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The American Journal of Managed Care December 2019
Clinical Characteristics and Treatment Patterns Among US Patients With HIV
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Delivery System Performance as Financial Risk Varies
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Delivery System Performance as Financial Risk Varies

Joseph P. Newhouse, PhD; Mary Price, MA; John Hsu, MD, MBA; Bruce Landon, MD, MBA; and J. Michael McWilliams, MD, PhD
One delivery system’s healthcare utilization in its Medicare Advantage product was notably less than in its Pioneer accountable care organization or in a traditional Medicare comparison group.
Although we compared various measures of utilization and total spending among the MA, ACO, and TM comparison groups, we could not obtain comparable spending values for specific types of services for the MA group because of differing aggregations of services in our data. For example, we could not determine MA emergency department (ED) spending because it was included with inpatient spending if the patient was admitted. Therefore, we instead made 3-way comparisons among MA, the ACO, and a TM comparison group for total medical spending and for various utilization measures but only a 2-way comparison of ACO and TM spending on specific medical services.

We faced 2 other issues in comparing the MA plan’s performance with that of the ACO and TM plans. About a quarter of hospital admissions in the MA plan were covered by a capitated contract, and for those admissions the paid claims files show a zero dollar amount. To obtain comparable spending figures, we imputed the mean payment for the relevant diagnosis-related group among the MA hospital claims with positive dollars. Second, all home health services in the MA plan were covered by a capitated contract and, consequently, show no individual-level spending. Therefore, we imputed spending for all home health claims using the estimated equation for risk-adjusted home health spending at the patient level in the ACO contract. Because home health services account for only 3% to 6% of total spending in the ACO, depending on the year, this approximation should induce little error.

Commercial. The commercial data come from Aetna for Maricopa County residents for 2010 to 2014. All medical and physician services are included, but drugs were excluded because they are sometimes covered under a separate contract.

Methodology

Our study was approved by the Harvard Medical School Institutional Review Board.

Medicare. Although the Pioneer program’s actual attribution of beneficiaries to ACOs was prospective and based on use in the prior 3 years, we used retrospective assignment to assign beneficiaries to providers in each study year. We could not apply the Pioneer program’s prospective assignment method consistently because we lacked data for 3 years prior to the study period; however, as a result, we avoided the problem of regression-to-the-mean effects that prospective assignment potentially introduces when applied to an initial cohort that is fixed.5,8

To avoid assignment to a time-varying panel of physicians, we kept the list of ACO physicians constant over time using National Physician Identifiers (NPIs) to isolate within-provider effects of the program. We used NPIs rather than Tax Identification Numbers (TINs) to identify physicians because Pioneer ACOs were not required to include all providers with the same TIN in the ACO. To define the set of physicians in our main analyses, we used the physicians in the Banner ACO as of 2012, although we also tested the sensitivity of using those in the ACO in 2014 instead. In short, we evaluated the performance of the same group of physicians before and after the ACO contracts began. The TM comparison group comprised TM beneficiaries in Maricopa County who were not attributed to the Banner ACO.

To maintain comparability with the ACO-attributed group, we excluded those beneficiaries in both the TM group and in the MA plan with no use of qualifying E&M services in the calendar year, because that group could not be attributed. This zero-use group constituted 10.2% to 10.5% of the TM group depending on the year; we cannot know what proportion of this group would have been attributed to Banner if they had used E&M services. The MA group had 3.3% to 4.9% of nonusers, depending on the year.

Although we have unique identification numbers for individual MA providers, they are idiosyncratic, not NPIs or TINs. We therefore analyzed the MA data using a constant set of providers, namely those providing services to MA beneficiaries in 2012. We tested the sensitivity of the results to those providing services in 2014 and to those providing services in the calendar year being analyzed (a nonconstant set of providers).

To increase comparability and in the spirit of doubly robust regression, we balanced the ACO, TM, and MA groups using inverse probability weights based on cells defined by age group (65-74, 75-84, and ≥85 years) and gender. Matching only on time-invariant factors, such as age and gender, avoids bias that can arise from matching on time-varying variables, such as pre-ACO period outcome measures.9

For all comparisons, we show annual risk-adjusted utilization rates, as well as total annual risk-adjusted spending per person for each year from 2010 to 2014 for the ACO, TM, and MA groups. To risk adjust, we used CMS Hierarchical Condition Categories (HCCs) version 12 and diagnoses from 2009.

We used standard linear regression methods for each group separately with the individual’s HCC risk score on the right-hand side. The predicted rates that we show set the risk score to 1.0. In equation form, we estimated the following equation for each of the 3 groups:
yit = αt + βHCCit + ϵit ,
where yit is an outcome measure (spending or utilization) for individual i in year t and α and β are constants to be estimated.

Because the trend in the 3-year post-ACO period is informative, we present our main results in the text using figures that show predicted annual utilization rates and spending from the equation above. The absolute values shown are centered at the mean risk score. In addition to calculating annual results, we carried out a standard DID analysis that compared the 2 years of the pre-ACO period (2010-2011) with the 3 years of the post-ACO period (2012-2014) for the ACO group relative to the TM or MA groups. Regression equations from the DID analysis are available in the eAppendix Tables. Although the trend lines appear reasonably parallel in the pre-ACO period, we cannot conduct a formal test with only 2 years of data.


 
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