Open Forum Infectious Diseases BRIEF REPORT CT) and physician data from the American Medical Association Characteristics of Primary Care (AMA) Physician Professional Data. Xponent, a proprietary Physicians Associated With database, includes data collected from US community phar- High Outpatient Antibiotic macies reporting their entire business weekly. QuintilesIMS reported capture >70% of outpatient prescriptions nationally in Prescribing Volume 2011 and used a routinely validated, patented method to recon- Katherine E. Fleming-Dutra, Monina Bartoces, Rebecca M. Roberts, and cile captured prescriptions to wholesale deliveries and project to Lauri A. Hicks 100% coverage . These data represent all outpatient antibiotic Office of Antibiotic Stewardship, Centers for Disease Control and Prevention, Atlanta, Georgia. prescriptions dispensed in nonfederal community and mail-or- der pharmacies. The AMA Physician Professional Data 2011 file Our objective was to identify characteristics associated with high-volume antibiotic prescribing among office-based pri- is AMA’s proprietary demographic database on US-based phy- mary care physicians to target antibiotic stewardship efforts. sicians and medical students. The AMA Physician Professional Physicians aged 40 years and older who were male, located Data also contain unique provider identification numbers in the South, and in solo or 2-physician practices prescribed that can be matched to provider identification numbers used higher volumes of antibiotics than their peers by specialty. in QuintilesIMS. This study was approved by the Centers for Keywords. antibiotic; antimicrobial stewardship; antibiotic Disease Control and Prevention’s (CDC’s) Institutional Review prescribing; antibiotic stewardship. Board with a waiver of informed consent. We included office-based physicians in the 50 US states (excluding territories) engaged in direct patient care as their Antibiotic use is the primary modifiable driver of antibiotic primary professional activity with primary specialties of fam- resistance, a major global public health threat , and the major- ily practice, pediatrics, or internal medicine. We excluded phy- ity of human antibiotic use occurs among outpatients [2, 3]. Half sicians with nonmatching primary specialties in the data sets, of the 260 million outpatient antibiotic prescriptions dispensed degrees other than medical doctor as non-MDs were not sys- in the United States in 2011 were prescribed by primary care tematically included in the AMA database, and any who were physicians: family practitioners, internists, and pediatricians “presumed dead.” To focus on primary care, we excluded phy- . At least 30% of US outpatient antibiotic prescribing is esti- sicians with self-designated primary specialties of pediatric or mated to be unnecessary . Clinicians vary in their propensity medical subspecialties, including infectious diseases. To select to prescribe antibiotics, even when controlling for diagnosis , physicians who are most likely to have similar practices, we but few US data exist regarding demographic groups of phy- excluded physicians who reported rounding in hospitals, had sicians who are most likely to prescribe antibiotics, whether 3 or more offices, had antibiotic prescriptions listed in multiple appropriately or inappropriately. Understanding these patterns states in 2011, or had no antibiotic prescriptions included in the can inform health systems and public health programs about database. Additionally, we excluded physicians in training pro- important physician groups to target with outpatient antibiotic grams, aged <30 or ≥65 years, and physicians less than 5 years stewardship efforts. Our objective was to identify characteris- aer g ft raduation to focus on physicians who were likely to be tics associated with high-volume antibiotic prescribing among in full-time practice as attending physicians during all of 2011. office-based family practitioners, internists, and pediatricians. Systemic oral antibiotics were extracted from the METHODS QuintilesIMS Xponent database for 2011. Antibiotic prescrip- tions, in this customized extract, were aggregated by clinician We conducted a retrospective study using antibiotic prescrip- with unique provider identification numbers. We determined tion data from QuintilesIMS Xponent (QuintilesIMS, Danbury, the median number and interquartile ranges of antibiotic pre- scriptions written by physicians stratified by each primary care specialty: family practice, pediatrics, and internal medicine. We Received 1 August 2017; editorial decision 21 December 2017; accepted 4 January 2018. investigated 6 factors—medical school location (within 50 US Correspondence: K. E. Fleming-Dutra, MD, Office of Antibiotic Stewardship, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road states vs outside); sex (male and female); age categories (30– NE, Mailstop A-31, Atlanta, GA 30329-4018 (email@example.com). <40, 40–<50, 50–<65 years); years since medical school gradu- Open Forum Infectious Diseases ation (5–<10, 10–<20, 20–<30, ≥30 years); office type (solo or Published by Oxford University Press on behalf of Infectious Diseases Society of America 2018. 