Successfully Reducing Antibiotic Prescribing in Nursing Homes Sheryl Zimmerman, PhD,a,b Philip D. Sloane, MD, MPH,a,c Rosanna Bertrand, PhD,d Lauren E. W. Olsho, PhD,d Anna Beeber, PhD, RN,a,e Christine Kistler, MD, MASc,a,c Louise Hadden, BA,d Alrick Edwards, MPH,d David J. Weber, MD, MPH,f and C. Madeline Mitchell, MURPa
OBJECTIVES: To determine whether antibiotic prescribing can be reduced in nursing homes using a quality improvement (QI) program that involves providers, staff, residents, and families. DESIGN: A 9-month quasi-experimental trial of a QI program in 12 nursing homes (6 comparison, 6 intervention) conducted from March to November 2011. SETTING: Nursing homes in two regions of North Carolina, roughly half of whose residents received care from a single practice of long-term care providers. PARTICIPANTS: All residents, including 1,497 who were prescribed antibiotics. INTERVENTION: In the intervention sites, providers in the single practice and nursing home nurses received training related to prescribing guidelines, including situations for which antibiotics are generally not indicated, and nursing home residents and their families were sensitized to matters related to antibiotic prescribing. Feedback on prescribing was shared with providers and nursing home staff monthly. MEASUREMENTS: Rates of antibiotic prescribing for presumed urinary tract, skin and soft tissue, and respiratory infections. RESULTS: The QI program reduced the number of prescriptions ordered between baseline and follow-up more in intervention than in comparison nursing homes (adjusted incidence rate ratio = 0.86, 95% confidence interval = 0.79–0.95). Based on baseline prescribing rates of From the aCecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, bSchool of Social Work, University of North Carolina at Chapel Hill, cDepartment of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; d Abt Associates, Inc., Cambridge, Massachusetts; eSchool of Nursing, University of North Carolina at Chapel Hill, and fDepartment of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. Address correspondence to Sheryl Zimmerman, Kenan Distinguished Professor, Associate Dean, and Co-Director, Program on Aging, Disability, and Long-Term Care, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, 725 Martin Luther King Jr. Boulevard, Chapel Hill, NC 27599. E-mail: [email protected] DOI: 10.1111/jgs.12784
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12.95 prescriptions per 1,000 resident-days, this estimated adjusted incidence rate ratio implies 1.8 prescriptions avoided per 1,000 resident-days. CONCLUSION: This magnitude of effect is unusual in efforts to reduce antibiotic use in nursing homes. Outcomes could be attributed to the commitment of the providers; outreach to providers and staff; and a focus on common clinical situations in which antibiotics are generally not indicated; and suggest that similar results can be achieved on a wider scale if similar commitment is obtained and education provided. J Am Geriatr Soc 62:907–912, 2014.
