This study sought to examine the relationship between rural residence and physical activity levels among US myocardial infarction (MI) survivors. We conducted a cross-sectional study using nationally ...representative Behavioral Risk Factor Surveillance System surveys from 2017 and 2019. We determined the survey-weighted percentage of rural and urban MI survivors meeting US physical activity guidelines. Logistic regression models were used to examine the relationship between rural/urban residence and meeting physical activity guidelines, accounting for sociodemographic factors. Our study included 22,732 MI survivors (37.3% rural residents). The percentage of rural MI survivors meeting physical activity guidelines (37.4%, 95% CI: 35.1%-39.7%) was significantly less than their urban counterparts (45.6%, 95% CI: 44.0%-47.2%). Rural residence was associated with a 28.8% (95% CI: 20.0%-36.7%) lower odds of meeting physical activity guidelines, with this changing to a 19.3% (95% CI: 9.3%-28.3%) lower odds after adjustment for sociodemographic factors. A significant rural/urban disparity in physical activity levels exists among US MI survivors. Our findings support the need for further efforts to improve physical activity levels among rural MI survivors as part of successful secondary prevention in US high-MI burden rural areas.
Effects of stroke (i.e., memory loss, paralysis) may make effective diabetes care difficult which can in turn contribute to additional diabetes related complications and hospitalization. However, ...little is known about US post-stroke diabetes care levels. This study sought to examine diabetes care levels among US adults with diabetes by stroke status.
Using 2015-2018 Behavioral Risk Factor Surveillance System surveys, the prevalence of nonadherence with the American Diabetes Association's diabetes care measures (<1 eye exam annually, <1 foot exam annually, <1 blood glucose check daily, <2 A1C tests annually, no receipt of annual flu vaccination) was ascertained in people with diabetes by stroke status. A separate logistic regression model was run for each diabetes care measure to determine if nonadherence patterns differed by stroke status after adjustment for stroke and diabetes associated factors.
Our study included 72,630 individuals, with 9.8% having had a stroke. Nonadherence levels varied for each diabetes care measure ranging from 20.4-42.2% for stroke survivors and 22.8-44.0% for those who had never had stroke. By stroke status, nonadherence with diabetes management measures was comparable except for stroke survivors having both a lower prevalence (30.2% versus 40.1%) and odds of nonadherence (OR: 0.73, 95% CI: 0.65, 0.82) with daily blood glucose check than those who had never had stroke.
While nonadherence with diabetes management does not vary by stroke status, considerable nonadherence still exists among stroke survivors with diabetes. Additional interventions to improve diabetes care may help to reduce risk of further diabetes complications in this population.
To conduct a cross-sectional nationwide study examining how exclusion of nursing home COVID-19 cases influences the association between county level social distancing behavior and COVID-19 cases ...throughout the US during the early phase of the pandemic (February 2020-May 2020).
Using county-level COVID-19 data and social distancing metrics from tracked mobile devices, we investigated the impact social distancing had on a county's total COVID-19 cases (cases/100,000 people) between when the first COVID-19 case was confirmed in a county and May 31st, 2020 when most statewide social distancing measures were lifted, representing the pandemic's exponential growth phase. We created a mixed-effects negative binomial model to assess how implementation of social distancing measures when they were most stringent (March 2020-May 2020) influenced total COVID-19 cases while controlling for social distancing and COVID-19 related covariates in two scenarios: (1) when COVID-19 nursing home cases are not excluded from total COVID-19 cases and (2) when these cases are excluded. Model findings were compared to those from February 2020, a baseline when social distancing measures were not in place. Marginal effects at the means were generated to further isolate the influence of social distancing on COVID-19 from other factors and determine total COVID-19 cases during March 2020-May 2020 for the two scenarios.
