Published Online: June 13, 2012
Serena Roth, MD; Ralph Gonzales, MD, MSPH; Tammy Harding-Anderer, PhD; Frederick J. Bloom, Jr, MD, MMM; Thomas Graf, MD; Melissa S. Stahl, MPH; Judith H. Maselli, MSPH; and Joshua P. Metlay, MD, PhD
Objectives: To determine whether diagnostic coding shifts might undermine apparent improvements resulting from the 2007 Healthcare Effectiveness Data and Information Set (HEDIS) measure on avoidance of antibiotics for the treatment of adults with acute bronchitis (International Classification of Diseases, Ninth Revision, Clinical Modification code 466.0).
Study Design: Time series analysis within a primary care network for 3 successive winter seasons from 2006 to 2009. Methods: All initial adult visits with a primary diagnosis code of 466.0 or 490 (bronchitis, not otherwise specified) were analyzed. Multivariable analysis accounted for clustering of observations by physician.
Results: The percentage of visits treated with antibiotics declined significantly for code 466.0 (76.8% to 74.4% to 27.0% of visits over the 3-year study period; P <.0001 for trend) but did not decline for code 490 (86.6% to 87.6% to 82.1% of visits; P = .33 for trend). Use of the 490 code rose significantly over the study period, from 1.5% of total bronchitis visits in year 1 to 84.6% of total bronchitis visits in year 3. As a result, the odds of an antibiotic prescription for codes 466 and 490 combined decreased slightly in year 3 compared with year 1 (odds ratio 0.88; 95% confidence interval 0.78-0.99).
Conclusions: While performance on the specific HEDIS measure improved dramatically during this study period, overall antibiotic prescribing did not decline substantially. Quality measures that assess performance on specific diagnosis codes are imperfect and do not account for shifts in diagnosis coding.
(Am J Manag Care. 2012;18(6):e217-e224)
Quality measures that are linked to specific disease diagnoses can create unintended shifts in diagnosis patterns that offset apparent gains in quality.
Diagnosis shifts are important consequences of quality measures that have narrow diagnostic specificity.
Programs should examine both intended and unintended effects of quality measures, specifically examining changes in behavior for related conditions not captured by the measure.
Acute respiratory tract infections (ARIs), including acute bronchitis, are the most common reasons for outpatient office visits by adults in the United States.1 Although numerous studies and guidelines have advised against routine use of antibiotics for these predominantly viral illnesses, 75% of all antibiotics prescribed in community settings are for ARIs.2 Extensive data have shown that overprescription of antibiotics is a major contributor to emerging antibiotic resistance,3,4 is costly,5 and exposes patients to potential side effects.6 Efforts utilizing public health campaigns, provider education, and practice guidelines like the Centers for Disease Control and Prevention’s “Principles for Appropriate Antibiotic Use for ARIs in Adults”7 have attempted to reduce antibiotic use for ARIs with varying degrees of success.8-15 Antibiotic prescriptions for acute bronchitis have been particularly refractory to change.14 Fewer than 10% of cases of acute bronchitis are bacterial in etiology, and antibiotic treatment has been shown to have minimal, if any, benefit.7 Guidelines state that acute bronchitis in healthy adults does not require antibiotic treatment, yet rates of antibiotic use remain very high. National Committee for Quality Assurance data from 2006 estimated that 71% of commercially insured patients diagnosed with acute bronchitis received antibiotics.16
In recognition of this issue, avoidance of antibiotics for the treatment of adults with acute bronchitis (defined as International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 466.0) was included as a component of the Healthcare Effectiveness Data and Information Set (HEDIS) reported to the National Committee for Quality Assurance in 2007. Data obtained from HEDIS indicators are used by employers and consumers to compare quality measures across health plans, and can have an impact on pay-for-performance reimbursement at both the individual and practice level. Desire to score well on the HEDIS measure is an additional financial incentive for providers and insurers to reduce antibiotic use for bronchitis. However, it remains to be seen if the HEDIS measure will result in true reductions in antibiotic use, or if clinicians will alter diagnostic coding for visits to justify antibiotic prescribing.
Using data from a large, integrated health system, we examined trends in antibiotic use for acute bronchitis from 2006 to 2009, years surrounding the implementation of the HEDIS acute bronchitis measure. In this network, an effort to improve quality of care and performance on the HEDIS measure led to an electronic health record–based intervention that attempted to provide real-time feedback to the physician that antibiotics were not necessary in uncomplicated acute bronchitis. The display text for code 466.0 was changed to “ACUTE BRONCHITIS (ABX NOT INDICATED)” and for code 490 it was changed to “ACUTE BRONCHITIS (COMPLICATED).” We first sought to determine current rates of antibiotic use for acute bronchitis within this health system. We then examined whether shifts in coding patterns occurred during this period, and if the shifts were associated with antibiotic prescription rates. Finally, we sought to determine which patient and provider factors were associated with receipt of an antibiotic in order to identify potential targets for future interventions.
