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The American Journal of Managed Care April 2017
Physician Variation in Lung Cancer Treatment at the End of Life
Jonas B. Green, MD, MPH, MSHS; Martin F. Shapiro, MD, PhD; Susan L. Ettner, PhD; Jennifer Malin, MD, PhD; Alfonso Ang, PhD; and Mitchell D. Wong, MD, PhD
Real-World Evidence and the Behavioral Economics of Physician Prescribing
Bruce Feinberg, DO
Provider Type and Management of Common Visits in Primary Care
Douglas W. Roblin, PhD; Hangsheng Liu, PhD; Lee F. Cromwell, MS; Michael Robbins, PhD; Brandi E. Robinson, MPH; David Auerbach, PhD; and Ateev Mehrotra, MD, MPH
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Can Primary Care Physicians Accurately Predict the Likelihood of Hospitalization in Their Patients?
Andrew S. Hwang, MD, MPH; Jeffrey M. Ashburner, PhD, MPH; Clemens S. Hong, MD, MPH; Wei He, MS; and Steven J. Atlas, MD, MPH
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The Breathmobile Improves the Asthma Medication Ratio and Decreases Emergency Department Utilization
Tricia Morphew, MSc; Wendy Altamirano, MPH, MBA; Stanley L. Bassin, EdD; and Stanley P. Galant, MD
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Eric A. Wright, PharmD, MPH; Steven R. Steinhubl, MD; J.B. Jones, PhD, MBA; Pinky Barua, MSc, MBA; Xiaowei Yan, PhD; Ryan Van Loan, BA; Glenda Frederick, BA; Durgesh Bhandary, MS; and David Cobden, Ph
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Barak D. Richman, JD, PhD; Nick Kitzman, JD; Arnold Milstein, MD, MPH; and Kevin A. Schulman, MD

The Breathmobile Improves the Asthma Medication Ratio and Decreases Emergency Department Utilization

Tricia Morphew, MSc; Wendy Altamirano, MPH, MBA; Stanley L. Bassin, EdD; and Stanley P. Galant, MD
An examination of the asthma medication ratio (≥0.50) as an informative metric in program evaluation and for healthcare organizations to measure quality of care provided to patients with asthma.
Among children whose parents accessed care offered through CalOptima in the CHOC Health Alliance BM, reduced average number of total claims (non-Rx), ED visits, IP stays, oral corticosteroid prescriptions, and increased controller medication use in the post compared with pre-period were significant (P <.05) (Table 2). Positive trends were observed in the UC group, but to a lesser extent, with the exception of reduction in patients requiring an IP stay. The BM group had 52% fewer ED days per 100 patients treated post year (P = .050) compared with only 13% fewer in the UC group (P = .662). Overall, the total claims (non-Rx) rate showed a greater reduction in the BM compared with UC cohort ([IRR, 0.59; [standard error (SE) = 0.08] versus IRR, 0.65 [SE = 0.07], respectively). In our population of patients who met the criteria for high-risk disease (baseline), only 22.6% of BM patients continued to have high-risk disease post year. In the UC cohort, improvement was also observed, although the percentage remaining at risk was slightly higher at 32.6%.

The percentage of patients who met HEDIS AMR ≥0.50 increased from 47.2% to 80.6% in the BM group (P <.001) and from 50.0% to 65.2% in the UC group (P = .013) (Figure 2). Although BM and UC groups were similar in their average AMR pre-year (0.42 [standard deviation (SD) = 0.24] and 0.40 [SD = 0.25], respectively; P = .649), the BM group had significantly higher average AMR post year compared with the UC group (0.60 [SD = 0.22] and 0.47 [SD = 0.28], respectively; P =.002). A significant shift in the medication usage pattern helped approach the benchmark of OCS Rx <1 per year in the BM group (pre vs post: 2.6 vs 1.0; P <.001) (Table 2). Trends were directionally similar in the UC group (2.9 vs 1.4; P <.001). This translated to 60% fewer OCS fills per 100 patients treated in the BM group compared with 52% fewer in the UC group. Increased controller medication fills in the BM group was in line with expectations post year for treating individuals with high-risk disease (7.6 compared with 3.9 pre-year; P <.001), whereas, a slight decrease in controller medication fills was observed in the UC group from 3.9 to 3.5 per year (P = .164). The difference in pharmacological therapy approach between the BM and UC groups was significant (P <.05). 


In this report, we have demonstrated the AMR to be an effective metric by assessing its relationship to improved asthma outcomes, particularly reduced ED visits in Hispanic children with high-risk persistent asthma. Utilizing the established optimal AMR threshold of ≥0.50, there were 49% fewer ED days per 100 patients compared with those with an AMR <0.50 (P <.05). Furthermore, using the change in the AMR as a quality improvement metric found that BM increased the percentage with an AMR ≥0.50 from 47.2% to 80.6% in the post year observation, whereas the comparator, UC, showed an increase from 50.0% to 65.2%. This difference was reflected in the significant 52% reduction in average number of ED visits for the BM cohort versus 13% for the UC cohort. The impact of the BM program was also shown by the reduction of OCS courses in the post year to approximately 1 compared with 1.4 in the UC group, suggesting continued risk of poor asthma control in the latter.

The concept wherein a provider proactively monitors their patient’s health status outside of the office setting, particularly electronically, enables them to interact before severe asthma exacerbation or loss of asthma control occurs. This is particularly important with a disease like asthma where signs and symptoms may be episodic and healthcare utilization high. For that reason, administrative data are used in HEDIS and by the National Committee on Quality Assurance to assess the quality of care by health plans, such as Medicaid, and are increasingly incorporated into the pay-for-performance reimbursement model, which encourages a preventative approach to asthma therapy.

