Chamberlain, Alanna M

Background Among patients with heart failure (HF), social risk factors (SRFs) are associated with poor outcomes. However, less is known about how co‐occurrence of SRFs affect all‐cause health care utilization for patients with HF. The objective was to address this gap using a novel approach to classify co‐occurrence of SRFs. Methods and Results This was a cohort study of residents living in an 11‐county region of southeast Minnesota, aged ≥18 years with a first‐ever diagnosis for HF between January 2013 and June 2017. SRFs, including education, health literacy, social isolation, and race and ethnicity, were obtained via surveys. Area‐deprivation index and rural‐urban commuting area codes were determined from patient addresses. Associations between SRFs and outcomes (emergency department visits and hospitalizations) were assessed using Andersen‐Gill models. Latent class analysis was used to identify subgroups of SRFs; associations with outcomes were examined. A total of 3142 patients with HF (mean age, 73.4 years; 45% women) had SRF data available. The SRFs with the strongest association with hospitalizations were education, social isolation, and area‐deprivation index. We identified 4 groups using latent class analysis, with group 3, characterized by more SRFs, at increased risk of emergency department visits (hazard ratio [HR], 1.33 [95% CI, 1.23–1.45]) and hospitalizations (HR, 1.42 [95% CI, 1.28–1.58]). Conclusions Low educational attainment, high social isolation, and high area‐deprivation index had the strongest associations. We identified meaningful subgroups with respect to SRFs, and these subgroups were associated with outcomes. These findings suggest that it is possible to apply latent class analysis to better understand the co‐occurrence of SRFs among patients with HF.

Background Larger within‐patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record–based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD‐free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06–1.37]). Results were similar for low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol. Conclusions In a large electronic health record–based population cohort, high variability in total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Objectives Obesity is a potentially modifiable risk factor that has been consistently associated with the development and progression of multi-morbidity (MM). However, obesity may be more problematic for some persons compared to others because of interactions with other risk factors. Therefore, we studied the effect of interactions between patient characteristics and overweight and obesity on the rate of accumulation of MM. Methods We studied 4 cohorts of persons ages 20-, 40-, 60-, and 80-years residing in Olmsted County, Minnesota between 2005 and 2014 using the Rochester Epidemiology Project (REP) medical records-linkage system. Body mass index, sex, race, ethnicity, education, and smoking status were extracted from REP indices. The rate of accumulation of MM was calculated as the number of new chronic conditions accumulated per 10 person years through 2017. Poisson rate regression models were used to identify associations between characteristics and rate of MM accumulation. Additive interactions were summarized using relative excess risk due to interaction, attributable proportion of disease, and the synergy index. Results Greater than additive synergistic associations were observed between female sex and obesity in the 20- and 40-year cohorts, between low education and obesity in the 20-year cohort (both sexes), and between smoking and obesity in the 40-year cohort (both sexes). Conclusions Interventions targeted at women, persons with lower education, and smokers who also have obesity may result in the greatest reduction in the rate of MM accumulation. However, interventions may need to focus on persons prior to mid-life to have the greatest effect.

Background Uncontrolled blood pressure (BP) remains a leading cause of death in the United States. The American Medical Association developed a quality improvement program to improve BP control, but it is unclear how to efficiently implement this program at scale across multiple health systems. Methods and Results We conducted BP MAP (Blood Pressure Measure Accurately, Act Rapidly, and Partner With Patients), a comparative effectiveness trial with clinic‐level randomization to compare 2 scalable versions of the quality improvement program: Full Support (with support from quality improvement expert) and Self‐Guided (using only online materials). Outcomes were clinic‐level BP control (<140/90 mm Hg) and other BP‐related process metrics calculated using electronic health record data. Difference‐in‐differences were used to compare changes in outcomes from baseline to 6 months, between intervention arms, and to a nonrandomized Usual Care arm composed of 18 health systems. A total of 24 safety‐net clinics in 9 different health systems underwent randomization and then simultaneous implementation. BP control increased from 56.7% to 59.1% in the Full Support arm, and 62.0% to 63.1% in the Self‐Guided arm, whereas BP control dropped slightly from 61.3% to 60.9% in the Usual Care arm. The between‐group differences‐in‐differences were not statistically significant (Full Support versus Self‐Guided=+1.2% [95% CI, −3.2% to 5.6%], P =0.59; Full Support versus Usual Care=+3.2% [−0.5% to 6.9%], P =0.09; Self‐Guided versus Usual Care=+2.0% [−0.4% to 4.5%], P =0.10). Conclusions In this randomized trial, 2 methods of implementing a quality improvement intervention in 24 safety net clinics led to modest improvements in BP control that were not statistically significant. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03818659.