2-physician practice, group, other, missing); and region of phy- This work is written by (a) US Government employee(s) and is in the public domain in the US. DOI: 10.1093/ofid/ofx279 sician practice (Northeast, Midwest, South, West). BRIEF REPORT • OFID • 1 Downloaded from https://academic.oup.com/ofid/article-abstract/5/1/ofx279/4791127 by Ed 'DeepDyve' Gillespie user on 16 March 2018 We then classified clinicians as “high-volume antibiotic pre- US studies demonstrating higher antibiotic prescribing in the scribers” or not based on a cutoff of the highest 25th percentile South [4, 5]. Because antibiotic prescribing was not normalized of number of antibiotic prescriptions in 2011 for each specialty to the number of visits or even the number of patients in these and performed multivariable log-binomial regression to com- practices, these findings reflect the number of antibiotic pre- pute adjusted prevalence ratios for factors associated with being scriptions per physician and their role in the overall antibiotic a high-volume antibiotic prescriber using SAS Proc Genmod prescribing burden on the patient population. However, under- (see the Supplementary Materials). As age and years since med- standing demographic groups of primary care physicians who ical school graduation are highly correlated, we included only prescribe more antibiotics on a per-physician level is critical to age in the model. Finally, we examined the top 5 antibiotic prioritizing antibiotic stewardship interventions. agents and classes for high-volume antibiotic prescribers and Antibiotic stewardship programs have traditionally focused on physicians who were not high-volume prescribers by specialty. improving antibiotic use in hospitals , and some hospital-based All analyses were conducted using SAS 9.3 (SAS Institute, Cary, antibiotic stewardship programs have begun to address antibiotic NC). We considered a P value of <.05 to be significant. prescribing in outpatient clinics within their health systems. This analysis demonstrates that antibiotic stewardship efforts RESULTS also need to include independent and small outpatient prac- Of 95 344 family practitioners in both data sets, 43 350 (45.9%) tices, especially solo and 2-physician practices. In 2016, the CDC met the inclusion criteria (Supplementary Table 1). For intern- released the Core Elements of Outpatient Antibiotic Stewardship ists, 41 313 (36.5%) of 113 301 were included, and 24 380 (40.4%) as a framework for antibiotic stewardship for outpatient set- of 60 201 pediatricians were included. The median number of tings, which contains recommendations that can be instituted in antibiotic prescriptions written per physician was higher among small, independent practices . In 2016, the Quality Innovation older age groups for both family practitioners and internists, Network–Quality Improvement Organizations (QIN-QIOs) was while median antibiotic prescriptions peaked for pediatricians at tasked by the Centers for Medicare and Medicaid Services to col- age 40–<50 years. Across all 3 specialties, the median number of laborate with regional partners including public health to recruit antibiotic prescriptions increased with each decade since medi- outpatient clinics serving Medicare beneficiaries and implement cal school graduation, was higher among male physicians, was the CDC’s Core Elements by July 2018 . Regional stewardship highest among physicians located in the South, and was high- collaboratives, such as the QIN-QIOs, may serve as a model for est among physicians in solo or 2-physician practices (Table 1). inclusion of small, independent clinics in antibiotic stewardship Family practitioners and internists who attended medical school efforts. Additionally, the American Academy of Pediatrics oer ff s in a US state had higher median antibiotic prescribing numbers quality improvement activities targeted at improving antibiotic than their peers educated in medical schools outside US states. prescribing in primary care pediatrics . Activities oer ff ed However, pediatricians educated in medical schools in US states through health care professional societies can help physicians had a lower median number of antibiotic prescriptions than in small practices improve antibiotic prescribing while meeting those with medical education outside US states. These same requirements to maintain licensure and certifications. factors were significantly