Journal of Managed Care Medicine, Volume 7, Number 4

Vol. 7, No. 4, February 2021

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Vol. 7, No. 4 February 2021

TABLE OF CONTENTS Articles Changes in Drug List Prices and Amounts Paid by Patients and Insurers Eric J. Yang, Emilio Galan, Robert Thombley, et al. . . . . . . . . .4 State Policies on Access to Vaccination Services for LowIncome Adults Charleigh J. Granade, Russell F. McCord, Alexandra A. Bhatti, et al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Comparison of Office-Based Physician Participation in Medicaid Managed Care and Health Insurance Exchange Plans in the Same US Geographic Markets Jacob Wallace, Anthony Lollo, Chima D. Ndumele . . . . . . . . 27 What Happened to Cancer Screening during COVID-19, and What We Can Do to Get Things Back on Track Sheryl Riley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Managed Care Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 New AAMCN Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

ISSN: 2374-359X. The Journal of Managed Care Nursing is published by AAMCN. Corporate and Circulation offices: 4435 Waterfront Drive, Suite 101, Glen Allen, VA 23060; Tel (804) 747-9698; Fax (804) 747-5316.

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Changes in Drug List Prices and Amounts Paid by Patients and Insurers Eric J. Yang, MD (1); Emilio Galan, MSc (2); Robert Thombley, BS (2); Andrew Lin, BS (2); Jaeyun Seo, BS (2); Chien-Wen Tseng, MD, MPH, MS (3); Jack S. Resneck, MD (4); Peter B. Bach, MD, MAPP (5); R. Adams Dudley, MD, MBA (6,7) (1) Department of Dermatology, Warren Alpert Medical School, Brown University, Providence, Rhode Island; (2) Center for Healthcare Value, Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco; (3) Department of Family Medicine and Community Health, University of Hawaii John A. Burns School of Medicine, Honolulu; (4) Department of Dermatology, University of California, San Francisco; (5) Center for Health Policy and Outcomes, Memorial Sloan Kettering Cancer Center, New York, New York; (6) School of Medicine, School of Public Health, Institute for Health Informatics, University of Minnesota, Minneapolis; (7) Minneapolis VA Medical Center, Minneapolis, Minnesota

Abstract Importance High out-of-pocket drug costs can cause patients to skip treatment and worsen outcomes, and high insurer drug payments could increase premiums. Drug wholesale list prices have doubled in recent years. However, because of manufacturer discounts and rebates, the extent to which increases in wholesale list prices are associated with amounts paid by patients and insurers is poorly characterized.

Objective To determine whether increases in wholesale list prices are associated with increases in amounts paid by patients and insurers for branded medications.

Design, Setting, and Participants Cross-sectional retrospective study analyzing pharmacy claims for patients younger than 65 years in the IBM MarketScan Commercial Database and pricing data from SSR Health, LLC, between January 1, 2010, and December 31, 2016. Pharmacy claims analyzed represent claims of employees and dependents participating in employer health benefit programs belonging to large employers. Rebate data were estimated from sales data from publicly traded companies. Analysis focused on the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers nationwide in 2014. Data were analyzed from July 2017 to July 2020.

Exposures Calendar year.

Main Outcomes and Measures Changes in inflation-adjusted amounts paid by patients and insurers for branded medications.

Results In this analysis of 14.4 million pharmacy claims made by 1.8 million patients from 20102016, median drug wholesale list price increased by 129% (interquartile range [IQR], 78%-133%), while median insurance payments increased by 64% (IQR, 28%-120%) and out-of-pocket costs increased by 53% (IQR, 42%-82%). The mean percentage of wholesale list price accounted for by discounts increased from 17% in 2010 to 21% in 2016, and the mean percentage of wholesale list price accounted for by rebates increased from 22% in 2010 to 24% in 2016. For specialty medications, median patient out-of-pocket costs increased by 85% (IQR, 73%-88%) from 2010 to 2016 after adjustment for inflation and 42% (IQR, 25%-53%) for nonspecialty medications. During that same period, insurer payments increased by 116% for specialty medications (IQR, 100%-127%) and 28% for nonspecialty medications (IQR, 5%-34%).

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Conclusions and Relevance This study’s findings suggest that drug list prices more than doubled over a 7-year study period. Despite rising manufacturer discounts and rebates, these price increases were associated with large increases in patient out-of-pocket costs and insurer payments.

Key Points Question How are increases in wholesale list prices for medications distributed among increases in patient out-of-pocket costs, insurer payments, and changes in rebates and discounts?

Findings In this cross-sectional analysis of 14.4 million pharmacy claims made by 1.8 million patients for the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers in 2014, the median drug wholesale list price (as defined by Average Wholesale Price) increased by 129% from 2010-2016, while median patient out-of-pocket costs increased by 53% and median insurance payments after rebates and discounts increased by 64%.

Meaning This study’s findings suggest that, after adjusting for inflation, increases in drug list prices are associated with increased patient out-of-pocket costs, which may have implications for costrelated nonadherence, and insurer payments.

INTRODUCTION Large increases in drug prices outpacing increases in income or general inflation in recent years have put the pharmaceutical industry under scrutiny.1-5 As cost-related drug nonadherence is already widespread, rising drug prices raise concerns that patients will be unable to afford their prescriptions, leading to negative health outcomes.6 Newly developed medications are often expected to be expensive because of high research and development costs, although there is debate about how to estimate these costs.7 Recent reports of substantial increases in list prices of drugs already on the market cannot be explained by development costs and have received considerable media attention3,4,8,9 and criticism from lawmakers, insurers, and consumers alike.1,10-12 However, pharmaceutical industry leaders argue that changes in wholesale list prices do not necessarily lead to large changes in amounts patients or insurers pay for drugs.13-15 Drug manufacturers have argued that increases in list prices are due to increases in discounts or rebates, not increases in actual amounts paid by patients and payers.16 Hernandez et al17 recently demonstrated that discounts

and rebates have increased, as manufacturers report.

However, after accounting for these price reductions, total payments manufacturers received rose faster than general inflation. What remains unknown, however, is how this mix of increases in list prices with rising discounts and rebates affects patients’ out-of-pocket costs. If discounts and rebates are large enough and targeted to patients, patient out-of-pocket costs may not rise as list prices increase. However, this hypothesis cannot be evaluated without evaluating discounts, rebates, and amounts paid by payers and by patients out of pocket in a single analysis. This issue is particularly relevant to clinical practice in which news about list price changes must be translated into discussions about drug costs with patients.

To address these issues, we studied a national sample of employer and commercial insurer pharmaceutical claims and sales data from 2010 to 2016 for drugs previously identified as contributing the most to total drug expenditures for commercial insurers.18 We analyzed changes over time in wholesale list prices and amounts paid by patients and insurers after accounting for discounts and rebates to understand how rising list prices are associated with patients’ out-of-pocket costs and the net cost of these drugs to insurers.

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METHODS Drugs Studied We focused our analyses on the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers nationwide in 2014.18,19 The most widely used specialty drugs include treatments for rheumatoid arthritis, Crohn disease, psoriasis, cancer, and HIV, among other conditions.20 Specialty medications commonly have high prices and have been implicated in driving rising national drug expenditures.18,21 Database and Study Population We used Health Insurance Portability and Accountability Act–compliant, deidentified, patient-level outpatient pharmacy claims data from the IBM MarketScan Commercial Database. This database represents the claims of 67.4 million employees and dependents participating in employer health benefit programs belonging to large employers between January 1, 2010, and December 31, 2016. The plans represented include a variety of fee-for-service, preferred provider organization, and capitated health plans. For clarity, we refer to these collectively as insurers. Claims for patients aged 65 years and older were excluded because of likely confounding differences in benefit design secondary to dual coverage with Medicare. We excluded claims in which reported prescribed dose exceeded the maximum clinical dose. This study was determined to be exempt from review by the institutional review board at the University of California, San Francisco because the patient claims data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

Average Wholesale Price (List Price), Discounts, and Out-of-Pocket Costs Each pharmacy claim reports the Average Wholesale Price (AWP) for the drug prescribed, actual payment to the pharmacy (ie, the discounted price paid by the insurer but not accounting for rebates), and the patient’s out-of-pocket cost for the medication. Because drug manufacturers report substantial discounts and rebates, for this analysis we use the AWP, equivalent to the manufacturer’s suggested retail price, as the starting wholesale list price. Another price available on claims is the Wholesale Acquisition Cost, but manufacturers would argue that this price already includes 6

a discount they offer, so we start with the higher AWP as the wholesale list price. We calculated the manufacturer’s discount for each claim as AWP minus actual payment to the pharmacy.

We converted all costs for each claim into unit price (ie, the cost of each claim was divided by the number of doses listed in that claim), to ensure that variation in prices was not due to differences in dosing. Mean AWP, patient out-of-pocket costs, and discounted price were calculated by totaling all claims for a drug and dividing by the number of claims. Discounts were calculated as the difference between AWP and the discounted price. Mean AWP per unit and discounts were calculated for each drug per quarter, whereas mean patient costs were calculated annually to account for patient deductibles.

Estimated Rebates Like Hernandez et al,17 we obtained rebate data from SSR Health, LLC, a health care–focused investment research firm, and Symphony Health. SSR Health compiles data from pharmaceutical company investor reports to determine total revenue after all price reductions (including discounts and rebates) have been factored in. This is divided by the total number of prescriptions, estimated by Symphony Health, to calculate the net payment for drugs, accounting for discounts and rebates.

SSR Health estimated discounts to Medicaid payers by using the sum of the Medicaid statutory rebate (23.1% of average manufacturer price) and the pricing penalty for price increases exceeding the Consumer Price Index increase. These are discounts offered to state Medicaid programs as part of “best price” regulations to make drug spending more affordable for Medicaid.

The number of units of each medication sold to Medicaid was obtained by SSR Health from the Centers for Medicare & Medicaid Services’ drug utilization files.

SSR Health then estimated total revenue from Medicaid by multiplying the discounted Medicaid price by the number of units sold to Medicaid. SSR Health calculated revenue from non-Medicaid sales for each drug by subtracting Medicaid revenue from total revenue. Similarly, the number of non-Medicaid units sold for each medication was estimated by subtracting Medicaid units sold from total units sold. SSR Health then divided non-Medicaid revenue by the number of non-Medicaid units sold to estimate the net price for

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non-Medicaid insurers. To allow for comparison with the pharmacy claims data (which does not include Medicaid payers), we used only the non-Medicaid sales data from this data set.

the Consumer Price Index for All Urban Consumers.

Analyses were performed with Excel 2013, version 15.0.4535.1507 (Microsoft Corp). Data were analyzed from July 2017 to July 2020.

We then calculated the mean rebate for non-Medicaid payers as the difference between the mean discounted price paid on claims and the non-Medicaid net price from SSR Health. With these figures, we could assess how the AWP for each drug could be broken down into discounts shown on the claim, rebates passed on to insurers, patient out-of-pocket costs, and insurer payments net of discounts and rebates such that

Sensitivity Analysis Some information about Medicaid discounts and rebates, such as best price discounts or rebates required by individual states, was not available to SSR Health. However, we could calculate a range of possible Medicaid prices. Because total discount and rebate amounts given by drug manufacturers are split between Medicaid and non-Medicaid purchasers, assuming higher Medicaid discounts and rebates would result in an estimation of lower non-Medicaid discounts and rebates.

AWP = (Discount + Rebate) + Insurer payments + Patient Out-of-Pocket Costs.

Independent and Dependent Variables Assessed Our primary independent variable was percentage of change in AWP from 2010 to 2016 for medications maintaining patent protection during this period. Our secondary independent variables were percentage of change in AWP from 2010 to 2014 and from 2010 to 2015 for medications maintaining patent protection during these respective periods. Our primary dependent variables were the percentage of change in patient out-of-pocket costs and insurer payments after rebates.

Secondary dependent variables included the mean percentage of change in discounts and rebates over the study period. In addition, differences in the changes in AWP, net payments (sum of insurer payments and patient out-of-pocket costs), insurer payments, and patient out-of-pocket costs between specialty medications and nonspecialty medications were evaluated.

The proportion of change in AWP accounted for by changes in discounts, rebates passed on to insurers, insurer payments, and patient out-of-pocket costs was computed for all medications.

Statistical Analysis We assessed differences in price trends between specialty and nonspecialty medications by comparing the proportion of increase in AWP from 2010 to 2016 accounted for by changes in rebates, discounts, insurer payments, and patient out-of-pocket costs between these 2 groups of drugs. Median values were used as a measure of central tendency to reduce the impact of outlier claims or benefits designs. All prices and payments shown were adjusted to 2016 dollars using

For our main analysis, the estimates made by SSR Health about Medicaid statutory rebates and pricing penalties led to the highest possible estimates of discounts and rebates to the commercial insurers being studied to maximally reflect manufacturer claims about high discount and rebate rates. As a sensitivity analysis and to estimate a range of patient out-ofpocket costs and insurer payments, we also calculated those outcomes with the assumption of every Medicaid unit in the SSR Health data set being rebated at 100% of average manufacturer price. Because this would be the maximum possible discount paid to Medicaid, it would consequently result in the lowest possible discounts and rebates to commercial insurers.

