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The American Journal of Managed Care August 2018
Impact of a Medical Home Model on Costs and Utilization Among Comorbid HIV-Positive Medicaid Patients
Paul Crits-Christoph, PhD; Robert Gallop, PhD; Elizabeth Noll, PhD; Aileen Rothbard, ScD; Caroline K. Diehl, BS; Mary Beth Connolly Gibbons, PhD; Robert Gross, MD, MSCE; and Karin V. Rhodes, MD, MS
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Michael E. Chernew, PhD
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Medicare Advantage Enrollees’ Use of Nursing Homes: Trends and Nursing Home Characteristics
Hye-Young Jung, PhD; Qijuan Li, PhD; Momotazur Rahman, PhD; and Vincent Mor, PhD

Medicare Advantage Enrollees’ Use of Nursing Homes: Trends and Nursing Home Characteristics

Hye-Young Jung, PhD; Qijuan Li, PhD; Momotazur Rahman, PhD; and Vincent Mor, PhD
The share of Medicare Advantage (MA) beneficiaries in the nursing home (NH) population has been steadily rising, while MA plans appear to be increasingly concentrating beneficiaries in select NHs with better performance on quality measures.

Share of MA patients in NHs. The primary dependent variable was the prevalence of MA patients in an NH. The numerator for the measure was defined as the number of MA enrollees receiving postacute or long-term care in the facility, and the denominator was defined as all patients in the facility, regardless of insurance coverage. Estimates of the numerator and denominator were calculated based upon NH occupancy on the first Thursday of April in a given year. These estimates were derived from the residential history file, which concatenates MDS assessments to determine where all patients using NHs were on each day, making it possible to estimate each facility’s census on a given day.12,13

Characteristics of NHs. We examined the relationships between the concentration of MA patients in NHs and multiple NH characteristics, including structural features, quality measures, aggregate patient characteristics, and geographic and market characteristics.14-22 Structural features of NHs included the number of beds, for-profit status, and chain affiliation, in addition to staffing as measured by direct care hours per patient per day for several types of nursing staff (registered nurses [RNs], licensed practical nurses [LPNs], and certified nursing assistants [CNAs]) and an indicator for the presence of a physician extender or nurse practitioner.23-25 We used 3 facility quality measures: the proportion of patients receiving antipsychotics, the proportion of patients physically restrained, and the facility’s 30-day rehospitalization rate.26-29 Aggregate patient characteristics included average age, percent female, racial composition, and patient case-mix variables: activities of daily living score at admission on a 28-point scale,30 an indicator for severe cognitive impairment (a score of 5 or 6 on the Cognitive Performance Scale),31 the proportion of Medicaid patients in the facility, and the proportion of patients admitted from hospitals. The MA penetration rate, urban location, and market competition measured by the Herfindahl index (with a range of 0 to 1, where higher values indicate a less competitive market) were included as county-level market characteristics. Lastly, we examined geographic variation by including indicators for 4 US regions (ie, Northeast, Midwest, South, and West). The selection of explanatory variables was based on previous studies of NH quality.14-22

Analytic Approach

First, we created longitudinal graphs to examine the aggregated trends over the 14-year study period in the proportion of all Medicare beneficiaries covered by MA, the proportion of NH patients enrolled in MA, the proportion of “high-MA” NHs with 25% or more of their patients enrolled in MA, and the proportion of NHs with any MA patients. National MA enrollment rates were calculated by using all Medicare beneficiaries as the denominator and MA enrollees as the numerator, based on estimates from January in a given year using Medicare enrollment records. National MA enrollment rates among Medicare beneficiaries were presented to show concurrent trends in MA concentration in NHs. Stratified trends were used to examine MA concentration in NHs by the following facility characteristics: for-profit, part of chain, large size (90th percentile of the distribution for bed count), high percentage of Medicaid patients (90th percentile), urban, US region, and MA penetration rates for counties (by deciles of the distribution).

Next, we used logistic regression models to evaluate the adjusted associations between facility characteristics and having a high proportion of MA patients. We used data from 2013, the most recent year in our study period. Three categories were selected for the outcome measure, based on the distribution of MA concentration in NHs in our sample: high-MA NHs, defined as those with 25% or more of their patients covered by MA, representing those in the highest quintile of the distribution; low-MA NHs, defined as those with 1% to 24% of their patients covered by MA; and no-MA NHs, defined as those with no patients covered by MA.

Our primary model estimated the relationship between facility characteristics and being a high-MA NH, with both low-MA and no-MA NHs included in the reference group (ie, 1, ≥25% MA; 0, 0%-24% MA). As a robustness check, we repeated our primary analysis using only non-MA NHs as the reference group (ie, 1, ≥25% MA; 0, 0% MA). In a secondary analysis, ordered logistic regression was used as an alternative regression model to fit multiple ordered response categories.

All analyses were performed using SAS version 9.2 (SAS Institute; Cary, North Carolina) and Stata MP version 12 (StataCorp; College Station, Texas).

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