694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

Download Full-text

On the Relationship between Development and Fertility: The Case of the United States

Comparative Population Studies ◽

10.12765/cpos-2015-13 ◽

2015 ◽

Vol 40 (4) ◽

Author(s):

Igor Ryabov

Keyword(s):

United States ◽

Human Development ◽

Negative Relationship ◽

The United States ◽

Median Income ◽

County Level ◽

Cross Sectional ◽

Development Indicators ◽

Rural Counties ◽

The Relationship

The present article addresses the question of whether there is a link between the spatial patterns of human development and period fertility in the United States at the county level. Using cross-sectional analyses of the relationship between Total Fertility Rate (TFR) and an array of human development indicators (pertaining to three components of the Human Development Index (HDI) – wealth, health, and education), this study sheds light on the relationship between fertility and human development. The analyses were conducted separately for urban, suburban and rural counties. According to the multivariate results, a negative association between selected human development indicators and TFR exists in suburban and rural counties, as well as in the United States as a whole. However, this is not the case for urban counties, where the results were inconclusive. Some indicators (e.g., median income per capita) were found to be positively, and some (e.g., the share of adults with at least bachelor’s degree) negatively, associated with TFR in urban counties. All in all, our results provide evidence of a negative relationship between human development indicators and period fertility in the United States at the county level, a finding which is consistent with the basic tenets of classic demographic transition theory.

Download Full-text

Predicting County-Level Overdose Death Rates Amid the Escalating Overdose Crisis in the United States: A Statistical Modeling Approach Predicting Deaths Through 2022

10.31219/osf.io/83zkh ◽

2022 ◽

Author(s):

Charles Marks ◽

Daniela Abramowitz ◽

Christl A. Donnelly ◽

Daniel Ciccarone ◽

Natasha Martin ◽

...

Keyword(s):

Negative Binomial ◽

The United States ◽

Similar Efficacy ◽

Original Model ◽

Mortality Data ◽

County Level ◽

Online Application ◽

Death Rates ◽

Overdose Death ◽

The Us

Aims. U.S. overdose (OD) deaths continue to escalate but are characterized by geographic and temporal heterogeneity. We previously validated a predictive statistical model to predict county-level OD mortality nationally from 2013 to 2018. Herein, we aimed to: 1) validate our model’s performance at predicting county-level OD mortality in 2019 and 2020; 2) modify and validate our model to predict OD mortality in 2022.Methods. We evaluated our mixed effects negative binomial model’s performance at predicting county-level OD mortality in 2019 and 2020. Further, we modified our model which originally used data from the year X to predict OD deaths in the year X+1 to instead predict deaths in year X+3. We validated this modification for the years 2017 through 2019 and generated future-oriented predictions for 2022. Finally, to leverage available, albeit incomplete, 2020 OD mortality data, we also modified and validated our model to predict OD deaths in year X+2 and generated an alternative set of predictions for 2022.Results. Our original model continued to perform with similar efficacy in 2019 and 2020, remaining superior to a benchmark approach. Our modified X+3 model performed with similar efficacy as our original model, and we present predictions for 2022, including identification of counties most likely to experience highest OD mortality rates. There was a high correlation (Spearman’s ρ = 0.93) between the rank ordering of counties for our 2022 predictions using our X+3 and X+2 models. However, the X+3 model (which did not account for OD escalation during COVID) predicted only 62,000 deaths nationwide for 2022, whereas the X+2 model predicted over 87,000.Conclusion. We have predicted county-level overdose death rates for 2022 across the US. These predictions, made publicly available in our online application, can be used to identify counties at highest risk of high OD mortality and support evidence-based OD prevention planning.

