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Background It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. There has been limited systematic evaluation of variation in U.S. local COVID-19-related policies. This study introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. Methods In January-March 2021, we collected an initial wave of cross-sectional data from government and media websites for 171 counties in 7 states on 22 county-level COVID-19-related policies within 3 policy domains that are likely to affect health: (1) containment/closure, (2) economic support, and (3) public health. We characterized the presence and comprehensiveness of policies using univariate analyses. We also examined the correlation of policies with one another using bivariate Spearman’s correlations. Finally, we examined geographical variation in policies across and within states. Results There was substantial variation in the presence and comprehensiveness of county policies during January-March 2021. For containment and closure policies, the percent of counties with no restrictions ranged from 0% (for public events) to more than half for public transportation (67.8%), hair salons (52.6%), and religious gatherings (52.0%). For economic policies, 76.6% of counties had housing support, while 64.9% had utility relief. For public health policies, most were comprehensive, with 70.8% of counties having coordinated public information campaigns, and 66.7% requiring masks outside the home at all times. Correlations between containment and closure policies tended to be positive and moderate (i.e., coefficients 0.4–0.59). There was variation within and across states in the number and comprehensiveness of policies. Conclusions This study introduces the UCCP Database, presenting granular data on local governments’ responses to the COVID-19 pandemic. We documented substantial variation within and across states on a wide range of policies at a single point in time. By making these data publicly available, this study supports future research that can leverage this database to examine how policies contributed to and continue to influence pandemic-related health and socioeconomic outcomes and disparities. The UCCP database is available online and will include additional time points for 2020–2021 and additional counties nationwide.

Alternative payment models frequently require attribution of patients to individual physicians to assign cost and quality outcomes. Our objective was to examine the performance of three methods for attributing a patient with cancer to the likeliest physician prescriber of anticancer drugs for that patient using administrative claims data. We used the HealthCore Integrated Research Environment to identify patients who had claims for anticancer medication along with diagnosis codes for breast, lung, or colorectal lung cancer between July 2013 and September 2017. The index date was the first date with a record for anticancer medication and cancer diagnosis code. Included patients had continuous medical coverage from 6 months before index to at least 7 days after index. Patients who received anticancer drugs during the 6 months prior to index were excluded. The three methods attributed each patient to the physician with whom the patient had the most evaluation and management (E&M) visits within a 90-day window around the index date (Method 1); the most E&M visits with no time window (Method 2); or the E&M visit nearest in time to the index date (Method 3). We assessed the performance of the methods using the percentage of the study cohort successfully attributed to a physician, and the positive predictive value (PPV) relative to available physician-reported data on patient(s) they treat. In total, 70,641 patients were available for attribution to physicians. Percentages of the study cohort attributed to a physician were: Method 1, 92.6%; Method 2, 96.9%; and Method 3, 96.9%. PPVs for each method were 84.4%, 80.6%, and 75.8%, respectively. We found that a claims-based algorithm - specifically, a plurality method with a 90-day time window - correctly attributed nearly 85% of patients to a prescribing physician. Claims data can reliably identify prescribing physicians in oncology.

The commercial health insurance market is characterized by consistently high enrollee turnover. Turnover can disrupt care continuity for patients and create challenges for insurers in managing the health of their enrollee populations. Yet the extent to which enrollees reenroll is not widely known. To characterize rates of disenrollment (hereafter, external turnover) and reenrollment in commercial health plans. In this retrospective longitudinal cohort study, trends in turnover and reenrollment in commercial health plans between January 1, 2006, and August 31, 2018, were analyzed. Data analysis was conducted from January 21, 2020, through December 23, 2021. Participants included 3 018 633 primary members and their dependents with employer-sponsored coverage. Primary outcomes included external turnover from commercial coverage and subsequent reenrollment into any line of business with the insurer (commercial, Medicaid Managed Care, and Medicare Advantage). Within commercial coverage, external turnover was analyzed separately for group (ie, employer-sponsored) and individual markets. In the sample of 3 018 633 members, 50.2% were men; mean (SD) age, including dependents, was 30.68 (19.05) years. A total of 2.2% of members experienced external turnover each month and 21.5% experienced external turnover each year. The individual market had the highest average monthly turnover rate of 3.4% compared with 2.1% in the group market. December had the highest rate of external turnover, with 13.8% experiencing external turnover in the individual market and 6.9% of members experiencing external turnover in the group market. Fourteen percent of the members who left the insurer from an individual plan reenrolled with the insurer after 1 year, and 34% had reenrolled after 5 years. Among members who left the insurer from a group plan, 12% reenrolled after 1 year and 32% reenrolled after 5 years. After 10 years, reenrollment reached 47% in the 2 markets. More than 80% of enrollees returned to the same line of business and within the same state, suggesting findings may generalize to smaller insurers. The findings of this cohort study suggest that insurers may benefit from investing in members' long-term health outcomes despite substantial short-term turnover rates.

