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1.
JAMA Netw Open ; 5(2): e220320, 2022 02 01.
Article in English | MEDLINE | ID: mdl-35201308

ABSTRACT

Importance: 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. Objective: To characterize rates of disenrollment (hereafter, external turnover) and reenrollment in commercial health plans. Design, Setting, and Participants: 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. Main Outcomes and Measures: 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. Results: 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. Conclusions and Relevance: 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.


Subject(s)
Insurance Coverage/statistics & numerical data , Insurance, Health/statistics & numerical data , Adolescent , Adult , Child , Female , Humans , Longitudinal Studies , Male , Managed Care Programs , Medicaid , Medicare Part C , Middle Aged , Retrospective Studies , United States , Young Adult
2.
J Public Econ ; 191: 104272, 2020 Nov.
Article in English | MEDLINE | ID: mdl-33518827

ABSTRACT

We quantify the causal impact of human mobility restrictions, particularly the lockdown of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. The lockdown of Wuhan reduced inflows to Wuhan by 76.98%, outflows from Wuhan by 56.31%, and within-Wuhan movements by 55.91%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities - the epicenter of the 2019-nCoV outbreak - on the destination cities' new infection cases. We also provide evidence that the enhanced social distancing policies in the 98 Chinese cities outside Hubei province were effective in reducing the impact of the population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV in the destination cities. We find that in the counterfactual world in which Wuhan were not locked down on January 23, 2020, the COVID-19 cases would be 105.27% higher in the 347 Chinese cities outside Hubei province. Our findings are relevant in the global efforts in pandemic containment.

3.
Am Econ Rev ; 107(2): 562-91, 2017 Feb.
Article in English | MEDLINE | ID: mdl-29553228

ABSTRACT

We propose a novel and easy-to-implement approach to detect potential overbilling based on the hours worked implied by the service codes which physicians submit to Medicare. Using the Medicare Part B Fee- for-Service (FFS) Physician Utilization and Payment Data in 2012 and 2013 released by the Centers for Medicare and Medicaid Services, we construct estimates for physicians' hours spent on Medicare beneficiaries. We find that about 2,300 physicians, representing about 3 percent of those with 20 or more hours of Medicare Part B FFS services, have billed Medicare over 100 hours per week. We consider these implausibly long hours.


Subject(s)
Fraud/prevention & control , Insurance, Health, Reimbursement/economics , Medicare Part B/economics , Fee-for-Service Plans , Humans , Insurance, Health, Reimbursement/statistics & numerical data , Time Factors , United States
4.
Am Econ Rev ; 101(7): 3047-77, 2011 Dec.
Article in English | MEDLINE | ID: mdl-29517886

ABSTRACT

We investigate the effects of the institutional settings of the US health care system on individuals' life-cycle medical expenditures. Health is a form of general human capital; labor turnover and labor-market frictions prevent an employer-employee pair from capturing the entire surplus from investment in an employee's health. Thus, the pair underinvests in health during working years, thereby increasing medical expenditures during retirement. We provide empirical evidence consistent with the comparative statics predictions of our model using the Medical Expenditure Panel Survey (MEPS) and the Health and Retirement Study (HRS). Our estimates suggest significant inefficiencies in health investment in the United States.


Subject(s)
Health Benefit Plans, Employee/economics , Health Expenditures/statistics & numerical data , Health Status , Personnel Turnover/economics , Retirement , Employment , Humans , Income , United States
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