RESULTS For 2010-2016, the MarketScan database included data for 67.4 million enrollees who filled at least 1 prescription, with 1.8 million enrollees having 14.4 million pharmacy claims for the drugs of interest (eTable 1 in the Supplement). Taken together, claims for the 14 drugs accounted for $13 billion in spending, or 6.6% of total drug spending during this study period. The drugs included in our analysis consisted of 5 specialty drugs and 9 nonspecialty drugs.

Not all drugs in our study retained patent protection through 2016. Figure 1 shows increases in AWP and net payments (the sum of patient and insurer payments after discounts and rebates) from 2010-2016. For the 14 drugs retaining patent protection through 2014, the

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median increase in AWP was 59% (interquartile range [IQR], 47%-71%), whereas median net payments increased by 78% (IQR, 27%-111%) above the rate of increase in the Consumer Price Index (eFigure 1 in the Supplement). For the 9 drugs retaining patent protection through 2016, the median increase in AWP was 129% (IQR, 78%-133%) from 2010 to 2016, whereas median net payments increased by 74% (IQR, 27%158%; eFigure 1 in the Supplement).

From 2010 to 2016, the median out-of-pocket patient payments and insurance payments increased by 53%

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(IQR, 42%-82%) and 64% (IQR, 28%-120%), respectively. During this time, manufacturer discounts and rebates passed on to insurers rose by a median of 163% (IQR, 130%-212%) and 103% (IQR, −18% to 293%). As a result, the mean percentage of wholesale list price accounted for by discounts increased from 17% in 2010 to 21% in 2016, and the mean percentage of wholesale list price accounted for by rebates increased from 22% in 2010 to 24% in 2016 (eFigure 2 in the Supplement).

The amounts paid by patients out of pocket and by in-

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surers increased for most drugs included in our analysis from 2010 to 2016 (Table 1). For Humalog and Nexium, insurer expenditures decreased slightly.

Among all study drugs that retained patent protection from 2010 to 2014, every $1 increase in AWP was associated with a median increase in patient out-ofpocket costs of $0.04 (range, $0.01-$0.19) (Table 2).

Patient out-of-pocket costs increased $0.04 (range, $0.03-$0.19) for nonspecialty drugs and $0.03 (range, $0.01-$0.04) for specialty drugs. Insurer payments for this period increased $0.63 (range, −$0.07 to $1.61) for all eligible study drugs during this period. Insurer payments increased $0.13 (range, −$0.07 to $1.61) for nonspecialty medications and $0.81 (range, $0.67$1.15) for specialty drugs for every $1 increase in AWP during this period.

Among all study drugs retaining patent protection from 2010 to 2016, every $1 increase in AWP was associated with a median increase in patient out-ofpocket costs of $0.04 (range, $0.01-$0.20). Patient out-of-pocket costs increased $0.05 (range, $0.02$0.20) for nonspecialty drugs and $0.03 (range, $0.01-$0.06) for specialty drugs. Insurer payments for this period increased $0.57 (range, −$0.04 to $1.02) for all eligible study drugs during this period. Insurer payments increased $0.13 (range, −$0.04 to $0.62) for nonspecialty drugs and $0.74 (range, $0.57-$1.02) for specialty drugs for every $1 increase in AWP during this period.

In total, median patient out-of-pocket costs for specialty medications increased by 85% (IQR, 73%-88%) after adjustment for inflation as compared with 42% (IQR, 25%-53%) for nonspecialty medications from

2010 to 2016, while insurer payments for specialty medications increased by 116% (IQR, 100%-127%) compared with 28% (IQR, 5%-34%) for nonspecialty medications (Figure 2).

In our sensitivity analysis in which we assessed the range of possible discounts and rebates to commercial insurers, patient out-of-pocket costs were unchanged.

Median insurer payment increases were slightly lower than those in our primary analysis, although still far above the rate of increase in the Consumer Price Index. With these assumptions, median insurer payments for drugs retaining patent protection through 2016 increased by 61% (IQR, 34%-122%) after adjustment for inflation.

DISCUSSION In a large, national sample of patients with commercial insurance, we found that increases in drug wholesale list prices were accompanied by increases in out-ofpocket payments by patients and by insurers. Increases in payments for specialty medications from 2010 to 2016 were greater than those for nonspecialty medications in our analysis over the study period.

Pharmaceutical manufacturers have argued that increases in drug wholesale list prices are accompanied by corresponding large increases in discounts and rebates, reducing the impact of list price changes on patients and insurers.13-15 Our analysis corroborates that rebates and discounts are large, and the proportion of AWP attributed to discounts and rebates did increase from 2010 to 2016 (eFigure 3 in the Supplement). However, after accounting for these changes, we show that increases in wholesale list prices were

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associated with increases in patient out-of-pocket costs and insurer payments for the drugs studied. This varies by drug, but wholesale list prices, insurer payments, and patient out-of-pocket costs all increased from 2010 to 2016 at rates above the rate of increase in the Consumer Price Index (eTable 2 in the Supplement).

Although patient out-of-pocket costs for drugs vary greatly, patients paid a median of 53% more for these drugs in 2016 than they did in 2010, after adjusting for general inflation. During the same period, median household income rose 8.6%,22 so these out-of-pockets costs have increased relative to income. Because we used claims to determine out-of-pocket costs, this finding persists despite coupons and patient assistance programs aimed at lowering patient spending. This observed rise in patient out-of-pocket costs with increasing drug list prices is of particular concern to clinicians because high drug costs can lead to poor drug regimen adherence, increased use of utilization management systems such as prior authorizations and step 10

therapy, and adverse health outcomes.6,20 Although patients paid a smaller percentage of the wholesale list price over time, the absolute amount that they paid for the drugs we studied continued to increase at rates higher than general inflation or the growth of income, which may have important implications for the ever larger population of patients with chronic diseases.23,24 This study highlights that increases in prices for drugs that have been on the market for several years increase the cost of treatment for patients.

Spending for specialty drugs has increased significantly over the past decade and is projected to contribute to rapidly increasing health care costs.20,25,26 Our results indicate that discounts and rebates for these drugs are lower relative to list prices than discounts and rebates for nonspecialty medications. Insurer payments for Nexium and Humalog did not outpace inflation in our analysis, presumably because they face more competition from similar drugs in their classes. Manufacturers of single-source specialty medications may

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offer fewer price concessions because most face little market competition.27 Consequently, insurer payments for specialty drugs are also rising at a much higher rate than payments for nonspecialty medications, also increasing coinsurance costs. Although patient out-ofpocket costs are a smaller proportion of the total price of specialty medications than nonspecialty medications, specialty drugs are still more costly to patients.

As use of these drugs increases, the high costs may affect patients through higher out-of-pocket spending per prescription and rising premiums, as well as both government programs and commercial insurers.18,28 Limitations Our study has several limitations. Given differences in pricing of generic medications with regard to discounts and rebates, we opted not to include medications that did not have patent protection during our study period. The findings of our study reflect the pricing breakdown of the 14 medications accounting for the greatest portion of insurer medication expenditures and may not be generalizable to other medications.

In addition, several of the analyzed medications lost patent protection shortly after the study period. Further study is needed to characterize how impending patent protection expiration affects list prices, discounts, rebates, and amounts paid for medications.

We were also unable to control for changes in cost sharing during this period, which may also contribute to changes in amounts paid for medications.

Furthermore, contracting and pricing are different for Medicare, Medicaid, the 340B program, and the Department of Veterans Affairs, so it is not clear how generalizable our findings are to those markets.

In addition, we could not obtain precise estimates of all Medicaid discounts and rebates, resulting in some uncertainty about the total discounts and rebates offered to the commercial insurers we studied. Therefore, our sensitivity analyses provide only a range of possible insurer payments rather than exact figures.

However, under any set of assumptions, we found that increases in list prices resulted in increases in insurer payments. This limitation has no effect on our estimates of patient out-of-pocket costs, since these were identified directly from claims data.

Our study also does not characterize who receives manufacturers’ rebates in detail. Rather, we report

what can be directly measured: the amount that is passed on to insurers. Insurers may then pass some of these savings on to employers, but rebates are also frequently paid—at least in part—to pharmaceutical benefits managers.29 We are not aware of data that allow description of how rebates are shared among these relevant parties, but they are not shared with patients.

Finally, we were not able to measure other mechanisms by which rising drug prices may increase patient out-of-pocket costs, such as increasing insurance premiums. Additional study of the association between rising insurer payments and these other patient out-ofpocket costs is needed to further clarify the total effect of these pricing changes on patients.

CONCLUSIONS In this cross-sectional study, we found that increases in drug wholesale list prices are associated with increases in net patient out-of-pocket costs and insurer payments. This finding suggests that, although discounts and rebates significantly reduce the amount paid for drugs and have increased over the past several years, they have not prevented an inflation-adjusted rise in patients’ and insurers’ costs. This could have both important clinical implications for patient outcomes and an impact on total health care expenditures.

This is an open access article distributed under the terms of the CC-BY License. © 2020 Yang EJ et al.

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CVS Specialty. Published October 2019. Accessed September 20, 2020. https://www.caremark.com/ portal/asset/IBM_Specialty_Drug_List.pdf 20. Lotvin AM, Shrank WH, Singh SC, Falit BP, Brennan TA. Specialty medications: traditional and novel tools can address rising spending on these costly drugs. Health Aff (Millwood). 2014;33(10):1736-1744. doi:10.1377/ hlthaff.2014.0511 21. Cuckler GA, Sisko AM, Poisal JA, et al. National health expenditure projections, 2017-26: despite uncertainty, fundamentals primarily drive spending growth. Health Aff (Millwood). 2018;37(3):482-492. doi:10.1377/ hlthaff.2017.1655 22. US Census Bureau. Real median household income in the United States [MEHOINUSA672N], retrieved from FRED, Federal Reserve Bank of St Louis. Updated September 16, 2020. Accessed September 20, 2020. https://fred.stlouisfed.org/se-

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ries/MEHOINUSA672N 23. Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis. 2014;11:E62. doi:10.5888/pcd11.130389 24. Piette JD, Heisler M, Wagner TH. Cost-related medication underuse among chronically ill adults: the treatments people forgo, how often, and who is at risk. Am J Public Health. 2004;94(10):17821787. doi:10.2105/AJPH.94.10.1782 25. Martin AB, Hartman M, Washington B, Catlin A; National Health Expenditure Accounts Team.

National health spending: faster growth in 2015 as coverage expands and utilization increases.

Health Aff (Millwood). 2017;36(1):166-176. doi:10.1377/hlthaff.2016.1330 26. Kesselheim AS, Avorn J, Sarpatwari A. The high cost of prescription drugs in the United States: origins and prospects for reform.

JAMA. 2016;316(8):858-871. doi:10.1001/ jama.2016.11237 27. Dusetzina SB, Bach PB. Prescription drugslist price, net price, and the rebate caught in the middle. JAMA. 2019;321(16):1563-1564. doi:10.1001/jama.2019.2445 28. Schumock GT, Li EC, Wiest MD, et al. National trends in prescription drug expenditures and projections for 2017. Am J Health Syst Pharm.

2017;74(15):1158-1173. doi:10.2146/ajhp170164 29. Dusetzina SB, Conti RM, Yu NL, Bach PB. Association of prescription drug price rebates in Medicare Part D with patient out-of-pocket and federal spending. JAMA Intern Med. 2017;177(8):1185-1188. doi:10.1001/jamainternmed.2017.1885 Article Information Accepted for Publication: October 4, 2020.

Published: December 9, 2020. doi:10.1001/jamanetworkopen.2020.28510 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Yang EJ et al. JAMA Network Open.

Corresponding Author: Eric J. Yang, MD, Department of Dermatology, Warren Alpert Medical School, Brown University, 593 Eddy St, Providence, RI 02903 (ericjyang@outlook.com).

of the data and the accuracy of the data analysis.

Concept and design: Yang, Galan, Bach, Dudley.

Acquisition, analysis, or interpretation of data: Yang, Galan, Thombley, Lin, Seo, Tseng, Resneck, Dudley.

Drafting of the manuscript: Yang, Galan, Bach, Dudley.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Yang, Bach.

Obtained funding: Galan, Dudley.

Administrative, technical, or material support: Galan, Thombley, Lin, Seo, Bach.

Supervision: Galan, Bach, Dudley.