associated with being a high-volume Additionally, these analyses demonstrate that family practi- antibiotic prescriber in multivariable modeling after adjustment tioners educated in US medical schools are as or more likely to for the additional factors except medical school location for be high-volume antibiotic prescribers than their peers educated internists, which switched direction for US medical schools to outside the 50 US states. This finding may highlight an oppor - have a small association with lower antibiotic prescribing (prev- tunity to incorporate antibiotic stewardship education into all alence ratio, 0.95; 95% confidence interval, 0.92–0.98) (Table 1). medical education, including by US medical schools and resi- Unadjusted prevalence ratios are shown in Supplementary dency training programs. Table 2. The top 5 antibiotic agents and classes were similar Our analysis was subject to at least the following limitations. among high prescribers and family practitioners/internists and es Th e data are the most recent years in which our team had identical among pediatricians (Supplementary Table 3). access to both data sets. While antibiotic prescriptions have decreased since 2011 for children, adult antibiotic prescribing DISCUSSION remained stable from 2011 to 2014 . Xponent data are col- In 2011, male primary care physicians aged 40 to 64 years were lected as dispensing data, which do not lend themselves to opti- more likely to be high-volume antibiotic prescribers than their mal use for public health. Xponent data lack diagnoses; thus, we younger female colleagues. Additionally, primary care phy- are unable to assess appropriateness. We cannot compare across sicians in the South and in solo or 2-physician practices pre- the 3 primary care specialties without diagnoses. Internists likely scribed more antibiotics than their peers. These findings mirror have more visits for chronic disease management, which would previous work in which older, male physicians in England were result in a lower number of antibiotic prescriptions per provider found to prescribe antibiotics at higher rates  and previous without ae ff cting appropriateness of antibiotic prescribing. We 2 • OFID • BRIEF REPORT Downloaded from https://academic.oup.com/ofid/article-abstract/5/1/ofx279/4791127 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Table 1. Median Number and Interquartile Range of Antibiotic Prescriptions per Provider and Adjusted Prevalence Ratio for Outcome of Being a High-Volume Antibiotic Prescriber (in top 25%) for Primary Care Physicians by Specialty—United States, 2011 Median Number of Antibiotic Prescriptions per Provider (IQR) Adjusted Prevalence Ratio (95% CI) Provider Characteristics Family Practitioners Internists Pediatricians Family Practitioners Internists Pediatricians Age group, y 30–<40 379 (675) 81 (230) 359 (693) Referent Referent Referent 40–<50 456 (769) 216 (521) 513 (864) 1.12 (1.08–1.17) 1.81 (1.71–1.91) 1.32 (1.25–1.40) a a a 50–<65 457 (821) 300 (591) 506 (981) 1.05 (1.01–1.10) 1.85 (1.75–1.96) 1.28 (1.21–1.36) Years since medical school graduation b b b 5–<10 293 (526) 62 (148) 250 (583) NA NA NA b b b 10–<20 355 (649) 76 (201) 340 (682) NA NA NA b b b 20–<30 457 (770) 171 (471) 483 (815) NA NA NA a a a b b b 30+ 473 (832) 310 (604) 523 (998) NA NA NA Sex Male 547 (887) 228 (562) 589 (1053) 1.81 (1.74–1.88) 1.45 (1.40–1.51) 1.45 (1.39–1.51) a a a Female 324 (595) 125 (377) 406 (733) Referent Referent Referent Medical school location Other 400 (739) 170 (484) 503 (987) Referent Referent Referent a a a US state 448 (771) 182 (485) 443 (792) 1.04 (1.00–1.08) 0.95 (0.92–0.98) 0.85 (0.81–0.89) Region West 271 (570) 106 (356) 275 (620) Referent Referent Referent Midwest 503 (773) 185 (487) 516 (850) 1.88 (1.78–1.98) 1.43 (1.36–1.52) 1.89 (1.75–2.05) Northeast 344 (555) 171 (448) 402 (694) 1.14 (1.05–1.22) 1.30 (1.23–1.37) 1.28 (1.18–1.39) a a a South 589 (936) 260 (599) 629 (1035) 2.33 (2.22–2.45) 1.73 (1.65–1.81) 2.38 (2.22–2.55) Primary present employment Solo or 2-physician practice 594 (848) 461 (618) 781 (1066) 1.09 (1.05–1.14) 1.36 (1.31–1.41) 1.34 (1.28–1.41) Group 486 (752) 246 (510) 540 (781) Referent Referent Referent Other 116 (389) 41 (160) 54 (219) 0.50 (0.43–0.58) 0.35 (0.29–0.42) 0.26 (0.19–0.35) a a a Missing 187 (545) 62 (172) 98 (468) 0.59 (0.56–0.62) 0.43 (0.41–0.46) 0.47 (0.44–0.51) Abbreviations: CI, confidence interval; IQR, interquartile range. P value <.001 for differences among the category within each specialty by Wilcoxon 2-sample/Kruskal Wallis. As physician age and years since medical school graduation are highly correlated, only age was included in the model. Primary present employment options were: “self-employed solo practice,” “2-physician practice full- or part-owner,” “other patient care,” “group