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Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States.

ABSTRACT: This longitudinal study aims to investigative the impacts of development density on the spread and mortality rates of COVID-19 in metropolitan counties in the United States. Multilevel Linear Modeling (MLM) is employed to model the infection rate and the mortality rate of COVID-19, accounting for the hierarchical (two-level) and longitudinal structure of the data. This study finds that large metropolitan size (measured in terms of population) leads to significantly higher COVID-19 infection rates and higher mortality rates. After controlling for metropolitan size and other confounding variables, county density leads to significantly lower infection rates and lower death rates. These findings recommend that urban planners and health professionals continue to advocate for compact development and continue to oppose urban sprawl for this and many other reasons documented in the literature, including the positive relationship between compact development and fitness and general health.

SUBMITTER: Hamidi S

PROVIDER: S-EPMC7315990 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States.

Hamidi Shima S Ewing Reid R Sabouri Sadegh S

Health & place 20200625

This longitudinal study aims to investigative the impacts of development density on the spread and mortality rates of COVID-19 in metropolitan counties in the United States. Multilevel Linear Modeling (MLM) is employed to model the infection rate and the mortality rate of COVID-19, accounting for the hierarchical (two-level) and longitudinal structure of the data. This study finds that large metropolitan size (measured in terms of population) leads to significantly higher COVID-19 infection rate ...[more]

PMID: 32738578

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Project description:BackgroundThe gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the "eight Americas," to explore the causes of the disparities that can inform specific public health intervention policies and programs.Methods and findingsThe eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15-44 y) and middle-aged (45-59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations.ConclusionsDisparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.

Project description:BackgroundThe COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level.MethodsUsing a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsMinneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week.ConclusionsThree of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Project description:BackgroundIn the United States, men who have sex with men (MSM) increasingly represent the majority of people living with and acquiring human immunodeficiency virus (HIV) infection. Local and federal surveillance programs estimate the number of persons living with an HIV diagnosis, persons living with HIV infection, and new diagnoses. Given the absence of population-based estimates of the number of MSM for US states, metropolitan statistical areas (MSAs), or counties, it is not possible to accurately estimate rates using these indicators at these levels, inhibiting the ability to understand HIV burden and to direct prevention efforts.ObjectiveTo synthesize recently published estimates of MSM population size with publicly available HIV surveillance data, in order to estimate the prevalence of HIV diagnosis and infection and the rate of new diagnoses, at the national, state, MSA, and county levels.MethodsThe number of MSM living with HIV infection in 2012 (prevalence), living with an HIV diagnosis in 2012 (diagnosed prevalence), and newly diagnosed with HIV infection in 2013 (new diagnosis), at state, MSA, and county levels, were obtained from publicly available data from AIDSVu.org and the US Centers for Disease Control and Prevention. The estimated number of MSM living in every US county was calculated using recently published methodology that utilized data from the National Health and Nutrition Examination Survey and American Community Survey. Estimated county-level MSM counts were aggregated to form MSA- and state-level totals. From this, we estimated HIV prevalence, diagnosed prevalence, and new diagnosis rates.ResultsThe estimated HIV prevalence among MSM in the United States in 2012 was 15.0% (666,900/4,452,772), the diagnosed HIV prevalence in 2012 was 11.1% (493,453/4,452,772), and the new diagnosis rate for 2013 was 0.7 per 100 MSM. For diagnosed prevalence at the state level, 6 states had both <15,000 cases and diagnosed prevalence rates of ≥15%, all in the South. Five highly populated states had ≥15,000 cases and rates between 10% and 15%. Georgia was the only state with ≥15,000 cases and ≥15% diagnosed prevalence rate. Of the 25 MSAs with the highest diagnosed prevalence rates in the United States, 21 were in the South and 6 had diagnosed prevalence of ≥25%. County-level data showed high diagnosed prevalence rates in both urban and rural counties of the South.ConclusionsHIV infection is hyperendemic among MSM in many areas of the United States, particularly in the South. Our data emphasize the priorities for HIV prevention and care set forth in the United States National HIV/AIDS Strategy (NHAS) and provide updatable local estimates of NHAS indicators. Jurisdictions can use these results to direct resources, programs, and policies to optimally benefit the health of MSM.

Dataset Information

Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States.

Publications

Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States.

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