Background {#Sec1}
==========

In the United States, 30% of outpatient antibiotic prescribing is estimated to be unnecessary, resulting in almost 47 million unnecessary antibiotic prescriptions each year \[[@CR1], [@CR2]\]. Excessive antibiotic prescribing drives the spread of antibiotic resistance, which contributes to increased morbidity, mortality, and economic costs associated with infections \[[@CR3]--[@CR5]\]. In response, the 2015 U.S. National Action Plan for Combatting Antibiotic-Resistant Bacteria set a goal of reducing inappropriate antibiotic prescribing in outpatient settings by 50% by 2020 \[[@CR6]\].

A major source of unnecessary outpatient antibiotic prescriptions is acute bronchitis cases \[[@CR7]--[@CR10]\]. Acute bronchitis is a common self-limited respiratory illness, characterized predominantly by cough, typically lasting less than 3 weeks \[[@CR7], [@CR11]\]. In the US in 2011, cough was the most common illness-related reason for ambulatory care visits, accounting for 2.6 million outpatient visits \[[@CR12]\]. A study in the UK estimated that 44/1000 adults are affected by acute bronchitis each year \[[@CR13]\]. Antibiotics are not effective for treating acute bronchitis, which is usually of viral etiology \[[@CR11]\], and long-standing professional guidelines recommend against antibiotics for uncomplicated cases \[[@CR14], [@CR15]\]. Nevertheless, US adults are prescribed antibiotics for acute bronchitis approximately 60--70% of the time \[[@CR7]--[@CR9], [@CR16]\]. Further, relative to other upper respiratory tract infections for which antibiotic treatment is not indicated (e.g., nasopharyngitis, laryngitis), providers are especially likely to prescribe for acute bronchitis \[[@CR8], [@CR17]--[@CR20]\]. Due to the prevalence of overprescribing, the U.S. Centers for Infectious Disease Control (CDC) has identified acute bronchitis cases as a major opportunity for reducing unnecessary outpatient antibiotic prescribing \[[@CR21]\].

Although acute bronchitis presents an opportunity to improve antibiotic stewardship, there is little consensus regarding effective stewardship interventions for ambulatory care \[[@CR22]--[@CR24]\]. A diversity of interventions have been proposed, but evidence supporting their effectiveness remains sparse \[[@CR23], [@CR24]\]. Implementation of outpatient stewardship programs could be aided by identifying the factors driving overprescribing, which might point to interventions that target those drivers \[[@CR22]\]. Factors driving antibiotic overprescribing may differ between hospital and outpatient settings and could include diagnostic uncertainty, real or perceived patient expectations for antibiotics, time pressures, or gaps in provider knowledge \[[@CR25], [@CR26]\].

Identifying drivers of prescribing for acute bronchitis could suggest potential interventions, but relatively few studies have focused on identifying these predictors. Prior studies of upper respiratory tract infection prescribing (including for acute bronchitis) in the USA have shown higher rates of antibiotic prescribing in rural (vs. urban) practices \[[@CR8], [@CR10]\], when patients have multiple diagnoses \[[@CR27]\] or illness of longer duration \[[@CR28]\], when providers are advanced practitioners rather than physicians \[[@CR9]\], and when providers experience greater diagnostic uncertainty \[[@CR27]\]. Since most studies have utilized data reported to insurance companies or national agencies \[[@CR13], [@CR16], [@CR23]\], few previous studies have examined how physical exam findings influence prescribing for acute bronchitis. In the few studies that have included data from patient charts, purulent nasal discharge, purulent sputum, abnormal respiratory exam, tonsillar exudate, and sinus tenderness have been reported to be moderately associated with prescribing \[[@CR20], [@CR29]\]. In addition, US prescribing rates for uncomplicated acute bronchitis are higher for younger adults (18--39) than older adults (40+) \[[@CR16]\], suggesting that factors influencing bronchitis prescribing for young adults are particularly good targets for evaluation and intervention.

University student health clinics provide an opportunity to study antibiotic prescribing in young adult patient populations. In the US, college students comprise a sizeable cohort of the population, with 20.1 million students enrolled in higher education, including 13.8 million students enrolled at 4-year degree-granting institutions \[[@CR30]\]. At these 4-year institutions, there are 165.5 annual visits to student health centers for every 100 enrolled students, 37% of which are for respiratory tract infections \[[@CR31]\]. Despite evidence that unnecessary antibiotic prescribing is high in young adult populations \[[@CR16]\], antibiotic stewardship programs are almost nonexistent at most student health centers, and best stewardship practices are not yet defined. Understanding what drives unnecessary antibiotic prescribing in student health centers is a first step towards evidence-based stewardship policies in these settings, and findings can also inform stewardship efforts with providers treating young adults in similar contexts (e.g., urgent care clinics).

The goal of this study was to identify patient and visit factors associated with antibiotic prescribing for young adults diagnosed with acute bronchitis at a high-volume student health center at a large US university. We conducted a retrospective chart review of all visits with an acute bronchitis diagnosis for the 2015--2016 academic year at the Pennsylvania State University's Student Health Center. This work is part of a multi-study interdisciplinary effort to improve antibiotic stewardship in emerging adult populations, with an initial focus on students at residential colleges.

