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The American Journal of Managed Care February 2018
Community Navigators Reduce Hospital Utilization in Super-Utilizers
Michael P. Thompson, PhD; Pradeep S.B. Podila, MS, MHA; Chip Clay, MDiv, BCC; Joy Sharp, BS; Sandra Bailey-DeLeeuw, MSHS; Armika J. Berkley, MPH; Bobby G. Baker, DMin, BCC; and Teresa M. Waters, PhD
Cost-Effectiveness of Collaborative Care for Depression and PTSD in Military Personnel
Tara A. Lavelle, PhD; Mallika Kommareddi, MPH; Lisa H. Jaycox, PhD; Bradley Belsher, PhD; Michael C. Freed, PhD; and Charles C. Engel, MD, MPH
Data Breach Locations, Types, and Associated Characteristics Among US Hospitals
Meghan Hufstader Gabriel, PhD; Alice Noblin, PhD, RHIA, CCS; Ashley Rutherford, PhD, MPH; Amanda Walden, MSHSA, RHIA, CHDA; and Kendall Cortelyou-Ward, PhD
ACA Marketplace Premiums and Competition Among Hospitals and Physician Practices
Maria Polyakova, PhD; M. Kate Bundorf, PhD, MBA, MPH; Daniel P. Kessler, JD, PhD; and Laurence C. Baker, PhD
Pricing of Monoclonal Antibody Therapies: Higher If Used for Cancer?
Inmaculada Hernandez, PharmD, PhD; Samuel W. Bott, BS; Anish S. Patel, BS; Collin G. Wolf, BS; Alexa R. Hospodar, BS; Shivani Sampathkumar, BS; and William H. Shrank, MD, MSHS
Leveraging Benefit Design for Better Diabetes Self-Management and A1C Control
Abiy Agiro, PhD; Yiqiong Xie, PhD; Kevin Bowman, MD; and Andrea DeVries, PhD
Development of a Tailored Survey to Evaluate a Patient-Centered Initiative
Marcy Winget, PhD; Farnoosh Haji-Sheikhi, MS; and Steve M. Asch, MD, MPH
Currently Reading
Claims-Based Risk Model for First Severe COPD Exacerbation
Richard H. Stanford, PharmD, MS; Arpita Nag, PhD, MBA, MS; Douglas W. Mapel, MD; Todd A. Lee, PhD; Richard Rosiello, MD; Michael Schatz, MD; Francis Vekeman, MS; Marjolaine Gauthier-Loiselle, PhD; J.F. Philip Merrigan, PhD; and Mei Sheng Duh, ScD
Variation in Markups on Outpatient Oncology Services in the United States
Angela Park; Tim Xu, MD, MPP; Michael Poku, MD, MBA; James Taylor, MBBChir, MPH, MRCS(Eng); and Martin A. Makary, MD, MPH

Claims-Based Risk Model for First Severe COPD Exacerbation

Richard H. Stanford, PharmD, MS; Arpita Nag, PhD, MBA, MS; Douglas W. Mapel, MD; Todd A. Lee, PhD; Richard Rosiello, MD; Michael Schatz, MD; Francis Vekeman, MS; Marjolaine Gauthier-Loiselle, PhD; J.F. Philip Merrigan, PhD; and Mei Sheng Duh, ScD
A health insurance claims-based risk assessment tool to predict patients’ first severe chronic obstructive pulmonary disease exacerbation has been developed and validated.

Objectives: To develop and validate a predictive model for first severe chronic obstructive pulmonary disease (COPD) exacerbation using health insurance claims data and to validate the risk measure of controller medication to total COPD treatment (controller and rescue) ratio (CTR).

Study Design: A predictive model was developed and validated in 2 managed care databases: Truven Health MarketScan database and Reliant Medical Group database. This secondary analysis assessed risk factors, including CTR, during the baseline period (Year 1) to predict risk of severe exacerbation in the at-risk period (Year 2).

Methods: Patients with COPD who were 40 years or older and who had at least 1 COPD medication dispensed during the year following COPD diagnosis were included. Subjects with severe exacerbations in the baseline year were excluded. Risk factors in the baseline period were included as potential predictors in multivariate analysis. Performance was evaluated using C-statistics.

