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The American Journal of Managed Care October 2018
Putting the Pieces Together: EHR Communication and Diabetes Patient Outcomes
Marlon P. Mundt, PhD, and Larissa I. Zakletskaia, MA
Primary Care Physician Resource Use Changes Associated With Feedback Reports
Eva Chang, PhD, MPH; Diana S.M. Buist, PhD, MPH; Matt Handley, MD; Eric Johnson, MS; Sharon Fuller, BA; Roy Pardee, JD, MA; Gabrielle Gundersen, MPH; and Robert J. Reid, MD, PhD
From the Editorial Board: Bruce W. Sherman, MD
Bruce W. Sherman, MD
Recent Study on Site of Care Has Severe Limitations
Lucio N. Gordan, MD, and Debra Patt, MD
The Authors Respond and Stand Behind Their Findings
Yamini Kalidindi, MHA; Jeah Jung, PhD; and Roger Feldman, PhD
The Characteristics of Physician Practices Joining the Early ACOs: Looking Back to Look Forward
Stephen M. Shortell, PhD, MPH, MBA; Patricia P. Ramsay, MPH; Laurence C. Baker, PhD; Michael F. Pesko, PhD; and Lawrence P. Casalino, MD, PhD
Nudging Physicians and Patients With Autopend Clinical Decision Support to Improve Diabetes Management
Laura Panattoni, PhD; Albert Chan, MD, MS; Yan Yang, PhD; Cliff Olson, MBA; and Ming Tai-Seale, PhD, MPH
Medicare Underpayment for Diabetes Prevention Program: Implications for DPP Suppliers
Amanda S. Parsons, MD; Varna Raman, MBA; Bronwyn Starr, MPH; Mark Zezza, PhD; and Colin D. Rehm, PhD
Clinical Outcomes and Healthcare Use Associated With Optimal ESRD Starts
Peter W. Crooks, MD; Christopher O. Thomas, MD; Amy Compton-Phillips, MD; Wendy Leith, MS, MPH; Alvina Sundang, MBA; Yi Yvonne Zhou, PhD; and Linda Radler, MBA
Currently Reading
Medicare Savings From Conservative Management of Low Back Pain
Alan M. Garber, MD, PhD; Tej D. Azad, BA; Anjali Dixit, MD; Monica Farid, BS; Edward Sung, BS, BSE; Daniel Vail, BA; and Jay Bhattacharya, MD, PhD
An Early Warning Tool for Predicting at Admission the Discharge Disposition of a Hospitalized Patient
Nicholas Ballester, PhD; Pratik J. Parikh, PhD; Michael Donlin, MSN, ACNP-BC, FHM; Elizabeth K. May, MS; and Steven R. Simon, MD, MPH
Gatekeeping and Patterns of Outpatient Care Post Healthcare Reform
Michael L. Barnett, MD, MS; Zirui Song, MD, PhD; Asaf Bitton, MD, MPH; Sherri Rose, PhD; and Bruce E. Landon, MD, MBA, MSc

Medicare Savings From Conservative Management of Low Back Pain

Alan M. Garber, MD, PhD; Tej D. Azad, BA; Anjali Dixit, MD; Monica Farid, BS; Edward Sung, BS, BSE; Daniel Vail, BA; and Jay Bhattacharya, MD, PhD
This instrumental variables analysis estimates that Medicare would realize $362 million in annual savings if all patients with newly diagnosed low back pain were managed conservatively.
Study Population

In identifying patients with idiopathic LBP, we sought to ensure that their initial management would be uncorrelated with the etiology of their back pain. To construct the analytic sample, we first identified all patients who had at least 2 diagnoses of LBP (International Classification of Diseases, Ninth Revision [ICD-9] code 724.2) within 6 weeks in the outpatient setting between 2006 and 2010 and who did not have another diagnosis of LBP in the preceding year. We restricted our sample to those who (1) had at least 1 year of Medicare claims prior to and following the index LBP diagnosis and (2) were continuously enrolled in Medicare parts A and B from the date of diagnosis until death or the end of the study period. Because most beneficiaries become eligible for Medicare at 65 years, we limited our sample to patients 66 years and older to allow for 1 year of prior claims data.