Methods {#Sec2}
=======

Study site {#Sec3}
----------

The Pennsylvania State University Health Services (UHS) offers on-campus medical care to PSU students and their dependents, serving over 40,000 students in more than 50,000 visits yearly. At the time of the study, 28 clinicians saw patients at UHS. During the study period, 21 of these clinicians (9 doctors of medicine (MDs), 2 doctors of osteopathic medicine (DOs), 8 physician assistants (PAs), and 2 nurse practitioners (NPs)) diagnosed at least one patient with acute bronchitis. The remaining clinicians did not diagnose acute bronchitis in the period studied, and therefore they do not appear in the data set.

Data collection and Curation {#Sec4}
----------------------------

UHS staff identified 1451 visits with acute bronchitis diagnoses during the 2015--2016 academic year (August--May). Honest brokers were then employed and trained to access the electronic medical records for these visits, extract deidentified data (data excluding information that could be used to identify individual patients), and enter it in the secure database manager REDCap for use by the researchers. Data extracted included patient characteristics, visit characteristics, symptoms recorded, exam findings, secondary diagnoses, tests ordered, and antibiotic prescriptions (see Table [1](#Tab1){ref-type="table"}). A double-entry procedure was used to provide a reliability check on data extracted from a randomly selected sample (*N* = 69; \~ 5%) of the visits. This check indicated adequate data quality (agreement \> 96% across all variables) for the intended analyses; identified discrepancies were corrected \[[@CR32]--[@CR34]\]. Table 1Descriptive statistics (*n* = 1031) and bivariate analysisVariableVisit Count (%)Odds Ratio (95% CI)Bivariate\
*p*-valueDate and Time visit date1028 (99.7%)0.99 (0.99-0.99)*p* \< 0.001 \*\* week day1028 (99.7%)range 0.64-1.28*p* = 0.63 time of day1021 (99.0%)0.99 (0.99-0.99)*p* = 0.24Patient Characteristics gender*p* = 0.03\*  female (reference group)636 (61.7%)-  male390 (37.8%)1.34 (1.02-1.76)  not recorded5 (0.5%)- race*p* = 0.43  white (reference group)594 (57.6%)-  multiple92 (8.9%)0.90 (0.55-1.44)  Asian50 (4.8%)0.97 (0.51-1.79)  black21 (2.0%)1.04 (0.38-2.54)  Hispanic6 (0.6%)-  international7 (0.7%)-  Pacific islander1 (0.1%)-  not recorded260 (25.2%)- academic status*p* = 0.27  undergraduate student932 (90.4%)-  graduate student85 (8.2%)0.86 (0.52-1.39)  spouse/dependent3 (0.3%)-  not recorded11 (1.1%)- height (inches)1013 (98.2%)1.04 (1.00-1.08)*p* = 0.02\* weight (pounds)1016 (98.5%)1.00 (0.99-1.01)*p* = 0.09Visit Characteristics providersee Fig 3range 0.05--4.63*p* \< 0.001\*\* days since onset (patient reported) ††1016 (98.5%)1.02 (1.01-1.03)*p* \< 0.001\*\* severity (patient reported)*p* = 0.009\*  mild (reference group)61 (5.9%)-  moderate342 (33.2%)0.48 (0.27-0.86)  severe32 (3.1%)1.13 (0.46-2.70)  not recorded596 (57.8%)- progression (patient reported)*p* \< 0.001\*\*  stable/no change (reference group)274 (26.6%)-  worsening317 (30.7%)2.27 (1.77-3.77)  improving108 (10.5%)1.06 (0.60-1.84)  not recorded332 (32.2%)- antibiotics in past month40 (3.9%)0.46 (0.19-1.00)*p* = 0.07Additional Diagnosis upper respiratory infection197 (19.1%)0.26 (0.16-0.39)*p* \< 0.001\*\* suspicious cough77 (7.5%)1.02 (0.61-1.67)*p* = 0.93 allergic rhinitis32 (3.1%)0.51 (0.19-1.17)*p* = 0.14 fever17 (1.6%)2.58 (0.98-6.92)*p* = 0.05 viral syndrome13 (1.3%)\-- tonsillitis6 (0.6%)\-- influenza4 (0.4%)\-- mononucleosis3 (0.3%)\--Common Symptoms Recorded† throat symptoms  sore throat402 (39.0%)1.42 (1.08-1.85)*p* = 0.01\*  painful swallowing130 (12.6%)0.58 (0.37-0.89)*p*= 0.02\*  hoarseness167 (16.2%)0.80 (0.55-1.56)*p* = 0.25  swollen glands in neck112 (10.9%)1.08 (0.70-1.63)*p* = 0.73 systemic symptoms  headache244 (23.7%)0.79 (0.57-1.09)*p* = 0.15  documented fever68 (6.6%)0.70 (0.42-1.17)*p* = 0.17  fever symptoms (patient reported)255 (24.7%)0.94 (0.69-1.28)*p* = 0.71  chills147 (14.2%)0.94 (0.65-1.37)*p* = 0.73  sweats159 (15.4%)1.19 (0.82-1.75)*p* = 0.36 nasal symptoms  stuffy nose665 (64.5%)0.86 (0.66-1.14)*p* = 0.29  sinus congestion344 (33.4%)0.73 (0.55-0.98)*p* = 0.03\*  clear nasal discharge215 (20.9%)0.71 (0.50-0.99)*p* = 0.04\*  purulent nasal discharge184 (17.8%)0.83 (0.58-1.19)p = 0.33  post-nasal drip sensation390 (37.8%)0.93 (0.70-1.21)*p* = 0.59  sinus pain79 (7.7%)1.69 (1.05-2.69)*p* = 0.03\*  sneezing101 (9.8%)0.39 (0.22-0.66)*p* \< 0.001\*\* pulmonary symptoms  sleep disruption due to cough610 (59.2%)1.22 (0.93-1.59)*p* = 0.15  sputum production638 (61.9%)0.98 (0.75-1.29)*p* = 0.91  shortness of breath348 (33.8%)0.74 (0.56-0.97)*p* = 0.03\*  chest tightness277 (26.8%)0.94 (0.69-1.26)*p* = 0.67  wheezing275 (26.7%)0.64 (0.48-0.86)*p* = 0.003\*  chest pain232 (22.5%)0.82 (0.60-1.12)*p* = 0.22  paroxysms of cough315 (30.6%)0.98 (0.73-1.30)*p* = 0.87 ear symptoms  ear pain48 (4.7%)1.02 (0.53-1.88)*p* = 0.94  ear pressure sensation122 (11.8%)0.89 (0.58-1.35)*p* = 0.60  decreased hearing34 (3.3%)0.81 (0.35-1.69)*p* = 0.58 GI symptoms  loss of appetite129 (12.5%)0.82 (0.54-1.23)*p* = 0.34  abdominal pain25 (2.4%)0.30 (0.07-0.87)*p* = 0.05  post-tussive vomiting74 (7.2%)0.67 (0.41-1.09)*p* = 0.10  nausea63 (6.1%)0.46 (0.22-0.86)*p* = 0.02\*  vomiting48 (4.6%)0.44 (0.19-0.89)*p* = 0.03\*  diarrhea34 (3.3%)1.24 (0.59-2.49)*p* = 0.56 neuro-vascular symptoms  lightheadedness47 (4.6%)1.29 (0.69-2.35)*p* = 0.41Commonly Ordered Labs chest x-ray177 (17.2%)2.09 (1.50-2.90)*p* \< 0.001\*\* rapid strep screen31 (3.0%)0.65 (0.26-1.45)*p* = 0.32 complete blood count85 (8.2%)1.40 (0.88-2.21)*p* = 0.15 Monospot27 (2.6%)1.83 (0.83-3.95)*p* = 0.12 influenza A + B16 (1.6%)0.32 (0.05-1.14)*p* = 0.13Common Exam Findings† ear exam  tympanic membrane (TM)27 (2.6%)0.08 (0.004-0.40)*p* = 0.01\* bulging  TM retraction42 (4.1%)0.79 (0.38-1.55)*p* = 0.51  visible fluid behind TM148 (14.3%)0.17 (0.09-0.29)*p* \< 0.001\*\*  cerumen in canal29 (2.8%)0.46 (0.15-1.12)*p* = 0.12 nose exam  mucosal edema580 (56.2%)0.83 (0.63-1.08)*p* = 0.16  mucosal erythema510 (49.5%)0.78 (0.59-1.01)*p* = 0.06  nasal discharge324 (31.4%)1.59 (1.20-2.09)*p* = 0.001\*\*  maxillary sinus tenderness30 (2.9%)1.75 (0.83-3.64)*p* = 0.13 throat exam  erythema209 (20.3%)0.79 (0.56-1.10)*p* = 0.17  lymphoid hyperplasia104 (10.1%)1.47 (0.96-2.22)*p* = 0.07  post-nasal drip157 (15.2%)1.44 (1.00-2.04)*p* = 0.04\*tonsil exam  surgically absent42 (4.1%)1.13 (0.57-2.14)*p* = 0.71  erythema84 (8.1%)0.43 (0.23-0.74)*p* = 0.004\*\*  enlarged47 (4.5%)1.17 (0.62-2.32)*p* = 0.64 lymphatics exam  anterior cervical lymphadenopathy, tender53 (5.1%)1.93 (1.10-3.38)*p* = 0.02\*  anterior cervical lymphadenopathy, non-tender87 (8.4%)0.70 (0.41-1.14)*p* = 0.16  posterior cervical lymphadenopathy, non-tender51 (4.9%)0.60 (0.29-1.16)*p* = 0.15  anterior cervical lymphadenopathy99 (9.6%)3.05 (2.01-4.66)*p* \< 0.001\*\*  posterior cervical lymphadenopathy26 (2.5%)1.00 (0.41-2.25)*p* = 0.10 lung exam  wheezing215 (20.9%)1.92 (1.40-2.61)*p* \< 0.001\*\*  rales21 (2.0%)10.05 (3.69-35.18)*p* \< 0.001\*\*  rhonchi223 (21.6%)2.33 (1.71-3.16)*p* \< 0.001\*\*  percussion abnormality25 (2.4%)9.55 (3.83-28.91)*p* \< 0.001\*\*