Regardless of whether nursing home COVID-19 cases were excluded from total COVID-19 cases, a 1% increase in average % of mobile devices leaving home was significantly associated with a 5% increase in a county's total COVID-19 cases between March 2020-May 2020 and about a 2.5% decrease in February 2020. When the influence of social distancing was separated from other factors, the estimated total COVID-19 cases/100,000 people was comparable throughout the range of social distancing values (25%-45% of mobile phone devices leaving home between March 2020-May 2020) when nursing home COVID-19 cases were not excluded (25% of mobile phones leaving home: 163.84 cases/100,000 people (95% CI: 121.81, 205.86), 45% of mobile phones leaving home: 432.79 cases/100,000 people (95% CI: 256.91, 608.66)) and when they were excluded (25% of mobile phones leaving home: 149.58 cases/100,000 people (95% CI: 111.90, 187.26), 45% of mobile phones leaving home: 405.38 cases/100,000 people (95% CI: 243.14, 567.62)).
Exclusion of nursing home COVID-19 cases from total COVID-19 case counts has little impact when estimating the relationship between county-level social distancing and preventing COVID-19 cases with additional research needed to see whether this finding is also observed for COVID-19 growth rates and mortality.
Short sleep duration (SSD) (<7 hours/night) is linked with increased risk of prediabetes to diabetes progression. Despite a high diabetes burden in US rural women, existing research does not provide ...SSD estimates for this population.
We used national Behavioral Risk Factor Surveillance System surveys to conduct a cross-sectional study examining SSD estimates for US women with prediabetes by rural/urban residence between 2016-2020. We applied logistic regression models to the BRFSS dataset to ascertain associations between rural/urban residence status and SSD prior to and following adjustment for sociodemographic factors (age, race, education, income, health care coverage, having a personal doctor).
Our study included 20,997 women with prediabetes (33.7% rural). SSD prevalence was similar between rural (35.5%, 95% CI: 33.0%-38.0%) and urban women (35.4%, 95% CI: 33.7%-37.1). Rural residence was not associated with SSD among US women with prediabetes prior to adjustment (Odds Ratio: 1.00, 95% CI: 0.87-1.14) or following adjustment for sociodemographic factors (Adjusted Odds Ratio: 1.06, 95% CI: 0.92-1.22). Among women with prediabetes, irrespective of rural/urban residence status, being Black, aged <65 years, and earning <$50,000 was linked with significantly higher odds of having SSD.
Despite the finding that SSD estimates among women with prediabetes did not vary by rural/urban residence status, 35% of rural women with prediabetes had SSD. Efforts to reduce diabetes burden in rural areas may benefit from incorporating strategies to improve sleep duration along with other known diabetes risk factors among rural women with prediabetes from certain sociodemographic backgrounds.
Due to the high prevalence of diabetes risk factors in rural areas, it is important to identify whether differences in diabetes screening rates between rural and urban areas exist. Thus, the purpose ...of this study is to examine if living in a rural area, rurality, has any influence on diabetes screening across the US.
Participants from the 2011, 2013, 2015, and 2017 nationally representative Behavioral Risk Factor Surveillance System (BRFSS) surveys who responded to a question on diabetes screening were included in the study (n = 1,889,712). Two types of marginal probabilities, average adjusted predictions (AAPs) and average marginal effects (AMEs), were estimated at the national level using this data. AAPs and AMEs allow for the assessment of the independent role of rurality on diabetes screening while controlling for important covariates.
People who lived in urban, suburban, and rural areas all had comparable odds (Urban compared to Rural Odds Ratio (OR): 1.01, Suburbans compared to Rural OR: 0.95, 0.94) and probabilities of diabetes screening (Urban AAP: 70.47%, Suburban AAPs: 69.31 and 69.05%, Rural AAP: 70.27%). Statistically significant differences in probability of diabetes screening were observed between residents in suburban areas and rural residents (AMEs: - 0.96% and - 1.22%) but not between urban and rural residents (AME: 0.20%).
While similar levels of diabetes screening were found in urban, suburban, and rural areas, there is arguably a need for increased diabetes screening in rural areas where the prevalence of diabetes risk factors is higher than in urban areas.