To encourage patients to select high-value providers, an insurer-initiated price transparency program that focused on elective advanced imaging procedures was implemented. Patients having at least one outpatient magnetic resonance imaging (MRI) scan in 2010 or 2012 were divided according to their membership in commercial health plans participating in the program (the intervention group) or in nonparticipating commercial health plans (the reference group) in similar US geographic regions. Patients in the intervention group were informed of price differences among available MRI facilities and given the option of selecting different providers. For those patients, the program resulted in a $220 cost reduction (18.7 percent) per test and a decrease in use of hospital-based facilities from 53 percent in 2010 to 45 percent in 2012. Price variation between hospital and nonhospital facilities for the intervention group was reduced by 30 percent after implementation. Nonparticipating members residing in intervention areas also observed price reductions, which indicates increased price competition among providers. The program significantly reduced imaging costs. This suggests that patients select lower-price facilities when informed about available alternatives.

To examine how health care utilization and spending vary for low-income employees compared with high-income employees enrolled in an employer-sponsored high-deductible health plan (HDHP). We use commercial medical claims data and administrative human resource data from a large employer between 2014 and 2018. We link the administrative data, which include details on salary and other benefit choices, to each employee in each year with medical claims. Our variables of interest include medical spending and utilization outcomes grouped into different care settings. Using multivariate regressions, we estimate the association between salary buckets and health care utilization and spending, controlling for demographic characteristics, comorbidities of employees, human resource health plan benefits, and geography. Employees earning less than $75,000 show lower rates of utilization and spending on preventive measures, such as outpatient visits and prescription drugs, while having higher rates of utilization of preventable and avoidable emergency department visits and inpatient stays, resulting in lower overall health care spending among lower-salary employees. Low-salary employees enrolled in HDHPs have higher rates of acute care utilization and spending but lower rates of primary care spending compared with high-salary employees. Results suggest that HDHPs discourage routine physician-patient care among low-salary employees.

To assess the association between the onset of the COVID-19 pandemic and change in low-value cancer services. In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March 31, 2021. We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non-guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. Among 117,116 members (median [IQR] age, 60 [53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862 (27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95% CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non-guideline-based antiemetics, and 2.71 (95% CI, -0.59 to 6.02) for aggressive end-of-life care. Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care.