We subsequently excluded data on 271 follow-up visits within UHS for previously diagnosed conditions and 149 visits with additional diagnoses for which antibiotics might be appropriate (sinusitis, pharyngitis, streptococcal pharyngitis, otitis media). One thousand thirty-one visits were included in the final analysis (Fig. [1](#Fig1){ref-type="fig"}). Fig. 1Flow of study inclusion and exclusion criteria for acute bronchitis visits (*n* = number of visits). Side arrows indicate exclusion criteria

Data from electronic patient charts included variables for all symptoms and exam findings listed in the record system. Many of these symptoms (e.g. eye discharge, mouth sores) were uncommon in acute bronchitis visits. To narrow the list to variables that might be important in acute bronchitis visits, as well as to eliminate variables with zero frequency cells in univariate contingency tables, we only considered symptoms and exam findings recorded for \> 20 patients for subsequent analysis (Table [1](#Tab1){ref-type="table"}).

Four visits had onset durations that were extreme outliers (\> 100 days since onset), and we substituted missing values for these onset durations. Models excluded visits with missing values in predictor variables. This strategy resulted in 33 visits being excluded from analysis in the final multivariate model due to missing values in predictor variables. It is important to note that for two patient-reported variables, severity and progression, "not recorded" was coded as a factor level, and these entries were not considered missing values.

Statistical methods {#Sec5}
-------------------

In all analyses, the response variable was whether an antibiotic was prescribed at a visit. All variables listed in Table [1](#Tab1){ref-type="table"} were tested as possible predictive factors. Bivariate logistic regression analyses were used to identify a narrowed list of potential predictors of antibiotic prescribing (Table [1](#Tab1){ref-type="table"}) \[[@CR35]\]. Provider traits were not included in the logistic regression analysis due to the small number of providers in the data set (21 total). All variables identified as significant in the bivariate analyses were entered into multivariate logistic regression analyses to identify independent predictors of antibiotic prescribing for acute bronchitis. Backward stepwise removal of nonsignificant variables was used to generate the final multivariate model \[[@CR35]\]. Factors were considered significant in the regression analyses when they had *p*-values \< 0.05. Analyses were carried out using R (version 3.4.3).

Results {#Sec6}
=======

Study population and antibiotic prescribing {#Sec7}
-------------------------------------------

The data set included 1031 visits with an acute bronchitis diagnosis (Table [1](#Tab1){ref-type="table"}). 61.7% of patients were female, and 90.1% of patients were undergraduate students. Antibiotics were prescribed at 30.8% of visits. Azithromycin was the most commonly prescribed antibiotic (83.9% of prescriptions) (Fig. [2](#Fig2){ref-type="fig"}a). Figure [2](#Fig2){ref-type="fig"}b shows the distribution of acute bronchitis visits and rates of antibiotic prescribing over the course of the 2015--2016 academic year. Table [1](#Tab1){ref-type="table"} shows the frequency of antibiotic prescribing by variable. Fig. 2Antibiotic prescribing. **a** Antibiotic prescriptions by drug. **b** Visits and antibiotic prescribing over time

Factors associated with antibiotic use {#Sec8}
--------------------------------------

Factors independently associated with antibiotic prescribing in a multivariate regression model are summarized in Table [2](#Tab2){ref-type="table"}. The factors with the greatest impacts on prescribing were individual providers and suspicious findings during lung examinations. The 21 providers in the data set had dramatically different rates of prescribing for acute bronchitis cases ranging from 0 to 80% (Fig. [3](#Fig3){ref-type="fig"}), and provider was an important predictor of prescribing (odds ratios (OR) ranged from 0.03 to 12.3 for individual providers). Suspicious findings during lung examinations were highly associated with antibiotic prescribing (rales OR 13.95, 95% CI 3.31--80.73; rhonchi OR 5.50, 95% CI 3.08--10.00; percussion abnormality OR 13.02, 95% CI 4.00--50.09). Table 2Factors independently associated with prescribing in a multivariate modelVariableOdds Ratio (95% CI)*p*-valueVisit and Patient Characteristics visit date (days)0.99 (0.99-0.99)*p* \< 0.001\*\* gender  female (reference group)  male1.68 (1.17-2.41)*p* = 0.005\*\* provider0.03-12.3*p* \< 0.001\*\* onset duration (days)1.04 (1.03-1.06)*p* \< 0.001\*\* progression  stable/no change (reference group)  worsening1.78 (1.03-3.10)*p* = 0.04\*  improving0.74 (0.35-1.54)*p* = 0.43  not recorded1.69 (0.87-3.27)*p* = 0.12Additional Diagnosis URI diagnosis0.33 (0.18-0.58)*p* \< 0.001\*\*Symptoms sneezing0.39 (0.17-0.86)*p* = 0.02\* vomiting0.31 (0.10-0.83)*p* = 0.03\*Exam Findings lymphatics  anterior cervical lymphadenopathy, tender3.85 (1.70-8.83)*p* = 0.001\*\*  anterior cervical lymphadenopathy2.63 (1.25-5.54)*p* = 0.01\* lungs  rales13.95 (3.31-80.73)*p* = 0.001\*\*  rhonchi5.50 (3.08-10.00)*p* \< 0.001\*\*  percussion abnormality13.02 (4.00-50.09)*p* \< 0.001\*\* Fig. 3Antibiotic prescribing rates by provider. Prescribing rates for acute bronchitis visits were highly variable among providers. The total number of acute bronchitis visits for each provider is shown above the bar, together with the national average \[[@CR7]--[@CR9], [@CR16]\], and the overall rate at the PSU health facility

