skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: How bad is it? Suicidality in the middle of the COVID‐19 pandemic
Abstract ObjectiveThe current paper examines the intersection between social vulnerability, individual risk, and social/psychological resources with adult suicidality during the COVID‐19 pandemic. MethodData come from a national sample (n = 10,368) of U.S. adults. Using an online platform, information was gathered during the third week of March 2020, and post‐stratification weighted to proportionally represent the U.S. population in terms of age, gender, race/ethnicity, income, and geography. ResultsNearly 15 percent of sampled respondents were categorized as high risk, scoring 7+ on the Suicide Behaviors Questionnaire‐Revised (SBQ‐R). This level of risk varied across social vulnerability groupings: Blacks, Native Americans, Hispanics, families with children, unmarried, and younger respondents reported higher SBQ‐R scores than their counterparts (p < .000). Regression results confirm these bivariate differences and also reveal that risk factors (food insecurity, physical symptoms, and CES‐D symptomatology) are positive and significantly related to suicidality (p < .000). Additionally, resource measures are significant and negatively related to suicidality (p < .000). ConclusionsThese results provide some insight on the impact COVID‐19 is having on the general U.S. population. Practitioners should be prepared for what will likely be a significant mental health fall‐out in the months and years ahead.  more » « less
Award ID(s):
2027148
PAR ID:
10455498
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Suicide and Life-Threatening Behavior
Volume:
50
Issue:
6
ISSN:
0363-0234
Page Range / eLocation ID:
p. 1241-1249
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; (, JAMA Network Open)
    ImportanceIdentifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic’s effects, yet it remains a challenging task. ObjectiveTo characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. Design, Setting, and ParticipantsInternet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium—the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution. Main Outcomes and MeasuresThe main outcomes were (1) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023 and (2) estimates of uncounted test-confirmed cases from February 1, 2022, to January 1, 2023. These estimates were compared with institutionally reported COVID-19 infections collected by Johns Hopkins University and wastewater viral concentrations for SARS-CoV-2 from Biobot Analytics. ResultsThe survey spanned 17 waves deployed from June 1, 2020, to January 31, 2023, with a total of 408 515 responses from 306 799 respondents (mean [SD] age, 42.8 [13.0] years; 202 416 women [66.0%]). Overall, 64 946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation,r = 0.96;P &lt; .001) from April 2020 to January 2022 (50-state correlation mean [SD] value,r = 0.88 [0.07]). This was before the government-led mass distribution of at-home rapid tests. After January 2022, correlation was diminished and no longer statistically significant (r = 0.55;P = .08; 50-state correlation mean [SD] value,r = 0.48 [0.23]). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r = 0.92;P &lt; .001) and after (r = 0.89;P &lt; .001) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79;P &lt; .001) with wastewater viral concentrations before January 2022, but poorly (r = 0.31;P = .35) after, suggesting that both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were likely unaccounted for in official records between January 2022 and January 2023. Conclusions and RelevanceThis study suggests that nonprobability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and health care officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future. 
    more » « less
  2. ; ; ; ; (, Frontiers in Public Health)
    BackgroundCOVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S. MethodsWe used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties. ResultsWe found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations. DiscussionThe present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron’s dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises. 
    more » « less
  3. ; ; ; ; (, BMC Public Health)
    Abstract BackgroundCOVID-19 mitigation strategies such as masking, social distancing, avoiding group gatherings, and vaccination uptake are crucial interventions to preventing the spread of COVID-19. At present, COVID-19 data are aggregated and fail to identify subgroup variation in Asian American communities such as Hmong Americans. To understand the acceptance, adoption, and adherence to COVID-19 mitigation behaviors, an investigation of Hmong Americans’ contextual and personal characteristics was conducted. MethodsThis study aims to describe COVID-19 mitigation behaviors among Hmong Americans and the contextual and personal characteristics that influence these behaviors. A cross-sectional online survey was conducted from April 8 till June 1, 2021, with Hmong Americans aged 18 and over. Descriptive statistics were used to summarize the overall characteristics and COVID-19 related behaviors of Hmong Americans. Chi-square and Fisher’s Exact Test were computed to describe COVID-19 mitigation behaviors by gender and generational status (a marker of acculturation). ResultsThe sample included 507 participants who completed the survey. A majority of the Hmong American participants in our study reported masking (449/505, 88.9%), social distancing (270/496, 55.3%), avoiding group gatherings (345/505, 68.3%), avoiding public spaces (366/506, 72.3%), and obtaining the COVID-19 vaccination (350/506, 69.2%) to stay safe from COVID-19. Women were more likely to socially distance (P = .005), and avoid family (P = .005), and social gatherings (P = .009) compared to men. Social influence patterns related to mitigation behaviors varied by sex. Men were more likely compared to women to be influenced by Hmong community leaders to participate in family and group gatherings (P = .026), masking (P = .029), social distancing (P = .022), and vaccination uptake (P = .037), whereas healthcare providers and government officials were social influencers for social distancing and masking for women. Patterns of social distancing and group gatherings were also influenced by generational status. ConclusionContextual and personal characteristics influence COVID-19 mitigation behaviors among English speaking Hmong Americans. These findings have implications for identifying and implementing culturally appropriate health messages, future public health interventions, policy development, and ongoing research with this population. 
