Citations

Introduction: US rural, compared with nonrural, counties have a higher burden of CVD. Obesity is a major CVD risk factor with 40% prevalence in adults. However, the rural-nonrural disparities in obesity are incompletely described. We aimed to evaluate differences in prevalence of obesity by county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined obesity (body mass index ≥30 kg/m 2 ) using self-reported weight and height. Rurality was based on six levels from large central metro (most urban) to noncore (most rural). We compared prevalence by rurality level using weighted least squares regression, adjusting for 13 county-level sociodemographic measures, and reported prevalence ratios (95% CI). Results: Counties with increasing rurality reflected older populations, as indicated by higher median age (from 35.6 years to 42.4 years). Also, increasing rurality was associated with differences in income and proportion uninsured. In age and sex ratio adjusted analysis (model 1), obesity prevalence was 1.05- to 1.06-fold higher in medium metro, small metro, and micropolitan counties compared with large central metro counties (global test, P <.001). With further adjustment for sociodemographic measures (model 2; model 1 covariates plus race, state, income, proportion uninsured) these relative differences were preserved but attenuated (each 1.03-fold higher), while a greater difference was revealed for large fringe metro (1.06-fold higher) counties. In both models, differences in obesity prevalence between noncore and large central metro counties were minimal. (Table) Conclusion: Counties with intermediate rurality were associated with higher obesity prevalence. The association was partly accounted for by nonmodifiable and modifiable sociodemographic measures, which may be targeted to reduce rural-nonrural disparities in obesity.

Abstract Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method–based risk prediction tools for outcome prediction after AIS in new data sets.

Introduction: The association of patient satisfaction with hospital ACS care and risk for long-term outcomes is incompletely described. This is particularly relevant to the Gulf region, where the ACS burden is high and literature on patient satisfaction and outcomes is lacking. The aim was to compare determinants of 1-year mortality based on patient satisfaction with ACS care in the Gulf region. Methods: In a prospective registry of 3566 ACS patients from Bahrain, Kuwait, Oman, and UAE (Gulf COAST registry), we grouped patients by their self-reported overall satisfaction with ACS care reported at 1-month following ACS (low satisfaction [n=1654] vs. high satisfaction [n=1912]; Table). We examined associations of baseline characteristics and risk for 1-year mortality in logistic regression models adjusted for Global Registry of Acute Coronary Events (GRACE) score. Results: Mortality at 1-year following ACS was higher in patients reporting low satisfaction (8.8%; n=146/1654) vs. high satisfaction (5.4%; n=103/1912) ( P <.0001). Women, compared with men, had higher 1-year mortality risk in both satisfaction groups, and statistically significant risk in the low satisfaction group (aOR 1.45 [1.01–2.07]; P <.05). Lack of employment (vs. any employment) was associated with higher 1-year mortality risk in both the low satisfaction (aOR 6.44 [2.58–16.03]) and high satisfaction (aOR 2.58 [1.35–4.93]) groups with borderline significant interaction ( P interaction = .08). In both satisfaction groups, lack of education, hypertension and prior angina were associated with higher 1-year mortality risk, whereas dyslipidemia and diabetes showed no association. Conclusions: Low patient satisfaction with ACS care was associated with higher 1-year mortality risk. Socioeconomic factors such as employment and education were associated with higher 1-year mortality, independent of patient satisfaction, and may guide mortality reduction interventions.

In July 2020, Mayo Clinic introduced a hospital-at-home program, known as Advanced Care at Home (ACH) as an alternate option for clinically stable medical patients requiring hospital-level care. This retrospective cohort study evaluates the impact of the addition of a dedicated ACH patient acquisition Advanced Practice Provider (APP) on average length of stay (ALOS) and the number of patients admitted into the program between in Florida and Wisconsin between 6 July 2020 and 31 January 2022. Patient volumes and ALOS of 755 patients were analyzed between the two sites both before and after a dedicated acquisition APP was added to the Florida site on 1 June 2021. The addition of a dedicated acquisition APP did not affect the length of time a patient was in the emergency department or hospital ward prior to ACH transition (2.91 days [Florida] vs. 2.59 days [Wisconsin], p = 0.22), the transition time between initiation of the ACH consult to patient transfer home (0.85 days [Florida] vs. 1.16 days [Wisconsin], p = 0.28), or the total ALOS (6.63 days [Florida] vs. 6.34 days [Wisconsin], p = 0.47). The average number of patients acquired monthly was significantly increased in Florida (38.3 patients per month) compared with Wisconsin (21.6 patients per month) (p < 0.01). The addition of a dedicated patient acquisition APP resulted in significantly higher patient volumes but did not affect transition time or ALOS. Other hospital-at-home programs may consider the addition of an acquisition APP to maximize patient volumes.

