substantial surge in hospitalization and ICU demand. The earlier studies also focused on COVID 19 transmission influence on COVID 19 hospitalization rates. While this emphasis is understandable, there is evidence to suggest that non COVID hospitalization demand is being displaced due to the hospitalization and ICU surge. Further, with the discovery of multiple mutated variants of COVID 19, it is important to remain vigilant in an effort to control the pandemic. Given these circumstances, the development of a high resolution framework that examines overall hospitalizations and ICU usage rate for COVID and non COVID patients would allow us to build a prediction system that can identify potential vulnerable locations for hospitalization capacity in the nation so that appropriate remedial measures can be planned. Method: The current study recognizes that COVID 19 has affected overall hospitalizations, not only COVID 19 hospitalizations. Drawing from the recently released Department of Health and Human services (DHH) weekly hospitalization data (or the time period August 28th , 2020 to January 22nd, 2021.), we study the overall hospitalization and ICU usage as two components: COVID 19 hospitalization and ICU per capita rates; and non COVID hospitalization and ICU per capita rates. A mixed linear mixed model is adopted to study the response variables in our study. The estimated models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under a host of COVID 19 transmission scenarios considering the new variants of COVID 19 and vaccination impacts. Findings: We find a significant association of the virus transmissibility with COVID (positive) and non COVID (negative) hospitalization and ICU usage rates. Several county level factors including demographics, mobility and health indicators are also found to be strongly associated with the overall hospitalization and ICU demand. Among the various scenarios considered, the results indicate a small possibility of a new wave of infections that can substantially overload hospitalization and ICU usage. In the scenario where vaccinations proceed as expected reducing transmission, our results indicate that hospitalizations and ICU usage rates are likely to reduce significantly. Interpretation: The research exercise presents a framework to predict evolving hospitalization and ICU usage trends in response to COVID 19 transmission rates while controlling for other factors. Our work highlights how future hospitalization demand varies by location and time in response to a range of pessimistic and optimistic scenarios. Further, the exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. The model will also allow hospitals to understand evolving displaced non COVID hospital demand.

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A Comprehensive Analysis of COVID-19 Transmission and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

Cited by 5 publications

References 23 publications

Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes

A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand

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