Conflict of Interest Disclosures: Dr Resneck reported serving as an American Medical Association (AMA) trustee and as a paid expert witness for the US Department of Justice on cases related to fraudulent prescribing of compounded medications outside the submitted work. Dr Bach reported receiving speaking fees and travel reimbursement from the American Society for Health-System Pharmacists, America’s Health Insurance Plans, Geisinger, Gilead Pharmaceuticals, Hematology Oncology Pharmacy Association, Kaiser Permanente Institute for Health Policy, Oncology Analytics, Oppenheimer & Co, United Rheumatology, and the US Congressional Budget Office; speaking fees from Anthem, Cello Health, Defined Health, JMP Securities, Magellan Health, Mercer, Morgan Stanley, the NYS Rheumatology Society, and WebMD; consulting fees from Foundation Medicine and Grail; stock from Arnold Ventures, EQRx, Grail, and Kaiser Permanente; and advisory fees from EQRx, all outside the submitted work. No other disclosures were reported.

Funding/Support: This research was supported by grant 5R25MD006832, which was received jointly from the PROFPATH program of the University of California, San Francisco, and the National Institutes of Health (NIH) National Institute on Minority Health and Health Disparities. Dr Tseng is supported by the Hawaii Medical Service Association Endowed Chair in Health Services and Quality Research.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The opinions expressed in this article are those of the authors and do not necessarily represent the views of the AMA.

Author Contributions: Drs Yang and Dudley had full access to all of the data in the study and take responsibility for the integrity www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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State Policies on Access to Vaccination Services for Low-Income Adults Charleigh J. Granade, MPH (1,2,3); Russell F. McCord, JD (4,5); Alexandra A. Bhatti, JD (4,5,6); Megan C. Lindley, MPH (1) 1. Immunization Services Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia; 2. Oak Ridge Institute for Science and Education, Department of Energy, Washington, DC; 3. Now with IHRC Inc, Atlanta, Georgia; 4. Public Health Law Program, Center for State, Tribal, Local, and Territorial Support, Centers for Disease Control and Prevention, Atlanta, Georgia; 5. Cherokee Nation Assurance, Arlington, Virginia; 6. Now with Global Policy, Communications & Population Health, Merck & Co Inc, North Wales, Pennsylvania

Abstract Importance State vaccination benefits coverage and access for adult Medicaid beneficiaries vary substantially. Multiple studies have documented lower vaccination uptake in publicly insured adults compared with privately insured adults.

Objective To evaluate adult Medicaid beneficiaries’ access to adult immunization services through review of vaccination benefits coverage in Medicaid programs across the 50 states and the District of Columbia.

Design, Setting, and Participants A public domain document review with supplemental semistructured telephone survey was conducted between June 1, 2018, and June 14, 2019, to evaluate vaccination services benefits in fee-for-service and managed care organization arrangements for adult Medicaid beneficiaries in the 50 states and the District of Columbia (total, 51 Medicaid programs).

Exposures Document review of benefits coverage for adult immunization services and supplemental survey with validation of document review findings.

Main Outcomes and Measures Benefits coverage for adult Medicaid beneficiaries and reimbursement amounts for vaccine purchase and administration.

Results Public domain document review was completed for all 51 jurisdictions. Among these, 44 Medicaid programs (86%) validated document review findings and completed the survey. Only 22 Medicaid programs (43%) covered all 13 Advisory Committee on Immunization Practices—recommended adult immunizations under both fee-for-service and managed care organization arrangements. Most fee-for-service arrangements (37 of 49) reimbursed health care professionals using any of the 4 approved vaccine administration codes; however, 8 of 49 programs did not separately reimburse for vaccine administration to adult Medicaid beneficiaries. Depending on administration route, median reimbursement for adult vaccine administration ranged from $9.81 to $13.98 per dose.

Median per-dose reimbursement for adult vaccine purchase was highest for 9-valent human papillomavirus vaccine ($204.87) and lowest for Haemophilus influenzae type b vaccine ($18.09). Median reimbursement was below the private sector price for 7 of the 13 included vaccines.

Conclusions and Relevance Even in programs with complete vaccination benefits coverage, reimbursement amounts to health care professionals for vaccine purchase and administration may not fully cover vaccination provision costs. Reimbursement amounts below costs may reduce incenwww.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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tives for health care professionals to vaccinate low-income adults and thereby limit Medicaid adult beneficiary access to vaccination.

Key Points Question What level of access to vaccination services do Medicaid programs provide to adult beneficiaries enrolled in fee-for-service and managed care organization arrangements?

Findings In this survey study of Medicaid programs, 22 of 51 programs covered all 13 adult vaccines recommended by the Advisory Committee on Immunization Practices for both fee-for-service and managed care organization enrollees. Reimbursement for vaccine administration was disparate; median vaccine purchase reimbursement was highly variable relative to manufacturer-reported private sector price.

Meaning These findings suggest that most adult Medicaid beneficiaries do not have access to all 13 Advisory Committee on Immunization Practices–recommended adult vaccines; low reimbursement for vaccine administration and purchase may disincentivize health care professionals to vaccinate low-income adults.

INTRODUCTION Medicaid traditionally provides health insurance to low-income children and parents, pregnant women, the elderly, and disabled individuals at little to no cost.

In 2010, the Patient Protection and Affordable Care Act extended Medicaid eligibility to include childless adults with incomes up to 138% of the federal poverty level.1 Enrollment rapidly increased; by 2017, adults enrolling under this expansion made up 19.4% of the total Medicaid population.2 Medicaid currently provides health insurance to an estimated 37.5 million adults across all eligibility groups in the United States.3 For the poorest citizens in the United States, Medicaid is the primary source of funding for health-related services, including vaccinations. Research suggests that individuals with health insurance have higher receipt than uninsured individuals of preventive services,4-6 but adults with public insurance generally have lower vaccination coverage than do privately insured individuals.7-10 Low adult immunization coverage can burden the US health care system: in 2015, vaccinepreventable diseases in adults cost the United States $9 billion in health care costs and lost productivity.11 Pneumonia and influenza are among the top causes of death for US adults, accounting for 55 672 deaths (2% of total deaths) in 2017.12 Knowledge regarding 16

current adult immunization policy within Medicaid is limited.13-15 Adult vaccination services are not a federally mandated benefit for traditionally eligible Medicaid beneficiaries and are therefore determined by individual states.

By contrast, benefits packages for adults who enrolled under the Medicaid expansion are required to cover 10 “essential health benefits,” including adult immunization services, with no cost sharing.16 Although the Medicaid expansion unified benefits among newly eligible adults, it did not address vaccination benefits for traditionally eligible adults. Copayments are a known barrier to receipt of health services such as vaccination, particularly for low-income populations.17,18 Medicaid programs were encouraged to limit copayments through the Section 4106 incentive, in which states received a 1% increase in the Federal Medical Assistance Percentage if their state matched preventive care benefits for adults who enrolled in Medicaid under the expansion and traditionally eligible Medicaid populations with no cost-sharing.19 Not all expansion states chose to leverage this increased Federal Medical Assistance Percentage incentive, resulting in persistent financial barriers for some traditionally eligible beneficiaries and potentially solidifying disparities in access between eligibility groups.

We evaluated adult Medicaid beneficiaries’ access to

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immunization services through review of vaccination benefits coverage in Medicaid programs across the 50 states and the District of Columbia. Specifically, this study examined benefits coverage and reimbursement amounts for vaccine purchase and administration as well as copayment practices for adult Medicaid beneficiaries for each vaccine recommended for adults in 2018 by the Advisory Committee on Immunization Practices (ACIP).20 METHODS The study was completed between June 1, 2018, and June 14, 2019. We conducted a public document review and developed and administered a survey assessing adult immunization policies for Medicaid fee-forservice (FFS) and managed care organization (MCO) arrangements. For the survey, respondent consent was assumed if the Medicaid director or their designee agreed to participate in the survey. To allow for transcription of responses collected via the telephone survey, respondents were asked to verbally consent to being audio recorded. The Centers for Disease Control and Prevention (CDC) determined this study to be research not involving human participants; therefore, it did not require institutional review board approval.

Document Review Public domain document review was conducted from April 2, 2018, to April 30, 2019. Using a standard search engine, information related to benefits coverage of, payment for, and copayments for Medicaid adult vaccination services was collected using the following search strings: “[X] state Medicaid plan,” “FFS [fee-for-service] fee schedule,” and “provider manuals Medicaid.”

Public domain review materials were organized into a brief document and integrated into the survey tool.

The document comprised Medicaid program population estimates, expansion status, Section 4106 status, income eligibility limits, distribution of FFS and MCO arrangements, federally qualified health center benefits, and FFS reimbursement fee schedules.

Survey Design and Administration The survey was developed by the the CDC’s Public Health Law Program and Immunization Services Division. Using the National Association of Medicaid

Directors online directory,21 we identified each jurisdiction’s Medicaid program director, who was sent an introductory email along with a summary of the public domain document review for their program and the survey instrument. Up to 5 additional emails were sent to nonresponding programs. Respondents included Medicaid directors and their designated representatives.

Respondents were asked to validate the public domain document review results for their program and to complete a semistructured survey. Both semistructured telephone surveys and written responses were collected from participating programs. The survey questions and probing used in both collection methods were identical, with response method determined by respondent preference. The survey and validation of the public domain document review was conducted from June 1, 2018, to June 14, 2019.

Adult Vaccination Access and Reimbursement To understand access to and reimbursement for adult immunization services at the Medicaid program level, we assessed coverage benefits for the following 2018 ACIP-recommended adult immunizations for persons 19 years or older20: influenza; tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap); measles, mumps, and rubella (MMR); varicella; recombinant zoster; 9-valent human papilloma virus (9vHPV); pneumococcal conjugate; pneumococcal polysaccharide (PPSV23); hepatitis A; hepatitis B; serogroup A, C, W, and Y meningococcal; serogroup B meningococcal; and Haemophilus influenzae type b (Hib) vaccines.

Thirty-one Current Procedural Terminology (CPT) codes for adult vaccines were evaluated using each program’s FFS fee schedule to determine adult vaccination benefits coverage; however, because numerous influenza vaccines are available on the market, only the most commonly covered inactivated influenza vaccine (code 90656), the live attenuated vaccine (code 90672), and the recombinant vaccine (code 90682) were used to assess benefits coverage. In addition, reimbursement to health care professionals for adult vaccine administration was evaluated for CPT codes 90471 to 90474, with each administration code dependent on method of delivery (injected vs intranasal) and number of vaccine doses administered during a health

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care visit.22 Median and range of vaccine purchase reimbursement amounts for the most commonly reimbursed CPT codes for each ACIP-recommended adult immunization were calculated using available FFS fee schedules and compared with publicly available CDC vaccine prices and manufacturer-reported private sector vaccine prices.23 Reimbursement amounts for adult vaccine purchase greater than 1.5 times the interquartile range were noted as outliers.

Medicaid programs were considered to provide coverage benefits consistent with the 2018 ACIP recommendations for adult immunization if their FFS fee schedule demonstrated reimbursement for any CPT code for each of the 13 immunizations examined, with the exception of influenza vaccine, for which reimbursement for any of the 3 codes noted above was counted.

To supplement document review findings, survey responses from participating Medicaid programs regarding adult vaccination coverage benefits under MCO arrangements, use of copayments for vaccination services under FFS and MCO arrangements, and factors associated with program decisions to cover ACIPrecommended adult immunizations were analyzed.

RESULTS Health care professional FFS reimbursement fee

18

schedules were evaluated for 49 of 51 Medicaid programs; the remaining 2 programs, in Hawaii and Tennessee, are both under 100% MCO arrangements.

In addition, 44 Medicaid programs (86%) validated public domain document review findings and completed the survey—34 (77%) via telephone and 10 (23%) in writing—and were included in analyses.

Medicaid Program Coverage of ACIP-Recommended Adult Vaccines Although most Medicaid programs provided some level of coverage for adult vaccines, only 22 Medicaid programs (43%) covered all 13 ACIP-recommended adult immunizations under both FFS and MCO arrangements (eTable 1 in the Supplement). Although 3 additional programs (in Arkansas, Iowa, and Utah) covered all ACIP-recommended vaccines under FFS, no information regarding their MCO vaccine benefits coverage was available, as these programs did not participate in the survey.

FFS Arrangements Most Medicaid program FFS arrangements provided adult vaccination benefits coverage for all or most ACIP-recommended adult immunizations (Figure 1).

Twenty-four of 49 FFS arrangements provided coverage for all ACIP-recommended adult immunizations.

Forty-eight FFS arrangements covered 1 or more

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influenza vaccines in addition to Tdap, MMR, varicella, and PPSV23. In contrast, benefits coverage was less common for the 9vHPV vaccine (43 of 49 FFS arrangements), Hib vaccine (37 of 49 FFS arrangements), and zoster vaccine (33 of 49 FFS arrangements).

MCO Arrangements Thirty-nine of 51 Medicaid programs (76%) covered some proportion of their beneficiaries via MCO arrangements. Because information regarding adult vaccination benefits coverage by individual CPT code for MCO arrangements was unavailable, coverage of the ACIP-recommended adult immunization schedule was evaluated using survey responses from 34 of 39 participating programs. Although adult vaccination benefits coverage varied by individual MCO, 21 programs provided coverage for all ACIP-recommended immunizations. Similar to FFS arrangements, coverage for the 9vHPV vaccine (25 of 34 participating programs), Hib vaccine (25 of 34 participating programs), and zoster vaccine (23 of 34 participating programs) was lower than for other vaccines under MCO arrangements (Figure 1).

