Policy implementation strategies to address rural disparities in access to care for stroke patients

ContextStroke systems of care (SSOC) promote access to stroke prevention, treatment, and rehabilitation and ensure patients receive evidence-based treatment. Stroke patients living in rural areas have disproportionately less access to emergency medical services (EMS). In the United States, rural counties have a 30% higher stroke mortality rate compared to urban counties. Many states have SSOC laws supported by evidence; however, there are knowledge gaps in how states implement these state laws to strengthen SSOC.ObjectiveThis study identifies strategies and potential challenges to implementing state policy interventions that require or encourage evidence-supported pre-hospital interventions for stroke pre-notification, triage and transport, and inter-facility transfer of patients to the most appropriate stroke facility.DesignResearchers interviewed representatives engaged in implementing SSOC across six states. Informants (n = 34) included state public health agency staff and other public health and clinical practitioners.OutcomesThis study examined implementation of pre-hospital SSOCs policies in terms of (1) development roles, processes, facilitators, and barriers; (2) implementation partners, challenges, and solutions; (3) EMS system structure, protocols, communication, and supervision; and (4) program improvement, outcomes, and sustainability.ResultsChallenges included unequal resource allocation and EMS and hospital services coverage, particularly in rural settings, lack of stroke registry usage, insufficient technologies, inconsistent use of standardized tools and protocols, collaboration gaps across SSOC, and lack of EMS stroke training. Strategies included addressing scarce resources, services, and facilities; disseminating, training on, and implementing standardized statewide SSOC protocols and tools; and utilizing SSOC quality and performance improvement systems and approaches.ConclusionsThis paper identifies several strategies that can be incorporated to enhance the implementation of evidence-based stroke policies to improve access to timely stroke care for all patient populations, particularly those experiencing disparities in rural communities

SEM results: Direct effects for <i>Cigarette Experimentation</i> among middle and high school students, NYTS, 2012^a.

<p>SEM, Structural Equation Model</p><p>NYTS, National Youth Tobacco Survey</p><p>^a Multivariate <i>N</i> = 24,654 based on all available cases across all variables used in analyses.</p><p>^b<i>B</i> = unstandardized regression coefficient, which represents the amount of change in the dependent variable per one-unit change in the independent variable.</p><p>^c β = standardized regression coefficient, which represents the SD change in the dependent variable per SD change in the independent variable.</p><p>^d Static exposure was defined as exposure to static tobacco advertisements on the Internet, in newspaper and magazines or retail stores.</p><p>^e Perception of peer tobacco use measured by student response to the questions (1) “Out of every 10 students in your grade at school, how many do you think smoke cigarettes?” and (2) “Out of every 10 students in your grade at school, how many do you think use tobacco products other than cigarettes?”</p><p>^f TV and movie exposure was defined as exposure to tobacco use in TV and movies.</p><p>^g Household member tobacco use was defined as number of tobacco products used by a family member or those living with the respondent.</p><p>^h Experimentation was defined as having puffed on a cigarette at least once but not having smoked a total of 100 lifetime cigarettes.</p><p>SEM results: Direct effects for <i>Cigarette Experimentation</i> among middle and high school students, NYTS, 2012<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134734#t003fn003" target="_blank">^a</a>.</p

SEM results: Direct effects, <i>Susceptibility to Cigarette Use</i> among middle and high school students, NYTS, 2012^a.

<p>SEM, Structural Equation Model</p><p>NYTS, National Youth Tobacco Survey</p><p>^a Multivariate <i>n</i> = 17,188 based on all available cases across all variables used in analyses.</p><p>^b<i>B</i> = unstandardized regression coefficient which represents the amount of change in the dependent variable per one-unit change in the independent variable.</p><p>^c β = standardized regression coefficient, which represents the SD change in the dependent variable per SD change in the independent variable.</p><p>^d Static exposure was defined as exposure to static tobacco advertisements on the Internet, in newspaper and magazines or retail stores.</p><p>^e Perception of peer tobacco use measured by student response to the questions (1) “Out of every 10 students in your grade at school, how many do you think smoke cigarettes?” and (2) “Out of every 10 students in your grade at school, how many do you think use tobacco products other than cigarettes?”</p><p>^f TV and movie exposure was defined as exposure to tobacco use in TV and movies.</p><p>^g Household member tobacco use was defined as number of tobacco products used by a family member or those living with the respondent.</p><p>^h Susceptibility was defined as never tried smoking cigarettes, even 1 or 2 puffs and responded in any way other than “no” to the question, “Do you think you will smoke a cigarette in the next year?” and responded in any way other than “definitely not” to either question: “Do you think you will smoke a cigarette soon?” or “If one of your best friends would offer you a cigarette, would you smoke it?”</p><p>SEM results: Direct effects, <i>Susceptibility to Cigarette Use</i> among middle and high school students, NYTS, 2012<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134734#t002fn003" target="_blank">^a</a>.</p

Demographic characteristics of respondents susceptible to cigarette use, cigarette experimenters, and current tobacco users, NYTS, 2012.