Key words: antibiotic quality improvement
prescribing;
nursing
homes;
A
ntibiotic resistance is one of the three biggest threats to health,1 and nursing home (NH) populations are an important setting within which to reduce overuse.2 Antibiotics are among the most commonly prescribed pharmaceuticals in NHs, with a recent study finding that, over 6 months, 42% of NH residents received antibiotics.3 Evidence further suggests that the high use of antibiotics contributes to antimicrobial resistance, with a point-prevalence survey of 117 NH residents finding that 43% were culture positive for antimicrobial-resistant pathogens, including methicillin-resistant staphylococcus aureus, extended-spectrum b-lactamase–producing Klebsiella pneumoniae or Escherichia coli, and vancomycin-resistant enterococci.4 Not only does overuse facilitate resistance, but also a notable proportion of antibiotic use may be unnecessary. (Studies indicate that 25–75% of prescriptions do not meet clinical guidelines for appropriate prescribing.)5–8 The most common clinical situation leading to inappropriate antibiotic therapy is suspected urinary tract infection,9 which accounts for 30% to 50% of antibiotics prescribed in NHs.10 One study found that one-third of residents who received antibiotics for urinary tract infection were being treated for asymptomatic bacteriuria—a condition
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for which antibiotics do not improve outcomes—rather than symptomatic disease.11 Furthermore, the inappropriate prescribing of antibiotics has been demonstrated in multiple randomized controlled trials to harm rather than benefit NH residents.11–13 Nationwide, 1.4 million people reside in 15,690 NHs.14 It is no surprise, then, that an editorial in the Journal of the American Geriatrics Society asserted that “with the growing body of evidence demonstrating the effectiveness of simple educational interventions, a proactive approach to curbing and eventually eliminating inappropriate antibiotic usage in NHs is no longer optional.”15 The challenge is to better define, refine, and test interventions that will optimize antibiotic use. To be successful, these interventions must address multiple risk factors and be acceptable to primary care providers, NH staff who oversee care and communicate signs and symptoms, and residents and their families.16,17
METHODS This 9-month quasi-experimental difference-in-differences trial of a quality improvement (QI) program was implemented in 12 NHs participating in the Collaborative Studies of Long-Term Care consortium based at the University of North Carolina at Chapel Hill (UNC). Six NHs in the Piedmont region of North Carolina served by one longterm care provider practice (Extended Care Physicians) were matched to six NHs in the mountain region of North Carolina according to bed size and profit status; the same practice group, but different providers, served the comparison sites. The two regions were adjacent and similar in terms of cultural and sociodemographic factors but were geographically separated, which avoided spillover effects. The sample size was determined based on a power calculation specifying a minimum detectable effect size of 15 percentage points for genitourinary and upper respiratory infections, because it was deemed that such a rate reduction would be significant based on evidence from prior work that approximately 86% of infections are treated with antibiotics,18 and 30% are not clinically indicated. Assuming an intraclass correlation of 0.03, an average cluster size of 116 residents per NH, and a two-sided alpha of 0.05, power to detect a 15-percentage-point change was estimated to be 0.87 for genitourinary infections and 0.86 for upper respiratory infections. Research nurses who were blind to study aims and condition obtained information related to antibiotic prescribing. They reviewed infection control logs and recorded information on prescriber, antibiotic name, start date, and suspected clinical indication for each resident who received an antibiotic between March and November 2011. Data were also obtained from staff regarding NH characteristics (e.g., estimates of resident case-mix, nurse staffing, proportion of residents served by the long-term care practice group). The institutional review boards of UNC and Abt Associates, Inc. approved all aspects of the research design, including a waiver of informed consent. The QI program was initiated in June 2011. The QI program targeted providers, NH staff, and residents and their families. Providers and NH nurses received on-site training from clinical and health services
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researchers from UNC (including a physician, nurse, infectious disease specialist, and experts in long-term care organization) on prescribing guidelines (e.g., components of the Loeb Minimum Criteria for initiation of antibiotics) and 12 common situations in which systemic antibiotics are generally not indicated: positive urine culture in an asymptomatic individual; urine culture ordered solely because of change in urine appearance; nonspecific symptoms or signs not referable to the urinary tract (with or without a positive urine culture); upper respiratory infection (common cold); bronchitis or asthma in an individual who does not have chronic obstructive pulmonary disease; infiltrate on chest X-ray in the absence of clinically significant symptoms; suspected or proven influenza in the absence of a secondary infection; respiratory symptoms in an individual with advanced dementia receiving palliative care or at the end of life; skin wound without cellulitis, sepsis, or osteomyelitis (regardless of culture result); small (.99 >.99 >.99 >.99 .08 .23 .62
1.2 15.9 9.9 50.2
0.2 14.1 4.4 12.1
1.3 9.1 13.6 38.9
0.4 5.3 3.4 8.6
.49 .29 .14 .09
0.2 1.8 1.8 7.8
0.4 2.1 1.5 4.0
0.5 3.2 6.0 17.2
0.6 2.8 7.2 16.5
.27 .34 .19 .20
21.6 21.5 30.5 24.2 2.3 67.4
6.2 9.3 5.2 10.6 0.9 9.1
8.0 14.9 27.9 42.7 6.3 78.1
6.0 14.5 5.5 20.2 4.7 14.0
.003 .38 .42 .07 .06 .15
84.3 10.1 0.3 0.1 0.2 70.0 48.0 1.22
10.8 4.6 0.5 0.2 0.3 6.3 16.9 0.08
77.7 8.2 0.0 0.4 0.9 54.9 34.9 1.14
18.0 13.7 0.0 0.9 1.7 24.3 27.7 0.13
.46 .76 .18 .50 .36 .17 .35 .23
69.0 10.0 55.3 28.4
.10 .31
58.1 10.5 41.4 14.7
Fisher exact test (two-sided) for categorical outcomes, two-sided t-test for continuous outcomes. Extended Care Physicians, LLC, the long-term care practice group participating in the project. SD = standard deviation; FTE = full-time equivalent.
