Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.

BACKGROUND: The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design. OBJECTIVE: Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design. DESIGN: Cohort study using data from 2012 to 2015. PATIENTS: Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10th percentile during 2014. MAIN MEASURES: Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups. KEY RESULTS: A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%). CONCLUSIONS: Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.

Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.

BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. OBJECTIVES: To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). RESEARCH DESIGN: Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. SUBJECTS: Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). MEASURES: Medical record data were observed from January 2016-December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. RESULTS: Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. CONCLUSIONS: In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time.

Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis.

BACKGROUND: Lifecourse research provides an important framework for chronic disease epidemiology. However, data collection to observe health characteristics over long periods is vulnerable to systematic error and statistical bias. We present a multiple-bias analysis using real-world data to estimate associations between excessive gestational weight gain and mid-life obesity, accounting for confounding, selection, and misclassification biases. METHODS: Participants were from the multiethnic Study of Women's Health Across the Nation. Obesity was defined by waist circumference measured in 1996-1997 when women were age 42-53. Gestational weight gain was measured retrospectively by self-recall and was missing for over 40% of participants. We estimated relative risk (RR) and 95% confidence intervals (CI) of obesity at mid-life for presence versus absence of excessive gestational weight gain in any pregnancy. We imputed missing data via multiple imputation and used weighted regression to account for misclassification. RESULTS: Among the 2,339 women in this analysis, 937 (40%) experienced obesity in mid-life. In complete case analysis, women with excessive gestational weight gain had an estimated 39% greater risk of obesity (RR = 1.4, CI = 1.1, 1.7), covariate-adjusted. Imputing data, then weighting estimates at the guidepost values of sensitivity = 80% and specificity = 75%, increased the RR (95% CI) for obesity to 2.3 (2.0, 2.6). Only models assuming a 20-point difference in specificity between those with and without obesity decreased the RR. CONCLUSIONS: The inference of a positive association between excessive gestational weight gain and mid-life obesity is robust to methods accounting for selection and misclassification bias.

Cardiovascular risk and midlife cognitive decline in the Study of Women's Health Across the Nation.

INTRODUCTION: Cardiovascular risk factors in midlife have been linked to late life risk for Alzheimer's disease and related dementias (ADRD). The relation of vascular risk factors on cognitive decline within midlife has been less studied. METHODS: Using data from the Study of Women's Health Across the Nation, we examined associations of midlife hypertension, elevated lipid levels, diabetes, fasting glucose, central adiposity, and Framingham heart age with rates of cognitive decline in women who completed multiple cognitive assessments of processing speed, and working and verbal memory during midlife. RESULTS: Diabetes, elevated fasting glucose, central obesity, and heart age greater than chronological age were associated with rate of decline in processing speed during midlife. Vascular risk factors were not related to rate of decline in working or verbal memory. DISCUSSION: Midlife may be a critical period for intervening on cardiovascular risk factors to prevent or delay later life cognitive impairment and ADRD.

The impact of dual VA-Medicare use on a data-driven clinical management tool for older Veterans with multimorbidity.

BACKGROUND: Healthcare systems are increasingly turning to data-driven approaches, such as clustering techniques, to inform interventions for medically complex older adults. However, patients seeking care in multiple healthcare systems may have missing diagnoses across systems, leading to misclassification of resulting groups. We evaluated the impact of multi-system use on the accuracy and composition of multimorbidity groups among older adults in the Veterans Health Administration (VA). METHODS: Eligible patients were VA primary care users aged ≥65 years and in the top decile of predicted 1-year hospitalization risk in 2018 (n = 558,864). Diagnoses of 26 chronic conditions were coded using a 24-month lookback period and input into latent class analysis (LCA) models. In a random 10% sample (n = 56,008), we compared the resulting model fit, class profiles, and patient assignments from models using only VA system data versus VA with Medicare data. RESULTS: LCA identified six patient comorbidity groups using VA system data. We labeled groups based on diagnoses with higher within-group prevalence relative to the average: Substance Use Disorders (7% of patients), Mental Health (15%), Heart Disease (22%), Diabetes (16%), Tumor (14%), and High Complexity (10%). VA with Medicare data showed improved model fit and assigned more patients with high accuracy. Over 70% of patients assigned to the Substance, Mental Health, High Complexity, and Tumor groups using VA data were assigned to the same group in VA with Medicare data. However, 41.9% of the Heart Disease group and 14.7% of the Diabetes group were reassigned to a new group characterized by multiple cardiometabolic conditions. CONCLUSIONS: The addition of Medicare data to VA data for older high-risk adults improved clustering model accuracy and altered the clinical profiles of groups. Accessing or accounting for multi-system data is key to the success of interventions based on empiric grouping in populations with dual-system use.