The model showed that prescribing rates decreased slightly over the course of the academic year (OR 0.99 per day, 95% CI 0.99--0.99). Male patients were more likely than female patients to be prescribed antibiotics (OR 1.68, 95% CI 1.17--2.41). Patients who reported longer duration since the onset of symptoms were slightly more likely to receive prescriptions (OR 1.04 per day, 95% CI 1.03--1.06), as were patients who reported their symptoms were worsening (OR 1.78, 95% CI 1.03--3.10). Visits with additional diagnoses or symptoms associated with viral infections or allergies were less likely to result in prescriptions (URI diagnosis OR 0.33, 95% CI 0.18--0.58; sneezing OR 0.39, 95% CI 0.17--0.86; vomiting OR 0.31, 95% CI 0.10--0.83). An exam finding of anterior cervical lymphadenopathy was associated with antibiotic prescribing (tender OR 3.85, 95% CI 1.70--8.83; general OR 2.63, 95% CI 1.25--5.54).

As a check, we repeated these analyses without excluding the data from follow-up visits (*N* = 149) for previously diagnosed conditions (Figure 4 in [Appendix](#Sec11){ref-type="sec"}). The results of this analysis were qualitatively similar to the primary analysis, with the addition of antibiotic prescriptions in the past month as a predictor of prescribing (Tables 3 and 4 in [Appendix](#Sec11){ref-type="sec"}). Patients who reported taking antibiotics in the past month were less likely to be prescribed antibiotics (OR 0.31, 95% CI 0.14--0.66). Provider and lung exam findings were the strongest predictors of prescribing in both analyses. Visit date, duration since onset, progression, URI diagnosis, sneezing, and anterior cervical lymphadenopathy were also significant predictors in both analyses.

Discussion {#Sec9}
==========

This study's results indicated two key drivers of antibiotic prescribing: variation between individual providers and diagnostic uncertainty. We take each of these in turn. Individual providers had extraordinarily variable rates of antibiotic prescribing for acute bronchitis (ranging from 0 to 80%), despite treating the same patient population at the same clinic. These results suggest that a subset of providers can drive a disproportionate amount of unnecessary antibiotic prescribing for acute bronchitis. In the current study, provider traits (e.g. provider specialty, age) were not included in the logistic regression analysis due to the small number of providers in the data set (21 total). Previous studies have identified provider specialty, provider age, and perceived patient demand for antibiotics as factors influencing provider prescribing rates for upper respiratory tract infections \[[@CR9], [@CR20], [@CR26], [@CR36], [@CR37]\].

A second important driver may be diagnostic uncertainty. In the present study, prescriptions were much more likely when findings of rales, rhonchi, or percussion abnormalities were recorded during lung examination, and somewhat more likely when external anterior cervical lymphadenopathy was reported. Rales and percussion abnormalities increased prescribing 13-fold, and rhonchi increased prescribing 5-fold. This increase in prescribing may reflect suspicion of pneumonia. Orders of chest x-rays, which also indicate suspicion of pneumonia, were a significant predictor of prescribing in a bivariate analysis, but were not significant in a multivariate model due to high correlation with other lung exam findings. Providers may prescribe antibiotics when there is suspicion of a condition that would respond to antibiotics or general diagnostic uncertainty \[[@CR27]\], and this may not be reflected in the diagnosis code.

Other predictors of prescribing in this study included symptoms of sneezing and vomiting, reported worsening of symptoms, diagnosis of an upper respiratory tract infection, duration of illness, and patient gender. Duration of illness has previously been associated with prescribing for upper respiratory tract infections \[[@CR28]\]. Patient gender has not typically been associated with prescribing rates for acute bronchitis \[[@CR16], [@CR20], [@CR28], [@CR29]\], although some studies have reported that males are more likely to get antibiotic prescriptions for upper respiratory tract infections \[[@CR8], [@CR17]\].

