This study creates a COVID-19 susceptibility scale at the county level, describes its components, and then assesses the health and socioeconomic resiliency of susceptible places across the rural-urban continuum. Methods:Factor analysis grouped 11 indicators into 7 distinct susceptibility factors for 3,079 counties in the conterminous United States. Unconditional mean differences are assessed using a multivariate general linear model. Data from 2018 are primarily taken from the US Census Bureau and CDC.Results: About 33% of rural counties are highly susceptible to COVID-19, driven by older and health-compromised populations, and care facilities for the elderly. Major vulnerabilities in rural counties include fewer physicians, lack of mental health services, higher disability, and more uninsured. Poor Internet access limits telemedicine. Lack of social capital and social services may hinder local pandemic recovery. Meat processing facilities drive risk in micropolitan counties. Although metropolitan counties are less susceptible due to healthier and younger populations, about 6% are at risk due to community spread from dense populations. Metropolitan vulnerabilities include minorities at higher health and diabetes risk, language barriers, being a transportation hub that helps spread infection, and acute housing distress. Conclusions:There is an immediate need to know specific types of susceptibilities and vulnerabilities ahead of time to allow local and state health officials to plan and allocate resources accordingly. In rural areas it is essential to shelterin-place vulnerable populations, whereas in large metropolitan areas general closure orders are needed to stop community spread. Pandemic response plans should address vulnerabilities.
The rapid increase of fatal opioid overdoses over the past two decades is a major U.S. public health problem, especially in non‐metropolitan communities. The crisis has transitioned from pharmaceuticals to illicit synthetic opioids and street mixtures, especially in urban areas. Using latent profile analysis, we classify n = 3,079 counties into distinct classes using CDC fatal overdose rates for specific opioids in 2002–2004, 2008–2012, and 2014–2016. We identify three distinct epidemics (prescription opioids, heroin, and prescription‐synthetic opioid mixtures) and one syndemic involving all opioids. We find that prescription‐related epidemic counties, whether rural or urban, have been “left behind” the rest of the nation. These communities are less populated and more remote, older and mostly white, have a history of drug abuse, and are former farm and factory communities that have been in decline since the 1990s. Overdoses in these places exemplify the “deaths of despair” narrative. By contrast, heroin and opioid syndemic counties tend to be more urban, connected to interstates, ethnically diverse, and in general more economically secure. The urban opioid crisis follows the path of previous drug epidemics, affecting a disadvantaged subpopulation that has been left behind rather than the entire community. County data on opioid epidemic class membership are provided.
Objectives. To examine associations of county-level demographic, socioeconomic, and labor market characteristics on overall drug mortality rates and specific classes of opioid mortality. Methods. We used National Vital Statistics System mortality data (2002–2004 and 2014–2016) and county-level US Census data. We examined associations between several census variables and drug deaths for 2014 to 2016. We then identified specific classes of counties characterized by different levels and rates of growth in mortality from specific opioid types between 2002 to 2004 and 2014 to 2016. We ran multivariate and multivariable regression models to predict probabilities of membership in each “opioid mortality class” on the basis of county-level census measures. Results. Drug mortality rates overall are higher in counties characterized by more economic disadvantage, more blue-collar and service employment, and higher opioid-prescribing rates. High rates of prescription opioid overdoses and overdoses involving both prescription and synthetic opioids cluster in more economically disadvantaged counties with larger concentrations of service industry workers. High heroin and “syndemic” opioid mortality counties (high rates across all major opioid types) are more urban, have larger concentrations of professional workers, and are less economically disadvantaged. Syndemic opioid counties also have greater concentrations of blue-collar workers. Conclusions. Census data are essential tools for understanding the importance of place-level characteristics on opioid mortality. Public Health Implications. National opioid policy strategies cannot be assumed universally applicable. In addition to national policies to combat the opioid and larger drug crises, emphasis should be on developing locally and regionally tailored interventions, with attention to place-based structural economic and social characteristics.
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