Reimbursement to Health Care Professionals for Vaccine Administration Reimbursement to health care professionals for administration of adult vaccines to FFS beneficiaries varied for each Medicaid program (Table). For the first dose of injected vaccine administered during a visit (CPT code 90471), the median reimbursement was $13.62 (range, $3.72 in South Carolina to $28.18 in Alaska); median reimbursement for first intranasal administration (CPT code 90473) was $13.98 (range, $3.00 in Michigan to $28.18 in Alaska). Median reimbursement for each subsequent dose of injected vaccine (CPT code 90472) was $9.81 and for each subsequent dose of intranasal vaccine (CPT code 90474) was $9.92, ranging from $2.00 in New York to $20.80 in New Mexico.

Most FFS arrangements (37 of 49) reimbursed health care professionals for adult vaccine administration using any of the 4 approved vaccine administration codes (Table). Eight programs (in Arkansas, Connecticut, Delaware, Georgia, Illinois, Maryland, Virginia, and West Virginia) did not provide separate reimbursement for vaccine administration to adult Medicaid

beneficiaries.

Eleven programs granted the same reimbursement for all 4 vaccine administration codes, ranging from $3.72 in South Carolina to $20.80 in New Mexico. The programs with the highest reimbursement amounts to health care professionals under FFS arrangements for a single injected vaccine administration (CPT code 90471) were Alaska ($28.18), Arizona ($22.32), and Nevada ($22.22). The programs with the lowest reimbursement amounts to health care professionals were South Carolina ($3.72), California ($4.46), Alabama ($5.00), and New Hampshire ($5.00).

Reimbursement to Health Care Professionals for Vaccine Purchase Information on reimbursement to health care professionals for vaccine purchase under FFS arrangements was available for 46 of 49 programs. Specific reimbursement amounts for West Virginia, Pennsylvania, and New York were not available, as the respective FFS schedule for each Medicaid program defined reimbursement as one of the following: “carrier-priced” (West Virginia), “national drug code” (Pennsylvania), and “manually priced” (New York). Among all ACIP-recommended adult immunizations, median reimbursement was highest for the 9vHPV vaccine (CPT code 90651), at $204.87 (Figure 2). The 9vHPV vaccine also demonstrated the largest per-dose reimbursement range, from $5.27 in Missouri to $491.38 in Mississippi. The ACIP-recommended adult vaccine with the lowest median reimbursement ($18.09) was the Hib vaccine (CPT code 90648), ranging from $5.27 in Missouri to $30.80 in Wisconsin.

Median reimbursement for the hepatitis A vaccine (CPT code 90632) was $58.65, ranging from $41.11 in the District of Columbia to $80.95 in New Jersey; that for the hepatitis B vaccine (CPT code 90746) was $63.30, ranging from $50.64 in the District of Columbia to $69.58 in California (eTable 2 in the Supplement). Median reimbursement was below the private sector price reported by manufacturers to the CDC for 7 of 13 ACIP-recommended adult vaccines, with the largest observed disparities for the varicella, 9vHPV, and Tdap vaccines (eTable 2 in the Supplement).

Factors Associated With ACIP-Recommended Adult Vaccination Benefits

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20

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Survey respondents were asked to describe factors associated with their program’s decision to cover ACIPrecommended adult immunizations. Respondents most commonly cited ACIP and CDC recommendations as the most important factor (38 of 44), followed by state and local health professional recommendations (18 of 44). Respondents also cited state health agency recommendations (10 of 44), public attention (8 of 44), and legislative interest (7 of 44). Other reasons for deciding to cover an ACIP-recommended adult vaccine included US Food and Drug Administration approval, “because it is the right thing to do,” and as a result of infectious disease outbreaks. In Oregon, respondents indicated that their Medicaid program collaborated with a health evidence committee when deciding whether to cover an ACIP-recommended adult immunization.

Copayments for Adult Vaccination Services Among the Medicaid programs surveyed, 12 of 44 confirmed that their jurisdiction implemented the Section 4106 incentive and therefore covered all ACIP-

recommended adult immunizations with no cost sharing. An additional 17 of 44 programs prohibited cost sharing for immunization services for adult Medicaid beneficiaries without use of the incentive. In the 15 remaining programs in which cost sharing is present, 14 permitted copayments for traditionally eligible adults who receive vaccination benefits under FFS arrangements. Specifically, copayments were permitted for vaccines received in the pharmacy setting (Maryland and Indiana) and for persons older than 21 years who are not pregnant (Oklahoma). In programs permitting copayments for FFS beneficiaries, copayments were prohibited for some populations, including pregnant women (8 of 14), nursing home residents (6 of 14), and individuals under hospice care (3 of 14).

Although most Medicaid programs prohibited MCO arrangements from establishing copayments for adult vaccination, MCOs within 6 of 15 programs permitted copayments for receipt of immunization services. In Florida, copayments were permitted for adult vaccination and are determined by vaccine type. In Geor-

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gia, MCO arrangements did not restrict copayments for adult immunization services; therefore, all adult Medicaid beneficiaries are subject to copayments.

Although Minnesota, Pennsylvania, Maryland, and Indiana are all expansion states, copayments for adult vaccination were permitted for certain beneficiary populations. However, copayments were prohibited for the following populations: pregnant women, individuals receiving hospice care, and nursing home residents (Minnesota and Pennsylvania); adults receiving vaccinations in the primary care setting (Maryland and Pennsylvania); Native American Indians (Minnesota); and recipients of breast and cervical cancer treatments (Pennsylvania). Cost-sharing policies for MCO arrangements in Indiana were unavailable.

DISCUSSION This study found that most Medicaid programs provided some level of reimbursement for adult vaccine administration and purchase. However, only 22 of 51 programs covered all 2018 ACIP-recommended adult immunizations for both FFS and MCO beneficiaries; of those, only 14 provided vaccination benefits 22

without copayments. Adult vaccination coverage and access varied between FFS and MCO arrangements within Medicaid programs. Inequities regarding access to adult immunization services are evident, and incomplete vaccination benefits coverage across Medicaid programs are likely associated with fewer Medicaid beneficiaries receiving the recommended vaccines.

Stewart and colleagues14 conducted a public document review and survey of Medicaid directors evaluating adult immunization policy under FFS arrangements, comparing benefits coverage with the 2012 ACIPrecommended adult immunization schedule, and found that 36 of 51 Medicaid FFS arrangements covered all recommended adult vaccines. In the present study, only 24 of 49 FFS arrangements and 21 of 34 MCO arrangements covered the 2018 ACIP-recommended adult immunization schedule. The number of routinely recommended adult vaccines has increased since 2012 from 10 to 13. Furthermore, the 9vHPV vaccine costs more per dose than the HPV vaccines used in 2012,23 and recombinant zoster vaccine recipients require 2 doses for complete vaccination,20 resulting in higher program costs to cover these vaccines. Among Medic-

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aid programs that do not cover all ACIP-recommended adult immunizations, most do not have full coverage as a result of not covering the 9vHPV vaccine, recombinant zoster vaccine, or both, consistent with previous studies.14 Historically, Medicaid reimbursement rates to health care professionals are significantly below Medicare or private sector rates for both vaccine purchase and administration.24-26 A 2014 survey of family and general internal medicine physicians found that 55% of respondents thought they lost money administering vaccines to adult Medicaid beneficiaries, while 25% or fewer of respondents thought they lost money administering vaccines to adults covered by other public and private payers.25 In our study, the median reimbursement amount to health care professionals for administration of a single adult vaccination via injection was $13.62 and for intranasal administration of a single adult vaccination was $13.98, falling within previously reported reimbursement ranges. These median reimbursements are below per-dose costs to administer vaccines to adults estimated by a recent study ($15$23, depending on type of health care professional).27 Similarly, median reimbursement for vaccine purchase was below manufacturer-reported private sector costs for 7 of 13 immunizations examined in this study.

Although vaccine purchase prices for individual health care professionals are negotiated with manufacturers or distributors,25 our findings regarding median reimbursement and the wide variation among programs suggest that Medicaid payments for adult vaccination might fail to cover health care professionals’ costs in many instances.

In a previous study, Stewart et al15 assessed reimbursement amounts to health care professionals for vaccine administration and purchase under Medicaid FFS arrangements. Since 2012, the median reimbursement for HPV vaccine purchase has increased from $131.36 to $204.87 (likely owing primarily to introduction of the 9vHPV vaccine formulation [CPT code 90651] in lieu of the previous quadrivalent HPV vaccine). In contrast, median reimbursement for PPSV23 vaccine purchase (CPT code 90732) decreased from $130.27 in 2012 to $103.30 in 2019, providing further evidence that payments for some vaccines are not keeping pace with costs to health care professionals in all Medicaid programs. Financial concerns reduce health care

professionals’ willingness to make vaccines available to adult patients28,29; Medicaid reimbursements that do not cover costs to health care professionals may thus reduce vaccination access for low-income adults.

Because Medicaid enrollee penetration varies by state, the effects of adult immunization policies on beneficiaries are disproportionate. Of the 10 largest Medicaid programs included in our survey (California, New York, Florida, Texas, Pennsylvania, Illinois, Ohio, Michigan, Washington, and Arizona), only 6 cover all ACIP-recommended adult immunizations, 5 with no cost sharing (California, New York, Illinois, Ohio, and Washington). However, while some of these programs grant complete vaccination coverage benefits, reimbursement for vaccine administration may be limited (California) or not permitted (Illinois), which may disincentivize health care professionals to vaccinate.

Although Florida has the third-largest state Medicaid program in the country,3 adult vaccination coverage benefits are available only for adults aged 19 to 20 years or limited to pharmacy services for FFS recipients living in residential facilities. As Florida has not implemented Medicaid program expansion under the Patient Protection and Affordable Care Act, observed barriers to adult immunization services likely reduce vaccination coverage substantially.

Limitations Our study has several limitations. First, not all jurisdictions responded to our survey, so we obtained information about exact reimbursement amounts to health care professionals under MCO arrangements for only 87% of programs covering at least some beneficiaries through MCOs; this lack of response restricted our ability to effectively evaluate policies on reimbursement to health care professionals and adult vaccine access under MCO arrangements. Second, because some Medicaid programs did not participate, our results may not provide a complete picture of US adult vaccination benefits coverage policies. However, our results include the 10 largest Medicaid programs and comprise jurisdictions representing more than 93% of the Medicaid beneficiary population.3 Third, low-income adults may receive vaccination services outside of the Medicaid program, so our findings are an imperfect proxy for vaccine access in this population. Fourth, ACIP vaccine recommendations and Medicaid policies change frequently, so information reported by respondents may not be current.

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CONCLUSIONS To our knowledge, this report presents the most comprehensive available examination of vaccination benefits coverage for low-income US adults and is the first to examine adult immunization coverage in both FFS and MCO arrangements. In many jurisdictions, adult Medicaid beneficiaries lack access to the full slate of ACIP-recommended vaccines. Even in programs providing complete vaccination coverage benefits, reimbursement amounts to health care professionals for vaccine purchase and administration may not fully cover costs to provide vaccination, disincentivizing health care professionals to vaccinate lowincome adults. Increased vaccination coverage benefits parity across Medicaid programs and between traditionally eligible and expansion adult populations could decrease income-based health disparities and reduce the proportion of limited program funds expended to treat vaccine-preventable diseases.

This is an open access article distributed under the terms of the CC-BY License. © 2020 Granade CJ et al. JAMA Network Open. Published: April 27, 2020. doi:10.1001/jamanetworkopen.2020.3316 The referenced supplemental content is available online: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2764810 REFERENCES 1. Medicaid.gov. Eligibility. Accessed October 1, 2019. https://www.medicaid.gov/medicaid/eligibility/index.html 2. Centers for Medicare & Medicaid Services. Medicaid enrollment—new adult group. Updated September 2, 2019. Accessed October 1, 2019. https:// catalog.data.gov/dataset/medicaid-enrollmentnew-adult-group 3. Medicaid.gov. November 2019 Medicaid & CHIP enrollment data highlights. Updated February 7, 2020. Accessed March 25, 2020. https://www. medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/report-highlights/ index.html 4. Jerant A, Fiscella K, Tancredi DJ, Franks P. Health insurance is associated with preventive care 24

but not personal health behaviors. J Am Board Fam Med. 2013;26(6):759-767. doi:10.3122/jabfm.2013.06.130054 5. Williams WW, Lu PJ, O’Halloran A, et al.