Download Full-text

United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

Healthcare ◽

10.3390/healthcare8030330 ◽

2020 ◽

Vol 8 (3) ◽

pp. 330

Author(s):

Rashid Ahmed ◽

Mark Williamson ◽

Muhammad Akhter Hamid ◽

Naila Ashraf

Keyword(s):

United States ◽

Case Fatality ◽

The United States ◽

Mortality Rates ◽

Small Sample ◽

County Level ◽

Death Rates ◽

Rural Counties ◽

Urban Classification ◽

Population Mortality

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

Download Full-text

De-unionization and Drug Death Rates

Social Currents ◽

10.1177/2329496518804555 ◽

2018 ◽

Vol 6 (1) ◽

pp. 4-13 ◽

Cited By ~ 1

Author(s):

Robert DeFina ◽

Lance Hannon

Keyword(s):

Fixed Effects ◽

Union Density ◽

The United States ◽

Sociological Research ◽

Social Forces ◽

Death Rates ◽

Drug Deaths ◽

Drug Death ◽

The Relationship ◽

Institutional Decline

Drug death rates in the United States have risen dramatically in recent years, sparking urgent discussions about causes. Most of these discussions have centered on supply-side issues, such as doctors overprescribing pain killers. However, there is increasing recognition of the need to go beyond proximate causes and to consider larger social forces that bear on the demand for pain-relieving drugs. Informed by sociological research linking labor unions to community health, we empirically examined the relationship between union density and drug death rates for the years 1999 to 2016. We found that states experiencing greater declines in unionization also tended to experience greater increases in drug deaths. Estimates from our fixed-effects models suggested that a one standard deviation decrease in union density was associated with a 42 percent increase in drug death rates over the period. Although the incorporation of a variety of statistical controls reduced this association, it remained negative and significant. Beyond variation in the availability of substances to misuse, our findings underscore the importance of considering institutional decline and broader social conditions as deeply relevant for contemporary drug death trends.

Download Full-text

What does and does not correlate with COVID-19 death rates

10.1101/2020.06.09.20126805 ◽

2020 ◽

Cited By ~ 2

Author(s):

Christopher R. Knittel ◽

Bora Ozaltun

Keyword(s):

Linear Regression ◽

Public Transportation ◽

Death Rate ◽

Negative Binomial ◽

Statistical Significance ◽

County Level ◽

Linear Regression Models ◽

Death Rates ◽

Socio Economic Variables ◽

Home Values

AbstractWe correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state’s death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California’s death rate is the lowest across all states.It is important to understand that this research, and other observational analyses like it, only identify correlations: these relationships are not necessarily causal. However, these correlations may help policy makers identify variables that may potentially be causally related to COVID-19 death rates and adopt appropriate policies after understanding the causal relationship.

Download Full-text

Exploring the Association Between the “Big Five” Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis (Preprint)

10.2196/preprints.24939 ◽

2020 ◽

Author(s):

Zhasmina Tacheva ◽

Anton Ivanov

Keyword(s):

United States ◽

Big Five ◽

Psychological Factors ◽

The United States ◽

Community Level ◽

Opioid Overdose ◽

County Level ◽

Psychological Traits ◽

Wide Range ◽

The Relationship

BACKGROUND Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction—the heart of this problem—ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. OBJECTIVE The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. METHODS We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level “Big Five” psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. RESULTS After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001). CONCLUSIONS Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

Download Full-text

Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

PLoS ONE ◽

10.1371/journal.pone.0248702 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248702

Author(s):

Brian Neelon ◽

Fedelis Mutiso ◽

Noel T. Mueller ◽

John L. Pearce ◽

Sara E. Benjamin-Neelon

Keyword(s):

Social Vulnerability ◽

Primary Care Physicians ◽

Negative Binomial ◽

Temporal Trends ◽

The United States ◽

Social Vulnerability Index ◽

Crossover Effect ◽

Death Rates ◽

The Impact ◽

Over Time

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating 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, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

Download Full-text

Sleep and Health

Integrative Sleep Medicine ◽

10.1093/med/9780190885403.003.0002 ◽

2021 ◽

pp. 17-32

Author(s):

Andrew S. Tubbs ◽

Michael A. Grandner

Keyword(s):

Emotional Health ◽

The United States ◽

Mental Illnesses ◽

Poor Sleep ◽

Drowsy Driving ◽

Insufficient Sleep ◽

Disturbed Sleep ◽

Risk Of Disease ◽

Common Problems

Sleep is a core component of human physiology, supporting physical, mental, and emotional health. Unfortunately, insufficient sleep and insomnia are increasingly common problems both in the United States and around the world. Poor sleep is strongly associated with diabetes, obesity, and other cardiometabolic diseases. Pro-inflammatory states associated with disrupted sleep schedules and insufficient sleep are hypothesized as risk factors for cancer. Sleep disturbance impairs cognition and memory, increasing the likelihood of accidents and drowsy driving. Poor sleep is a common and exacerbating feature of many mental illnesses and may impair treatment response. Finally, the burden of insufficient sleep is calculated both in financial costs and increased mortality. Given this evidence, clinicians should recognize and treat disturbed sleep to decrease the risk of disease and improve overall quality of life.

