Jiang, Ruoxiang

Abstract Introduction: Lung cancer rates differ across rural and urban areas, but few studies have evaluated rural-urban differences across the risk-screening continuum. We examined smoking, cessation interventions, and lung cancer screening patterns across the rural-urban continuum in a geographically defined area over 10 years. Materials and Methods: A retrospective cohort study (2014-2023) was conducted using clinical data from the Rochester Epidemiology Project, derived from healthcare encounters in a 27-county region of the midwestern United States. Patients ages 40-80 were included. We used Rural-Urban Commuting Area codes to assign residence as urban, rural, or highly rural. We examined yearly smoking prevalence, cessation intervention (pharmacotherapy, counseling), and low-dose computed tomography (LDCT) lung cancer screening. Results: Over the 10-year study period, the sample size ranged from 305,530 to 340,411 people annually with 36-38% urban, 56-57% rural, and 6-7% highly rural. Current smoking prevalence declined from 14 to 12% over the 10-year period (range=12-16%; p=0.06) and was consistently lower in urban areas (range=10-14%) than rural (range=12-17.0%) and highly rural areas (range=12-18%; p=<0.001). Among individuals who currently smoked with no history of lung cancer, yearly smoking cessation intervention ranged from 16% to 23%, increasing over the 10-year period (p=<0.001). Cessation medication (varenicline, bupropion, or nicotine replacement) prescription consistently increased overall over time but remained higher in urban than rural and highly rural areas (p=<0.001). Cessation counseling rates also increased overall over time and were similar between urban and rural areas and lower in highly rural areas (p=<0.001). Among people who had ever-smoked aged 50-80 years with no diagnosis of lung cancer, LDCT screening increased from 0.0% to 3.1% over the study period. Increases were higher in urban areas (0.0-3.6%) and similar in rural and highly rural areas (0.0-2.8% and 0.0-2.9%, respectively; p=<0.001). Among people who currently smoked, LDCT screening also increased over the study period (0.0-6.4%), with higher increases in urban (0.1-8.0%) than rural (0.0-5.7%) or highly rural (0.1-6.1%) areas (p=<0.001). After the new screening guidelines in 2021, rates in all areas increased year-on-year. Conclusion: In this cohort, we found that smoking prevalence, cessation interventions, and lung cancer screening improved from 2014-2023 across the rural-urban continuum. Unfortunately, smoking prevalence remained higher and cessation interventions and screening rates lower in rural compared to urban areas. The persistent patterns underscore the need for strategies to overcome barriers for rural residents and improve lung cancer prevention and early detection in all areas. Citation Format: Brianna Tranby, Paul A. Decker, Jiang Ruoxiang, David Midthun, Lori C. Sakoda, Melinda C. Aldrich, Debra Friedman, Adoma Manful, Oindrila Bhattacharyya, Christi Patten, Chyke A. Doubeni. Patterns of tobacco use, cessation interventions, and lung cancer screening in rural vs. urban areas over time; a 10-year retrospective cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5045.

AbstractPurposeThe Collaborative care model (CoCM) is the leading model for integrating behavioral health into primary care for patients with major depressive disorder (MDD). However, CoCM requires engagement and ongoing participation. We aimed to assess whether two area‐based measures, the area‐deprivation index (ADI) and rurality, were associated with enrollment, participation, and outcomes with CoCM.MethodsThis was an observational analysis of Mayo Clinic patients eligible for CoCM: adults aged ≥18 years, empaneled in primary care, and with a PHQ‐9 of ≥10. We operationalized ADI as quintiles with Q1 being least deprived and Q5 being most deprived and rurality using RUCA codes with two categories: urban and rural. We evaluated enrollment in CoCM, drop out defined by leaving the program early, the count and type of contacts with the care coordinator, and clinical improvement measured using the PHQ‐9.FindingsWe identified 54,030 individuals with 16,532 (30.6%) residing in rural areas and 11,122 (20.6%) residing in the most deprived ADI quintile (Q5). Living in a rural area was associated with lower enrollment in CoCM (–2.3 percentage points [95% confidence interval (CI): –2.5, 2.2]), longer length in CoCM (18.6 days [95% CI: 5.7, 31.5]), more contacts with the care coordinator (1.1 contacts [95% CI: 0.2, 2.0]), and worse response and remission. In contrast, ADI Q5 was only associated with worse response and remission.ConclusionsRurality was associated with lower enrollment, greater engagement, and worse clinical outcomes. More work may be needed to address enrollment barriers for individuals living in rural areas to improve clinical outcomes.