b
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to 11.80 (pooled difference-in-differences = 2.75 prescriptions per 1,000 resident-days, P = .05). There were no statistically significant difference-in-differences according to specific indication at the P < .05 level, but prescribing for urinary and skin and soft tissue indications fell more in intervention sites than comparison sites (both P = .09). Adjusting for NH-level confounders (Table 2), there was a statistically significant reduction in prescribing rates for all indications combined in intervention NHs (IRR = 0.86, 95% confidence interval (CI) = 0.79–0.95; P = .002) but not for urinary or skin and soft tissue indications considered separately. In these multivariate models, the QI program reduced the prescribing rate for respiratory indications (IRR = 0.71, 95% CI = 0.56–0.90; P = .005). Statistical power considerations may explain why the effect on urinary and skin and soft tissue prescribing was not statistically significant, because point estimates for these indications indicate greater prescribing declines in intervention sites than in comparison sites after the QI program. Based on the baseline prescribing rate of 12.95 prescriptions per 1,000 resident-days, the estimated adjusted IRR of 0.86 implies a reduction to 11.1 prescriptions per 1,000 resident-days attributable to the QI program, or approximately 1.8 prescriptions avoided per 1,000 resident-days.
DISCUSSION Programs to address inappropriate prescribing in NHs have ranged from establishing prescribing guidelines19 to designing interventions to improve prescribing practices.22 To be effective, NH antibiotic stewardship programs must consider challenges related to NH understaffing (which limits the time available to complete additional paperwork), absence of electronic records, and prescribers who
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are not directly employed by the NH. Nursing homes vary in the number and intensity of providers who are involved in care.23 The most common pattern is for a NH to have a medical director (or medical group) who manages the majority of residents but whose main work is office-based and who employs a nurse practitioner or physician assistant because the physician visits the nursing home for at most a half-day a week. Long-term care specialty practices, in which a few providers (usually a mix of physicians, nurse practitioners, and physician assistants) manage the majority of (or all) residents, often visiting the NH several times a week, serve a growing minority of NHs.24 In both cases, a large number of additional community physicians who treat a few residents each supplement these main providers.25 “Closed” provider models in which all physicians are actively involved in numerous facets of care, such as participating in regular medical staff meetings, are uncommon.23 The procedures employed in this QI program were sensitive to these concerns. The magnitude of effect achieved in this study is unusual for efforts to reduce antibiotic use in NHs. Although the study design did not allow the contribution of separate components of the QI program to be discerned, the data give clues as to their effectiveness. During postproject interviews, prescribers indicated that training related to situations in which antibiotics are generally not indicated changed their thinking and influenced their prescribing, and NH staff reported that the medical care referral form increased communication regarding relevant signs and symptoms. Antibiotic prescribing was not differentially reduced between the providers who attended the training and other providers, suggesting that NH staff communication was in part operative in achieving the lower antibiotic prescribing rates. That said, successful reduction in prescribing can nonetheless be attributed to working with a long-term
Table 2. Effect of Quality Improvement Program on Antibiotic Prescribing Rates: Total and According to Indication All Indications Characteristic
Effect of quality improvement program Intervention site Nurse staffing ratio Director of nursing turnover Nursing staff turnovera Case-mix Age group Herfindahl–Hirschman Index Proportion white Proportion Medicaid Proportion dementia Case-mix index Proportion physician prescribersb Proportion ECPc