practice,” “HMO,” “medical school,” “city/ county/state other than hospital,” and “no classification.” “Self-employed solo practice” and “2-physician practice full- or part-owner” were categorized as solo or 2-physician practice for this analysis; “group practice” as group. All others were placed into the other category. Missing information was categorized as missing. Acknowledgments are unable to determine practice location. For example, we can- We thank Robert Hunkler of QuintilesIMS for his review of the methods not determine if physicians work in urgent care, which would and manuscript. ae ff ct their case mix and volume of antibiotic prescribing. Disclaimer. e fin Th dings and conclusions in this report are those of the As Xponent data are prescription based, we are unable to authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. determine the volume of visits to physicians. Physicians who Financial support. This work was supported by the Centers for Disease see more patients will likely have higher volumes of antibiotic Control and Prevention. prescriptions, which may not be associated with appropriate- Potential conifl cts of interest. All authors: no reported conflicts of ness. Visit volume may vary by physician age or gender. It is interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to also likely that adherence to antibiotic prescribing guidelines the content of the manuscript have been disclosed. may also vary with physician age and gender. Previous work has suggested that female physicians may more oen p ft rovide References guideline-concordant care . These analyses will help iden- 1. Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2013. Available at: http://www.cdc.gov/drugresistance/threat-re- tify physician groups who prescribe high volumes of antibiotics, port-2013/index.html. Accessed 4 December 2017. and thus inform the CDC’s efforts to target important physician 2. Suda KJ, Hicks LA, Roberts RM, Hunkler RJ, Danziger LH. A national evalu- ation of antibiotic expenditures by healthcare setting in the United States, 2009. J groups to include in outpatient antibiotic stewardship efforts. Antimicrob Chemother 2013; 68:715–8. Understanding physician demographics associated with the 3. Swedres-Svarm Summary 2014. Available at: http://www.sva.se/en/antibiotics-/ svarm-reports. Accessed 26 May 2015. highest volume or burden of antibiotic prescribing can help dir- 4. Hicks LA, Bartoces MG, Roberts RM, et al. US outpatient antibiotic prescribing ect stewardship efforts. Innovative stewardship interventions variation according to geography, patient population, and provider specialty in that engage outpatient physicians in small practices are needed. 2011. Clin Infect Dis 2015; 60:1308–16. BRIEF REPORT • OFID • 3 Downloaded from https://academic.oup.com/ofid/article-abstract/5/1/ofx279/4791127 by Ed 'DeepDyve' Gillespie user on 16 March 2018 5. Fleming-Dutra K, Hersh A, Shapiro D, et al. Prevalence of inappropriate antibiotic pre- 10. Quality Improvement Organizations. QIO News: March 2017 Patient Safety Q&A scriptions among US ambulatory care visits, 2010–2011. JAMA 2016; 315:1864–73. with Yolanda Jones, RN. Available at: http://qioprogram.org/qionews/articles/ 6. Jones BE, Sauer B, Jones MM, et al. Variation in outpatient antibiotic prescribing patient-safety-qa-yolanda-jones-rn. Accessed 24 April 2017. for acute respiratory infections in the veteran population: a cross-sectional study. 11. American Academy of Pediatrics. EQIPP: Judicious Use of Antibiotics. Available Ann Intern Med 2015; 163:73–80. at: https://shop.aap.org/eqipp-judicious-use-of-antibiotics/. Accessed 4 7. Wang KY, Seed P, Schofield P, et al. Which practices are high antibiotic prescrib- December 2017. ers? A cross-sectional analysis. Br J Gen Pract 2009; 59:e315–20. 12. Centers for Disease Control and Prevention. Measuring Outpatient Antibiotic 8. Pollack LA, Srinivasan A. Core elements of hospital antibiotic stewardship pro- Prescribing. Available at: http://www.cdc.gov/antibiotic-use/community/ grams from the Centers for Disease Control and Prevention. Clin Infect Dis 2014; programs-measurement/measuring-antibiotic-prescribing.html. Accessed 4 59(Suppl 3):S97–100. December 2017. 9. Sanchez GV, Fleming-Dutra KE, Roberts RM, Hicks LA. Core elements of outpa- 13. Berthold HK, Gouni-Berthold I, Bestehorn KP, et al. Physician gender is associ- tient antibiotic stewardship. MMWR Recomm Rep 2016; 65:1–12. ated with the quality of type 2 diabetes care. J Int Med 2008; 264:340–350. 4 • OFID • BRIEF REPORT Downloaded from https://academic.oup.com/ofid/article-abstract/5/1/ofx279/4791127 by Ed 'DeepDyve' Gillespie user on 16 March 2018
Open Forum Infectious Diseases – Oxford University Press
Published: Jan 1, 2018
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