Objective: Over 3 million Americans have heart failure with preserved ejection fraction (HFpEF). Effective guideline directed medical treatments (GDMT) are increasingly available but patients with HFpEF are likely under-detected (particularly women) and not receiving GDMT. Limited data are available informing the extent of HFpEF under-detection in the community. Hypothesis: Among patients with undifferentiated dyspnea and a high risk for unrecognized HFpEF, fewer than 50% will receive a HFpEF diagnosis during two years of follow-up; underrecognition will be greater among women. Methods: We identified 22,134 patients aged ≥30 in the Rochester Epidemiology Project (a medical records linkage system comprising a 27-county region of southeastern Minnesota and western Wisconsin) with evidence of unexplained dyspnea (≥2 ICD codes for dyspnea separated by >1 day) without prior documented etiology including HF or lung disease, during the time period 2018-2022. We calculated the recently validated HFpEF-Age-BMI-Atrial Fibrillation (HFpEF-ABA) score to estimate the probability of underlying HFpEF. Cox proportional hazards models regressed risk for incident HFpEF diagnosis on HFpEF-ABA score (dichotomized as ≥80% vs. <80%) with multivariable adjustment for sex, race and ethnicity. HRs(95%CIs) are presented. Results: Patients were 59±16 yrs, 50.8% women, 88.6% white, 3.1% Black, 3.7% other race, 4.6% Hispanic. Atrial fibrillation prevalence was 9.4% and mean BMI=31.4±7.8. Prevalence of HFpEF-ABA≥80% was 22.4%(n=4,958): 22.7% among women and 22.1% among men. The cumulative incidence of clinically-recognized HFpEF diagnosis at two years follow-up was 9.7% among women and 12.8% among men (log-rank p<0.01). HFpEF incidence was elevated with high HFpEF-ABA scores(≥80%): HR(95%CI) among women=3.72 (3.35, 4.14), and men=3.42 (3.11, 3.76). Among those with clinically diagnosed HFpEF during follow-up, mean time from date of first dyspnea ICD code to HFpEF diagnosis was 1.26 years overall: 1.31 and 1.21 years in women and men, respectively (p=0.05). Conclusions: One in five patients with undifferentiated dyspnea have probable undiagnosed HFpEF, and of that group, only 30% receive a clinical diagnosis, requiring an average of over 1 year to be recognized. The rate of diagnosis is even lower among women than men. Further study is warranted to implement evidence-based algorithms supporting detection of undiagnosed HFpEF in the community, especially in women.

Abstract Objectives The aims of the study were to identify conditions diagnosed in at least 10% of midlife women living in the US upper midwest and to assess prevalence by age, race, ethnicity, and sociodemographic status. Methods The Rochester Epidemiology Project was used to conduct a cross-sectional prevalence study of 86,946 women between 40 and 59 years residing in a 27-county region of the United States on January 1, 2020. Diagnostic billing codes were extracted and grouped into broader condition categories using the Clinical Classification System Refined. The prevalence of 424 conditions was calculated by age, race, ethnicity, and area deprivation index quartiles. Logistic regression was used to examine associations between participant characteristics and conditions that affected 10% or more of the study population. Results Twenty-eight conditions affected ≥10% of women, and eight conditions increased by ≥45% between the ages of 40 and 59 (disorders of lipid metabolism, hypertension, sleep/wake disorders, thyroid disorders, esophageal disorders, osteoarthritis, tendon and synovial disorders, and menopausal disorders; all test for trend P < 0.01). Black women had a significantly higher prevalence of hypertension and esophageal disorders at all ages (adjusted P values <0.05). Women living in more deprived areas had a significantly higher prevalence of hyperlipidemia, hypertension, sleep/wake disorders, and esophageal disorders (adjusted P values <0.05). Women living in less deprived areas had a significantly higher prevalence of thyroid disorders at age 40 to 44 and menopausal disorders at ages 50 to 59 (adjusted P values <0.05). Conclusions These data suggest that additional attention should focus on Black women and women with a lower socioeconomic status to ensure that common midlife conditions are diagnosed and treated.

