Population Health Management最新文献

Devastating mortality, morbidity, economic, and quality of life impacts have resulted from disasters in the United States. This study aimed to validate a preexisting machine learning (ML) model of household disaster preparedness. Data from 2021 to 23 Federal Emergency Management Agency's National Household Surveys (n = 21,294) were harmonized. Importance features from the preexisting random forest ML model were transferred and tested in multiple linear and logistic regression models with updated datasets. Multiple regression models explained 42%-53% of the variance in household disaster preparedness. Features that improved the odds of overall disaster preparedness included detailed evacuation plans (odds ratios [OR] = 3.5-5.5), detailed shelter plans (OR = 4.3-11.0), having flood insurance (OR = 1.5-2.0), and higher educational attainment (OR = 1.1). Having no specified source of disaster information lowered preparedness odds (OR = 0.11-0.53). When stratified further by older adults with Black racial identities (n = 350), television as a main source of disaster-related information demonstrated associations with increased preparedness odds (OR = 2.2). These results validate the importance of detailed evacuation and shelter planning and the need to consider flood insurance subsidies in population health management to prepare for disasters. Tailored preparedness education for older adults with low educational attainment and targeted television media for subpopulation disaster-related information are indicated. By demonstrating a feasible use case to import ML model findings for regression testing in new datasets, this process promises to enhance population management health equity for those in sites that do not yet utilize local ML.

Personal health factors and direct and indirect costs of obesity affect employers and employees. This research aimed to understand perceptions of obesity management and anti-obesity medications (AOMs) among employers and employees. In 2022, people with obesity and employers completed cross-sectional surveys about perceptions of obesity and its management, including AOMs. Data were analyzed with descriptive statistics. Data from 461 employed people with obesity (EwO) and 51 employer representatives (ER) were analyzed. Both EwO and ER acknowledged the impact of obesity on future health problems (88.3%; 100.0%) and perceived obesity as a disease (60.5%; 80.4%) to varied degrees. Both groups perceived an incremental value in combining self-directed lifestyle changes and AOMs (57.5%; 66.7%) and perceived healthcare provider-guided lifestyle change alongside AOMs as the most effective approach for maintaining long-term weight reduction (56.4%; 66.6%). More than two-thirds (68.6%) of ER expressed willingness to revisit their AOM coverage decisions, though cost of medication coverage (72.5%) and affordability of medications for employees (68.7%) were identified as barriers. ER believed that data showing reductions in premiums and claims at their organizations (78.4%) would be helpful in supporting the coverage of AOMs. While EwO and ER were receptive toward AOMs, organization-level barriers existed with AOM coverage. Evidence demonstrating the benefits of evidence-based obesity care, direct/indirect cost reductions, and the impact of obesity may address barriers to AOM coverage and improve obesity care and outcomes of their workforces.

Virtual urgent care (VUC) and emergency department at home (ED at home) are two emerging interventions that may help address avoidable health care costs driven by inadequate access to primary care. This study evaluates the integration of VUC and ED at home as a combined mobile integrated care program, into a value-based primary care model that serves Medicaid and dual-eligible populations. Use of embedded VUC and ED at home among individuals with claim-identified physical health needs was associated with a statistically significant 27% reduction in inpatient admissions (P = 0.05), a 61% reduction in readmission (P = 0.04), and a 240% increase in engagement with primary care and care coordination (P < 0.001). Use of these services was also associated with a total cost of care decrease of $550 per member per month (P = 0.07). Findings suggest that virtual and home-based acute care services may be a promising lever for value-based payment models to enhance engagement and realize goals of improved cost and outcomes among populations with complex medical and social needs.

High-need, high-cost (HNHC) adults require comprehensive strategies that address both clinical and social determinants of health (SDOH). This retrospective, propensity score-matched study (n = 526) evaluated a care model integrating monthly SDOH screenings, medication oversight, and real-time admission-discharge-transfer alerts in four urban primary care clinics. Compared to usual care, the intervention significantly reduced acute utilization within 60 days: emergency department (ED) visits decreased by 0.17 (P < 0.001) and hospital admissions declined by 0.12 (P < 0.001). Gross per-participant costs fell from $6,019 to $2,422 (a $3,597 reduction); after accounting for intervention expenses, net savings reached $3,222 (P < 0.001), yielding an estimated 6.9:1 return on investment. Patient-reported outcomes also demonstrated significant gains: EQ-5D-5L scores increased by 0.082 (P < 0.001) in the intervention cohort, exceeding the threshold for clinically meaningful change, while Net Promoter Scores rose by 8.8 points (P < 0.001). Subgroup analyses revealed slightly smaller quality-of-life gains among non-White cohorts, highlighting the need for culturally tailored approaches to advance equity. These findings align with prior Population Health Management research showing that integrated care models can reduce costs and enhance patient satisfaction. Overall, this multifaceted model effectively curbs avoidable ED visits and admissions, generates short-term cost savings, and boosts patient satisfaction-key outcomes under value-based care contracts. Future research should investigate longer-term outcomes and refine equity-focused strategies to ensure sustained and inclusive benefits.

