Rural US residents recognize anxiety better than urbanites and suburbanites but hold similar stigma.

PURPOSE: Recognizing signs of psychological distress is a critical first step in assisting people who are struggling with poor mental health to access help. However, community-level factors that impact recognition and stigma are underexplored. The purpose of this study is to investigate the relationship between rurality, other community-level variables, and individual variables with regard to the recognition and stigma of anxiety. METHODS: We use a survey of US adults (N = 627), including a rural oversample, and a cloaked vignette approach. We assess the ability to identify anxiety and measure associated stigma. The analysis applies an ecological model in multinomial logistic regressions. FINDINGS: About half of the respondents recognize anxiety from a list of possibilities when provided with a vignette detailing common anxiety symptoms. Respondents living in rural areas are nearly twice as likely to correctly identify anxiety than nonrural respondents. About one-fifth of respondents agree with a statement designed to measure stigma: that exhibiting the symptoms is a sign of personal weakness. Respondents able to identify anxiety show less stigma. Respondents from counties with high mental health provider access were less likely to endorse the stigma statement. CONCLUSIONS: Rural areas seem poised to reduce the stigma associated with anxiety, because residents are more adept at identifying anxiety than people living elsewhere. Future work could focus on effective mechanisms for reducing stigma associated with anxiety in rural areas, and whether anxiety recognition and stigma are changing.

Social Status Influence on Stigma Towards Mental Illness and Substance Use Disorder in the United States.

We describe the relationship between socio-demographic membership and stigma towards any mental illness (AMI) and substance use disorder (SUD) in the United States using a national survey (N = 2512). We hypothesize that participants from higher status socio-demographic groups may be more likely to report stigmatizing attitudes than participants from lower status socio-demographic groups. We find support for our hypothesis using multiple linear regression. Participants who were college-educated, male, or had household incomes above the national median were more likely to report stigmatizing attitudes toward both AMI and SUD in comparison to participants that were not college-educated, were female, or had household incomes below the national median. In contrast to our hypothesis, we find that participants who identified as Hispanic were more likely to report stigmatizing attitudes toward AMI than non-Hispanic whites. Younger and urban participants were more likely to report stigmatizing attitudes than their older and non-urban counterparts.

A broadband integrated time series (BITS) for longitudinal analyses of the digital divide.

To evaluate actions taken to implement the Telecommunications Act of 1996, the primary goal of which was to foster competition in the industry, the FCC created a standardized form (Form 477) to collect information about broadband deployment and competition in local telephone service. These data represent the best publicly available record of broadband provision in the United States. Despite the potential benefits offered by this database, there are several nuances to these data related to shifting geographies and reporting requirements that uncorrected, prevent them from being used as an uninterrupted time series for longitudinal analyses. Given the analytical challenges associated with the FCC Form 477 data, the purpose of this paper is to present a solution to the fragmented nature of these data which prevents meaningful longitudinal analyses of the digital divide. Specifically, this paper develops and describes a procedure for producing an integrated broadband time series (BITS) for the last decade (2008-2018). This includes the procedures for using these data, their value to social and economic analysis, and their underlying limitations. The core contribution of this paper is the creation of data infrastructure for investigating the evolution of the digital divide.

Estimating determinants of healthcare establishment locations with restricted federal administrative data.

We model the locational determinants of nine categories of healthcare services in the contiguous United States using restricted access federal establishment data. These data enable close examination of rural health services, which are subject to suppression in publicly published data sources. After reviewing differences in public and unsuppressed restricted data and testing underlying data generation processes for each healthcare industry, including the Poisson, negative binomial, and their zero-inflated counterparts, we estimate marginal effects for four categories of independent variables: place-based factors, financial access, characteristics of population, and industry interdependencies. Findings show establishments are less likely to be found with high concentrations of Medicare and Medicaid recipients, while agglomerations are associated with more establishments. Nonemployer establishments serve a broader spectrum of people, but the rural poor still experience less access to health care.

Recognition and stigma of prescription drug abuse disorder: personal and community determinants.

BACKGROUND: Prescription drug abuse (PDA) disorders continue to contribute to the current American opioid crisis. Within this context, our study seeks to improve understanding about stigma associated with, and symptom recognition of, prescription drug abuse. AIMS: Model the stigma and symptom recognition of PDA in the general population. METHODS: A randomized, nation-wide, online, vignette-focused survey of the general public (N = 631) was implemented with an oversample for rural counties. Logit estimation was used for analysis, with regional and county-level sociodemographic variables as controls. RESULTS: Individual respondents that self-identify as having or having had "a prescription drug abuse issue" were less likely to correctly identify the condition and were 4 times more likely to exhibit stigma. Male respondents were approximately half as likely to correctly identify PDA as female respondents while older respondents (55+) were more likely to correctly identify PDA, relative to those aged 35-54. Being both male and younger was associated with slightly more stigma, in that they were less likely to disagree with the stigma statement. CONCLUSIONS: In light of the continued risks that individuals with PDA behaviors face in potentially transitioning to illicit opioid use, the findings of this survey suggested a continued need for public education and outreach. Of particular note is the perspective of those who have self-identified with the condition, as this population faces the largest risks of adverse health outcomes from illicit drug use within the survey respondents.

Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data.

BACKGROUND: Vital statistics on the number of, alcohol-induced death (AICD) drug-induced death (DICD), and suicides at the local-level are only available after a substantial lag of up to two years after the events occur. We (1) investigate how well Google Trends search data explain variation in state-level rates in the US, and (2) use this method to forecast these rates of death for 2015 as official data are not yet available. METHODS: We tested the degree to which Google Trends data on 27 terms can be fit to CDC data using L1-regularization on AICD, DICD, and suicide. Using Google Trends data, we forecast 2015 AICD, DICD, and suicide rates. RESULTS: L1-regularization fit the pre-2015 data much better than the alternative model using state-level unemployment and income variables. Google Trends data account for substantial variation in growth of state-level rates of death: 30.9% for AICD, 23.9% for DICD, and 21.8% for suicide rates. Every state except Hawaii is forecasted to increase in all three of these rates in 2015. LIMITATIONS: The model predicts state, not local or individual behavior, and is dependent on continued availability of Google Trends data. CONCLUSIONS: The method predicts state-level AICD, DICD, and suicide rates better than the alternative model. The study findings suggest that this methodology can be developed into a public health surveillance system for behavioral health-related causes of death. State-level predictions could be used to inform state interventions aimed at reducing AICD, DICD, and suicide.

Angry, Scared, and Unsure: Mental Health Consequences of Contaminated Water in Flint, Michigan.

Natural and manmade crises impact community-level behavioral health, including mental health and substance use. This article shares findings from a larger project about community behavioral health, relevant to the ongoing water crisis in Flint, Michigan, using data from a larger study, involving monthly surveys of a panel of key informants from Genesee County. The data come from open-response questions and are analyzed as qualitative data using grounded theory techniques. Although respondents were not asked about the water issues in Flint, participants commented that the water situation was increasing stress, anxiety, and depression among the city's population. Participants thought these mental health issues would affect the entire community but would be worse among low-income, African American populations in the city. Mental health consequences were related not only to the water contamination but to distrust of public officials who are expected and have the authority to resolve the issues. The mental health effects of this public health crisis are significant and have received inadequate attention in the literature. Public health response to situations similar to the water issues in Flint should include sustained attention mental health.