Yeganeh, Hanieh Sadat Tabatabaei

Introduction: Older age and multimorbidity observed in individuals with dementia may impact both the care they receive and their outcomes in the setting of acute coronary syndrome (ACS). We conducted an analysis of ACS hospitalizations in the US to examine whether disparities exist between patients with and without dementia in percutaneous coronary intervention (PCI) use and ACS-related outcomes after accounting for age and comorbidities. Methods: We used the Nationwide Readmissions Database 2021 to identify adults hospitalized with a primary diagnosis of ACS, including STEMI and NSTEMI. Propensity scores for dementia were calculated using 33 baseline characteristics, including age, sex, insurance, income, hospital characteristics, Charlson comorbidity index, and other relevant comorbidities. We performed 1:1 nearest-neighbor matching with a caliper of 0.2 standard deviations. After matching, we compare the following outcomes of people with and without dementia: receipt of PCI, mechanical complications of ACS, cardiogenic shock, mechanical circulatory support, inpatient mortality, length of stay, and hospitalization cost, and reported odds ratios or mean difference, 95% confidence interval, p-value. Survey weights were applied to ensure national representativeness. Results: Among 295,396 patients hospitalized with ACS, 7,143 had dementia. After matching, 7,142 patients with and without dementia were identified for analysis ( Figure 1 ). Covariate balance was achieved (all standardized mean differences <.1). Patients with dementia were 28% less likely to undergo PCI (0.72 [0.67–0.77], p<.001) and 78% more likely to develop cardiogenic shock (1.76 [1.58–1.95], p<.001), 82% more likely to require mechanical circulatory support (1.82 [1.61–2.05], p<.001), and 16% more likely to die during hospitalization (1.16 [1.04–1.30], p<.01) ( Figure 2 ). No significant difference was observed in mechanical complications of ACS (0.89 [0.33–2.33], p=.81). Patients with dementia had longer hospital stays (9.27 vs. 5.89 days; mean difference 3.38 [3.06-3.7] days, p<.001) and higher hospitalization costs ($182,649 vs. $130,233; mean difference $52,416 [$44,618- $60,215], p<.001). Conclusion: In this analysis of US hospitalizations for ACS, dementia was independently associated with lower PCI rates and worse ACS-related outcomes despite accounting for age and comorbidities. Further research is needed to understand and address these inequities in ACS management in patients with dementia.

Introduction: US rural areas have a disproportionate burden of CVD and hypertension. Physical inactivity is a major risk factor, with 25% prevalence in US adults. However, rural disparities in physical inactivity are incompletely described. The aim was to evaluate differences in prevalence of physical inactivity based on 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 physical inactivity as self-reported lack of leisure time physical activity in the month preceding the survey. Rurality was based on six categories ordered from least rural (large central metro) to most rural (noncore). We used weighted least squares regression to compare prevalence by rurality level, adjusting for 13 county-level sociodemographic factors; results reported as prevalence ratios (95% CI) with partial F -tests and P -values. Results: In age and sex ratio adjusted analysis (model 1), we observed a gradient in the prevalence of physical inactivity across levels of rurality, with significantly higher rates in small metro (1.03 [1.00–1.06]), micropolitan (1.05 [1.02–1.07]), and noncore (1.05 [1.02–1.08]) compared with large central metro counties ( F -statistic=7.5; P <.001). With further adjustment for other sociodemographic factors (model 2), the relative differences were negated in noncore counties (0.99 [0.97-1.02]), attenuated in intermediate counties, and accentuated in large fringe metro counties (1.06 [1.04-1.08]) ( F -statistic=14.3; P <.001). Further adjustment for obesity (model 3) largely attenuated the association of rurality with physical inactivity, resulting in no significant differences relative to large central metro counties ( F -statistic=4.8; P <.001). Conclusions: County-level rurality was associated with prevalence of physical inactivity. These disparities were largely accounted for by obesity, which may be targeted for intervention.

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.