Abstract BACKGROUND Uncertainty remains over the relationship between blood pressure (BP) variability (BPV), measured in hospital settings, and clinical outcomes following acute ischemic stroke (AIS). We examined the association between within-person systolic blood pressure (SBP) variability (SBPV) during hospitalization and readmission-free survival, all-cause readmission, or all-cause mortality 1 year after AIS. METHODS In a cohort of 862 consecutive patients (age [mean ± SD] 75 ± 15 years, 55% women) with AIS (2005–2018, follow-up through 2019), we measured SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) from a median of 16 SBP readings obtained throughout hospitalization. RESULTS In the cumulative cohort, the measured SD and CV of SBP in mmHg were 16 ± 6 and 10 ± 5, respectively. The hazard ratios (HR) for readmission-free survival between the highest vs. lowest quartiles were 1.44 (95% confidence interval [CI] 1.04–1.81) for SD and 1.29 (95% CI 0.94–1.78) for CV after adjustment for demographics and comorbidities. Similarly, incident readmission or mortality remained consistent between the highest vs. lowest quartiles of SD and CV (readmission: HR 1.29 [95% CI 0.90–1.78] for SD, HR 1.29 [95% CI 0.94–1.78] for CV; mortality: HR 1.15 [95% CI 0.71–1.87] for SD, HR 0.86 [95% CI 0.55–1.36] for CV). CONCULSIONS In patients with first AIS, SBPV measured as quartiles of SD or CV based on multiple readings throughout hospitalization has no independent prognostic implications for the readmission-free survival, readmission, or mortality. This underscores the importance of overall patient care rather than a specific focus on BP parameters during hospitalization for AIS.

Importance US rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown. Objective To examine rates and trends in diabetes mortality based on county urbanization. Design, Setting, and Participants In this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022. Exposures County urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West). Main Outcomes and Measures Annual diabetes mortality rate per 100 000 people. Results From 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1]; P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8]; P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1]; P &lt; .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (−14.0; 95% CI, −17.7 to −10.3) ( P &lt; .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) ( P &lt; .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0; P &lt; .001). Conclusions and Relevance In this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

AbstractBackgroundThe early phase of the coronavirus disease 2019 (COVID‐19) pandemic had a negative impact on the wellness of hospitalists and hospital medicine advanced practice providers (APPs). However, the burden of the pandemic has evolved and the change in hospitalist and hospital medicine APP wellness is unknown.ObjectiveTo evaluate the longitudinal trend in wellness of hospitalists and hospital medicine APPs during the COVID‐19 pandemic and guide wellness interventions.Design, Setting and ParticipantsBetween May 4, 2020, and June 6, 2021, we administered three surveys to Internal Medicine hospitalists (physicians) and hospital medicine APPs (nurse practitioners and physician assistants) at 16 Mayo Clinic hospitals in four U.S. states.MeasurementsWe evaluated the association of hospitalist and hospital medicine APP characteristics with PROMIS® measures of global wellbeing‐mental health, global wellbeing‐social activities and relationships, anxiety, social isolation, and emotional support, using logistic and linear regression models.ResultsThe response rates were 52.2% (n=154/295; May 2020), 37.1% (n=111/299; October 2020) and 35.5% (n=114/321; May 2021). In mixed models that included hospitalist and hospital medicine APP characteristics and survey period, APPs, compared with physicians, had lower odds of top global wellbeing‐social activities and relationships (adjusted odds ratio 0.42 [0.22–0.82]; p = .01), whereas survey period showed no association. The survey period showed an independent association with higher anxiety (May 2020 vs. others) and higher social isolation (October 2020 vs. others), whereas profession showed no association. Concern about contracting COVID‐19 at work was significantly associated with lower odds of top global wellbeing‐mental health and global wellbeing‐social activities and relationships, and with higher anxiety and social isolation. Hospitalist and hospital medicine APP characteristics showed no association with levels of emotional support.ConclusionsIn this longitudinal assessment of hospitalists and hospital medicine APPs, concern about contracting COVID‐19 at work remained a determinant of wellness. The trend for global wellbeing, anxiety, and social isolation may guide wellness interventions.