Download Full-text

Legacies of Marginalization: System Avoidance among the Adult Children of Unauthorized Immigrants in the United States

International Migration Review ◽

10.1177/0197918319885640 ◽

2019 ◽

Vol 54 (3) ◽

pp. 707-739

Author(s):

Sarah Desai ◽

Jessica Houston Su ◽

Robert M. Adelman

Keyword(s):

Los Angeles ◽

Adult Children ◽

Regression Models ◽

Social Institutions ◽

The United States ◽

Immigrant Parents ◽

Formal Employment ◽

The Relationship ◽

Institutional Participation

The threat of deportation shapes the way that unauthorized immigrants and their families interact with social institutions. For example, the adult children of unauthorized immigrants might avoid institutions that keep formal records (“surveilling” institutions) because such institutions could potentially expose their families to deportation. Using intergenerational data from the Immigration and Intergenerational Mobility in Metropolitan Los Angeles survey, we examine the relationship between immigrant parents’ authorization status and their adult children’s institutional participation ( n = 3,283). Results from Poisson and propensity-weighted regression models suggest that the adult children of unauthorized immigrants were more likely to avoid surveilling institutions, such as formal employment, than those with authorized parents. In contrast, parental immigration status was unrelated to their attachment to non-surveilling institutions, such as community groups or religious organizations. This finding suggests that the adult children of unauthorized immigrants are not systematically disengaged from all institutions but may avoid surveilling institutions in particular due to fear of their family’s deportation. This type of system avoidance may have long-term consequences for their social and economic mobility.

Download Full-text

Disproportionate Impact of COVID-19 Pandemic on Racial and Ethnic Minorities

The American Surgeon ◽

10.1177/0003134820973356 ◽

2020 ◽

pp. 000313482097335

Author(s):

Brad Boserup ◽

Mark McKenney ◽

Adel Elkbuli

Keyword(s):

Ethnic Groups ◽

Social Vulnerability ◽

Negative Binomial ◽

Demographic Data ◽

National Level ◽

The United States ◽

Death Rates ◽

Social Distancing ◽

The Impact ◽

Racial Ethnic

Background Health disparities are prevalent in many areas of medicine. We aimed to investigate the impact of the COVID-19 pandemic on racial/ethnic groups in the United States (US) and to assess the effects of social distancing, social vulnerability metrics, and medical disparities. Methods A cross-sectional study was conducted utilizing data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC). Demographic data were obtained from the US Census Bureau, social vulnerability data were obtained from the CDC, social distancing data were obtained from Unacast, and medical disparities data from the Center for Medicare and Medicaid Services. A comparison of proportions by Fisher’s exact test was used to evaluate differences between death rates stratified by age. Negative binomial regression analysis was used to predict COVID-19 deaths based on social distancing scores, social vulnerability metrics, and medical disparities. Results COVID-19 cumulative infection and death rates were higher among minority racial/ethnic groups than whites across many states. Older age was also associated with increased cumulative death rates across all racial/ethnic groups on a national level, and many minority racial/ethnic groups experienced significantly greater cumulative death rates than whites within age groups ≥ 35 years. All studied racial/ethnic groups experienced higher hospitalization rates than whites. Older persons (≥ 65 years) also experienced more COVID-19 deaths associated with comorbidities than younger individuals. Social distancing factors, several measures of social vulnerability, and select medical disparities were identified as being predictive of county-level COVID-19 deaths. Conclusion COVID-19 has disproportionately impacted many racial/ethnic minority communities across the country, warranting further research and intervention.

Download Full-text