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The American Journal of Managed Care March 2019
Fragmented Ambulatory Care and Subsequent Emergency Department Visits and Hospital Admissions Among Medicaid Beneficiaries
Lisa M. Kern, MD, MPH; Joanna K. Seirup, MPH; Mangala Rajan, MBA; Rachel Jawahar, PhD, MPH; and Susan S. Stuard, MBA
Incorrect and Missing Author Initials in Affiliations and Authorship Information
From the Editorial Board: Austin Frakt, PhD
Austin Frakt, PhD
Implications of Eligibility Category Churn for Pediatric Payment in Medicaid
Deena J. Chisolm, PhD; Sean P. Gleeson, MD, MBA; Kelly J. Kelleher, MD, MPH; Marisa E. Domino, PhD; Emily Alexy, MPH; Wendy Yi Xu, PhD; and Paula H. Song, PhD
Factors Influencing Primary Care Providers’ Decisions to Accept New Medicaid Patients Under Michigan’s Medicaid Expansion
Renuka Tipirneni, MD, MSc; Edith C. Kieffer, PhD, MPH; John Z. Ayanian, MD, MPP; Eric G. Campbell, PhD; Cengiz Salman, MA; Sarah J. Clark, MPH; Tammy Chang, MD, MPH, MS; Adrianne N. Haggins, MD, MSc; Erica Solway, PhD, MPH, MSW; Matthias A. Kirch, MS; and Susan D. Goold, MD, MHSA, MA
Did Medicaid Expansion Matter in States With Generous Medicaid?
Alina Denham, MS; and Peter J. Veazie, PhD
Access to Primary and Dental Care Among Adults Newly Enrolled in Medicaid
Krisda H. Chaiyachati, MD, MPH, MSHP; Jeffrey K. Hom, MD, MSHP; Charlene Wong, MD, MSHP; Kamyar Nasseh, PhD; Xinwei Chen, MS; Ashley Beggin, BS; Elisa Zygmunt, MSW; Marko Vujicic, PhD; and David Grande, MD, MPA
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Medicare Annual Wellness Visit Association With Healthcare Quality and Costs
Adam L. Beckman, BS; Adan Z. Becerra, PhD; Anna Marcus, BS; C. Annette DuBard, MD, MPH; Kimberly Lynch, MPH; Emily Maxson, MD; Farzad Mostashari, MD, ScM; and Jennifer King, PhD
Specialty Care Access for Medicaid Enrollees in Expansion States
Justin W. Timbie, PhD; Ashley M. Kranz, PhD; Ammarah Mahmud, MPH; and Cheryl L. Damberg, PhD
Gender Differences in Prescribing of Zolpidem in the Veterans Health Administration
Guneet K. Jasuja, PhD; Joel I. Reisman, AB; Renda Soylemez Wiener, MD, MPH; Melissa L. Christopher, PharmD; and Adam J. Rose, MD, MSc
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Health Insurance Literacy: Disparities by Race, Ethnicity, and Language Preference
Victor G. Villagra, MD; Bhumika Bhuva, MA; Emil Coman, PhD; Denise O. Smith, MBA; and Judith Fifield, PhD

Medicare Annual Wellness Visit Association With Healthcare Quality and Costs

Adam L. Beckman, BS; Adan Z. Becerra, PhD; Anna Marcus, BS; C. Annette DuBard, MD, MPH; Kimberly Lynch, MPH; Emily Maxson, MD; Farzad Mostashari, MD, ScM; and Jennifer King, PhD
In the context of 2 primary care physician–led accountable care organizations, Medicare Annual Wellness Visits were associated with lower healthcare costs and improved clinical care quality for beneficiaries.
Outcome Measures

The primary outcome was the differential change in healthcare cost (in the post-AWV period compared with the pre-AWV period) between those who did and did not receive an AWV. Costs in the month that the AWV was performed, including the approximately $175 cost of the AWV itself, were excluded. Cost was evaluated in separate analyses as total Medicare cost (all parts A and B Medicare spending) and category-specific costs within Part A (hospital acute care; hospital outpatient; hospital outpatient non–emergency department [ED]; skilled nursing facility, home health, other outpatient facility spending) and Part B (provider/supplier, durable medical equipment). The secondary outcomes included counts of ED visits and hospitalizations in the pre- and post-AWV periods. The outcomes for the quality measures analysis were 16 clinical quality measures with definitions specified by Medicare in 3 domains: preventive health, clinical care for at-risk populations, and care coordination.25

Statistical Analysis

Similar to other observational studies using real-world evidence, this study’s approach accounted for the likelihood that individuals who received AWVs differed from other patients in substantial ways that might affect the outcomes measured. In particular, issues may include that practices did not reach out to all patients with equal likelihood, practices succeeded in reaching patients at different rates, and patients who were willing and able to come in for a primary care visit differed from patients who were not. We used several analytic methods to explore the impact of such selection bias and account for it. We employed propensity score matching to identify control subjects who were similar to patients who received AWVs in key respects, including level of engagement with primary care in the pre-AWV period. We estimated the impact of AWVs by specifying a series of difference-in-differences (DID) regression models.

Specifically, for the primary data source, we checked the parallel trends assumptions and used a DID study design to assess changes in subsequent 11-month healthcare costs, ED visits, and hospitalizations between beneficiaries who did and did not receive an AWV in the index month. We used a mixed-effects negative binomial model for total cost and mixed-effects zero-inflated negative binomial (ZINB) models for category-specific costs and utilization. For the secondary data source, we used mixed-effects logistic regression models to assess the association between receiving an AWV and quality measures. In order to account for multiple comparisons, the Hochberg sequential procedure was used.26,27 For additional details on the modeling approach, see the eAppendix.

We also conducted a series of sensitivity analyses to evaluate the robustness of our results. First, we evaluated whether the intervention effect differed among those patients who received outreach using the Aledade app in 2015 versus those patients who did not receive outreach in 2015. Second, we used coarsened exact matching instead of propensity score matching to identify the comparison group. Third, we repeated our primary analysis (including the matching) only among beneficiaries who were continuously attributed throughout the entire pre- and post-AWV period. Fourth, we excluded intervention patients who matched to controls who had an AWV in the post-AWV period. Finally, we evaluated whether the AWV associations were different for “early” (January 2015–July 2015) versus “late” AWVs (August 2015–December 2015). (See eAppendix for additional details.)

All statistical analyses were conducted using R version 3.3.3 (R Foundation for Statistical Computing; Vienna, Austria). Propensity score and coarsened exact matching were conducted using the MatchIt package, mixed-effects logistic models were fit using the lme4 package, and the mixed-effects negative binomial and ZINB models were fit using the glmmTMB package.28-30 Graphics and plots were generated using the ggplot2 package.31

RESULTS

Baseline Characteristics by AWV Status

The primary analysis sample of matched intervention and control beneficiaries included 8917 beneficiaries, of whom 4789 (54%) received a first-time AWV in 2015 (Table 223). Differences in clinical and sociodemographic characteristics between the matched intervention and control groups were small. (For characteristics of the population of beneficiaries who entered the matching process, see the eAppendix.) The only covariate that was significantly different between those who did and did not receive an AWV in the final analytic sample was the specific ACO of the patient, and thus it was adjusted for in final regression models.


 
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