The Affordable Care Act (ACA) expanded health care access in the United States. This study examines the long-term impact of the ACA on private health insurance enrollment using National Health Interview Survey (NHIS) data. A repeated cross-sectional study using NHIS data from 2015 to 2022 was analyzed. Given the repeal of the ACA's individual mandate in 2019, stratified analyses compared Marketplace enrollment before (2015, 2018) and after (2019, 2022) the repeal. The study included US adults aged 26-64 years. Unadjusted enrollment rates were compared across age, sex, race/ethnicity, social determinants of health (SDOH), chronic conditions, body mass index, and smoking. Multivariable logistic regression assessed enrollment trends and associated factors. Marketplace enrollment increased by 1.4 percentage points post-mandate (P < 0.001), with no significant change pre-mandate (0.5-point decline, P = 0.235). Some subgroups (ages 26-39, Midwest, West) saw declines pre-mandate, while many experienced increased enrollments post-mandate. After adjustment, individuals in 2022 had 27% higher odds of enrollment than in 2019 (adjusted odds ratio [aOR] = 1.27, 95% confidence interval [CI] = 1.13-1.43, P < 0.001), whereas no significant change occurred between 2015 and 2018 (aOR = 1.02, 95% CI = 0.89-1.16, P = 0.818). Age, racial minority status, and unfavorable SDOH were associated with higher post-mandate enrollment odds. Marketplace enrollment grew post-mandate, particularly among vulnerable populations. While the repeal of the individual mandate may have contributed, other policy changes-expanded enrollment windows, increased subsidies, enhanced outreach, and streamlined applications-likely played a role, particularly in response to COVID-19.

The authors aimed to investigate potential differences between health care use and related payments for patients with complex needs and high costs in Health Resources and Services Administration-funded health centers (HCs) and with other safety net primary care providers. The authors used data from the California Health Homes Program that was designed to improve health outcomes and reduce expenditures of such Medicaid managed care beneficiaries. The authors used 2018 data prior to program implementation and conducted propensity score-matched regressions. The authors then estimated predicted rates of use across seven service categories and payment values for each category and for overall payments. The authors found that 29% of the sample were HC patients and had lower estimated average total payment values ($21,220) than group provider patients ($23,180). HC patients also had lower values for hospitalizations and long-term facility stays and higher values for primary and mental health services than all other providers. Payment differences were generally consistent with differences in predicted rates of use. These findings suggest that HC approaches to managing patient care access and integrated mental health services may explain these differences in use and payment patterns.

The aim was to compare clinical outcomes for older adults with diabetes who received telehealth (TH) as an adjunct to in-person care (F2F) compared with those who received in-person only care (F2F). Systematic literature search was performed using the following databases: Ovid MEDLINE, Embase, Scopus, Web of Science, Cochrane, CINAHL, and ClinicalTrials.gov to include studies involving TH care for older adults with diabetes. Two authors independently reviewed the full text of shortlisted articles. A total of four studies that met the eligibility criteria were included. One study showed slight worsening in glycemic control in the TH group, but the remaining three showed improvement or no difference between the two groups. This review shows that TH modality, when utilized as an adjunct to F2F care, has comparability to F2F alone, with similar or better glycemic control for older adults with type II diabetes, especially those residing in rural communities, those older than age 75, and those with multiple comorbidities who had multiple clinical encounters.

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities exacerbated by shortages of medical professionals and other resources. However, implementing AI effectively and responsibly in these settings requires careful consideration of context-specific needs and barriers to equitable care. This article explores the practical deployment of AI in low-resource environments through a review of existing literature and interviews with experts, ranging from health care providers and administrators to AI tool developers and government consultants. The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices. By addressing these aspects, the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.