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Unintended Consequences of a Quality Measure for Acute Bronchitis

The American Journal of Managed CareJune 2012
Volume 18
Issue 6

A quality measure reduced antibiotic use for patients with acute bronchitis but led to use of an alternative diagnosis, offsetting most of the observed improvement.


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.


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).


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 Source

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.

Study Population

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 .

Statistical Analyses

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.


Figure 1

Table 1

There were 18,542 eligible visits for acute bronchitis (combining ICD-9-CM codes 466.0 and 490) in the years 2006-2009. 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). 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.

Figure 2

Table 3

For 2006-2009, antibiotics were prescribed for 74.9% of all patients with acute bronchitis (466.0 + 490 codes combined), and the overall rate declined from 76.9% in 2006-2007 to 74.6% in 2008-2009 (P = .03 for trend, ). Compared with the first year of the study, the odds of antibiotic prescriptions were slightly lower in the second year (OR 0.9; 95% CI 0.80-1.01) and third year (OR 0.88; 95% CI 0.78-0.99) of the study (). Across all 3 years, antibiotic prescriptions were significantly more common for visits coded as 490 (82.3% of visits) than for visits coded as 466.0 (71.5% of visits) (OR 2.18 for 490 vs 466.0; 95% CI 1.92-2.47; P <.0001). When analyzed by visit year, the percentage of visits where antibiotics were prescribed was consistently high for visits coded as 490 at 86.6%, 87.6%, and 82.1% of visits in 2006-2007, 2007-2008, and 2008-2009, respectively (P = .33 for trend). In contrast, the percentage of visits where antibiotics were prescribed was high for visits coded as 466.0 in years 2006-2007 (76.8%) and 2007-2008 (74.4%), but dropped substantially to 27.0% in years 2008-2009 (P <.0001 for trend).

No patient factors were significantly associated with the odds of an antibiotic prescription, including patient age, sex, ethnicity, current smoking, or presence of comorbidities (Table 3). However, in comparing provider types, patients seen by a nurse practitioner or physician assistant were more likely to have an antibiotic prescription compared with patients seen by a physician (OR 2.77; 95% CI 1.69-4.52).


Acute bronchitis is a very common reason for outpatient office visits.1 Extensive data support avoidance of antibiotics for uncomplicated acute bronchitis,7 and quality improvement measures such as HEDIS have been designed to incentivize evidence-based practice for this condition.16 In this study of outpatient visit data from an integrated health plan, we found that a large decrease (ie, improvement) in antibiotic prescribing for acute bronchitis (466.0) as defined by the HEDIS measure was largely accounted for by diagnostic coding shifts. Antibiotic use for visits coded as bronchitis NOS (490) remained high throughout the period 2006-2009, and antibiotic prescription rates for the combined group of ICD-9-CM codes 466 and 490 showed little change over time.

The high rates of antibiotic use (74.9% overall) for acute bronchitis seen in our study are comparable to those found in earlier studies with HEDIS plans as well as with national ambulatory office visit surveys.14,16,17 Previous studies have elucidated many provider, patient, and health system factors that contribute to continued antibiotic use for bronchitis, including physician diagnostic uncertainty (ie, use of antibiotics due to difficulty in ruling out community-acquired pneumonia),18 provider19-24 and patient demographics and knowledge factors, and expectation of antibiotic treatment.25-28 Efforts at provider and patient education including decision support tools and management algorithms have shown some efficacy in lowering rates of antibiotic use.9,10,13-15,29 The HEDIS bronchitis measure has significant potential to alter practice patterns, as health systems and providers have a financial incentive to prescribe fewer antibiotics. However, it is crucial to ensure that improved performance on the HEDIS measure translates to a true reduction in antibiotic use. This study suggests that diagnostic coding patterns should be analyzed when assessing HEDIS outcomes, and perhaps should be considered in future revisions of the HEDIS bronchitis measure.

The very simple electronic health record coding intervention had a significant effect on the use of antibiotics for acute bronchitis (466.0) after implementation in the third year of the study. However, this small effect was overshadowed by the large impact of coding changes on the overall use of antibiotics for patients with bronchitis. The use of diagnostic coding pattern shifts to improve ratings is a potential concern in the reporting of HEDIS measures. A study from 2000 found that after an educational intervention to reduce antibiotic use for acute bronchitis among family physicians, rates of antibiotic prescription declined, and there was no significant shift in coding from acute bronchitis to other ARIs such as sinusitis or pneumonia; however, coding shifts between acute bronchitis and bronchitis NOS were not assessed.30