Surveillance of vaccination coverage among adult populations—United States, 2015. MMWR Surveill Summ. 2017;66(11):1-28. doi:10.15585/ mmwr.ss6611a1 6. Abbas KM, Kang GJ, Chen D, Werre SR, Marathe A. Demographics, perceptions, and socioeconomic factors affecting influenza vaccination among adults in the United States. PeerJ. 2018;6:e5171. doi:10.7717/peerj.5171 7. Lu PJ, O’Halloran A, Williams WW. Impact of health insurance status on vaccination coverage among adult populations. Am J Prev Med. 2015;48(6):647-661. doi:10.1016/j. amepre.2014.12.008 8. McNamara M, Buck PO, Yan S, et al. Is patient insurance type related to physician recommendation, administration and referral for adult vaccination? a survey of US physicians. Hum Vaccin Immunother. 2019;15(9):2217-2226. doi:10.1080/ 21645515.2019.1582402 9. Lindley MC, Kahn KE, Bardenheier BH, et al.

Vital signs: burden and prevention of influenza and pertussis among pregnant women and infants—United States. MMWR Morb Mortal Wkly Rep. 2019;68(40):885-892. doi:10.15585/mmwr. mm6840e1 10. Merritt TA, Rasmussen SA, Bright MA, et al. Variation in Tdap and influenza vaccination coverage among pregnant women by insurance type—Florida, 2016–2018. MMWR Morb Mortal Wkly Rep. 2020;69(3):72-76. doi:10.15585/mmwr. mm6903a4 11. Ozawa S, Portnoy A, Getaneh H, et al. Modeling the economic burden of adult vaccine-preventable diseases in the United States. Health Aff (Millwood). 2016;35(11):2124-2132. doi:10.1377/ hlthaff.2016.0462 12. Heron M. Deaths: leading causes for 2017. National Vital Statistics Reports. Published June 24, 2019. Accessed October 1, 2019. https://www.cdc. gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf 13. Rosenbaum S, Stewart A, Cox M, Lee A. The epidemiology of US immunization law: a national study for the National Immunizations Program, Centers for Disease Control and Pre-

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vention: Medicaid coverage of immunizations for non-institutionalized adults. Published November 2003. Accessed October 1, 2019. https:// hsrc.himmelfarb.gwu.edu/cgi/viewcontent. cgi?article=1266&context=sphhs_policy_facpubs 14. Stewart AM, Lindley MC, Chang KH, Cox MA. Vaccination benefits and cost-sharing policy for non-institutionalized adult Medicaid enrollees in the United States. Vaccine. 2014;32(5):618-623. doi:10.1016/j.vaccine.2013.11.050 15. Stewart AM, Lindley MC, Cox MA. Medicaid provider reimbursement policy for adult immunizations. Vaccine. 2015;33(43):5801-5808. doi:10.1016/j.vaccine.2015.09.014 16. Ku L, Paradise J, Thompson V. Data note: Medicaid’s role in providing access to preventive care for adults. Published May 17, 2017. Accessed October 1, 2019. https://www.kff.org/medicaid/ issue-brief/data-note-medicaids-role-in-providingaccess-to-preventive-care-for-adults/ 17. Stoecker C, Stewart AM, Lindley MC. The cost of cost-sharing: the impact of Medicaid benefit design on influenza vaccination uptake. Vaccines (Basel). 2017;5(1):1-8. doi:10.3390/vaccines5010008 18. Artiga S, Ubri P, Zur J. The effects of premiums and cost sharing on low-income populations: updated review of research findings. Published June 1, 2018. Accessed October 1, 2019. https://www. kff.org/medicaid/issue-brief/the-effects-of-premiums-and-cost-sharing-on-low-income-populationsupdated-review-of-research-findings/ 19. Gates A, Ranji U, Snyder L. Coverage of preventive services for adults in Medicaid: appendices.

Published November 13, 2014. Accessed October 1, 2019. https://www.kff.org/report-section/coverage-of-preventive-services-for-adults-in-medicaidappendices/ 20. Kim DK, Riley LE, Hunter P; Advisory Committee on Immunization Practices. Recommended immunization schedule for adults aged 19 years or older, United States, 2018. Ann Intern Med. 2018;168(3):210-220. doi:10.7326/M17-3439 21. National Association of Medicaid Directors. Medicaid directors. Published 2019. Accessed October 8, 2019. https://medicaiddirectors.org/about/ medicaid-directors/ 22. American Academy of Professional Coders. What is CPT? Accessed on October 1, 2019. https://

www.aapc.com/resources/medical-coding/cpt.aspx 23. Centers for Disease Control and Prevention.

CDC vaccine price list. Accessed August 1, 2019. https://www.cdc.gov/vaccines/programs/vfc/ awardees/vaccine-management/price-list/index. html 24. Long SK. Physicians may need more than higher reimbursements to expand Medicaid participation: findings from Washington state. Health Aff (Millwood). 2013;32(9):1560-1567. doi:10.1377/ hlthaff.2012.1010 25. Lindley MC, Hurley LP, Beaty BL, et al. Vaccine financing and billing in practices serving adult patients: a follow-up survey. Vaccine.

2018;36(8):1093-1100. doi:10.1016/j.vaccine.2018.01.015 26. Zuckerman S, Williams AF, Stockley KE. Trends in Medicaid physician fees, 2003-2008.

Health Aff (Millwood). 2009;28(3)(suppl 1):w510w519. doi:10.1377/hlthaff.28.3.w510 27. Yarnoff B, Kim D, Zhou F, et al. Estimating the costs and income of providing vaccination to adults and children. Med Care. 2019;57(6):410416. doi:10.1097/MLR.0000000000001117 PubMedGoogle ScholarCrossref 28. Hutton DW, Rose A, Singer DC, et al. Importance of reasons for stocking adult vaccines. Am J Manag Care. 2019;25(11):e334-e341.

29. Hurley LP, Lindley MC, Allison MA, et al.

Primary care physicians’ perspective on financial issues and adult immunization in the era of the Affordable Care Act. Vaccine. 2017;35(4):647-654. doi:10.1016/j.vaccine.2016.12.007 Article Information Accepted for Publication: February 21, 2020.

Published: April 27, 2020. doi:10.1001/jamanetworkopen.2020.3316 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Granade CJ et al. JAMA Network Open.

Corresponding Author: Charleigh J. Granade, MPH, Immunization Services Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Mailstop H24-4, Atlanta, GA 30329 (ori9@ cdc.gov).

Author Contributions: Ms Granade had full access to all of the data in the study and takes responsibility for the integrity of the

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data and the accuracy of the data analysis.

Supervision: Bhatti.

Concept and design: McCord, Bhatti, Lindley.

Conflict of Interest Disclosures: Ms Bhatti worked on this project during her tenure at the Centers for Disease Control and Prevention (CDC); however, in January 2019 she transitioned from the CDC to Merck & Co Inc, but contributions to research and the article were not made as a Merck employee. No other disclosures were reported.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Granade, McCord.

Critical revision of the manuscript for important intellectual content: McCord, Bhatti, Lindley.

Statistical analysis: Granade, Bhatti.

Administrative, technical, or material support: McCord, Bhatti.

Disclaimer: The findings and conclusions of this report are those of the authors and do not necessarily represent the official position of the CDC. This document was coauthored by Cherokee Nation Assurance contractors in the Public Health Law Program in the Center for State, Tribal, Local, and Territorial Support at the CDC.

AAMCN Would Like to Recognize Our Corporate Partners Gold: TCS Healthcare Silver: Home Instead Senior Care® Humana, Inc. Mallinckrodt Pharmaceuticals Bronze: Gilead Sciences, Inc. Novocure 26

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Comparison of Office-Based Physician Participation in Medicaid Managed Care and Health Insurance Exchange Plans in the Same US Geographic Markets Jacob Wallace, PhD (1); Anthony Lollo, PhD (1); Chima D. Ndumele, PhD (1) 1. Yale School of Public Health, New Haven, Connecticut

Abstract Importance Several recent policy proposals have sought to expand the role of Medicaid in providing health insurance for low-income Americans, but there is little recent information on how physician participation in Medicaid compares with alternative forms of coverage for low-income Americans.

Objective To compare the number of office-based physicians included in Medicaid managed care and health insurance exchange plans that operate in the same geographic markets.

Design, Setting, and Participants This cross-sectional study used administrative data from physician network directories and survey data from office-based physicians for Kansas, Nebraska, New York, Tennessee, and Washington. The number of participants totaled 67 057 office-based physicians in the 5 sample states. Data were collected and analyzed from May 2018 to June 2019.

Exposures Physician participation in a Medicaid managed care or health insurance exchange plan network.

Main Outcomes and Measures The percentage of office-based physicians in a county who indicated during a phone survey that they participated in Medicaid; the percentage of office-based physicians in a county who participated in each Medicaid managed care and health insurance exchange plan network; and the percentage of office-based physicians in a county who participated in at least one Medicaid managed care plan or, separately, at least one health insurance exchange plan.

Results Of the 67 057 office-based physicians in our sample, 49 983 reported in a telephone survey that they accepted Medicaid. This survey-based measure undercounted the percentage of physicians participating in Medicaid by 5.2% (95% CI, 2.3%-8.1%; P < .001) relative to a measure based on physician network directories. Medicaid managed care physician networks covered a mean (SD) of 63.4% (20.5%) of office-based physicians compared with health insurance exchange physician networks, which covered 51.0% (25.2%). In adjusted analyses, Medicaid managed care plans covered 6.2% (95% CI, 3.2%-9.3%, P < .001) more office-based physicians than health insurance exchange plans operating in the same counties. In the states where the same insurers participated in both markets (New York, Tennessee, Washington), the Medicaid managed care physician networks were 6.5% (95% CI, 3.2%-9.8%, P < .001) larger than the health insurance exchange networks offered by the same insurer.

Conclusions and Relevance In this cross-sectional study of physician network data, Medicaid managed care physician networks included more office-based physicians than the physician networks of health insurance exchange plans operating in the same geographic markets. This suggests that www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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Medicaid remains a viable option for expanding coverage in the United States.

Key Points Question How does the percentage of office-based physicians who participate in Medicaid compare with participation in health insurance exchange plans?

Findings In this cross-sectional study of 67 057 office-based physicians in 5 states, Medicaid managed care plans included more physicians than health insurance exchange plans in the same geographic markets.

Meaning These findings indicate that physicians are likelier to participate in Medicaid physician networks than previously believed, with important implications for the ongoing debate about the role of Medicaid in expanding health insurance and reforming the US health care system.

INTRODUCTION Medicaid is now the largest single insurer in the United States,1 providing coverage for approximately one in five Americans. Policy makers continue to debate the merits of expanding it further, either through expansions or proposals that would allow low-income individuals to buy into Medicaid,2-4 but these efforts have been stymied by widely cited concerns about whether the program offers adequate access to physicians and hospitals.5-8 Historically, Medicaid fee-forservice reimbursement rates have been lower than the rates paid to physicians by Medicare or commercial insurers,9 limiting physician participation in the program.10-13 However, recent evidence indicates access may be improving in Medicaid, with recipients reporting similar levels of satisfaction as commercial populations,14 suggesting a reexamination of access is warranted.

Some policy observers have linked changes in access in Medicaid to the rapid uptake of managed care across states. Currently, more than 80% of Medicaid enrollees receive care through managed care organizations (MCOs),15 private health plans that limit patients to a restricted set of physicians and hospitals. This model, known as Medicaid managed care, represents a departure from Medicaid fee-for-service, where states offer contracts to all physicians willing to accept Medicaid reimbursement rates. As a result, for most Medicaid beneficiaries the number of physicians participating in their health plan is a function of the size of the health plan physician networks (hereafter referred to 28

as physician networks) offered by each MCO rather than the percentage of physicians willing to accept fee-for-service Medicaid reimbursement rates.16 Given the prevalence of Medicaid managed care, a clearer understanding of how MCO physician networks compare to physician networks offered on the health insurance exchanges (HIX) is warranted. This is especially important as states grapple with whether to expand health insurance coverage to low-income, nonelderly Americans through private or public mechanisms.17 In this study, we examined the number of physicians participating in Medicaid in several ways. First, we examined how survey-based measures of physician participation in Medicaid, the traditional approach to measuring physician participation, compare with measures of physician participation based on a novel data set of MCO physician network directories obtained directly from state Medicaid agencies. Second, we used physician network directory data to compare the size of MCO and HIX plan networks. We compared two distinct measures of physician network size: (1) what percentage of physicians participate in at least one MCO vs at least one HIX plan, and (2) the size of MCO and HIX networks at the individual plan level, since these are the networks that ultimately determine the set of physicians that are in-network for patients based on the plans they’ve chosen.

METHODS Overview This study, conducted from May 2018 to June 2019,

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was approved by the institutional review board at Yale Medical School. The requirement for informed consent was waived because participation involved no more than minimal risk to the study participants.

The confidentiality of individual practices has been protected. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies.

Data The primary source of data for this study was the physician network directories for MCOs and HIX plans operating in Kansas, Nebraska, New York, Tennessee, and Washington. We measured physician network size at the county level, where we have data on the set of practicing physicians that participate in each Medicaid and HIX network (eTable 1 in the Supplement).