The identification of provider variation and diagnostic uncertainty as drivers of prescribing suggests possible interventions for this clinic and similar settings. Provider variation points to a need for provider-targeted interventions such as audit and feedback, communication training, provider education, or clinical decision support tools \[[@CR22], [@CR23]\]. In an 'audit and feedback' intervention, individual clinicians receive personalized, ongoing feedback on their prescribing rates \[[@CR22]--[@CR24], [@CR38], [@CR39]\]. In one study, quarterly feedback resulted in a 50% relative reduction in broad-spectrum antibiotic use for respiratory tract infections \[[@CR24]\]. Provider communication training has also been shown to decrease unnecessary antibiotic prescribing \[[@CR23]\]. Communication training addresses provider concerns related to patient satisfaction and patient expectation for antibiotics \[[@CR23]\]. In some cases, diagnostic uncertainty may be addressed through point of care diagnostic testing \[[@CR22]\]. Point of care diagnostics are available for respiratory tract infections including Group A *Streptococcus* and influenza \[[@CR22]\]. There is some evidence supporting point of care testing to reduce antibiotic prescribing for respiratory tract infections \[[@CR23], [@CR40]\].

While unnecessary prescribing for acute bronchitis was common in our data, the rate of prescribing was substantially lower than the nationwide average. In the 2015--2016 academic year, antibiotics were prescribed at less than a third of acute bronchitis visits, compared to national rates near 70% \[[@CR7]--[@CR9], [@CR16]\]. There is still room for improvement, but overall, this suggests that lower rates of prescribing for acute bronchitis are achievable.

Our study is unique in its focus on antibiotic prescribing practices at a university health center. University health services are important centers for antibiotic prescribing serving millions of patients, yet they have largely been overlooked as sites for antibiotic stewardship. To our knowledge, the Pennsylvania State University is the first university with a student antibiotic stewardship program. This study is the first to identify drivers of antibiotic prescribing in a university health center, and one of the few to focus on young adults or consider exam findings and symptoms from patient charts as possible predictors of prescribing. We hope that these findings can be used to inform antibiotic stewardship initiatives at university health centers and similar clinical contexts. Our results suggest that unnecessary antibiotic prescribing is disproportionately driven by a subset of clinicians, and interventions targeting providers may be effective at reducing unnecessary prescribing.

Conclusions {#Sec10}
===========

Reducing unnecessary antibiotic prescribing for acute bronchitis cases is a national antibiotic stewardship goal, yet rates of unnecessary antibiotic prescribing remain stubbornly high nationwide. Here we identified factors that influence antibiotic prescribing for acute bronchitis cases at a large university health center. Suspicious findings during lung examinations (rales, rhonchi, percussion abnormality) and individual healthcare providers were the most influential factors affecting antibiotic prescribing rates for acute bronchitis visits. Patient gender, worsening symptoms, duration of illness, symptoms associated with viral infections or allergies, and anterior cervical lymphadenopathy also influenced prescribing rates.

Appendix {#Sec11}
========

Analysis including follow-up visits {#Sec12}
-----------------------------------

The logistic regression analyses described in the main text were repeated with a data set including follow-up visits for previously diagnosed conditions, which had been excluded from the original analysis. Antibiotics were prescribed at 30.0% of visits.

Fig. 4Flow of study inclusion and exclusion criteria for acute bronchitis visits (*n* = number of visits). Side arrows indicate exclusion criteria

