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The American Journal of Managed Care April 2019
Time to Fecal Immunochemical Test Completion for Colorectal Cancer
Cameron B. Haas, MPH; Amanda I. Phipps, PhD; Anjum Hajat, PhD; Jessica Chubak, PhD; and Karen J. Wernli, PhD
From the Editorial Board: Kavita K. Patel, MD, MS
Kavita K. Patel, MD, MS
Comment on Generalizability of GLP-1 RA CVOTs in US T2D Population
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Deprescribing in the Context of Multiple Providers: Understanding Patient Preferences
Amy Linsky, MD, MSc; Mark Meterko, PhD; Barbara G. Bokhour, PhD; Kelly Stolzmann, MS; and Steven R. Simon, MD, MPH
The Health and Well-being of an ACO Population
Thomas E. Kottke, MD, MSPH; Jason M. Gallagher, MBA; Marcia Lowry, MS; Sachin Rauri, MS; Juliana O. Tillema, MPA; Jeanette Y. Ziegenfuss, PhD; Nicolaas P. Pronk, PhD, MA; and Susan M. Knudson, MA
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Deaths Among Opioid Users: Impact of Potential Inappropriate Prescribing Practices
Jayani Jayawardhana, PhD; Amanda J. Abraham, PhD; and Matthew Perri, PhD
Do Health Systems Respond to the Quality of Their Competitors?
Daniel J. Crespin, PhD; Jon B. Christianson, PhD; Jeffrey S. McCullough, PhD; and Michael D. Finch, PhD
Impact of Clinical Training on Recruiting Graduating Health Professionals
Sheri A. Keitz, MD, PhD; David C. Aron, MD; Judy L. Brannen, MD; John M. Byrne, DO; Grant W. Cannon, MD; Christopher T. Clarke, PhD; Stuart C. Gilman, MD; Debbie L. Hettler, OD, MPH; Catherine P. Kaminetzky, MD, MPH; Robert A. Zeiss, PhD; David S. Bernett, BA; Annie B. Wicker, BS; and T. Michael Kashner, PhD, JD
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Does Care Consultation Affect Use of VHA Versus Non-VHA Care?
Robert O. Morgan, PhD; Shweta Pathak, PhD, MPH; David M. Bass, PhD; Katherine S. Judge, PhD; Nancy L. Wilson, MSW; Catherine McCarthy; Jung Hyun Kim, PhD, MPH; and Mark E. Kunik, MD, MPH

Does Care Consultation Affect Use of VHA Versus Non-VHA Care?

Robert O. Morgan, PhD; Shweta Pathak, PhD, MPH; David M. Bass, PhD; Katherine S. Judge, PhD; Nancy L. Wilson, MSW; Catherine McCarthy; Jung Hyun Kim, PhD, MPH; and Mark E. Kunik, MD, MPH
Uncoordinated multisystem use is problematic for Veterans Health Administration (VHA) patients with dementia. The Partners in Dementia Care intervention is successful in changing the pattern of VHA versus non-VHA use.
VHA Data

Data on hospital and ED use (including urgent care) from, or paid for by, the VHA were obtained from the VHA National Patient Care Database. Records were extracted for the periods corresponding to the time frames covered by the interviews: the 6 months prior to the baseline interview, and the two 6-month periods between the baseline, 6-month, and 12-month interviews.26 Two dichotomous outcome measures representing whether veterans had any hospital admissions and any ED visits were created to represent utilization during these 3 time intervals. Hospital admissions and ED use were treated independently. An ED visit resulting in a hospital admission contributed to both indicators.

Overall health burden, measured by the Charlson-Deyo Index,27 was calculated from the 6 months of prebaseline utilization data. Each veteran’s VHA priority for service score was also collected from VHA administrative data. Priority scores range from 1 to 8b, with 1 being the highest service priority. Veterans were grouped into 3 priority groups (ie, 1, 2-6, and 7a-8b), broadly differentiating co-payment levels and out-of-pocket maximums.28

Non-VHA Use, Patient Symptoms, and Background Data

Data on non-VHA hospital and ED use (including urgent care) came from the structured caregiver interviews. Caregivers were given dates of their baseline and 6-month interviews to use as reference points when reporting service use between months 1 to 6 and 7 to 12. Non-VHA utilization was represented using a dichotomous indicator of any non-VHA hospital admissions and any non-VHA ED visits during the reporting periods. As with the VHA indicators, hospital admissions and ED visits were treated independently.

Additionally, caregiver reports of cognitive impairment (eg, difficulties with remembering names and addresses, knowing the day of the week, repeating things) and behavior problems (eg, acting agitated, yelling or swearing, interfering with family members) were measured using subscales from a previously published 22-item instrument.29 In our sample, the cognitive impairment subscale had a Cronbach’s α of 0.84 and the behavior problems subscale had an α of 0.77. Personal care dependency was measured using the activities of daily living (ADLs).30 Seven background or context characteristics were collected to control for baseline differences. These were patient race, patient and caregiver ages, patient and caregiver education levels, caregiver status as a spouse versus nonspouse, and caregiver location at a Northeast or Southwest study site. We excluded self-rated patient and caregiver health due to substantial missing data for those variables, and we excluded caregiver race due to high collinearity with other variables in the model.

Statistical Methods

Baseline differences between PDC and UC participants were examined using t tests for continuous measures and contingency table analysis with a χ2 test statistic or Fisher’s exact test for categorical measures.

Our analyses examined change over time in site of care using patient-level outcome measures, with patients clustered by VHA site. Consequently, we conducted a within-patient, difference-in-differences (DID) analysis using a generalized estimating equation (GEE) with a population-averaged approach to model dichotomous dependent variables representing either hospital or ED use. For our models, our data consisted of longitudinal data with multiple observations on the same patient over three 6-month intervals, beginning with the preintervention baseline. We adjusted our standard errors to account for clustering by VHA site and included patient baseline characteristics, as described above, to help control for patient differences across sites.

All models included variables distinguishing PDC/UC groups (1, PDC; 0, UC); baseline, 6-month, and 12-month cognitive, behavioral, and ADL scores; baseline Charlson-Deyo Index scores; VHA/non-VHA site of encounter (1, VHA; 0, non-VHA); VHA priority level; and the 7 previously mentioned background and context characteristics. Because we did not have information on Medicare enrollment for the veterans in our sample, we constructed a dichotomous variable to indicate patients 65 years or older (1, 65 years or older; 0, younger than 65 years) as a proxy for Medicare enrollment. We also added the geodesic distance to the nearest VHA medical center, using patient residential zip codes, as a covariate to adjust for geographic access to care in our model. In all, we had 6 observations per participant, representing VHA and non-VHA use over the 6-month periods preceding each interview (baseline, 6-month, and 12-month).

If the PDC intervention affected choice of site of care, we expected to see a change in the relative likelihood of VHA versus non-VHA use over time among the intervention group, with no change among the UC group. Consequently, our primary DID test was an interaction between the PDC/UC group, VHA/non-VHA site of care, and time (baseline, 6 months, 12 months). Because distance from VHA facilities has been shown to affect use of VHA services, we also included our distance measure, yielding a 4-way test of interaction. Analyses were conducted using Stata version 13 (StataCorp LLC; College Station, Texas).


 
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