Excessive Gestational Weight Gain and Long-Term Maternal Cardiovascular Risk Profile: The Study of Women's Health Across the Nation.

Background: Excessive gestational weight gain (GWG) is consistently linked with maternal risk of obesity. However, the literature on its long-term cardiovascular risk is minimal and conflicting. We evaluated whether excessive GWG is associated with a high-risk cardiovascular profile among parous women in midlife. Materials and Methods: Participants were women in the multiethnic cohort Study of Women's Health Across the Nation with a history of live birth(s). Excessive GWG was defined according to Institute of Medicine guidelines and collected by self-recall. Outcomes were the atherosclerotic cardiovascular disease (ASCVD) risk score and C-reactive protein (CRP), measured at the study baseline when mean age was 47 years, and at 10 follow-up visits (1996-2017). We estimated the association of excessive GWG with outcomes through linear mixed model regression. Results: The analytic sample included 1318 women with 3049 singleton births. Over 40% (536) reported one or more pregnancies with excessive GWG. Longitudinal models estimated that at a mean age of 67, women with a history of excessive GWG had a 9.8% (9.2, 10.5) 10-year ASCVD risk, compared to 9.5% (8.9, 10.1) for those without, and mean CRP of 2.20 mg/L (1.89, 2.57) versus 1.85 mg/L (1.61, 2.14), respectively, adjusted for participant characteristics. Conclusions: In this multiethnic cohort of parous women, a history of excessive GWG was associated with a small, but statistically significant difference in ASCVD risk, and a moderate, statistically significant difference in CRP across midlife. More research is necessary to understand the mechanistic pathway between excessive GWG and long-term maternal cardiovascular health.

The Effect of Gestational Weight Gain Across Reproductive History on Maternal Body Mass Index in Midlife: The Study of Women's Health Across the Nation.

Background: Excessive weight gain during pregnancy is common and has been shown to be associated with increased long-term maternal weight. However, less is known on whether there is a cumulative effect of excessive gestational weight gain (GWG) over multiple pregnancies. Methods: Data from the Study of Women's Health Across the Nation were used, restricted to parous women with no history of stillbirth or premature birth. The effect of the number of excessive GWG pregnancies on body mass index (BMI) in midlife (age 42-53) was analyzed using multivariable linear regression. Fully adjusted models included parity, inadequate GWG, demographic, and behavioral characteristics. Results: The 1181 women included in this analysis reported a total of 2693 births. Overall, 466 (39.5%) were categorized as having at least one pregnancy with excessive GWG. The median BMI at midlife was 26.0 kg/m2 (interquartile range 22.5-31.1). In fully adjusted models, each additional pregnancy with excessive GWG was associated with 0.021 higher estimated log BMI (p = 0.031). Among women with 1-3 births, adjusted mean (95% confidence interval) BMI for those with 0, 1, 2, and 3 excessive GWG pregnancies was 25.4 (24.9-25.9), 26.8 (26.1-27.5), 27.5 (26.6-28.4), and 28.8 (27.3-30.5), respectively. Conclusions: In this multiethnic study of women with a history of term live births, the number of pregnancies with excessive GWG was associated with increased maternal BMI in midlife. Our findings suggest that prevention of excessive GWG at any point in a woman's reproductive history can have an impact on long-term maternal health.