Urinary Tract Infection
Respiratory Infection
Skin and Soft Tissue Infection
Incidence Rate Ratio (95% Confidence Interval)
0.86 1.15 0.99 0.91 1.20
(0.79–0.95)d (0.76–1.75) (0.98–1.00)d (0.69–1.20) (0.38–3.77)
0.84 1.67 0.98 0.92 1.23
(0.66–1.05) (0.96–2.89) (0.97–1.00)d (0.61–1.38) (0.26–5.77)
0.71 1.14 0.98 0.52 2.32
(0.56–0.90)d (0.57–2.25) (0.97–1.00)d (0.31–0.86)d (0.35–15.17)
0.89 1.25 0.99 0.79 1.66
(0.62–1.28) (0.52–3.01) (0.97–1.01) (0.45–1.38) (0.16–17.10)
1.00 0.99 0.99 1.01 1.58 0.99 1.01
(1.00–1.00) (0.98–1.00) (0.98–0.99)d (1.01–1.02)d (0.16–15.94) (0.97–1.01) (0.99–1.02)
1.00 0.99 0.99 1.02 1.90 1.01 1.01
(1.00–1.00) (0.98–1.01) (0.98–1.00) (1.00–1.03)d (0.09–41.02) (0.98–1.04) (0.99–1.03)
1.00 0.98 0.99 1.02 1.34 0.98 1.00
(1.00–1.00) (0.96–1.00) (0.97–1.00) (1.01–1.04)d (0.03–55.89) (0.94–1.01) (0.98–1.02)
1.00 0.99 0.98 1.02 0.56 0.98 1.01
(1.00–1.00) (0.97–1.02) (0.96–1.00) (0.99–1.04) (0.01–55.94) (0.93–1.02) (0.98–1.04)
All results adjusted for clustering of prescriptions within residents within nursing homes. a Includes registered nurses, licensed practical nurses, licensed vocational nurses, and certified nursing assistants. b Includes Doctor of Dental Surgery, Doctor of Dental Medicine, Doctor of Medicine, Doctor of Osteopathy, and Doctor of Podiatry. c The long-term care practice group participating in the project. Statistic refers to the proportion of Extended Care Physicians (ECP) providers in the nursing home setting. d P < .05.
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care–only medical group. The advantages of working with such a group included the need to train fewer providers (i.e., seven providers wrote 41–55% of prescriptions), night and weekend coverage by providers who knew the residents and procedures of the practice and of the NH, and a greater interest in long-term care medicine than is typical. Therefore, one challenge for replicating these findings is determining whether similar results can be obtained in settings with more-diffuse medical coverage and lessinvested providers. Another element of successful prescribing is related to the matter that even comparison sites reduced their prescribing, suggesting that the act of reviewing infections may itself increase attention to prescribing. In addition, although the data indicate that reduction was significant only in prescribing for respiratory infections, prescribing for all indications fell, suggesting that not achieving statistical significance for other infection types might well have been an artifact of their denominator. The size, design, and length of this study were not adequate to address questions about the intervention’s effects on hospitalization or death rates. These are timely issues, especially with the current healthcare emphasis on decreasing hospitalizations—an emphasis that could conceivably increase prescribing in an attempt to avoid hospitalization.26 Furthermore, data were not available to identify infections that were not treated with antibiotics. In addition, it was not possible to control for differences in case-mix and processes of care between conditions (e.g., the intervention group had younger residents, proportionately more CNAs, and fewer physicians, although the difference-in-differences analytical procedure tempers concern about this limitation), and the accuracy of the infection logs was not ascertained (although there is no reason to expect bias between the intervention and comparison sites in their recording practices). In addition, although the intervention and comparison sites were geographically separate, it is possible that changes in practice occurring in one geographic region might have been communicated to another region—the results of which would have tempered the intervention effect. Furthermore, the extent to which the prescription of an antibiotic is the proper course of treatment is one that is best determined on an individual basis. Despite these potential limitations, this study’s success in lowering prescription rates is impressive and stands in contrast to other efforts.13 Whether such results can be replicated, sustained, and improved upon remains to be seen, as does whether achieving prescribing reductions can slow the development of antibiotic resistance and help maintain the effectiveness of existing antibiotics.
