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The American Journal of Managed Care September 2018
Food Insecurity, Healthcare Utilization, and High Cost: A Longitudinal Cohort Study
Seth A. Berkowitz, MD, MPH; Hilary K. Seligman, MD, MAS; James B. Meigs, MD, MPH; and Sanjay Basu, MD, PhD
Language Barriers and LDL-C/SBP Control Among Latinos With Diabetes
Alicia Fernandez, MD; E. Margaret Warton, MPH; Dean Schillinger, MD; Howard H. Moffet, MPH; Jenna Kruger, MPH; Nancy Adler, PhD; and Andrew J. Karter, PhD
Hepatitis C Care Cascade Among Persons Born 1945-1965: 3 Medical Centers
Joanne E. Brady, PhD; Claudia Vellozzi, MD, MPH; Susan Hariri, PhD; Danielle L. Kruger, BA; David R. Nerenz, PhD; Kimberly Ann Brown, MD; Alex D. Federman, MD, MPH; Katherine Krauskopf, MD, MPH; Natalie Kil, MPH; Omar I. Massoud, MD; Jenni M. Wise, RN, MSN; Toni Ann Seay, MPH, MA; Bryce D. Smith, PhD; Anthony K. Yartel, MPH; and David B. Rein, PhD
“Precision Health” for High-Need, High-Cost Patients
Dhruv Khullar, MD, MPP, and Rainu Kaushal, MD, MPH
From the Editorial Board: A. Mark Fendrick, MD
A. Mark Fendrick, MD
Health Literacy, Preventive Health Screening, and Medication Adherence Behaviors of Older African Americans at a PCMH
Anil N.F. Aranha, PhD, and Pragnesh J. Patel, MD
Early Experiences With the Acute Community Care Program in Eastern Massachusetts
Lisa I. Iezzoni, MD, MSc; Amy J. Wint, MSc; W. Scott Cluett III; Toyin Ajayi, MD, MPhil; Matthew Goudreau, BS; Bonnie B. Blanchfield, CPA, SM, ScD; Joseph Palmisano, MA, MPH; and Yorghos Tripodis, PhD
Economic Evaluation of Patient-Centered Care Among Long-Term Cancer Survivors
JaeJin An, BPharm, PhD, and Adrian Lau, PharmD
Fragmented Ambulatory Care and Subsequent Healthcare Utilization Among Medicare Beneficiaries
Lisa M. Kern, MD, MPH; Joanna K. Seirup, MPH; Mangala Rajan, MBA; Rachel Jawahar, PhD, MPH; and Susan S. Stuard, MBA
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High-Touch Care Leads to Better Outcomes and Lower Costs in a Senior Population
Reyan Ghany, MD; Leonardo Tamariz, MD, MPH; Gordon Chen, MD; Elissa Dawkins, MS; Alina Ghany, MD; Emancia Forbes, RDCS; Thiago Tajiri, MBA; and Ana Palacio, MD, MPH

High-Touch Care Leads to Better Outcomes and Lower Costs in a Senior Population

Reyan Ghany, MD; Leonardo Tamariz, MD, MPH; Gordon Chen, MD; Elissa Dawkins, MS; Alina Ghany, MD; Emancia Forbes, RDCS; Thiago Tajiri, MBA; and Ana Palacio, MD, MPH
Evaluating the impact of a high-touch primary care model among a Medicare Advantage population in comparison with a standard practice–based model.
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Our primary outcome was healthcare utilization. We defined healthcare utilization as total healthcare costs and the number of hospital admissions. We collected annual healthcare utilization costs based on the total incurred costs (medical and pharmacy) during a 12-month period. We added the costs from the medical claim files of both models of care. The total healthcare costs represent the costs incurred by the health benefits company and the member’s responsibility. We reported per member per month (PMPM) costs.11 We also collected hospital admissions during the same 12-month period.12 We counted all admissions to any hospital.

A secondary outcome was use of medications such as statins, aspirin, β-blockers, angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), and diuretics. We defined medication use as refilling at least 1 prescription in each of those medication classes during the study period.13

Other Variables

Using claims, we collected demographic information, including age and gender, and presence of comorbidities. We calculated the Charlson Comorbidity Index (CCI) score as a measure of disease burden. This is a validated method to assess comorbidity status.14

Statistical Analysis

We compared the unmatched baseline characteristics between the 2 models of care using t tests and χ2 tests. In light of model differences between both models of care, we calculated a propensity score using logistic regression. The propensity score calculated the probability of a patient being part of model 1 controlling for CCI score, age, and gender. We then matched by propensity score with a margin of 0.01.15

After we completed the propensity matching and to account for the skewed nature of cost data, we conducted several analyses. First, we reported median costs removing 5% of costs on both tails. Second, we reported median costs removing 5% of the lower-cost tail to account for those who had zero cost over the year.16 Third, we used generalized linear models to account for residual confounding, adjusting for Elixhauser comorbid conditions identified during the analysis of the baseline characteristics.17

For medication use, we calculated in the propensity score–matched groups the differences between those using specific medications in models 1 and 2.

The fitness of the data was assessed using the deviance ratio. Analyses were performed using STATA 14.0 (StataCorp; College Station, Texas), and all significance tests were 2-tailed.


Baseline Characteristics

Tables 2 and 3 show the unmatched and matched baseline characteristics. We included 17,711 unmatched primary care patients. Both groups had significant differences in CCI score, age, and gender (P <.01 for all).

We were able to match 5695 patients from both models of care. The characteristics used for matching—namely, CCI score, age, and gender—were similar when comparing both types of models of care (P >.05 for all). The mean number of primary care visits was higher in model 1 of care compared with model 2 (8.7 ± 4.6 vs 3.8 ± 3.8; P <.01).

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