Introduction: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of elderly individuals. It is unknown how the FDRS variables predict dementia in heart failure (HF) and atrial fibrillation (AF) populations. Thus, we aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of individuals with HF and AF and to determine whether the addition of other comorbidities and risk factors improves risk prediction for dementia. Methods: Residents aged ≥50 from 7 southeast MN counties with a first diagnosis of HF (ICD-9 428/ICD-10 I50) or AF (ICD-9 427.3/ICD-10 I48 with AF on electrocardiogram/Holter monitor) between 1/1/2013 and 12/31/2017 were identified. Patients with dementia (defined by ICD codes) prior to or within 6 months after their index event and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict dementia ≥ 6 months post index including the variables in the FDRS (age, marital status, BMI, stroke/TIA, diabetes and cancer). Additional comorbidities and risk factors were added to the models. For all models c-statistics were calculated. Results: Among 3,291 patients with HF (mean age 76 years, 53% male), 626 cases of dementia occurred and among 4,107 patients with AF (mean age 74 years, 57% male), 736 cases of dementia occurred. Among patients with HF, the FDRS variables predicted dementia with a c-statistic of 0.68 (Table). Adding additonal comorbidities and risk factors impoved the c-statistic slightly to 0.69 (p=0.01). Among patients with AF, the FDRS variables also performed well (c-stastic: 0.73; Table) and adding other comorbidities and risk factors improved the performance (c-statistic: 0.75; p<0.01). Conclusions: The variables from the FDRS predict dementia well in both HF and AF populations. The addition of comorbidities and other CVD risk factors only modestly improved prediction, thus indicating that the FDRS variables are appropriate to predict dementia in patients with HF and AF.

Background Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. Methods and Results We identified all Olmsted County, Minnesota residents aged ≥18 with a first‐ever International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD‐10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD‐10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. Conclusions We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

Introduction: Heart failure (HF) is a clinical syndrome with high mortality. Left ventricular ejection fraction (EF) determines clinical phenotyping and HF is classified most often as preserved (HFpEF) or reduced (HFrEF) EF. The importance of expanding phenotyping beyond EF is recognized. We hypothesized that machine learning applied to echocardiography will provide information important for deep phenotyping. Methods: We collected clinical and echo data in a HF community cohort between 2013 and 2017. Unsupervised clustering was applied to 23 echo variables using partitioning around medoids (PAM), with Gower dissimilarity used to address the mixed variable types. Proportional hazards regression examined the association between cluster assignment and death. Results: We identified 4978 persons with HF [mean age 73.9 years (SD 13.9), 47% women] with echocardiography within 6 months of diagnosis. We identified 2 primary clusters using echo variables, as depicted using t-Distributed Stochastic Neighbor Embedding (t-SNE) applied to the dissimilarity matrix (Figure). Diastolic function variables exhibited the largest differences by clusters as shown by absolute standardized differences (Table). After 3.2 + 1.9 years of follow up, 2001 deaths occurred. Assignment to cluster 2 was associated with a near 20% excess risk of death independent of age, sex, comorbidity, and EF [HR 1.17 (1.07, 1.28) p<0.001]. Conclusions: Unsupervised clustering applied to echocardiography in a large community HF cohort identified 2 distinct clusters, which defined HF subgroups with different characteristics and were independently associated with risk of death.