Choosing Wisely interventions to reduce antibiotic overuse in the safety net

Abstract

Objectives: Physician pay-for-performance (P4P) programs frequently target inappropriate antibiotics. Yet little is known about P4P programs' effects on antibiotic prescribing among safety-net populations at risk for unintended harms from reducing care. We evaluated effects of P4P-motivated interventions to reduce antibiotic prescriptions for safety-net patients with acute respiratory tract infections (ARTIs).

Study design: Interrupted time series.

Methods: A nonrandomized intervention (5/28/2015-2/1/2018) was conducted at 2 large academic safety-net hospitals: Los Angeles County+University of Southern California (LAC+USC) and Olive View-UCLA (OV-UCLA). In response to California's 2016 P4P program to reduce antibiotics for acute bronchitis, 5 staggered Choosing Wisely-based interventions were launched in combination: audit and feedback, clinician education, suggested alternatives, procalcitonin, and public commitment. We also assessed 5 unintended effects: reductions in Healthcare Effectiveness Data and Information Set (HEDIS)-appropriate prescribing, diagnosis shifting, substituting antibiotics with steroids, increasing antibiotics for ARTIs not penalized by the P4P program, and inappropriate withholding of antibiotics.

Results: Among 3583 consecutive patients with ARTIs, mean antibiotic prescribing rates for ARTIs decreased from 35.9% to 22.9% (odds ratio [OR], 0.60; 95% CI, 0.39-0.93) at LAC+USC and from 48.7% to 27.3% (OR, 0.81; 95% CI, 0.70-0.93) at OV-UCLA after the intervention. HEDIS-inappropriate prescribing rates decreased from 28.9% to 19.7% (OR, 0.69; 95% CI, 0.39-1.21) at LAC+USC and from 40.9% to 12.5% (OR, 0.72; 95% CI, 0.59-0.88) at OV-UCLA. There was no evidence of unintended consequences.

Conclusions: These real-world multicomponent interventions responding to P4P incentives were associated with substantial reductions in antibiotic prescriptions for ARTIs in 2 safety-net health systems without unintended harms.

Figure 1.

Flow of participants in non-randomized trial of antibiotic prescribing.

Figure 2.

Estimated probabilities over time of antibiotic prescribing and diagnosis shifting. Each panel depicts the estimated probability of the outcome event (Y-axis) at the start of data collection (start of curve), T1 (first vertical dotted line), T2 (second vertical dotted line) and T3 (end of curve). The slopes of the curves connecting the probability point estimates correspond to the odds ratios (ORs), and the changes in slopes from one time period to another correspond to the ratio of ORs (rORs). Thus, change in slopes were interpretable as the change in rate at which the probability of the outcome changed during the given time period compared to the indicated prior time period, assuming that trends in outcome event rates would have paralleled pre-intervention trends in the absence of the intervention (e.g., trends at T2 would have been parallel to trends at T1 had the intervention not occurred). Time point T1 was the period before any intervention implementation at both sites, T2 was the period after audit and 1:1 feedback (the primary intervention) had been implemented at LAC+USC but not OV-UCLA (though OV-UCLA had other interventions implemented during this time), and T3 was the period after all interventions had been implemented at both sites. Brackets and upside-down brackets depict within-site statistical significance for OV-UCLA and LAC+USC, respectively, between the time periods corresponding with the start and end of the brackets. Asterisks represent statistical significance by ITS analysis at the following levels: * if P≤.05, ** if P≤.01, *** if P≤.001.The shaded areas around the solid lines represent 95% CIs. Panels A and B are the primary outcomes of the intervention (A depicts total antibiotic prescribing, and B depicts HEDIS-inappropriate prescribing). Panel C shows HEDIS-appropriate prescribing, and demonstrates no significant changes in appropriate prescribing after intervention implementation at LAC+USC (T2) or OV-UCLA (T3). Pre-intervention trends in any metric (e.g., declines in HEDIS-appropriate prescribing, increases in coding for alternative ICD codes) likely represent secular trends independent from the interventions. Panels D and E are the pre-specified measures of diagnosis shifting to antibiotic-appropriate ICD codes, and show that there were no statistically significant increases in coding for antibiotic-appropriate conditions or comorbidities after the interventions at both sites (there were statistically significant decreases but not increases after the interventions, arguing against any diagnostic gaming to avoid forfeiting the PRIME incentive payments).

Figure 3.

Unadjusted Antibiotic Prescribing for Acute Respiratory Tract Infections Before and After All Interventions at A) LAC+USC and B) OV-UCLA. The pre-period refers to the time before any interventions were implemented (T1 at both sites—see Figure 2). The post-period refers to the time after the case-audit feedback (the main intervention) was implemented at each site (T2 at LAC+USC and T3 at OV-UCLA). Error bars depict 95% CIs. Per the analysis plan, tests of statistical significance were not performed on unadjusted antibiotic prescribing rates.

Figure 4.

Adjusted Odds Ratios for Antibiotic Prescribing for Acute Respiratory Tract Infections at A) LAC+USC and B) OV-UCLA. Odds ratios (ORs) during each time period were obtained from the within-group interrupted-time-series analysis consisting of segmented age- and sex-adjusted logistic regressions on the dependent variable (antibiotic prescribing) clustered by patient. Results were interpretable as the OR of prescribing antibiotics during a post-intervention period divided by the OR of prescribing antibiotics during the period before all interventions (i.e., ratio of ORs). A ratio of ORs (rOR) equal to 0.60 indicated that the odds of prescribing antibiotics decreased by 40% from the pre-intervention period. All interventions at LAC+USC were implemented during T2 (see Figure 2); Panel A depicts the change in odds of prescribing antibiotics after all interventions were implemented compared to before any interventions were implemented (i.e., T2 v. T1). At OV-UCLA some interventions were implemented during T2, while case-audit feedback (the main intervention) was implemented during T3; Panel B depicts both the change in odds of prescribing antibiotics after some interventions were implemented (post-partial interventions; T2 v. T1) as well as after all interventions were implemented (post-all interventions; T3 v. T1). Thus, LAC+USC had no “post-partial intervention” period. Asterisks represent statistical significance by ITS analysis at the following levels: * if P≤.05, ** if P≤.01, *** if P≤.001. Error bars depict 95% CIs.