Results: The analysis included 223,824 patients. The greatest risk factors for first severe exacerbation were advanced age, chronic oxygen therapy usage, COPD diagnosis type, dispensing of 4 or more canisters of rescue medication, and having 2 or more moderate exacerbations. A CTR of 0.3 or greater was associated with a 14% lower risk of severe exacerbation. The model performed well with C-statistics, ranging from 0.711 to 0.714.

Conclusions: This claims-based risk model can predict the likelihood of first severe COPD exacerbation. The CTR could also potentially be used to target populations at greatest risk for severe exacerbations. This could be relevant for providers and payers in approaches to prevent severe exacerbations and reduce costs.

Am J Manag Care. 2018;24(2):e45-e53
Takeaway Points
  • To prevent worsening of chronic obstructive pulmonary disease (COPD) and its associated costs, identifying patients at high risk of a first severe exacerbation is critical.
  • A risk model for first severe COPD exacerbation was developed and validated using health insurance claims.
  • Total COPD treatment (controller and rescue) ratio (CTR) can predict first severe COPD exacerbation using health insurance claims.
  • COPD patients with a CTR of 0.3 or greater are at reduced risk of having a severe COPD exacerbation in the subsequent year.
  • Moderate exacerbation history, advanced age, use of rescue medication, chronic oxygen therapy, and type of COPD were also risk factors for first severe COPD exacerbation.
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States, with more than 147,000 patients dying because of it in 2014.1 COPD poses a substantial economic burden on patients, healthcare systems, and society. In the United States, $32.1 billion in medical costs were associated with COPD in 2010, with costs projected to rise to $49.0 billion by 2020.2

A major portion of COPD-related healthcare costs are attributable to exacerbations (sustained worsening of COPD from stable state, beyond normal day-to-day variations).3,4 Exacerbations are acute in onset and warrant additional treatment.3,4 Increased frequency of exacerbations leads to more hospital and emergency department (ED) visits.5 In 2009, approximately 740,000 hospitalizations were related to COPD.6 Hospitalization costs represent 85% of direct medical costs associated with COPD and are largely associated with exacerbations.7,8 Based on a Healthcare Cost and Utilization Project analysis, Perera et al showed that severe exacerbations, defined as hospitalizations for acute COPD exacerbations, were associated with increased aggregate costs, rising from $2.96 billion in 2006 to $3.47 billion in 2010.9 Furthermore, in a retrospective analysis of the Thompson Reuters MarketScan administrative database, Yu et al reported that COPD-related costs for patients with severe exacerbations were $7014 per 90 days compared with $658 per 90 days for patients with no exacerbations, with 83% of the costs associated with inpatient expenses.10 In another retrospective study, Abudagga et al reported that the costs associated with severe exacerbations were $18,120 and increased with each additional prior exacerbation.11

Given the high costs associated with COPD exacerbations, it is important to identify patients at risk of exacerbation to target them for early treatment and improved prevention. Prior history of exacerbations is among the most important risk factors for the development of subsequent exacerbations.12 Previous clinical studies have identified additional risk factors for COPD exacerbations, with several groups developing exacerbation risk models.13-22 However, risk assessment models for COPD exacerbations utilizing readily accessible claims data that are available to health plans and healthcare groups, such as national quality-of-care organizations, are not available.

Identifying patients at risk for their first severe exacerbation is critical to target early and effective treatment, potentially slowing progression and associated cost increases. The present study was a secondary analysis from a wider study in which a medication ratio using pharmacy claims and a risk model were developed and validated. The primary results of this study have been published.23 In this secondary analysis, we aimed to construct and validate a predictive model of first severe COPD exacerbation event using health insurance claims, a source of data readily available to health plans and most quality-of-care organizations. This secondary analysis aimed to further validate the ability of the controller medication to total COPD treatment (controller and rescue) ratio (CTR) to predict first severe COPD exacerbation. The CTR is similar to the current Healthcare Effectiveness Data and Information Set (HEDIS) measure in asthma, the asthma medication ratio, and could have the potential to be used in a similar capacity.24


Study Objectives

The primary objectives of this study have been reported elsewhere.23 There were 2 objectives of the present analysis: 1) construction and validation of a risk model for first severe COPD exacerbation using patient demographics, comorbid conditions, and COPD treatment claims data as covariates; and 2) validation of the CTR as a measure of severe COPD exacerbation risk. The CTR is the ratio of total controller medications (inhaled corticosteroids [ICS], long‑acting β2-adrenergic agonists [LABA], ICS + LABA, long‑acting muscarinic antagonists, phosphodiesterase-4 inhibitors, and methylxanthines) to total controller plus rescue medications (short-acting β-agonists or short-acting muscarinic antagonists [SAMA]).