Abstract Disclosure: M. Lansang: Abbott Laboratories, Dexcom, Neuro Solutions 100. L.C. Petito: None. E. Hegermiller: None. C. Indhumathy: None. R. Carnahan: None. M.E. McDonnell: Dexcom. G. Sylwestrzak: None. A. DeVries: None. E. Priest: AstraZeneca, Boehringer Ingelheim, CSL Vifor, Lilly USA, LLC, Owkin. V. Willey: None. V. Nair: None. S. Goel: None. A. Kaul: None. A. Turchin: Eli Lilly & Company, Novo Nordisk, Proteomics International. Introduction and Objective: Though newer ADA guidelines recommend initiating GLP1 receptor agonists (GLP1) or SGLT2 inhibitors (SGLT2i) for T2DM with high CV risk, metformin was previously 1st-line and there are no guidelines for moderate CV risk. We assessed US-wide trends in 2nd-line medication (med) use: (DPP4 inhibitor [DPP4i], GLP1, SGLT2i and sulfonylurea [SU]) after metformin in high vs moderate CV risk T2DM from 2013-2023. Methods: This was a secondary analysis of BESTMED data (https://bestmed.org) using electronic health records and insurance claims for T2DM with high and moderate CV risk in10 health systems and 2 insurance plans. Prescription (Rx) class was regressed on calendar time via multinomial logistic regression to model trends in Rx patterns, adjusted for sociodemographic variables; CIs and p-values for differences were calculated via nonparametric bootstrap. Significance was interpreted at p<0.05 and annual rate of change ≥0.5%. Results: Of 104,474 eligible, 28% had high CV risk. After metformin, meds initiated were DPP4i (high/moderate CV risk: 17%/18%), GLP1(12%/17%), SGLT2i (20%/18%), and SU (51%/47%). The uptake of GLP1(2.5 vs 3.6%/year) and SGLT2i (4.0 vs 3.0%/year) rose but differed by high vs moderate CV risk. Greater annual increases in GLP1 occurred in women, <65y old, and obesity class >2. In moderate but not high CV risk, GLP1 increase was higher in privately insured (3.6 vs 3%/year), Hispanic vs non (3.8 vs 3.1%/year) and lower (better) than higher social deprivation index (<30th vs >70th percentile: 3.9 vs 3.2%/year) (all p<0.01). Greater annual increases in SGLT2i were seen in men, ≥65y, less than class 2 obesity. Among moderate CV risk, increase for SGLT2i was higher in Black vs white (3.4 vs 2.9%/year, p =0.001). In high CV risk, increase was higher in publicly vs privately insured (5.3 vs 3.8%/year, p<0.001). Conclusion: GLP1 and SGLT2i increased, not just in high but also in moderate CV riskT2DM even if there are no guidelines. Difference in rise in SGLT2i and GLP1 among socioeconomic strata merit further exploration. Presentation: Saturday, July 12, 2025

This cohort study examines time trends in officially reported SARS-CoV-2 case counts and unreported home test positivity.

Wraparound programs that provide comprehensive evidence-based outpatient treatment, transportation, social services, and housing supports have shown promise for improving clinical behavioral health-related outcomes to reduce the need for institutionalized care; however, the majority of evidence is based on wraparound programs for children. This study examined the impact of a wraparound program for adult Medicaid managed care organization members with serious mental health or substance use disorders on health care costs and utilization. This retrospective observational study used 2013–2018 claims data collected from a large Medicaid managed care organization operating in multiple states. We used an intention-to-treat difference-in-difference study design to examine the association of the wraparound with costs and utilization. Adult Medicaid members with an emergency department (ED) or inpatient visit for a behavioral health condition (index visit) were eligible for the study. Outcomes included all-cause and behavioral health-related costs and utilization during follow-up after the index visit’s admission date. Outcomes were calculated overall, as well as separately by inpatient, ED, and outpatient/wraparound settings. We found that during the first post-admission month, the wraparound program was associated with 27.6 percentage points (PP) and 27.2 PP reductions in the number of behavioral health-related inpatient nights and costs, respectively. However, during subsequent months (median follow-up ranging from 7 to 10 months) there were no associations with per-member-per-month total all-cause or behavioral health-related costs. Nonetheless, the wraparound program was associated with 12.3 PP reduction in all-cause cost during the entire study period among a subset of members who were high cost at the baseline. Reduced hospital utilization and costs during the first month of wraparound services were fully counteracted by outpatient, housing, and other wraparound services costs during the following months. This indicates the importance of proper payment arrangements with value-based contracting or performance targets with wraparound services providers to align the objective of reducing inpatient use. Future wraparound programs may consider a more focused recruitment from high-cost members with complex care needs. However, our estimates were conservative given that it’s from a single Medicaid managed care organization’s perspective and some benefit from investing in addressing social needs may be realized in longer term (beyond our study period). States’ Medicaid programs may consider the longer-term cost and broader, societal benefit of wraparound investment.