•Around 9% of US myocardial infarction (MI) survivors binge drink.•This weighted percentage translates to 1,100,000 US MI survivors who binge drink.•Being young, male, and earning more were linked ...with more post-MI binge drinking.
Although binge drinking is associated with higher myocardial infarction (MI) incidence, little is known about binge drinking patterns in US MI survivors, at elevated risk for recurrent MIs.
To determine the prevalence of and what factors are associated with binge drinking in US MI survivors.
We compared the prevalence of binge drinking between MI survivors and those without a MI history in 2016-2018 Behavioral Risk Factor Surveillance System data. Logistic regression was used to examine which sociodemographic factors are associated with binge drinking in these groups.
8.7% of MI survivors (1.1 million people nationwide) were binge drinkers. Among MI survivors; being young, male, Hispanic, having higher income, and having lower educational attainment were associated with increased binge drinking.
The sizable number of US MI survivors who binge drink suggests interventions to reduce this behavior are warranted, especially among specific sociodemographic groups of this population.
Although hypertension is a contributing factor to higher stroke occurrence in the Stroke Belt, little is known about post‐stroke hypertension medication use in Stroke Belt residents. Through the use ...of national Behavioral Risk Factor Surveillance System surveys from 2015, 2017, and 2019; we compared unadjusted and adjusted estimates of post‐stroke hypertension medication use by Stroke Belt residence status. Similar levels of post‐stroke hypertension medication use were observed between Stroke Belt residents (OR: 1.09, 95% CI: 0.89, 1.33) and non‐Stroke Belt residents. After adjustment, Stroke Belt residents had 1.14 times the odds of post‐stroke hypertension medication use (95% CI: 0.92, 1.41) compared to non‐Stroke Belt residents. Findings from this study suggest that there is little difference between post‐stroke hypertension medication use between Stroke Belt and non‐Stroke Belt residents. However, further work is needed to assess whether use of other non‐medicinal methods of post‐stroke hypertension control differs by Stroke Belt residence status.
To determine US diabetes screening estimates in Whites, Blacks, Hispanics, Asians, Native Hawaiians/Pacific Islanders, American Indians/Alaska Natives, and Others at the national, regional, and state ...level.
In this study of 2011, 2013, 2015, and 2017 Behavioral Risk Factor Surveillance System data, we used logistic regression results to generate national, regional, and state screening marginal probabilities (average adjusted predictions (AAPs)) for each race in the two American Diabetes Association recommended screening groups1: asymptomatic overweight/obese people <45y with ≥1 diabetes risk factor and2 people ≥45y.
Even after adjusting for sociodemographic and clinical factors, significant racial disparities in screening (p-value<.05) persist at all three geographic levels. Asians had the worst national, regional, and state level AAPs of all the races. Across all races, the Northeast had the highest regional screening levels (regional AAP: 48.4–78.58%) while the West had the lowest (regional AAP: 41.98–75.18%).
Study findings indicate that sociodemographic and clinical factors do not fully explain racial disparities in diabetes screening. Further research on clinician and patient attitudes towards diabetes screening are warranted in order to design and implement initiatives in US areas where certain racial groups have particularly low diabetes screening levels.
•Racial disparity in diabetes screening exists at the national level in the US.•Little known about whether it extends to the regional and state level.•Asians have lowest diabetes screening of all races at all geographic levels.•Racial disparity in diabetes screening varies between regions and states.
Despite evidence-based guidelines,
stroke rehabilitation remains underutilized, particularly among women and minorities.
Telerehabilitation is a promising alternative to traditional in-person ...rehabilitation and offers a novel strategy to overcome access barriers,
which intensified during the COVID-19 pandemic.
A broadband connection is a prerequisite for its wide adoption but its availability varies across the United States (https://broadbandnow.com/national-broadband-map). Little is known about demographic and geographic variation in internet use among stroke survivors. In this study, we sought to compare internet use in a nationally representative sample of individuals with and without stroke.
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