We obtained Medicaid network data directly from the states that included lists of the physicians under contract within its MCO network. We identified the set of counties MCOs participated in using publicly available documentation from each state. We linked the Medicaid network data with information from Vericred Solutions Inc, provided with support from the Robert Wood Johnson Foundation. The Vericred data contained HIX physician networks as of August 1, 2017, obtained either online or from machine-readable physician directories made available by HIX insurers.

The data, which we linked at the National Provider Identifier level to our Medicaid network data, have been used in prior research on physician networks.18-20 To determine which counties each HIX plan operates in, we used the Health Insurance Exchange Compare data set. We only included network-associated plans that were actively marketed in 2017 on a state or federal marketplace. For HIX insurers that offered multiple plans that shared a network, we included the unique network only once. To construct our final sample we excluded a small number of networks where data quality was a concern. We merged the MCO and HIX physician network directories to health care information services firm SK&A’s Office-Based Physician Database (SK&A), a phone-based survey that identifies whether physicians are in active practice and includes their answer to the question “Do you accept Medicaid (yes or no)?” (eAppendix 1 in the Supplement). We restricted our sample to office-based physicians and removed geriatric specialties, since

the Medicaid program generally serves as a primary source of coverage for individuals aged 0 to 64 years.

Variables Our primary outcome was an assessment of the size of Medicaid managed care and HIX physician networks.

We constructed three measures of network size. First, we measured the percentage of office-based physicians in a county who answered “yes” to the question “Do you accept Medicaid?” in the SK&A data. Second, we measured the mean percentage of office-based physicians in a county covered by Medicaid managed care and HIX physician networks in that county. Third, we measured the percentage of office-based physicians who participate in at least one Medicaid managed care plan in a county and the percentage of office-based physicians who participate in at least one HIX plan in a county.

We constructed county-level covariates from several sources. From the 2010 United States Census, we obtained each county’s nonelderly population and racial composition. County-level poverty rates were obtained from the Area Health Resources File. From the Kaiser Family Foundation, we obtained data on health insurance coverage rates and the percentage of Medicaid recipients in managed care in 2017.

Statistical Analysis In unadjusted analyses, we presented our measures of physician network size by state, county geographic designation, and physician specialty. We used multivariable regression to attempt to adjust for county using a dummy variable for each county in ordinary least squares regression models of the following form: Ypc = β0 + β1Medicaidpc + γc + εpc, where the subscript p denotes a plan and c denotes a county. The independent variable Medicaidpc is an indicator that plan p offered in county c was a Medicaid (rather than HIX) plan. In our adjusted specification, we include dummy variables for each county, γc, which attempt to adjust for unobserved factors at the county level so that our estimates can be thought of as comparing the size of Medicaid and HIX physician networks offered within the same county. Standard errors were clustered at the county level and results are reported with 95% confidence intervals and 2-tailed P values.

We weighted regressions by the county proportion of a state’s population (eAppendix 2, eFigure 1 in the

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Supplement). To assess the association between the size of Medicaid and HIX physician networks at the state level, we used the Pearson correlation coefficient (denoted by ρ).

Sensitivity analyses tested the robustness of our results to alterations in the statistical model, including the addition of dummy variables for each insurer so that our estimates can be thought of as comparing the size of Medicaid and HIX physician networks offered by the same insurer within the same county.

RESULTS Population Our final sample included 2642 physician network– county pairs from 102 physician networks operated by 33 unique issuers in 370 counties in our 5 sample states (eTable 2 in the Supplement). The distribution of physician specialties was qualitatively similar across sample states (eTable 3 in the Supplement).

Our sample states were similar to the national average in their demographic characteristics with the exception that, by design, the percentage of the Medicaid

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population in managed care (89.98%) was higher than average (Table 1).

Measuring Participation in Medicaid Managed Care Of the 67 057 office-based physicians who met our inclusion criteria, 49 983 reported in a telephone survey that they accepted Medicaid. The percentage of office-based physicians who accepted Medicaid per survey-based reports of participating physicians was 5.2% (95% CI, 2.3%-8.1%; P < .001) lower than the percentages that were listed in at least 1 MCO physician network (eTable 4 in the Supplement). This pattern held for Kansas, New York, and Tennessee, but the opposite was true in Washington (Figure 1).

Participation in Medicaid Managed Care and the Health Insurance Exchanges Figure 2 compares the size of Medicaid managed care and HIX physician networks by state. In our study sample, we found that a mean (SD) of 87.7% (10.1%) of office-based physicians participated in at least 1 HIX plan, an estimate that closely mirrors prior work (Table 2).18 A similar, but lower, mean (SD) percentage of physicians (86.6% [10.2%]) participated in at

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least 1 MCO physician network. This result masks considerable heterogeneity. In three of our five states (Nebraska, New York, Washington), a higher percentage of office-based physicians participated in the HIX than Medicaid managed care. We also examined participation in Medicaid managed care and the HIX by physician specialty, based on the difference between

participation in at least one HIX physician network relative to at least one Medicaid physician network.

We found a higher percentage of psychiatrists (7.1% [95% CI, 2.7%-11.5%]; P = .002) and obstetriciangynecologists (2.1% [95% CI, 0.1%-4.1%]; P = .04) participated in HIX networks relative to Medicaid.

These findings were robust to the inclusion of county

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dummy variables to adjust for potential differences in where MCOs and HIX plans operate.

Size of Individual Medicaid Managed Care and HIX Physician Networks In 2017, the mean (SD) Medicaid managed care physician network covered 63.4% (20.5%) of office-based physicians as compared with the average HIX physician network, which covered 51.0% (25.2%) of officebased physicians (Figure 2). On an adjusted basis, Medicaid managed care physician networks covered 6.2% (95% CI, 3.2%-9.3%; P < .001) more officebased physicians than HIX physician networks in the same counties (Table 2). When we compared Medicaid managed care and HIX physician networks in metropolitan and nonmetropolitan counties, we found that the difference in size is largest in metropolitan areas, where Medicaid managed care physician networks covered 7.5% (95% CI, 4.3%-10.8%; P < .001) more office-based physicians. Medicaid managed care physician networks, on average, include more physicians even when comparing physician networks offered in the same county by the same insurer (eFigure 2 in the 32

Supplement). We tested this formally by estimating a model with county and insurer dummy variables in the three states where insurers participate in both markets. Managed care physician networks covered 6.5% (95%, 3.2%-9.8%; P < .001) more office-based physicians than HIX physician networks offered by the same insurer (eTable 5 in the Supplement). Insurers participating in both Medicaid and the HIX included more physicians in their physician networks than insurers participating in only one of those markets (eFigure 3 in the Supplement).

Similar to prior work,18,19 we found large differences across states in the size of the Medicaid and HIX physician networks offered. There was a strong correlation (ρ = 0.92) at the state level between Medicaid managed care and HIX network size. Physician networks in less urban states (Kansas, Nebraska, Washington) covered a greater percentage of physicians. In subanalyses by specialty, Medicaid managed care physician networks included more physicians than HIX physician networks for all but one specialty (psychiatry).

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DISCUSSION This study compared physician participation in Medicaid managed care and the HIX. We found evidence that traditional, survey-based approaches to measuring the number of physicians in Medicaid (ie, tabulating the percentage of physicians that say in telephone surveys that they accept Medicaid) conflict with measures based on physician network directory data. Furthermore, we found that no simple explanation (or adjustment) could reconcile the two measures. One possibility is that not all physicians who contract with private, Medicaid managed care plans identify as accepting Medicaid. This finding highlights the importance of incorporating physician network data into measures of physician participation as the percentage of Medicaid recipients in managed care continues to grow.

Using physician network directory data, we examined the relative size of Medicaid managed care and HIX physician networks. We did not find evidence that physicians are more likely to participate in at least one MCO than at least one HIX plan. On the other hand, the average MCO physician network covers a much larger percentage of office-based physicians than the average HIX physician network. This pattern holds for both primary care and specialty physicians, and is robust to comparisons of the physician networks within insurers that participate in both. Despite being less likely to participate in the average HIX physician network than the average Medicaid physician network, physicians were equally likely to participate in at least one HIX physician network as they were to participate in at least one Medicaid physician network. This is partially due to HIX plans being more numerous at the county level, as well as MCO physician networks overlapping more across plans than HIX networks (eFigure 4, eTable 6, eAppendix 3 in the Supplement).

Given prior evidence that the Medicaid population is served by a concentrated set of physicians,21 the differences in overlap across plans between Medicaid and the HIX remain an important area for future work.

at the payer level in the HIX relative to Medicaid may make it likelier that HIX consumers can find plans that include their usual sources of care.22,23 However, this relies on consumers navigating a large number of plan choices and complex features, such as physician network size. Prior work suggests consumers will struggle when faced with such choices,24,25 although there is evidence consumers take network size into account when selecting plans.26,27 Given that consumers value larger physician networks, and may not be fully informed about their future health care needs, it is also important to measure the size of physician networks at the plan level, where network restrictions ultimately bind and limit consumers to a set of contracted physicians.

Our findings also have implications for the regulation of physician networks. For most Medicaid recipients, the number of physicians included in their network is now a function of the size of the physician networks offered by plans participating in Medicaid managed care. Network size is shaped by how states regulate Medicaid managed care, particularly in how they set network adequacy standards—rules for how many and what types of physicians plans must include in their networks. These standards vary widely by state. Theoretical work on health care markets suggests that strict network adequacy standards will limit the flexibility of plans to build high-value networks and negotiate for discounts.28 Insufficient network adequacy standards, however, raise the specter of substandard access and prompt concerns that networks may be intentionally designed to avoid the sickest patients.29,30 Our findings indicate that Medicaid networks may not be as narrow as once thought.

Our work also contributes to a growing base of evidence that access in Medicaid may be better than previously believed, with important implications for the ongoing debate about how to reform the US health care system. Historical survey data and audits of physicians tended to find reduced access in Medicaid relative to other insurance types.31,32 However, One implication of our findings is that only measuring recent surveys found that Medicaid recipients report whether physicians participate in at least one physician comparable levels of health care satisfaction and, for network at the payer level (ie, Medicaid vs HIX) may example, experience similar rates of low-value care to not capture important differences in physician network those with other forms of coverage.14,33,34 Our findings size at the plan level (a particular Medicaid vs a paroffer one possible explanation for this—the number of ticular HIX plan). For example, broader participation physicians in Medicaid is now largely a function of the www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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size of the physician networks offered by participating plans, networks that appear comparable to, if not broader than, what is being offered on the health insurance exchanges in the states we studied.

This finding has important implications for the ongoing debate about the role of Medicaid in expanding coverage to the remaining uninsured. While roughly a third of states have opted not to expand Medicaid via the Affordable Care Act, Medicaid buy-in and other Medicaid expansion proposals are being debated at the state and federal level.2-4 Typically, these proposals laud the efficiency of the Medicaid program while raising concerns about whether there is substandard access to physicians and hospitals. Our findings suggest that concerns about limited physician networks in Medicaid may be overstated, and that Medicaid physician networks may include more physicians than plans on the health insurance exchanges.

Limitations Our study has several limitations. First, our study is based on a sample of 5 states in 2017, for which Medicaid managed care and HIX physician network information was available. In Table 1 we demonstrate that our sample states are similar to the rest of the nation, although our conclusions may not apply to all states and time periods. Second, we measure the size of MCO and HIX physician networks using data from physician network directories. Previous studies have shown that physician network directories may contain inaccurate information.35 This is a particular concern if the physician network data from Medicaid is of a different quality than the physician network data from the HIX. We address this concern by standardizing all physician network data using the National Plan and Provider Enumeration System’s National Provider Identifier (NPPES NPI) Registry and limiting our sample to NPIs that merged with the SK&A OfficeBased Physician Database. However, differences in data quality may persist. Third, because we did not have access to administrative claims data, we weight each office-based physician equally. However, prior research suggests that physician characteristics (eg, proximity to patients) will affect their relative importance to Medicaid or HIX enrollees.36 Furthermore, measuring physician participation with physician network directories may not account for other factors that impact physician access, including physician capacity, 34

reimbursement rates, or administrative burden.19,37-39 CONCLUSIONS In this cross-sectional study of 67 057 office-based physicians operating across 5 states in 2017, we present evidence that traditional, survey-based approaches to measuring physician participation in Medicaid undercount participation relative to measures based on physician network directory data. When participation was measured using physician network data, we found more physicians participating in the average Medicaid plan than the average health insurance exchange plan.

Our data suggest that Medicaid physician networks include more physicians than previously believed.