Study Design and Setting
We conducted an analysis of antibiotic prescription patterns for adults with ARIs over a 3-year time period. The initial study population included all adults who sought care for any ARI at any of 33 primary care practice sites within the network.
Data for these analyses were collected from the integrated electronic health record that is utilized across all outpatient and inpatient practice sites within the healthcare system. We extracted data for all visits meeting inclusion criteria for the 3 successive periods of October 1 through March 31 for the years 2006 to 2009. Eligible encounters included outpatient visits to any of the 33 primary care practice sites by adult patients with an ARI (defined below). For each visit, we extracted the date of the visit, primary care site, patient demographic variables (age, sex), primary and secondary diagnoses for the visit, documented vital signs, and the presence of specific comorbidities based on the electronic problem list. In addition, we captured the prescription of an oral antibiotic at each eligible visit based on documentation in the electronic health record.
Visits for ARIs were identified based on the primary diagnosis code, using any of the following ICD-9-CM codes: 460.0 through 487 (acute respiratory infections,
other diseases of the respiratory tract, pneumonia, and influenza), 490 (bronchitis not specified as acute or chronic, hereafter bronchitis NOS), 034.0 (streptococcal
sore throat), 079.0 (adenovirus), 784.1 (throat pain), and 786.2 (cough). For the analyses in this study, we limited our visits of interest to those with a primary diagnosis code of 466.0 (acute bronchitis) and 490 (bronchitis NOS). In addition, we excluded any visits for acute bronchitis within 30 days of a prior visit for an ARI as defined above. Additional exclusion criteria, based on the HEDIS measure, included age <18 years or >65 years, and the presence of specific comorbidities and/or antibiotic-responsive secondary diagnoses. Specific comorbidities included HIV, cystic fibrosis, malignancy, chronic bronchitis or emphysema, or other chronic respiratory diseases, and were identified based on the problem list maintained in the electronic health record and documented as of the date of visit. Antibiotic-responsive secondary diagnoses were documented at the time of the visit for acute bronchitis. The full list of diagnoses is included in the Appendix.
Analyses were limited to visits with a primary diagnosis of either acute bronchitis (466.0) or bronchitis NOS (490). Descriptive statistics were performed to examine the distribution of patient characteristics across all participating sites. We next performed unadjusted comparisons of antibiotic prescription rates across each of the study years using the Mantel-Haenszel chi-square analysis for trend. Analyses were conducted for visits coded as 466.0 and 490 separately and combined. Next, generalized estimating equations (PROC GENMOD) were used to control for clustering of antibiotic prescribing patterns by provider, and to evaluate independent predictors of antibiotic prescribing, including study year and ICD-9-CM diagnosis code. All statistical computations were performed using the SAS statistical software program (version 9.2; SAS Institute Inc, Cary, North Carolina). This study was approved by the Institutional Review Board at the participating clinical site and at the University of Pennsylvania and University of California, San Francisco.
There were 18,542 eligible visits for acute bronchitis (combining ICD-9-CM codes 466.0 and 490) in the years 2006-2009. Figure 1 displays the total number of visits for bronchitis; when stratified by ICD-9-CM code, visits for bronchitis were coded almost exclusively as 466.0 for the years 2006-2007 (98.5% of cases) and 2007-2008 (98.4% of cases). The electronic health record diagnosis display text was changed in October 2008. In 2008-2009, the frequency of visits coded as 466.0 declined, and the use of code 490 rose significantly and accounted for a total of 84.6% of visits for bronchitis (P <.0001 for trend). Table 1 displays the characteristics of patients
diagnosed with acute bronchitis, stratified by study year and diagnosis code. Overall, vital sign abnormalities were generally uncommon among patients across the 3 study years and comparing both diagnosis codes. Similarly, the documented presence of other comorbidities (not considered exclusion criteria for the HEDIS measure) was uncommon among patients diagnosed with either 466.0 or 490.
Table 2 summarizes factors associated with the use of code 466.0 versus code 490. In both unadjusted and adjusted models, the odds of the 490 diagnosis went up substantially in year 3 compared with year 1 (odds ratio [OR] 370; 95% confidence interval [CI] 97-1416). Female patients had a lower odds of diagnosis with 490 versus 466.0 (OR 0.7; 95% CI 0.6-0.9); and current smokers had a higher odds of diagnosis with 490 versus 466.0 (OR 1.38; 95% CI 1.16-1.65). No other patient characteristics were associated with the choice of 466.0 versus 490 diagnosis codes.
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