Table 3Descriptive statistics (*n* = 1270) and bivariate analysisVariableVisitsOdds Ratio (95% CI)Bivariate*p*-valueDate and Time visit date1267 (99.8%)0.99 (0.99-0.99)*p* \< 0.001 \*\* week day1267 (99.8%)range 0.60-1.40*p* = 0.26 time of day1258 (99.0%)0.99 (0.99-1.00)*p* = 0.61Patient Characteristics gender*p* = 0.07  female (reference group)788 (62.0%)-  male477 (46.3%)1.25 (0.98-1.60)  not recorded5 (0.4%)- race*p* = 0.20  white (reference group)737 (58.0%)-  multiple117 (9.2%)0.76 (0.48-1.17)  Asian61 (4.8%)0.88 (0.49-1.54)  black31 (2.4%)1.00 (0.45-2.11)  Hispanic7 (0.6%)-  international8 (0.6%)-  Pacific islander2 (0.1%)-  not recorded307 (24.2%)- academic status*p* = 0.70  undergraduate student (reference group)1153 (90.8%)-  graduate student101 (7.9%)0.83 (0.52-1.30)  spouse/dependent4 (0.3%)-  not recorded12 (0.9%)- height (inches)1242 (97.8%)1.03 (1.00-1.07)*p* = 0.04\* weight (pounds)1236 (97.3%)1.00 (0.99-1.01)*p* = 0.15Visit Characteristics provider1254 (98.7%)range 0.07 -3.27*p* \< 0.001\*\* onset duration (patient reported)1250 (98.4%)0.99 (0.99-1.00)*p* \< 0.001\*\* severity (patient reported)*p* = 0.02\*  mild (reference group)69 (5.4%)-  moderate381 (30.0%)0.54 (0.32-0.95)  severe37 (2.9%)1.20 (0.52-2.72)  not recorded783 (61.6%)- timing (patient reported)*p* \< 0.001\*\*  stable/no change (reference group)322 (25.3%)-  worsening370 (29.1%)2.59 (1.84-3.65)  improving176 (13.8%)0.67 (0.40-1.09)  not recorded402 (31.6%)- antibiotics in past month85 (6.7%)0.44 (0.23-0.77)*p* = 0.006\*\*Additional Diagnosis URI216 (17.0%)0.30 (0.19-0.44)*p* \< 0.001\*\* suspicious cough102 (8.0%)1.30 (0.84-1.98)*p* = 0.22 allergic rhinitis38 (3.0%)0.43 (0.16-0.96)*p* = 0.06 fever19 (1.5%)2.13 (0.84-5.31)*p* = 0.10 viral syndrome15 (1.2%)\-- tonsillitis6 (0.4%)\-- influenza5 (0.4%)\-- mononucleosis7 (0.5%)\--Common Symptoms Recorded† throat symptoms  sore throat459 (36.1%)1.45 (1.13-1.86)*p* = 0.003\*\*  painful swallowing146 (11.5%)0.60 (0.39-0.89)*p* = 0.01\*  hoarseness186 (14.6%)0.84 (0.59-1.18)*p* = 0.32  swollen glands in neck126 (9.9%)1.19 (0.80-1.75)*p* = 0.39 systemic symptoms  headache272 (21.4%)0.88 (0.65-1.18)*p* = 0.40  documented fever73 (5.7%)0.63 (0.39-1.04)*p* = 0.06  fever symptoms (patient reported)285 (22.4%)1.10 (0.83-1.46)*p* = 0.51  chills164 (12.9%)0.86 (0.60-1.22)*p* = 0.38  sweats182 (14.3%)1.02 (0.73-1.45)*p* = 0.92 nasal symptoms  stuffy nose782 (61.6%)1.04 (0.81-1.33)*p* = 0.76  sinus congestion402 (39.0%)0.83 (0.64-1.08)*p* = 0.16  clear nasal discharge251 (19.8%)0.86 (0.63-1.16)*p* = 0.33  purulent nasal discharge212 (16.7%)0.96 (0.69-1.32)*p* = 0.79  post-nasal drip sensation444 (35.0%)1.10 (0.86-1.41)*p* = 0.46  sinus pain85 (6.7%)1.79 (1.14-2.79)*p* = 0.01\*  sneezing108 (8.5%)0.47 (0.28-0.77)*p* = 0.004\*\* pulmonary symptoms  sleep disruption due to cough710 (55.9%)1.08 (0.85-1.37)*p* = 0.54  sputum production743 (58.5%)0.94 (0.73-1.20)*p* = 0.61  shortness of breath412 (32.4%)0.69 (0.53-0.88)*p* = 0.003\*\*  chest tightness331 (26.1%)0.82 (0.62-1.07)*p* = 0.14  wheezing340 (26.8%)0.64 (0.49-0.84)*p* = 0.001\*\*  chest pain269 (21.2%)0.75 (0.56-0.99)*p* = 0.047\*  paroxysms of cough362 (28.5%)0.91 (0.70-1.18)*p* = 0.46 ear symptoms  ear pain59 (4.6%)1.03 (0.57-1.78)*p* = 0.93  ear pressure sensation143 (11.2%)0.93 (0.63-1.36)*p* = 0.71  decreased hearing41 (3.2%)0.96 (0.47-1.87)*p* = 0.92 GI symptoms  loss of appetite146 (11.5%)0.80 (0.53-1.17)*p* = 0.26  abdominal pain29 (2.3%)0.26 (0.06-0.75)*p* = 0.03\*  post-tussive vomiting85 (6.7%)0.59 (0.38-0.92)*p* = 0.02\*  nausea72 (5.7%)0.50 (0.26-0.89)*p* = 0.02\*  vomiting58 (4.6%)0.73 (0.38-1.32)*p* = 0.32  diarrhea37 (2.9%)1.27 (0.62-2.49)*p* = 0.49 neuro-vascular symptoms  lightheadedness59 (4.6%)1.30 (0.74-2.24)*p* = 0.33Commonly Ordered Labs chest x-ray223 (17.6%)2.17 (1.61-2.91)*p* \< 0.001\*\* rapid strep screen37 (2.9%)0.54 (0.21-1.16)*p* = 0.14 complete blood count115 (9.0%)1.38 (0.92-2.06)*p* = 0.11 monospot36 (2.8%)1.33 (0.65-2.61)*p* = 0.41 influenza A + B19 (1.5%)0.27 (0.04-0.95)*p* = 0.08Common Exam Findings† ear exam  tympanic membrane (TM) bulging32 (2.5%)0.07 (0.004-0.34)*p* = 0.01\*  TM retraction48 (3.8%)0.77 (0.38-1.46)*p* = 0.44  visible fluid behind TM173 (13.6%)0.18 (0.10-0.30)*p* \< 0.001\*\*  cerumen in canal37 (2.9%)0.64 (0.27-1.34)*p* = 0.26 nose exam  mucosal edema707 (55.7%)0.83 (0.65-1.06)*p* = 0.14  mucosal erythema606 (47.7%)0.80 (0.63-1.02)*p* = 0.07  nasal discharge382 (30.1%)1.58 (1.22-2.04)*p* \< 0.001\*\*  maxillary sinus tenderness35 (2.7%)2.01 (1.01-3.95)*p* = 0.04\* throat exam  erythema240 (18.9%)0.76 (0.55-1.04)*p* = 0.09  lymphoid hyperplasia114 (9.0%)1.59 (1.07-2.36)*p* = 0.02\*  post-nasal drip181 (14.2%)1.41 (1.01-1.95)*p* = 0.04\* tonsil exam  surgically absent56 (4.4%)0.93 (0.50-1.65)*p* = 0.81  erythema97 (7.6%)0.47 (0.27-0.79)*p* = 0.006\*\*  enlarged57 (4.5%)1.10 (0.62-2.04)*p* = 0.74 lymphatics exam  anterior cervical lymphadenopathy, tender64 (5.0%)1.88 (1.12-3.12)*p* = 0.01\*  anterior cervical lymphadenopathy, non-tender103 (8.1%)0.81 (0.51-1.27)*p* = 0.38  posterior cervical lymphadenopathy, non-tender62 (4.9%)0.67 (0.35-1.20)*p* = 0.19  anterior cervical lymphadenopathy132 (10.4%)2.80 (1.94-4.04)*p* \< 0.001\*\*  posterior cervical lymphadenopathy38 (3.0%)1.08 (0.52-2.12)*p* = 0.83 lung exam  wheezing253 (19.9%)1.82 (1.37-2.43)*p* \< 0.001\*\*  rales22 (1.7%)8.26 (3.24-25.27)*p* \< 0.001\*\*  rhonchi273 (21.5%)2.30 (1.74-3.03)*p* \< 0.001\*\*  percussion abnormality30 (2.4%)9.89 (4.27-26.89)*p* \< 0.001\*\*^†^ includes symptoms and findings recorded for \> 20 visits

