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The American Journal of Managed Care September 2019
VA Geriatric Scholars Program’s Impact on Prescribing Potentially Inappropriate Medications
Zachary Burningham, PhD; Wei Chen, PhD; Brian C. Sauer, PhD; Regina Richter Lagha, PhD; Jared Hansen, MStat; Tina Huynh, MPH, MHA; Shardool Patel, PharmD; Jianwei Leng, MStat; Ahmad Halwani, MD; and B. Josea Kramer, PhD
The Sociobehavioral Phenotype: Applying a Precision Medicine Framework to Social Determinants of Health
Ravi B. Parikh, MD, MPP; Sachin H. Jain, MD, MBA; and Amol S. Navathe, MD, PhD
From the Editorial Board: Jan E. Berger, MD, MJ
Jan E. Berger, MD, MJ
Medicaid Managed Care: Issues for Enrollees With Serious Mental Illness
Jean P. Hall, PhD; Tracey A. LaPierre, PhD; and Noelle K. Kurth, MS
Multi-Payer Advanced Primary Care Practice Demonstration on Quality of Care
Musetta Leung, PhD; Christopher Beadles, MD, PhD; Melissa Romaire, PhD; and Monika Gulledge, MPH; for the MAPCP Evaluation Team
Physician-Initiated Payment Reform: A New Path Toward Value
Suhas Gondi, BA; Timothy G. Ferris, MD, MPH; Kavita K. Patel, MD, MSHS; and Zirui Song, MD, PhD
Managed Care for Long-Stay Nursing Home Residents: An Evaluation of Institutional Special Needs Plans
Brian E. McGarry, PT, PhD; and David C. Grabowski, PhD
Changes in Ambulatory Utilization After Switching From Medicaid Fee-for-Service to Managed Care
Lisa M. Kern, MD, MPH; Mangala Rajan, MBA; Harold Alan Pincus, MD; Lawrence P. Casalino, MD, PhD; and Susan S. Stuard, MBA
Did Medicare Advantage Payment Cuts Affect Beneficiary Access and Affordability?
Laura Skopec, MS; Joshua Aarons, BA; and Stephen Zuckerman, PhD
Medicare Shared Savings Program ACO Network Comprehensiveness and Patient Panel Stability
Cassandra Leighton, MPH; Evan Cole, PhD; A. Everette James, JD, MBA; and Julia Driessen, PhD
Currently Reading
Which Patients Are Persistently High-Risk for Hospitalization?
Evelyn T. Chang, MD, MSHS; Rebecca Piegari, MS; Edwin S. Wong, PhD; Ann-Marie Rosland, MD, MS; Stephan D. Fihn, MD, MPH; Sandeep Vijan, MD; and Jean Yoon, PhD, MHS

Which Patients Are Persistently High-Risk for Hospitalization?

Evelyn T. Chang, MD, MSHS; Rebecca Piegari, MS; Edwin S. Wong, PhD; Ann-Marie Rosland, MD, MS; Stephan D. Fihn, MD, MPH; Sandeep Vijan, MD; and Jean Yoon, PhD, MHS
Most patients in a large integrated healthcare system who were high-risk for hospitalization were at substantially lower risk within 2 years.
We categorized patients with nonmissing CAN scores as persistently high-risk, intermittently high-risk, or initially high-risk then persistently low-risk for every month based on all patient-month observations in the study period. Patient-month observations were coded as “persistently high-risk” if hospitalization probability was 10% or greater during the current month and all prior months. “Initially high-risk” was defined as probability less than 10% during all subsequent months. “Intermittently high-risk” was defined as having a combination of high (≥10%) and low (<10%) hospitalization risk across the months and not meeting the criteria for persistently high-risk or initially high-risk. These categorizations ignore the patient-months designated as “hospitalization” or “dropped out of care.” By definition, patients can only move out of the persistently high-risk group, patients can only move into the initially high-risk group, and patients may move into or out of the intermittently high-risk group.

Patient Characteristics and Utilization

We examined patient characteristics measured at baseline: demographics (age, gender, marital status, ethnicity, urban vs rural residence), medical and psychiatric comorbidities, any cancer diagnosis, and markers of social instability.14 Markers of social instability included discharges against medical advice4; number of zip code changes in the year prior4; International Classification of Diseases, Ninth Revision (ICD-9) diagnosis of nonadherence to medical treatment; and homelessness. Homelessness was measured by use of housing services15 or ICD-9 diagnosis codes indicating lack of housing. ICD-9 diagnoses for comorbidities were adapted from previously published research16 and from the VHA Health Economics Resource Center17 (eAppendix Table 2). Numbers of hospitalizations and outpatient visits and no-show rates were calculated from the index date over the subsequent 2 years. No-shows were recorded for each instance in which a patient did not arrive to a scheduled appointment.18

For outpatient visits during the follow-up period, we counted VHA encounters that occurred face-to-face or by telephone with any healthcare provider (eg, physician, nurse, psychologist). We categorized outpatient visits based on the specific type of service (ie, primary care, mental health, palliative care, ED).19 Mental health visits included primary care–mental health integration encounters.20 Inpatient admissions were categorized into medical, surgical, and psychiatric admissions.19

Analyses

Our dependent variable in bivariable and multivariable analyses was the patient’s risk category (ie, persistently high, intermittently high, or initially high) in the final study month (n = 194,597). We used analysis of variance to examine whether there were differences in means among the continuous variables (inpatient/outpatient utilization, ED visits, and zip code changes) across the high-risk categories. In bivariable analyses, we performed ordered logit regression to estimate a single equation for each independent variable over the 3 ordered levels of the dependent variable: persistently, intermittently, and initially high-risk. Proportional odds ratios (ORs) were generated from the ordered logit models to estimate the change in odds in each predictor for patients at (1) persistently high-risk versus intermittently high-risk and (2) intermittently high-risk versus initially high-risk.

We performed multivariable logistic regression to predict being persistently high-risk versus the other 2 groups after adjusting for independent variables found to be statistically significant (P <.05) from the ordered logit regression analyses described previously. Model covariates included age, gender, and race/ethnicity, and we excluded variables used to calculate the CAN probabilities (eAppendix Table 1).

For continuous independent variables that are not normally distributed (ie, number of discharges against medical advice, palliative care encounters, mental health visits, and zip code changes), we applied a log (base 10) transformation to create a less skewed distribution. As a result, one would need to have a 10-fold increase in the independent variables to achieve the ORs indicated for the transformed variables.

Because the ordered logit model assumes proportional odds, we also estimated a generalized ordered logistic regression in sensitivity analysis. This model estimates ORs for variables that violate the proportional odds assumption separately for each comparison.21 OR estimates from the generalized ordered logit model were of similar size and significance as those from the ordered logit model (eAppendix Table 3). In sensitivity analyses, we also used a multinomial logit model to compare patients who were persistently high-risk with those who were intermittently high-risk and to compare those who were persistently high-risk with those who were initially high-risk. Directionality and significance were similar to the ordered logit model (eAppendix Table 4).

We conducted stratified analyses by age group (≥65 years and <65 years) to examine whether there were differences in risk patterns between VHA enrollees eligible for Medicare and younger veterans; results were similar for both age groups (eAppendix Table 5), so we report only the combined results.

Analyses were conducted with SAS version 9.4 (SAS Institute Inc; Cary, North Carolina) and SAS Enterprise Miner.

Institutional Review Board

This evaluation was designed to support VHA operations and quality improvement for the VHA Offices of Primary Care and Clinical Systems Development and Evaluation and was exempt from institutional review board approval or waiver.22,23


 
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