Objectives To compare the agreement between percentile ranks from 4 multi-morbidity scores. Design Population-based descriptive study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). Results We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. Conclusions The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.

Background Persons who accumulate chronic conditions at a rate faster than their peers may experience accelerated aging and poor health outcomes, including functional limitations. Methods Adults aged ≥40 years who resided in Olmsted County, Minnesota on 1 January 2006 were identified. The prevalence of 21 chronic conditions was ascertained, and age-specific quartiles of the number of chronic conditions was estimated within 4 age groups: 40–54, 55–64, 65–74, and ≥75 years. Difficulty with nine patient-reported functional limitations (including basic and instrumental activities of daily living and mobility activities) were ascertained through 31 October 2018. Cox regression was used to model associations of chronic condition quartiles with new-onset functional limitations considered separately. We estimated absolute risk differences and hazard ratios stratified by age group, and adjusted for sex, race, ethnicity, marital status, education, and the residual effect of age. Results Among 39,624 persons (44.5% men, 93.2% white), the most common reported new functional limitations were difficulty with climbing stairs, walking, and housekeeping. For all functional limitations, the absolute risk differences were largest among the oldest age group (≥75 years). Approximately twofold increased hazard ratios were observed among those in the highest vs. lowest quartile for the three oldest age groups, and approximately threefold or higher hazard ratios were observed for persons aged 40-54 years. Conclusion Persons with increased accumulation of chronic conditions experience increased risks of developing functional limitations compared to their peers. These findings underscore the importance of assessing health status and of employing interventions to prevent and effectively manage multi-morbidity at all ages.

Cardiovascular disease surveillance involves quantifying the evolving population‐level burden of cardiovascular outcomes and risk factors as a data‐driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more “national” surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care–seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information‐gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

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.

Background Associations between depression, incident heart failure (HF), and mortality are well documented in predominately White samples. Yet, there are sparse data from racial minorities, including those who are women, and depression is underrecognized and undertreated in the Black population. Thus, we examined associations between baseline depressive symptoms, incident HF, and all‐cause mortality across 10 years. Methods and Results We included Jackson Heart Study (JHS) participants with no history of HF at baseline (n=2651; 63.9% women; median age, 53 years). Cox proportional hazards models tested if the risk of incident HF or mortality differed by clinically significant depressive symptoms at baseline (Center for Epidemiological Studies–Depression scores ≥16 versus <16). Models were conducted in the full sample and by sex, with hierarchical adjustment for demographics, HF risk factors, and lifestyle factors. Overall, 538 adults (20.3%) reported high depressive symptoms (71.0% were women), and there were 181 cases of HF (cumulative incidence, 0.06%). In the unadjusted model, individuals with high depressive symptoms had a 43% greater risk of HF ( P =0.035). The association remained with demographic and HF risk factors but was attenuated by lifestyle factors. All‐cause mortality was similar regardless of depressive symptoms. By sex, the unadjusted association between depressive symptoms and HF remained for women only ( P =0.039). The fully adjusted model showed a 53% greater risk of HF for women with high depressive symptoms ( P =0.043). Conclusions Among Black adults, there were sex‐specific associations between depressive symptoms and incident HF, with greater risk among women. Sex‐specific management of depression may be needed to improve cardiovascular outcomes.