Because our study was observational, we cannot conclude that the implementation of the HEDIS measure and health system response were directly responsible for the observed code shifting and change in antibiotic prescribing patterns. It is possible that other factors influenced the change from use of code 466.0 to use of code 490, such as differences in patient presentation over the years, or concerns about postinfluenza bacterial illnesses in the wake of the influenza epidemic in 2008-2009. In addition, it is possible that provider turnover during the study period may have contributed to the observed change in coding patterns noted in 2008, if newer providers had different coding habits. Another limitation involves the abstraction of data on bronchitis diagnosis and antibiotic use from medical records, which may overestimate or underestimate actual antibiotic use rates depending on errors in the coding. However, previous studies to determine the accuracy of quality measurements such as HEDIS have shown that review of ICD-9-CM coding and claims data provides information comparable to that obtained by full chart review and that claims data review appears to underestimate actual antibiotic use by providers.31,32

Our study highlights the importance of ensuring that quality assessment measures obtain a complete picture of their designated outcomes. While the HEDIS bronchitis measure is an important step to encourage adoption of evidence-based management of acute bronchitis and reduce antibiotic overuse, the potential to work around its data collection measurements, either intentionally or accidentally, should be acknowledged. In future versions of the HEDIS bronchitis measure, the inclusion of bronchitis NOS (490) may be useful to obtain a complete assessment of antibiotic practice patterns.Author Affiliations: From the Department of Medicine (SR, JPM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Medicine (RG, JHM), University of California, San Francisco, San Francisco, CA; and the Geisinger Health System (TH-A, FJB, TG, MSS), Danville, PA.

Funding Source: Supported by grant R01-CI000611 from the Centers for Disease Control and Prevention and grant K24-AI073957 from the National Institutes of Health.

Author Disclosures: Dr Bloom reports receiving a speaker’s bureau invitation from Merck regarding patient-centered medical home topics. Dr Graf reports receiving lecture fees from Merck. Dr Metlay reports receiving grants from the Centers for Disease Control and Prevention. The other authors (SR, RG, TH-A, MSS, JHM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (RG, TH-A, FJB, TG, JPM); acquisition of data (RG, TH-A, TG, MSS, JHM, JPM); analysis and interpretation of data (SR, TH-A, TG, JHM, JPM); drafting of the manuscript (SR, TH-A, FJB, JPM); critical revision of the manuscript for important intellectual content (SR, RG, FJB, TG, MSS, JPM); statistical analysis (SR, JHM); provision of study materials or patients (TG, MSS); obtaining funding (RG, JPM); and administrative, technical, or logistic support (FJB, MSS, JPM).

Address correspondence to: Joshua P. Metlay, MD, PhD, 1232 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104. E-mail: jmetlay@mail.med.upenn.edu.1. Schappert SM. Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 1997. Vital Health Stat 13. 1999(143):i-iv, 1-39.

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18. Hrisos S, Eccles M, Johnston M, et al. An intervention modelling experiment to change GPs’ intentions to implement evidence-based practice: using theory-based interventions to promote GP management of upper respiratory tract infection without prescribing antibiotics #2. BMC Health Serv Res. 2008;8:10.

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27. Macfarlane J, Holmes W, Macfarlane R, Britten N. Influence of patients’ expectations on antibiotic management of acute lower respiratory tract illness in general practice: questionnaire study. BMJ. 1997;315(7117):1211-1214.

28. Mangione-Smith R, McGlynn EA, Elliott MN, Krogstad P, Brook RH. The relationship between perceived parental expectations and pediatrician antimicrobial prescribing behavior. Pediatrics. 1999;103(4, pt 1):711-718.

29. Metlay JP, Camargo CA Jr, MacKenzie T, et al; IMPAACT Investigators. Cluster-randomized trial to improve antibiotic use for adults with acute respiratory infections treated in emergency departments. Ann Emerg Med. 2007;50(3):221-230.

30. Hueston WJ, Slott K. Improving quality or shifting diagnoses? what happens when antibiotic prescribing is reduced for acute bronchitis? Arch Fam Med. 2000;9(9):933-935.

31. Linder JA, Bates DW, Williams DH, Connolly MA, Middleton B. Acute infections in primary care: accuracy of electronic diagnoses and electronic antibiotic prescribing. J Am Med Inform Assoc. 2006;13(1):61-66.

32. Maselli JH, Gonzales R; Colorado Medical Society Joint Data Project Investigators. Measuring antibiotic prescribing practices among ambulatory physicians: accuracy of administrative claims data. J Clin Epidemiol. 2001;54(2):196-201.

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