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The American Journal of Managed Care December 2018
Feasibility of Expanded Emergency Department Screening for Behavioral Health Problems
Mamata Kene, MD, MPH; Christopher Miller Rosales, MS; Sabrina Wood, MS; Adina S. Rauchwerger, MPH; David R. Vinson, MD; and Stacy A. Sterling, DrPH, MSW
From the Editorial Board: Jonas de Souza, MD, MBA
Jonas de Souza, MD, MBA
Risk Adjusting Medicare Advantage Star Ratings for Socioeconomic Status
Margaret E. O’Kane, MHA, President, National Committee for Quality Assurance
Reducing Disparities Requires Multiple Strategies
Melony E. Sorbero, PhD, MS, MPH; Susan M. Paddock, PhD; and Cheryl L. Damberg, PhD
Cost Variation and Savings Opportunities in the Oncology Care Model
James Baumgardner, PhD; Ahva Shahabi, PhD; Christopher Zacker, RPh, PhD; and Darius Lakdawalla, PhD
Patient Attribution: Why the Method Matters
Rozalina G. McCoy, MD, MS; Kari S. Bunkers, MD; Priya Ramar, MPH; Sarah K. Meier, PhD; Lorelle L. Benetti, BA; Robert E. Nesse, MD; and James M. Naessens, ScD, MPH
Patient Experience During a Large Primary Care Practice Transformation Initiative
Kaylyn E. Swankoski, MA; Deborah N. Peikes, PhD, MPA; Nikkilyn Morrison, MPPA; John J. Holland, BS; Nancy Duda, PhD; Nancy A. Clusen, MS; Timothy J. Day, MSPH; and Randall S. Brown, PhD
Relationships Between Provider-Led Health Plans and Quality, Utilization, and Satisfaction
Natasha Parekh, MD, MS; Inmaculada Hernandez, PharmD, PhD; Thomas R. Radomski, MD, MS; and William H. Shrank, MD, MSHS
Primary Care Burnout and Populist Discontent
James O. Breen, MD
Adalimumab Persistence for Inflammatory Bowel Disease in Veteran and Insured Cohorts
Shail M. Govani, MD, MSc; Rachel Lipson, MSc; Mohamed Noureldin, MBBS, MSc; Wyndy Wiitala, PhD; Peter D.R. Higgins, MD, PhD, MSc; Sameer D. Saini, MD, MSc; Jacqueline A. Pugh, MD; Dawn I. Velligan, PhD; Ryan W. Stidham, MD, MSc; and Akbar K. Waljee, MD, MSc
The Value of Novel Immuno-Oncology Treatments
John A. Romley, PhD; Andrew Delgado, PharmD; Jinjoo Shim, MS; and Katharine Batt, MD
Medicare Advantage Control of Postacute Costs: Perspectives From Stakeholders
Emily A. Gadbois, PhD; Denise A. Tyler, PhD; Renee R. Shield, PhD; John P. McHugh, PhD; Ulrika Winblad, PhD; Amal Trivedi, MD; and Vincent Mor, PhD
Currently Reading
Provider-Owned Insurers in the Individual Market
David H. Howard, PhD; Brad Herring, PhD; John Graves, PhD; and Erin Trish, PhD

Provider-Owned Insurers in the Individual Market

David H. Howard, PhD; Brad Herring, PhD; John Graves, PhD; and Erin Trish, PhD
Provider-owned insurers sell individual policies in areas that cover 62% of the US population and have premiums similar to policies of traditional insurers.
METHODS

Data

We used the Robert Wood Johnson Foundation’s 2017 HIX Compare data set (the February 2, 2018, version), compiled from insurers’ mandatory submissions to the CMS Health Insurance Oversight System. The data cover every plan sold on the exchanges, including plans sold on federally facilitated, state partnership, federally supported, and state-based Marketplaces, as well as off-exchange, ACA-qualified plans. The data cover every plan’s characteristics but do not include information on the number and characteristics of enrollees. Observations are at the plan-market level. Insurers sell multiple plans across and within markets.

Insurer Classification

We classified an insurer as provider owned if the insurer was owned by or shared a common owner with a hospital system or multispecialty physician clinic. (We hypothesized that insurers that owned only primary care clinics would not have sufficient leverage or control over the delivery of healthcare to substantially reduce costs.)

We identified provider-owned insurers by examining the About Us section of each insurer’s website. We looked for statements indicating that (1) the insurer was owned by a health system or (2) the insurer owned and operated hospitals and clinics. We then examined network directories to determine if the network included hospitals or physician clinics affiliated with the insurer. A research assistant classified each insurer, and then one of the authors reviewed each classification, using the same methods, to determine its accuracy. eAppendix Table 1 (eAppendix available at ajmc.com) contains a list of these insurers and their classifications.

We classified observations at the insurer rather than plan or product level. For example, UnitedHealthcare is a traditional insurer but had a recently closed subsidiary, Harken Health, that operated its own physician clinics. We classified both UnitedHealthcare and Harken Health as traditional insurers because only a small share of the United plans were sold under the Harken brand and because United may have used the profits from Harken to cross-subsidize its other products or vice versa.

Corporate names are used inconsistently in the HIX Compare data (for example, some plans sold by Harvard Pilgrim are associated with “Harvard Pilgrim”; others, with “Harvard Pilgrim Health Care”). We therefore recoded the carrier variable in the HIX Compare data so that all plans sold by an insurer had a common code. This step was necessary to correctly adjust standard errors for clustering of plans by insurer. We grouped all of the Blue Cross Blue Shield plans owned by Anthem, Inc, together. Plans sold by non-Anthem Blue Cross Blue Shield insurers were assigned distinct codes.

Analysis

We assessed the presence and number of provider-owned and traditional insurers in each market (as defined by exchange rating areas). We merged the HIX Compare data with the Health Resources and Services Administration’s Area Resource File to determine the characteristics of the markets in which provider-owned insurers operate. We measured the association between market characteristics and the presence of at least 1 provider-owned insurer in the market using logistic regression.

We estimated differences in premiums between plans sold by provider-owned and traditional insurers using a correlated random-effects model.11,12 We restricted attention to each insurer’s lowest-cost Silver plan for an individual aged 50 years in each market. There is 1 observation per insurer per market.

Similar to a fixed-effects model, the correlated random-effects model estimates within-market differences in premiums. However, similar to a mixed-effects model, it also permits adjustment of standard errors for clustering at the market and insurer levels. Plans are nested in markets and in insurers, but neither markets nor insurers are nested in each other (they are “crossed” effects). A traditional fixed-effects model cannot accommodate the complex error structure. The model is of the following form:
yijm = β1POIj + β2POIm + αj + μm + εijm,
where i indexes plans, j represents insurers, and m means markets. POI indicates whether the insurer is provider owned. Controlling for the market-level average of the share of provider-owned insurers, POI, ensures that the coefficient on the provider-owned insurer indicator, β1, is equivalent to an estimate from a fixed-effects model. The variables αj and μm represent insurer and market random effects, respectively.


 
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