Finally, to ensure that we were comparing patients who were similar at initial diagnosis, we restricted the final cohort to patients who did not have any red flag diagnoses in the 1 month prior to or 8 weeks following the index diagnosis and to patients surviving the first 6 weeks after the index diagnosis. Our red flag diagnoses are standard indications for early imaging in the context of LBP: spinal fracture, cancer, infection, disc herniation, spinal cord compression, aortic aneurysm, and nephrolithiasis. We also excluded patients with diagnoses in which imaging may have been contraindicated (patients with renal failure, pheochromocytoma, or hyperthyroidism; patients with implanted medical devices; patients relying on mechanical ventilation). After these exclusions, the remaining population largely consisted of patients whose LBP was idiopathic in nature.

Diagnostic Strategy Designation

We determined initial diagnostic strategies based on procedure codes within the Medicare claims data submitted within 6 weeks of the index diagnosis. We categorized patients as either receiving no lumbar spine imaging or receiving lumbar spine imaging (stratified as MRI or CT). Standard Current Procedural Terminology codes were used to define each lumbar spine imaging strategy.

Cost Analysis

When used to assess the relation of overall costs of care to early imaging of LBP, standard multivariable statistical methods can be compromised by confounding by indication: Physicians are more likely to order imaging studies early for patients with LBP who are in worse initial health.

To address this problem, we used risk-adjusted physician-level propensity to use imaging on patients without LBP over the previous 12 months (hereafter, physician propensity) as an IV. An IV adjusts for unobserved confounders by acting as a quasi-randomizer; it stratified our population using a variable that is unrelated to the unobserved confounder (in this case, severity of patient illness) but is associated with likelihood of receiving the treatment being studied (in this case, referral for early imaging). Here, we stratify by physician propensity to refer for imaging to remove the potential unobserved confounder of severity of the patient’s illness.

The IV method has been used in the medical literature to study a wide variety of topics for which unobserved confounders may introduce bias, including treatment of acute myocardial infarctions,23,24 effects of health insurance,25 management of bladder and pancreatic cancer,26,27 screening for congenital birth defects,28 and interpretation of neuroimaging data.29 To the best of our knowledge, the IV approach has not yet been used in the medical literature to study management of LBP.

To adjust appropriately for confounding by indication, the IV method requires that physician propensity be (1) strongly correlated with the use of imaging in initial management of patients with LBP and (2) uncorrelated with the unobserved determinants of clinical management outcomes and costs among patients with LBP after the initial treatment period. Our statistical work strongly supported the first requirement, whereas the second requirement is true by construction: Physician propensity is measured for patients without LBP, so it cannot be correlated with the unobserved health of those with LBP. Regression analysis illustrating the strong correlation between physician propensity and the use of imaging in initial management of patients with LBP is available in the eAppendix Table (eAppendix available at ajmc.com).

In addition to the IV approach, we adjusted for patient age, race, sex, Medicaid eligibility (as a proxy for socioeconomic status), inpatient visits during the previous 12 months (binary variables for 1 or 2 or more visits), skilled nursing facility (SNF) or hospice stays during the previous 12 months (binary variables for 0 or 1 or more visits in either setting), Elixhauser comorbid medical conditions (using ICD-9 diagnosis codes during the year prior to index diagnosis),30 and geography (using a complete set of indicator variables for each hospital referral region).

To calculate cost outcomes, we identified all Medicare parts A and B claims and summed these to total Medicare costs by year from date of diagnosis for each patient. We inflated costs to 2010 US$ using the gross domestic product deflator. We accounted for the possibility of nonnormal distribution of costs by log-transforming costs and employing a standard statistical method that eliminates bias that might arise from the presence of patients with LBP with zero costs after the initial management phase.31

Medicare Savings Model

To better illustrate the implications of our statistical analyses, we simulated the effects of switching all Medicare patients with LBP to a strategy of no imaging during the initial management phase (the first 6 weeks after diagnosis). The parameters and structure of our simulation matched the statistical models we used to analyze costs in the year after the initial visit. Using the simulation, we first calculated 1-year expenditures for patients with a particular set of demographic and health characteristics (as per the IV models described previously) if they had received no imaging instead of MRI or CT. We then compared this estimate with the original calculated cost for each diagnostic group and extrapolated the difference across the Medicare population.


 
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