Study Design

This retrospective study (GlaxoSmithKline study #HO-11-732) used the Truven MarketScan Commercial Claims and Encounter Database and Medicare Supplemental and Coordination of Benefits database (MarketScan database, Truven Health Analytics; Ann Arbor, Michigan) from 2006 to 2011 to construct and validate a risk model of prediction of first severe COPD exacerbation and to test the CTR measure. The Reliant Medical Group (Reliant database, Reliant Medical Group; Worcester, Massachusetts) was used for cross-validation.

The study start date (index date) was the second encounter with a COPD diagnosis (defined in study population section) within a year. Risk factors were assessed during the baseline period, which spanned 1 year, starting at the index date. This was followed by a 1‑year at-risk period during which the occurrence of severe COPD exacerbations, the primary endpoint, was assessed (Figure).

Study Population

Patients with COPD who were 40 years and older were included in the study. The index date of COPD status was defined as the second outpatient and/or ED encounter with a COPD diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 491.x [chronic bronchitis], 492.x [emphysema], or 496 [chronic airway obstruction]) within 1 year. Patients were also required to have at least 1 dispensing for an inhaled COPD medication or oral theophylline during the year following the index date and to have had continuous insurance eligibility (no more than 45 days without coverage) for at least 2 years following the index date.

Patients were excluded if they had a severe COPD exacerbation, defined as a COPD-related hospitalization during the baseline period, or at least 1 claim with an ICD-9-CM diagnosis code for cancer (except cancer types that are typically considered to have little effect on lung function [eg, breast, prostate, or skin cancer except melanoma]) or other non-COPD lung disease during the baseline period.

Data Sources

The MarketScan database was used for model development and validation. This database includes health insurance claims data from 2006 to 2011, representing approximately 18 million covered individuals, and contains information on enrollment history and claims for medical (provider and institutional) and pharmacy services. The Reliant database was used for external validation. It includes longitudinal member-linked medical claims (physician and facility), pharmacy claims, enrollment records, laboratory results, and electronic health record information.


The primary endpoint of this study was the occurrence of a severe COPD exacerbation during the at-risk period (Year 2). A severe COPD exacerbation was defined as a hospitalization with either a primary diagnosis of COPD (excluding obstructive chronic bronchitis without exacerbation; ICD-9-CM code 491.20) or a secondary diagnosis for COPD with a primary diagnosis of respiratory failure (ICD-9-CM codes 518.81, 518.82, or 518.84). The choice of a 1-year window for the baseline and at-risk periods for the base case scenario made it possible to account for the seasonality of COPD exacerbations. Yet, given that risk factors were evaluated in the year prior to the at-risk period, they may not accurately reflect risk factors during the totality of the at-risk period. For example, the CTR may reflect more reliably the COPD medications patients received at the beginning of the at-risk period than those received toward the end of the at-risk period. As such, sensitivity analyses were conducted that limited the at-risk period to 90 days or 180 days following the baseline year (Year 1).

Statistical Methods

Candidate risk factors were assessed during the baseline period and used as predictors for severe exacerbation risk in the following year. Candidate risk factors were gender, age, region, insurance plan, H1N1 flu season (October 2009 to May 2010), pulmonologist visit, county characteristics (altitude; number of pulmonologists per 100,000 inhabitants; number of hospitals per 100,000 inhabitants; proportion of households below the low-income margin; median household income; proportion of patients without health insurance; proportions of high school dropouts and college graduates; urban/suburban/rural localization; and proportion of white, black, Hispanic, and Asian patients, derived from the Area Resource File25), type of COPD diagnosis, exacerbation history (moderate only [ie, defined as outpatient treated or ED visit for COPD with a dispensing for an oral corticosteroid within 7 days]), COPD medications (based on at least 1 dispensing), concomitant medications, procedures (flu and pneumococcal vaccines, use of chronic oxygen therapy, nebulizer, and spirometry; all based on Current Procedural Terminology and Healthcare Common Procedure Coding System codes), and comorbidities based on ICD-9-CM codes.26 Based on univariate associations among risk factors and the probability of an exacerbation in the at-risk period, risk factors were excluded if P >.1. P values were calculated using χ2 tests for discrete variables and Wilcoxon tests for continuous variables. Risk factors present in less than 0.5% of the sample were excluded.

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