This is an open access article distributed under the terms of the CC-BY License. © 2020 Wallace J et al. JAMA Network Open. Published: April 13, 2020. doi:10.1001/jamanetworkopen.2020.2727 The referenced supplemental content is available online: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2764348 REFERENCES 1. Iglehart JK, Sommers BD. Medicaid at 50— from welfare program to nation’s largest health insurer. N Engl J Med. 2015;372(22):2152-2159. doi:10.1056/NEJMhpr1500791 2. Goldman AL, Sommers BD. Medicaid expansion gains momentum: postelection prospects and potential implications. JAMA. 2019;321(3):241-242. doi:10.1001/jama.2018.20484 3. Sommers BD, Fry CE, Blendon RJ, Epstein AM. New approaches in Medicaid: work requirements, health savings accounts, and health care access. Health Aff (Millwood). 2018;37(7):10991108. doi:10.1377/hlthaff.2018.0331 4. Andrews M. States’ “Medicaid buy-in” plans would expand affordable health coverage. Kaiser Health News. Published February 26, 2019. Accessed February 14, 2020. https://khn.org/news/ progressives-tout-medicare-for-all-but-states-eyemedicaid-buy-in/ 5. Decker SL. In 2011 nearly one-third of physicians said they would not accept new Medicaid patients, but rising fees may help. Health Aff (Millwood). 2012;31(8):1673-1679. doi:10.1377/

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hlthaff.2012.0294 6. Decker SL. Two-thirds of primary care physicians accepted new Medicaid patients in 2011-12: a baseline to measure future acceptance rates.

Health Aff (Millwood). 2013;32(7):1183-1187. doi:10.1377/hlthaff.2013.0361 7. Bisgaier J, Rhodes KV. Auditing access to specialty care for children with public insurance. N Engl J Med. 2011;364(24):2324-2333. doi:10.1056/NEJMsa1013285 8. Polsky D, Candon M, Saloner B, et al. Changes in primary care access between 2012 and 2016 for new patients with Medicaid and private coverage. JAMA Intern Med. 2017;177(4):588-590. doi:10.1001/jamainternmed.2016.9662 9. Zuckerman S, Williams AF, Stockley KE. Trends in Medicaid physician fees, 2003-2008.

Health Aff (Millwood). 2009;28(3):w510-w519. doi:10.1377/hlthaff.28.3.w510 10. Decker SL. Medicaid payment levels to dentists and access to dental care among children and adolescents. JAMA. 2011;306(2):187-193. doi:10.1001/jama.2011.956 11. Rosenbaum S. Medicaid payments and access to care. N Engl J Med. 2014;371(25):2345-2347. doi:10.1056/NEJMp1412488 12. Polsky D, Richards M, Basseyn S, et al. Appointment availability after increases in Medicaid payments for primary care. N Engl J Med. 2015;372(6):537-545. doi:10.1056/NEJMsa1413299 13. Chou SC, Deng Y, Smart J, Parwani V, Bernstein SL, Venkatesh AK. Insurance status and access to urgent primary care follow-up after an emergency department visit in 2016. Ann Emerg Med. 2018;71(4):487-496.e1. doi:10.1016/j. annemergmed.2017.08.045 14. Barnett ML, Sommers BD. A national survey of Medicaid beneficiaries’ experiences and satisfaction with health care. JAMA Intern Med. 2017;177(9):1378-1381. doi:10.1001/jamainternmed.2017.3174 15. Kaiser Family Foundation. Total Medicaid MCO enrollment. Revised July 1, 2017. Accessed February 21, 2020. https://www.kff.org/other/stateindicator/total-medicaid-mco-enrollment/ 16. Ndumele CD, Staiger B, Ross JS, Schlesinger MJ. Network optimization and the continuity of physicians in Medicaid managed care. Health

Aff (Millwood). 2018;37(6):929-935. doi:10.1377/ hlthaff.2017.1410 17. Sommers BD, Maylone B, Blendon RJ, Orav EJ, Epstein AM. Three-year impacts of the Affordable Care Act: improved medical care and health among low-income adults. Health Aff (Millwood). 2017;36(6):1119-1128. doi:10.1377/ hlthaff.2017.0293 18. Polski D, Weiner J, Zhang Y. Narrow networks on the individual marketplace in 2017. LDI Issue Brief. 2017;21(8):1-6.

19. Polsky D, Candon MK, Chatterjee P, Chen X. Scope of primary care physicians’ participation in the health insurance marketplaces. Health Aff (Millwood). 2018;37(8):1252-1256. doi:10.1377/ hlthaff.2018.0179 20. Zhu JM, Zhang Y, Polsky D. Networks in ACA marketplaces are narrower for mental health care than for primary care. Health Aff (Millwood). 2017;36(9):1624-1631. doi:10.1377/ hlthaff.2017.0325 21. Cunningham P, May J. Medicaid patients increasingly concentrated among physicians. Track Rep. 2006;(16):1-5.

22. Shepard M. Hospital network competition and adverse selection: evidence from the Massachusetts health insurance exchange. National Bureau of Economic Research working paper 22600.

Published September 2016. Accessed February 21, 2020. https://www.nber.org/papers/w22600 23. Higuera L, Carlin CS, Dowd B. Narrow provider networks and willingness to pay for continuity of care and network breadth. J Health Econ. 2018;60:90-97. doi:10.1016/j.jhealeco.2018.06.006 24. Abaluck J, Gruber J. Choice inconsistencies among the elderly: evidence from plan choice in the Medicare part D program. Am Econ Rev. 2011;101(4):1180-1210. doi:10.1257/ aer.101.4.1180 25. Handel BR, Kolstad JT. Health insurance for “humans”: information frictions, plan choice, and consumer welfare. Am Econ Rev. 2015;105(8):2449-2500. doi:10.1257/aer.20131126 26. Ericson KM, Starc A. Measuring consumer valuation of limited provider networks. Am Econ Rev. 2015:105(5):115-119. doi:10.1257/aer.p20151082 27. Drake C. What are consumers willing to pay for a broad network health plan? evidence from cov-

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ered California. J Health Econ. 2019;65:63-77. doi:10.1016/j.jhealeco.2018.12.003 28. Ho K, Lee RS. Equilibrium provider networks: bargaining and exclusion in health care markets.

Am Econ Rev. 2019;109(2):473-522. doi:10.1257/ aer.20171288 29. Yasaitis L, Bekelman JE, Polsky D. Relation between narrow networks and providers of cancer care. J Clin Oncol. 2017;35(27):3131-3135. doi:10.1200/JCO.2017.73.2040 30. Baicker K, Levy H. How narrow a network is too narrow? JAMA Intern Med. 2015;175(3):337338. doi:10.1001/jamainternmed.2014.7763 31. Rhodes KV, Kenney GM, Friedman AB, et al. Primary care access for new patients on the eve of health care reform. JAMA Intern Med. 2014;174(6):861-869. doi:10.1001/jamainternmed.2014.20 32. Asplin BR, Rhodes KV, Levy H, et al. Insurance status and access to urgent ambulatory care follow-up appointments. JAMA. 2005;294(10):1248-1254. doi:10.1001/ jama.294.10.1248 33. Barnett ML, Linder JA, Clark CR, Sommers BD. Low-value medical services in the safety-net population. JAMA Intern Med. 2017;177(6):829837. doi:10.1001/jamainternmed.2017.0401 34. Charlesworth CJ, Meath THA, Schwartz AL, McConnell KJ. Comparison of low-value care in Medicaid vs commercially insured populations. JAMA Intern Med. 2016;176(7):998-1004. doi:10.1001/jamainternmed.2016.2086 35. Haeder SF, Weimer DL, Mukamel DB. Secret shoppers find access to providers and network accuracy lacking for those in marketplace and commercial plans. Health Aff (Millwood). 2016;35(7):1160-1166. doi:10.1377/ hlthaff.2015.1554 36. Wallace J. What does a provider network do? evidence from random assignment in Medicaid managed care. Published May 7, 2019. Accessed March 2, 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544928 37. Neprash HT, Zink A, Gray J, Hempstead K. Physicians’ participation in Medicaid increased only slightly following expansion. Health Aff (Millwood). 2018;37(7):1087-1091. doi:10.1377/ hlthaff.2017.1085 38. Gottlieb JD, Shapiro AH, Dunn A. The complexity of billing and paying for physician care.

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Health Aff (Millwood). 2018;37(4):619-626. doi:10.1377/hlthaff.2017.1325 39. Decker SL. No association found between the Medicaid primary care fee bump and physicianreported participation in Medicaid. Health Aff (Millwood). 2018;37(7):1092-1098. doi:10.1377/ hlthaff.2018.0078 Article Information Accepted for Publication: February 18, 2020.

Published: April 13, 2020. doi:10.1001/jamanetworkopen.2020.2727 Correction: This article was corrected on May 7, 2020, to fix reporting of statistical measures of physician network coverage in the Abstract and Results sections.

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Wallace J et al. JAMA Network Open.

Corresponding Author: Jacob Wallace, PhD, Department of Health Policy and Management, Yale School of Public Health, 60 College St, New Haven, CT 06510 (jacob.wallace@yale.edu).

Author Contributions: Dr Wallace had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Wallace, Lollo.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Wallace, Lollo.

Obtained funding: Ndumele.

Administrative, technical, or material support: Wallace, Ndumele.

Supervision: Wallace, Ndumele.

Conflict of Interest Disclosures: Dr Wallace reported having a spouse who is the Associate Director for Medicaid Policy at a consulting firm. No other disclosures were reported.

Funding/Support: The conduct of this research was funded by a grant from the National, Heart, Lung, and Blood Institute (5R01HL144644).

Role of the Funder/Sponsor: The funders had no role in the de-

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sign and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Dominique Desroches, BA, of the Yale School of Public Health provided research assistance for this study and was compensated as a paid team member.

www.AAMCN.org Spring Managed Care Forum 2021 • Visit the AAMCN website at www.aamcn.org to register for the Virtual Spring Managed Care Forum Social Media • Members of AAMCN can join our Facebook discussion group at www.facebook.com/groups/ AAMCN • LinkedIn

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What Happened to Cancer Screening during COVID-19, and What We Can Do to Get Things Back on Track Sheryl Riley RN, OCN, CMCN (1) 1. Chief Nursing Officer, Beacon Oncology Nurse Advocate, St. Petersburg, Florida

For over three decades, nurses and health care professionals have worked diligently to help the population understand that cancer screening is a valuable tool in finding and preventing cancer for people of all ages.

This was not something that happened overnight and came without struggles. Nurses and doctors had to overcome myths, misinformation, and bias to convince men and women to get screened yearly or according to the guidelines set forth by the Center for Disease Control (CDC) and the American Cancer Society (ACS).

For years doctors told their patients and the population at large: do not delay; do not put screening off; screening saves lives. Then when COVID-19 hit hard, many panicked, and the verbiage changed.

much to talk a person out of going in for a colonoscopy.”3

The population was told to stay at home and to delay having any test or procedure that was not urgent.

When the American Cancer Society and the Center for Disease Control and Prevention recommend delay, people are inclined to listen because no one looks forward to a mammogram, pap smear, or a colonoscopy.

The American Society of Clinical Oncology called for cancer screening that required clinic or center visits, such as mammograms and colonoscopies, to be postponed “for the time being” to “conserve health system resources and reduce patient contact with health screening facilities.”1

Another study conducted by Komodo Health analyzed the billing records of 320 million patients in the US. They found that screening for cervical cancer was down 68% from March 19 to April 20, compared to the previous 11 weeks and a comparable period last year.4

Routine cancer screenings have plummeted during the pandemic. According to a white paper by Epic, the electronic medical record vendor, US appointments for screenings for cancers of the cervix, colon, and breast were down between 86% and 94% in March, compared to average volumes in 2017, 2018, and 2019.2 Epic President Carl Dvorak told STAT, “We’re also fairly convinced that even once they lift the lockdowns, we’ll still see the concerned patients a little bit more reluctant to go in [….] Truthfully, it doesn’t take 38

Doctors that every year instructed patients not to delay screening are now saying just the opposite. This is hard to believe, considering 60% of all cancer are diagnosed in people over the age of 65. What message does this send the average American, maybe that screening is not as important as the doctors said last year vs. this year? What this tells us as clinicians is: we have our work cut out for us! We need to create a positive and uplifting message that helps people feel safe about going to medical centers, doctor’s offices, and free-standing facilities to get their screening.

Depending upon your insurance or where you live, cancer screenings generally take place at diagnostic centers run by larger health systems. There’s been variability in how these facilities have responded to the pandemic: some have closed their doors altogether, while others have stayed open for emergencies or maintained a skeleton crew of staff; but most community oncology practices have done their best to stay open and meet the needs of the oncology patients.

One of the startling effects of this pandemic is that COVID-19, in only 3 months, undid 30 years of hard work and dedication by thousands of health care professionals around the world, and the trend appears to be continuing. Even though clinicians as well as administrators see these staggering numbers, there are

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still hospitals and centers continuing to tell people to stay home and delay.

As a nurse of over 30 years and one who volunteered in a COVID-19 recovery unit for months during the pandemic, I find it difficult to believe that facilities and staff could not find ways to continue testing and screening during the pandemic—especially for those patients that were considered high risk. It was too easy to tell the population to just stay home and avoid getting the virus even though delaying many of these screenings could lead to increases in later stage cancer diagnosis. Moreover, how we handled screening during the pandemic will define who we are as an oncology community.

