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JAMA ; 326(3): 250-256, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1338163


Importance: Medical debt is an increasing concern in the US, yet there is limited understanding of the amount and distribution of medical debt, and its association with health care policies. Objective: To measure the amount of medical debt nationally and by geographic region and income group and its association with Medicaid expansion under the Affordable Care Act. Design, Setting, and Participants: Data on medical debt in collections were obtained from a nationally representative 10% panel of consumer credit reports between January 2009 and June 2020 (reflecting care provided prior to the COVID-19 pandemic). Income data were obtained from the 2014-2018 American Community Survey. The sample consisted of 4.1 billion person-month observations (nearly 40 million unique individuals). These data were used to estimate the amount of medical debt (nationally and by geographic region and zip code income decile) and to examine the association between Medicaid expansion and medical debt (overall and by income group). Exposures: Geographic region (US Census region), income group (zip code income decile), and state Medicaid expansion status. Main Outcomes and Measures: The stock (all unpaid debt listed on credit reports) and flow (new debt listed on credit reports during the preceding 12 months) of medical debt in collections that can be collected on by debt collectors. Results: In June 2020, an estimated 17.8% of individuals had medical debt (13.0% accrued debt during the prior year), and the mean amount was $429 ($311 accrued during the prior year). The mean stock of medical debt was highest in the South and lowest in the Northeast ($616 vs $167; difference, $448 [95% CI, $435-$462]) and higher in poor than in rich zip code income deciles ($677 vs $126; difference, $551 [95% CI, $520-$581]). Between 2013 and 2020, the states that expanded Medicaid in 2014 experienced a decline in the mean flow of medical debt that was 34.0 percentage points (95% CI, 18.5-49.4 percentage points) greater (from $330 to $175) than the states that did not expand Medicaid (from $613 to $550). In the expansion states, the gap in the mean flow of medical debt between the lowest and highest zip code income deciles decreased by $145 (95% CI, $95-$194) while the gap increased by $218 (95% CI, $163-$273) in the nonexpansion states. Conclusions and Relevance: This study provides an estimate of the amount of medical debt in collections in the US based on consumer credit reports from January 2009 to June 2020, reflecting care delivered prior to the COVID-19 pandemic, and suggests that the amount of medical debt was highest among individuals living in the South and in lower-income communities. However, further study is needed regarding debt related to COVID-19.

Financing, Personal/economics , Health Expenditures/statistics & numerical data , Healthcare Disparities/economics , Humans , Income , Insurance, Health/economics , Medicaid/economics , Medically Uninsured , Social Determinants of Health , United States
Eur J Health Econ ; 22(7): 1005-1016, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1310570


The COVID-19 pandemic has led to disruptions in healthcare utilization and spending. While some changes might persist (e.g. substitution of specialist visits by online consultations), others will be transitory (e.g. fewer surgical procedures due to cancellation of treatments). This paper discusses the implications of transitory changes in healthcare utilization and spending for risk adjustment of health plan payment. In practice, risk adjustment methodologies typically consist of two steps: (1) calibration of payment weights for a given set of risk adjusters and (2) calculation of payments to insurers by combining the calibrated weights with enrollee characteristics. In this paper, we first introduce a simple conceptual framework for analyzing the (potential) distortions from the pandemic for both steps and then provide a hypothetical illustration of how these distortions can lead to under- or overpayment of insurers. The size of these under-/overpayments depends on (1) the impact of the pandemic on patterns in utilization and spending, (2) the distribution of risk types across insurers, (3) the extent to which insurers are disproportionately affected by the pandemic, and (4) features of the risk adjustment system.

COVID-19 , Insurance Carriers , Insurance, Health/economics , Risk Adjustment/methods , Health Expenditures , Humans , Pandemics , SARS-CoV-2
Am J Cardiol ; 140: 148-149, 2021 02 01.
Article in English | MEDLINE | ID: covidwho-1222834
J Health Polit Policy Law ; 46(4): 599-609, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1050485


In January 2021, the incoming Biden administration inherited urgent priorities for curbing health care spending and expanding health care coverage to millions of Americans while also addressing the COVID-19 pandemic and resulting economic downturn. Among these competing priorities is the issue of access to and affordability of prescription drugs. Here, the authors outline Biden's plan for directly lowering prescription drug spending for payers and patients and for expanding access to prescription medications through improved health insurance coverage. These policies could provide important financial protections for Americans against high prescription drug prices. Despite widespread public support for addressing prescription drug prices, many of Biden's plans rely on congressional action, which will be complicated by the narrow majority held by Democrats in the House and an evenly divided Senate. However, there may be other opportunities for reducing prescription drug spending and improving health insurance enrollment among the uninsured. While directly lowering drug prices would provide the most widespread savings for payers and patients alike, any successful effort for increasing the number of Americans enrolled in health insurance or rendering it more affordable will still likely effectively lower patients' out-of-pocket costs and improve access to prescription drugs.

Health Expenditures , Health Services Accessibility , Insurance Coverage/economics , Insurance, Health/economics , Prescription Drugs/economics , Humans , Insurance Coverage/legislation & jurisprudence , Insurance, Health/legislation & jurisprudence , Politics , United States