Of the 3 HEDIS process measures for persistent asthma, the AMR has shown superiority to ≥1 controller medication prescriptions per year.21 This was also evident in our population, as ≥80% of high-risk children had received ≥1 controller in the pre-year. Yoon et al recently evaluated the MMA as a quality-of-care metric and found the MMA was not related to improved asthma outcomes assessed by rescue medication prescriptions, ED visits, and IP stays.18

One potential reason given for the superiority of the AMR, compared with the first 2 HEDIS process measures, is that they lack measure of short acting beta-agonist (SABA) prescriptions, which may indicate asthma exacerbations and poor asthma control. Schatz et al found the 2 measures that correlated with asthma outcomes were the number of SABA prescriptions and the AMR30—the latter includes both controller and rescue medication. These authors reported that the number of SABA prescriptions per year was inversely correlated with asthma outcomes. We hypothesized that the lack of change in SABA use in our BM cohort, which was less than 4 prescriptions, a threshold associated with increased risk of ED visits and OCS use,30 may reflect general recommendations for use as a safety precaution prior to vigorous exercise (eg, running or basketball).

In the pediatric population, particularly in those with persistent asthma, several studies have shown the AMR to be a useful quality-of-care metric in relationship to improved asthma outcomes. Rust et al evaluated a Medicaid population of children aged 5 to 12 years with either 1 hospitalization or 2 outpatient visits for asthma and found that only 33.4% had an AMR ≥0.50 over a 90-day follow-up period. Those with an AMR ≥0.50 had a 17% reduced odds of a future ED visit.31 The positive impact of achieving AMR ≥0.50 was also found in our cohort with the 49% reduction in average number of concurrent ED visits. Rust et al noted that the proportion of prescribed days (≥50% vs <50%), similar to the MMA, did not correspond to improved asthma outcomes,31 which mirrored the findings of Yoon et al.18 In a subsequent paper by Rust et al, utilizing the same population, these investigators established the cost savings with a greater percentage reaching ≥0.50 AMR as a marker of greater adherence.32 For example, they projected a cost savings of $523.53 per patient due to reduced healthcare utilization by increasing the percentage reaching this threshold from 33.5% to 70%. This 36.5% absolute percentage point increase is similar to the 33.4% improvement (from 47.2% to 80.6%) observed in our BM cohort. Prior cost-benefit evaluations of the BM program showed similar positive cost reductions but preceded standardization on achieving AMR ≥0.50 and evaluation of attributable cost savings.28,33

 In a novel approach, Beck et al calculated a pharmacy-level AMR (PH-AMR) and found children in census tracts with a PH-AMR reaching the ≥0.50 threshold had significantly less utilization than those with PH-AMR <0.50 (P = .001).23  They reported that for every 0.1 increase in the PH-AMR, the asthma emergent care utilization rate decreased by 9.5 events per 1000 children (P = .03). After adjusting for poverty level and access to care, they concluded that the pharmacy may be a community leverage point to improve population-level asthma control through targeted interventions. For every 0.1 increase in AMR in our community setting, the rate of ED visits per 100 high-risk patients decreased approximately 9% (P = .06).

Stanford et al found that the optimal AMR threshold, depending on population, type of insurance, and timeframe being evaluated, ranged from ≥0.50 to ≥0.70. 24 However, AMR defined at commonly used and effective thresholds of ≥0.50 and ≥0.70 was found to be a significant predictor of subsequent exacerbations and OCS usage in both children and adults. This relationship was also seen in those receiving either Medicaid or commercial insurance, particularly in those defined as being high-risk for persistent asthma.

The Breathmobile Program

The concept of providing mobile healthcare to patients who have poor accessibility to adequate medical care is not new and has been successful in several rural and low socioeconomic status (SES)/underserved communities for both adults and children.25,27,34,35 The concept of the BM model of healthcare delivery was originated in Los Angeles by Jones et al in 1995.25 This program addressed several major barriers to preventative care including accessibility, cultural compatibility, affordability, and continuity of care. Outcomes shown by several BM programs distributed in underserved communities throughout the United States have consistently shown self-reported reduction in healthcare utilization, improvement in asthma control, and reduced school absenteeism.25-27,28,33


Most studies evaluating the AMR as a population-based healthcare metric have utilized extensive Medicaid databases, while our study evaluated far fewer patients selected on the basis of being high risk for asthma exacerbations. This limitation may have affected our power to detect the significance of clinically meaningful differentials in health resource use reductions between intervention groups in the post period.

Restricting our study to low-SES Hispanic patients may have limited the generalizability of our findings. Although our study was not a randomized clinical trial, selection bias was expected to be minimal as both BM and UC patients met criteria for high-risk asthma and were similar in distribution of baseline characteristics described in Table 1. Access to disease management strategies offered through CalOptima may have contributed to the higher-than-expected baseline percentage of patients whose AMR value was ≥0.50 in both BM and UC cohorts (pre-year: 47.2% and 50.0%, respectively), based on the much lower percentage reported for a similar population in the Rust article (33.4%).31 Our approach differed from some other AMR studies in that it was not designed sequentially to predict changes in the postintervention observational period,18,24 but rather concomitantly to determine the relationship of AMR to healthcare utilization and pharmacological outcomes.22,23 Outcome improvement may occur during subsequent years as shown by others.18,24 Population health metrics, such as the AMR, provide insightful and comparative data for application in evaluation of disease management programs; approaches to increase accessibility to parameters necessary to calculate this measure across multiple settings and populations should be explored. 


Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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