I am aware that many people have died during COVID-19 and are still dying, and I do not want to minimize that fact, but if we had kept open freestanding clinics, imaging centers, and practices that had screening capabilities, we could have continued to screen patients. We could have spaced them out more, leaving time for cleaning in between patients, and we could have focused on those of our patients that were of a higher risk first and then expanded to the general screening as well. During the pandemic, we have built field hospitals out of convention centers and recovery centers out of hotels. We retrained nurses and doctors in areas they had never worked before, so I think we should have tried harder for our patients and the population that looks to us to guide them. What we do as the cancer community over the next three to six months will define just how devastating the pandemic will be to cancer patients.

In a recent study published online in JAMA Network Open, researchers with Quest Diagnostics examined

weekly changes in the number of patients with newly diagnosed cancers before and during the pandemic.

Quest found that there was a 46% weekly drop in diagnosis for 6 common cancers combined and a 52% weekly drop in breast cancer identifications. In this same study, Quest did a cross-sectional study that included patients from across the US whose screenings for breast, colorectal, lung, pancreatic, gastric, or esophageal cancers were processed from January 1, 2018 to April 18, 2020. Mean (SD) weekly numbers of newly diagnosed patients were compared between the baseline period (January 6, 2019 to February 29, 2020) and the early US months of COVID-19 (March 1 to April 18, 2020). Significant declines were seen in all six cancer types during the pandemic period studied.5 Researcher Harvey W. Kaufman, MD, and colleagues from Quest Diagnostics stated “Our results indicate a significant decline in newly identified patients with six common types of cancer, mirroring findings from other countries [….] When cancer screenings and resulting cancer diagnoses are postponed, some of these cancers are likely to later be identified at more advanced stages, which will result in poorer outcomes and even increased death rates.”5 So, what does all this mean? While cancer diagnoses appear to be declining in the data, cancer itself is not.

The Dana-Farber Cancer Institute in Boston echoed this point in a statement: “The true incidence of these cancers did not drop,” said Dr. Craig Bunnell, DanaFarber’s chief medical officer. “The decline clearly represents a delay in making the diagnoses, and delays matter with cancer. It means we need to safely perform these diagnostic tests and the public needs to not think of them as optional. Their lives could depend on them.”6 Laura Makaroff, senior vice president of pre-

Originally published in JAMA Network Open: “Changes in the Number of US Patients With Newly Identified Cancer Before and During the Coronavirus Disease 2019 (COVID-19) Pandemic”5 www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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vention and early detection with the American Cancer Society, agreed, warning that “cancer certainly isn’t stopping due to the pandemic.”6 Now that we understand the problem; let’s put forth some solutions to fix it! Start with a plan that incorporates strategies below, but one size does not fit all— everyone has to work with the population and demographics they have. However, I do believe this outline of key tasks can help you get started.

• Positive messaging • General public messaging • Reaching out by phone to those that are at risk • Reaching out by phone to those that are overdue • Positive education and encouragement • General education and messaging on TV and radio with endorsements from CDC, ACS and large cancer centers • United message in all forms of media; do not add to the confusion • Special education to those patients and populations at higher risk • Enhanced safety protocols and measures • Make patients aware of the protocols that have been put in place—temp check, screening questions, where to wait, increased wait times, etc.

• Let them know your cleaning protocols and why screening might take longer due to cleaning procedures • Let them know that they need to wear a mask and the team they are working with will be wearing masks at all times • Let them know all high touch items like magazines, books, etc. have been removed to decrease spread • Advise that no food or drink will be allowed • Let them know if they have to wait in their car until called or texted

population are counting on us.

REFERENCES 1. American Society of Clinical Oncology. Cancer screening, diagnosis, staging & surveillance. Updated June 22, 2020. www.asco.org/asco-coronavirusresources/care-individuals-cancer-during-covid-19/ cancer-screening-diagnosis-staging. Accessed August 21, 2020.

2. Epic Health Research Network. Preventive cancer screenings during COVID-19 pandemic. May 1, 2020. https://ehrn.org/wp-content/uploads/PreventiveCancer-Screenings-during-COVID-19-Pandemic.pdf.

Accessed August 21, 2020.

3. Robbins R. Routine cancer screenings have plummeted during the pandemic, medical records data show. May 4, 2020. www.statnews.com/2020/05/04/ cancer-screenings-drop-coronavirus-pandemic-epic/.

Accessed August 21, 2020.

4. Respaut R, Nelson DJ. Exclusive: U.S. medical testing, cancer screenings plunge during coronavirus outbreak-data firm analysis. April 28, 2020. www.reuters. com/article/us-health-coronavirus-usa-screenings-exc/ exclusive-u-s-medical-testing-cancer-screeningsplunge-during-coronavirus-outbreak-data-firm-analysis-idUSKCN22A0DY. Accessed August 20, 2020.

5. Kaufman HW, Chen Z, Niles J, Fesko Y. Changes in the number of US patients with newly identified cancer before and during coronavirus disease 2019 (COVID-19) pandemic. JAMA Netw Open.

2020;3:e2017267.

6. Mozes A. Cancer diagnoses plunge as Americans avoid screening during pandemic. August 4, 2020. www.medicinenet.com/script/main/art.asp?artic

We, as health care professionals, need to send a clear message to the oncology community and the population at large that screening is vital, and we are safely open for business! Most of all, we need to make it our mission to try our best to undo the missed steps that happened during COVID-19. Our patients and the 40

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Managed Care UPDATES

Trends in Outpatient Care Delivery and Telemedicine During the COVID-19 Pandemic in the US Research Letter, JAMA Intern Med The coronavirus disease 2019 (COVID-19) pandemic has dramatically altered patterns of health care delivery in the US. In the context of declining in-person outpatient visits, many clinicians began using telemedicine for the first time, spurred in part by regulatory changes that expanded public and private insurer reimbursement for a wider range of telemedicine services. To understand how telemedicine compensated for declining outpatient volume and geographic variation in changing patterns of outpatient care, we examined telemedicine and in-person outpatient visits in 2020 among a national sample of 16.7 million individuals with commercial or Medicare Advantage insurance. Read more at https://jamanetwork.com/journals/ jamainternalmedicine/fullarticle/2773059 Saving Medicare for Baby Boomers and Beyond—A Looming Fiscal Crisis Editor’s Comment, JAMA Health Forum The fiscal future of Medicare is an unavoidable challenge for the US federal government. The Medicare trustees’ annual report to Congress in April 2020 projected that the Medicare Hospital Insurance Trust Fund will become insolvent in 2026, when revenues will cover only 90% of expected Part A expenditures. Financed primarily through Medicare payroll taxes on working adults (nearly 90% of revenue) and income taxes on Social Security benefits of retired adults (approximately 8% of revenue), this trust fund covers Part A benefits for Medicare beneficiaries receiving hospital, skilled nursing, home health, and hospice care. Read more at https://jamanetwork.com/channels/health-forum/fullarticle/2773054 Cost Minimization Analysis of a Teledermatology Triage System in a Managed Care Setting JN Learning, JAMA Dermatology Interview with Erin Amerson, MD, author of Cost Minimization Analysis of a Teledermatology Triage System in a Managed Care Setting. Listen here https://edhub.ama-assn.org/jn-learning/ audio-player/18561081 Association of Race, Health Insurance Status, and Household Income With Location and Outcomes of Ambulatory Surgery Among Adult Patients in 2 US States Original Investigation, JAMA Surgery In this cohort study of 13 million patients who received ambulatory surgery in New York and Florida between 2011 and 2013, the likelihood of receiving surgery at a freestanding ambulatory surgery center compared with a hospital-based outpatient department was significantly lower among patients who were Black, had public health insurance, and resided in rural areas. Read more at https:// jamanetwork.com/journals/jamasurgery/article-abstract/2770165 www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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Unemployment Insurance, Health-Related Social Needs, Health Care Access, and Mental Health During the COVID-19 Pandemic Research Letter, JAMA Internal Medicine More than 30 million jobs have been lost during the coronavirus disease 2019 (COVID-19) pandemic. Unemployment insurance (UI) was temporarily expanded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, but further reform is under debate. Key CARES Act provisions were adding $600 weekly federal payments to state payments (Federal Pandemic Unemployment Compensation), longer benefit duration (Pandemic Emergency Unemployment Compensation), and broadened eligibility for minimum-wage, self-employed, contract, and gig workers (Pandemic Unemployment Assistance).

Read more at https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2773234 Catastrophic Health Expenditures Across Insurance Types and Incomes Before and After the Patient Protection and Affordable Care Act Research Letter, JAMA Network Open One decade after passage of the Patient Protection and Affordable Care Act (ACA), despite substantial gains in insurance coverage, health care affordability remains a major concern among US residents. Premiums are increasingly unaffordable, and underinsurance—incomplete financial protection despite coverage—is increasingly common. Although previous research has shown that the ACA’s Medicaid expansions decreased out-of-pocket spending among low-income adults, broader trends in outof-pocket spending have not been well characterized. We thus sought to analyze changes in financial risk protection associated with ACA implementation across all income strata and insurance types. Read more at https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2770949 The Uncertain Future of Children’s Health Insurance: New and Ongoing Threats Viewpoint, JAMA Pediatrics Children’s health in the United States has largely been a success story. Since the adoption of the Children’s Health Insurance Program (CHIP) in 1997, the rate of uninsured children has decreased from 25% to less than 6% nationally. Improvements to children’s access to health insurance have been further supported by the Affordable Care Act (ACA) and Medicaid expansion. Yet, these successes conceal new and ongoing threats to the future of children’s health. The number of uninsured children has increased since 2016, with more than 4 million children uninsured. Partisan conflict, misaligned policies, and persistent racial and ethnic disparities threaten the future of children’s health. The 2020 election represents a potential turning point for strengthening or further crippling children’s access to health care. Read more at https://jamanetwork. com/journals/jamapediatrics/article-abstract/2769781 ACA Marketplaces Gain More Insurers for Third Year in a Row, Offering Consumers More Options In the News, JAMA Health Forum US consumers buying their own health insurance for 2021 have more options to choose from as the number of companies participating in the Patient Protection and Affordable Care Act (ACA) insurance exchanges increased for the third year in a row, according to a new analysis from the Kaiser Family Foundation (KFF). The KFF researchers found that during the enrollment period now underway for 2021, 30 insurers are entering the ACA marketplaces across 20 states, and 61 insurers are expanding within states they already serve. Read more at https://jamanetwork.com/channels/health-forum/fullarticle/2773769

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Rebecca Robinson Nodeane Robotham Lynn Rodeghiero Jennifer Rodriguez Brian Roffe Megan Rogers Wendy Rogers Donna Rose, RN, BSN, CCM, CPHQ, BSW Henrietta Rudy Lisa Rumler Sally Ruppert Todd Rush Rebecca Ruth Sue Ryan Shannon Saladee Katrina Salley Jinima San Valentin Claudia Sanchez Olga Sanchez Vickie Sanders, RN Luz Santana Cindy Schmidt Susan Scott Lori Shaney Kathryn Sharkey Cassandra Shatley Michelle Shaw Stephanie Shephard Tamijka Shinholster Rebecca Sichau Barbara Simons Althea Smalling Linda Smiley Karen Childs Smith Kelli Smith Rhea Sobremesan Gary Solmerin, RN Kymberly Spady-Grove Katharine Starnes Roslyn Stephens Sarah Stephenson Sheri Stevens Currie Stewart Lisa Stiltner Joann Storey Deanna Stotts Katie Stramara Nerine Sullivan Megan Summers

Shan Sun Susan Szanyi Carol Szer Sophia Tagship Deb Taylor Monique Taylor Melissa Te Irene Tecson Evelyn Tejeda C. Thigpen-Oliver Kaitlin Thomas, RN Amanda Thompson Jennifer Thompson Kara Thompson Heather Thornton, RN, MSN, CNL, CCM Latanya Townsend-Gates Kara Traverse Natalie Treigle Monique Triplett Tashi Tsundu Karen Valley Samanatha Van Dyk Christy Vann Michelle Vann Allison Vekas Patricia Vetter Charlean Viera Jennifer Vincent Jonathan Vitti, RN Ngoc-Han Vo Kim Von Asten Erin Walker Audrey Ward Mikhaila Warhola Juanita Washington Storie Weissman, RN Chris Wenzel Sandra Westerbeck Daphne White MaryAnn White Renee White Sharon White, RN Sharee Williams Sharonda Williams Kristina Willis Pamela Willmar Becky Wolfe Gwendolyn Woody Tonya Wooten www.aamcn.org | Vol. 7, No. 4 | Journal of Managed Care Nursing

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Christine Wright Della Wright Tuyet Wright, RN Dayna Yokooji Holly Young Michelle Young Smotha Young Brooke Young, RN Lisa Young, Rn Michelle Zang Kelly Zanin Contance Zimmer Kristin Zimmerman

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