    more » « less
  4. ; ; (, PLOS ONE)
    Nabi, Mohammad Hayatun (Ed.)
    BackgroundAlthough research shows that the Covid-19 pandemic has led to declines in mental health, the existing research has not identified the pathways through which this decline happens. AimsThe current study identifies the distinct pathways through which COVID-induced stressors (i.e., social distancing, disease risk, and financial stressors) trigger mental distress and examines the causal impact of these stressors on mental distress. MethodsWe combined evidence of objective pandemic-related stressors collected at the county level (e.g., lack of social contact, infection rates, and unemployment rates) with self-reported survey data from over 11.5 million adult respondents in the United States collected daily for eight months. We used mediation analysis to examine the extent to which the objective stressors influenced mental health by influencing individual respondents’ behavior and fears. ResultsCounty-level, day-to-day social distancing predicted significantly greater mental distress, both directly and indirectly through its effects on individual social contacts, worries about getting ill, and concerns about finances. Economic hardships were indirectly linked to increased mental distress by elevating people’s concerns about their household’s finances. Disease threats were both directly linked to mental distress and indirectly through its effects on individual worries about getting ill. Although one might expect that social distancing from people outside the home would have a greater influence on people who live alone, sub-analyses based on household composition do not support this expectation. ConclusionThis research provides evidence consistent with the thesis that the COVID-19 pandemic harmed the mental well-being of adults in the United States and identifies specific stressors associated with the pandemic that are responsible for increasing mental distress. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; (, Journal of Extreme Events)
    null (Ed.)
    We examine the uneven social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability in the U.S. epicenter, New York City (NYC). As of July 17th, 2020, NYC, despite having only 2.5% of the U.S. population, has [Formula: see text]6% of all confirmed cases, and [Formula: see text]16% of all deaths, making it a key learning ground for the social dynamics of the disease. Our analysis focuses on the multiple potential social, economic, and demographic drivers of disproportionate impacts in COVID-19 cases and deaths, as well as population rates of testing. Findings show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability. Results highlight the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities. Significance Statement Communities around the world have variable success in mitigating the social impacts of COVID-19, with many urban areas being hit particularly hard. Analysis of social vulnerability to COVID-19 in the NYC, the U.S. national epicenter, shows strongly disproportionate impacts of the pandemic on low income populations and communities of color. Results highlight the class and racial inequities of the coronavirus pandemic in NYC, and the need to unpack the drivers of social vulnerability. To that aim, we provide a replicable framework for examining patterns of uneven social vulnerability to COVID-19- using publicly available data which can be readily applied in other study regions, especially within the U.S.A. This study is important to inform public and policy debate over strategies for short- and long-term responses that address the injustice of disproportionate impacts of COVID-19. Although similar studies examining social vulnerability and equity dimensions of the COVID-19 outbreak in cities across the U.S. have been conducted (Cordes and Castro 2020, Kim and Bostwick 2002, Gaynor and Wilson 2020; Wang et al. 2020; Choi and Unwin 2020), this study provides a more comprehensive analysis in NYC that extends previous contributions to use the highest resolution spatial units for data aggregation (ZCTAs). We also include mortality and severity rates as key indicators and provide a replicable framework that draws from the Centers for Disease Control and Prevention’s Social Vulnerability indicators for communities in NYC. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email