Table 4Factors independently associated with prescribing in a multivariate modelVariableOdds Ratio (95% CI)p-valueVisit and Patient Characteristics visit date (days)0.99 (0.99-0.99)*p* \< 0.001\*\* height (inches)1.05 (1.00-1.09)*p* = 0.03\* providerrange 0.04-8.42*p* \< 0.001\*\* onset duration (days)1.04 (1.03-1.05)*p* \< 0.001\*\* progression  stable/no change (reference group)  worsening1.79 (1.11-2.90)p = 0.02\*  improving0.43 (0.23-0.81)*p* = 0.01\*  not recorded1.43 (0.83-2.48)*p* = 0.20 antibiotics in past month0.32 (0.14-0.65)*p* = 0.003\*\*Diagnosis URI diagnosis0.36 (0.21-0.62)*p* \< 0.001\*\*Symptoms sore throat1.46 (1.04-2.05)*p* = 0.03\* sneezing0.48 (0.22-0.97)*p* = 0.048\*Exam Findings lymphatics  anterior cervical lymphadenopathy, tender2.55 (1.27-5.13)*p* = 0.008\*\*  anterior cervical lymphadenopathy2.88 (1.55- 5.39)*p* \< 0.001\*\* lungs  rales10.21 (3.16-60.02)*p* \< 0.001\*\*  rhonchi5.08 (3.10-8.49)*p* \< 0.001\*\*  percussion abnormality9.69 (3.47-30.79)*p* \< 0.001\*\*

GI

:   Gastrointestinal

PSU

:   Pennsylvania State University

TM

:   Tympanic membrane

UHS

:   University Health Services

URI

:   Upper respiratory tract infection

**Publisher's Note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We are grateful to the University Health Services Antibiotic Stewardship committee for their enthusiastic support. Karen Bascom, Karen Anderson, and Julia Lundy were instrumental to data entry. Ruth Anne Snyder, Bettyann Milliron, Carmel Kamens, and Cristie Happekotte helped coordinate access and training for the honest brokers. We also thank Sue Johnson for her help and support with computing. This study grew out of discussions initiated by Lewis Logan. We thank reviewers Katherine Ka Wai Lam, Yu Feng, and Sofiane Bakour and editor Simon Ching Lam for their helpful comments on this manuscript.

VJM analyzed the data and wrote the manuscript. EPCF and RRV were major contributors to data collection and processing. YZ assisted with literature review and manuscript writing. MZ, AFR, and ELM conceived, designed, and managed the study, and contributed substantially to manuscript writing. All authors read and approved the final manuscript.

This study was funded by the Huck Institutes of the Life Sciences at Pennsylvania State University. The funding body did not have a role in designing the study, in collection, analysis, and interpretation of data, or in writing the manuscript.

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

The Pennsylvania State University Institutional Review Board (IRB) approved this research. Honest brokers were employed and trained to access electronic medical records for clinical visits, extract deidentified data (data excluding information that could be used to identify individual patients), and enter it in the secure database manager REDCap for use by the researchers. Informed consent was waived by the IRB given the retrospective nature of the study and the use of honest brokers for deidentification.

Not applicable.

The authors declare that they have no competing interests.
