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

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.

The Opioid Epidemic and Homicide in the United States

2021 ◽  
pp. 002242782098684
Author(s):  
Richard Rosenfeld ◽  
Joel Wallman ◽  
Randolph Roth
Keyword(s):  
United States ◽  
Death Rate ◽  
The United States ◽  
Opioid Epidemic ◽  
Drug Markets ◽  
Cross Sectional ◽  
Homicide Rates ◽  
Overdose Death ◽  
Sectional Analysis ◽  
The Relationship

Objectives: Evaluate the relationship between the opioid epidemic and homicide rates in the United States. Methods: A county-level cross-sectional analysis covering the period 1999 to 2015. The race-specific homicide rate and the race-specific opioid-related overdose death rate are regressed on demographic, social, and economic covariates. Results: The race-specific opioid-related overdose death rate is positively associated with race-specific homicide rates, net of controls. The results are generally robust across alternative samples and model specifications. Conclusions: We interpret the results as reflecting the violent dynamics of street drug markets, although more research is needed to draw definitive conclusions about the mechanisms linking opioid demand and homicide.

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

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

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

Examining the Relationship Between Food Insecurity and Academic Performance: Implications for Diversity and Equity in Higher Education

2021 ◽  
pp. 152102512110538
Author(s):  
Heather Mechler ◽  
Kathryn Coakley ◽  
Marygold Walsh-Dilley ◽  
Sarita Cargas
Keyword(s):  
Higher Education ◽  
United States ◽  
Food Insecurity ◽  
Educational Equity ◽  
The United States ◽  
Cross Sectional Study ◽  
Drop Out ◽  
Cross Sectional ◽  
Professional Students ◽  
The Relationship

In recent years, researchers have increasingly focused on the experience of food insecurity among students at higher education institutions. Most of the literature has focused on undergraduates in the eastern and midwestern regions of the United States. This cross-sectional study of undergraduate, graduate, and professional students at a Minority Institution in the southwestern United States is the first of its kind to explore food insecurity among diverse students that also includes data on gender identity and sexual orientation. When holding other factors constant, food-insecure students were far more likely to fail or withdraw from a course or to drop out entirely. We explore the role that higher education can play in ensuring students’ basic needs and implications for educational equity.

The cost of linguistic loyalty

2009 ◽  
Vol 6 (1) ◽  
pp. 7-25 ◽  
Author(s):  
Devin L. Jenkins
Keyword(s):  
United States ◽  
Language Maintenance ◽  
Census Data ◽  
The United States ◽  
Spanish Language ◽  
Median Income ◽  
Social Variables ◽  
Model Set ◽  
The Cost ◽  
The Relationship

In a census-related study on language maintenance among the Hispanic/Latino population in the southwest United States, Hudson, Hernández-Chávez and Bills (1995) stated that, given negative correlations between language maintenance and years of education and per capita income, “educational and economic success in the Spanish origin population are purchased at the expense of Spanish language maintenance in the home” (1995: 179). While census figures from 1980 make this statement undeniable for the Southwest, the recent growth of the Spanish-language population in the United States, which has grown by a factor of ~2.5 over the last twenty years, begs a reexamination of these correlations. A recent study on the state of Colorado (McCullough & Jenkins 2005) found a correlational weakening, especially with regard to the relationship between language maintenance and median income.
 The current study follows the model set forth by Hudson et al. (1995) in examining the interrelationship between the measures of count, density, language loyalty and retention based on 2000 census data, as well as the relationship between these metrics and socioeconomic and demographic variables, including income and education. While some relationships existed in 2000 much in the same way that they did in the 1980 data, especially with regard to count and density, the measures of loyalty and retention saw marked reductions in their correlations with social variables.

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

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, <i>P</i>&lt;.001), neuroticism (β=.248, <i>P</i>&lt;.001), and conscientiousness (β=.229, <i>P</i>&lt;.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.

scholarly journals The evolution of infant mortality inequality in the United States, 1960–2016

2020 ◽  
Vol 6 (29) ◽  
pp. eaba5908
Author(s):  
Nick Turner ◽  
Kaveh Danesh ◽  
Kelsey Moran
Keyword(s):  
United States ◽  
Infant Mortality ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Level Data ◽  
Antipoverty Programs ◽  
The Relationship ◽  
Over Time ◽  
Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

scholarly journals Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study

2020 ◽  
Author(s):  
Xiao Wu ◽  
Rachel C Nethery ◽  
M Benjamin Sabath ◽  
Danielle Braun ◽  
Francesca Dominici
Keyword(s):  
United States ◽  
Air Pollution ◽  
Death Rate ◽  
The United States ◽  
Sensitivity Analyses ◽  
List Type ◽  
County Level ◽  
Cross Sectional ◽  
Increased Risk

AbstractObjectivesUnited States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.DesignA nationwide, cross-sectional study using county-level data.Data sourcesCOVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.Main outcome measuresWe fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.ResultsWe found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.ConclusionsA small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.Summary BoxWhat is already known on this topicLong-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease.PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS.Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection.What this study addsThis is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders.This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available.A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.

scholarly journals COVID-19 and excess mortality in the United States: A county-level analysis

PLoS Medicine ◽  
2021 ◽  
Vol 18 (5) ◽  
pp. e1003571
Author(s):  
Andrew C. Stokes ◽  
Dielle J. Lundberg ◽  
Irma T. Elo ◽  
Katherine Hempstead ◽  
Jacob Bor ◽  
...  
Keyword(s):  
United States ◽  
Formal Education ◽  
Excess Mortality ◽  
Time Lag ◽  
The United States ◽  
County Level ◽  
Level Variation ◽  
Cross Sectional ◽  
All Cause Mortality ◽  
Excess Deaths

Background Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. Methods and findings In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. Conclusions In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

scholarly journals The Impact of Intergenerational Mobility on Msa Growth in the United States

2020 ◽  
Vol 15 (1) ◽  
pp. 127-141
Author(s):  
Mauro Joseph
Keyword(s):  
United States ◽  
Intergenerational Mobility ◽  
Negative Relationship ◽  
The United States ◽  
Income Mobility ◽  
Absolute Income ◽  
Time Period ◽  
Two Measures ◽  
The Impact ◽  
The Relationship

AbstractThis paper explores the relationship between economic growth and intergenerational mobility in the United States. Data from metropolitan statistical areas in the U.S. is used to examine how two measures of intergenerational mobility impact growth rates. More precisely, I examine how absolute income mobility and relative income mobility are related the growth rate of real gross metropolitan product (RGMP) from 2001 to 2011. I find that absolute mobility has a positive relationship with RGMP growth over the time period, and that relative mobility exhibits a negative relationship with RGMP. Results are found to be robust to two stage least squares estimation.

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