<p>NYTS, National Youth Tobacco Survey</p><p>^a Never-smokers who are susceptible to cigarette use was defined as never tried smoking cigarettes, even one or two puffs and responded other than “definitely not” to the following questions: “Do you think you will smoke a cigarette in the next year?” and “Do you think you will smoke a cigarette soon?” and “If one of your best friends would offer you a cigarette, would you smoke it?”</p><p>^b Reported <i>n</i> based on univariate analyses with missing values excluded.</p><p>^c Cigarette experimentation was defined as having puffed on a cigarette at least once but not having smoked a total of 100 lifetime cigarettes.</p><p>^d Current tobacco use was defined as using on at least 1 day in the past 30 days any of the following tobacco products: cigarettes, cigars, smokeless tobacco, pipe, bidis, kreteks, snus, hookah, roll-your-own cigarettes, dissolvable tobacco products, electronic cigarettes, or some other new tobacco product.</p><p>Demographic characteristics of respondents susceptible to cigarette use, cigarette experimenters, and current tobacco users, NYTS, 2012.</p

SEM results: Direct effects for <i>Current Tobacco Use</i> among middle and high school students, NYTS, 2012^a.

<p>SEM, Structural Equation Model</p><p>NYTS, National Youth Tobacco Survey</p><p>^a Multivariate <i>N</i> = 24,654 based on all available cases across all variables used in analyses.</p><p>^b<i>B</i> = unstandardized regression coefficient, which represents the amount of change in the dependent variable per one-unit change in the independent variable.</p><p>^c β = standardized regression coefficient, which represents the SD change in the dependent variable per SD change in the independent variable.</p><p>^d Static exposure was defined as exposure to static tobacco advertisements on the Internet, in newspaper and magazines or retail stores.</p><p>^e Perception of peer tobacco use measured by student response to the questions (1) “Out of every 10 students in your grade at school, how many do you think smoke cigarettes?” and (2) “Out of every 10 students in your grade at school, how many do you think use tobacco products other than cigarettes?”</p><p>^f TV and movie exposure was defined as exposure to tobacco use in TV and movies.</p><p>^g Household member tobacco use was defined as number of tobacco products used by a family member or those living with the respondent.</p><p>^h Current use was defined as using on at least 1 day in the past 30 days any of the following tobacco products: cigarettes, cigars, smokeless tobacco, pipe, bidis, kreteks, snus, hookah, roll-your-own cigarettes, dissolvable tobacco products, electronic cigarettes, or some other new tobacco product.</p><p>SEM results: Direct effects for <i>Current Tobacco Use</i> among middle and high school students, NYTS, 2012<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134734#t004fn003" target="_blank">^a</a>.</p

Direct and indirect effects of protobacco media exposure on susceptibility, experimentation, and current use.

<p>Conceptual model demonstrating the direct and indirect effects of exposure to static ads and tobacco use in TV and movies on susceptibility to smoking cigarettes, cigarette experimentation, and current tobacco use among US youth. Variables presented in rectangular boxes are observed, whereas unmeasured (latent) variables, including static ad exposure and peer tobacco use, are represented within ellipses. Straight lines with a unidirectional arrow depict direct relationships between variables. Curved lines with bidirectional arrows represent covariation between variables. Covariates that were included in the SEM analyses and tables, but not depicted in the diagram: household member tobacco use, sex, grade in school, black race/ethnicity, Hispanic race/ethnicity, and other ethnicity.</p

Public Attitudes, Behaviors, and Beliefs Related to COVID-19, Stay-at-Home Orders, Nonessential Business Closures, and Public Health Guidance - United States, New York City, and Los Angeles, May 5-12, 2020

SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to be transmitted mainly by person-to-person contact (1). Implementation of nationwide public health orders to limit person-to-person interaction and of guidance on personal protective practices can slow transmission (2,3). Such strategies can include stay-at-home orders, business closures, prohibitions against mass gatherings, use of cloth face coverings, and maintenance of a physical distance between persons (2,3). To assess and understand public attitudes, behaviors, and beliefs related to this guidance and COVID-19, representative panel surveys were conducted among adults aged ≥18 years in New York City (NYC) and Los Angeles, and broadly across the United States during May 5-12, 2020. Most respondents in the three cohorts supported stay-at-home orders and nonessential business closures* (United States, 79.5%; New York City, 86.7%; and Los Angeles, 81.5%), reported always or often wearing cloth face coverings in public areas (United States, 74.1%, New York City, 89.6%; and Los Angeles 89.8%), and believed that their state's restrictions were the right balance or not restrictive enough (United States, 84.3%; New York City, 89.7%; and Los Angeles, 79.7%). Periodic assessments of public attitudes, behaviors, and beliefs can guide evidence-based public health decision-making and related prevention messaging about mitigation strategies needed as the COVID-19 pandemic evolves