ACKNOWLEDGMENTS The authors thank the staff of the NHs participating in the Collaborative Studies of Long-Term Care for their ongoing efforts to promote the quality of care in nursing homes and other residential long-term care settings. Thanks also are extended to Nancy Siebens, RN, and Mary Lou Vanrite, RN, for their expert data collection and Sarah Shoemaker, PharmD, for her critical review of the analysis. Finally, the authors thank the members of Extended Care Physicians, PA, for their involvement in
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this project, most especially Steven Corder, MD, and Douglas Nelson, MD. Funded by Agency for Healthcare Research and Quality (AHRQ) Contract HHSA290200600019i, Task Order No. 11. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Study concept and design: Zimmerman, Sloane, Bertrand. Acquisition of data: Mitchell. Analysis and interpretation of data: Zimmerman, Sloane, Bertrand, Olsho, Beeber, Kistler, Hadden, Edwards, Webber, Mitchell. Preparation of manuscript: Zimmerman, Sloane, Bertrand, Olsho, Beeber, Kistler, Hadden, Edwards, Webber, Mitchell. Sponsor’s Role: This project was funded by AHRQ. Staff at AHRQ participated in the development of the scope of work. Approval from AHRQ was required before the manuscript could be submitted for publication, but the authors are solely responsible for the content and the decision to submit it for publication.
REFERENCES 1. Leung E, Weil DE, Raviglione M et al. The WHO policy package to combat antimicrobial resistance. Bull World Health Organ 2011;89: 390–392. 2. Centers for Disease Control and Prevention. Antibiotic Use in Nursing Homes [on-line]. Available at http://www.cdc.gov/getsmart/campaign-materials/week/downloads/gsw-factsheet-nursinghomes.pdf Accessed December 27, 2012. 3. Benoit SR, Nsa W, Richards CL et al. Factors associated with antimicrobial use in nursing homes: A multilevel model. J Am Geriatr Soc 2008;56:2039–2044. 4. Trick WE, Weinstein RA, DeMarais PL et al. Colonization of skilled-care facility residents with antimicrobial-resistant pathogens. J Am Geriatr Soc 2001;49:270–276. 5. Warren JW, Palumbo FB, Fitterman L et al. Incidence and characteristics of antibiotic use in aged nursing home patients. J Am Geriatr Soc 1991;39:963–972. 6. Nicolle LE, Bentley DW, Garibaldi R et al. Antimicrobial use in long-termcare facilities. SHEA Long-Term-Care Committee. Infect Control Hosp Epidemiol 2000;21:537–545. 7. Katz PR, Beam TR Jr, Brand F et al. Antibiotic use in the nursing home. Physician practice patterns. Arch Intern Med 1990;150:1465–1468. 8. Zimmer JG, Bentley DW, Valenti WM et al. Systemic antibiotic use in nursing homes. A quality assessment. J Am Geriatr Soc 1986;34:703–710. 9. Loeb M, Brazil K, Lohfeld L et al. Effect of a multifaceted intervention on number of antimicrobial prescriptions for suspected urinary tract infections in residents of nursing homes: Cluster randomised controlled trial. BMJ 2005;331:669. 10. Loeb M, Brazil K, Lohfeld L et al. Optimizing antibiotics in residents of nursing homes: Protocol of a randomized trial. BMC Health Serv Res 2002;2:17. 11. Loeb M, Simor AE, Landry L et al. Antibiotic use in Ontario facilities that provide chronic care. J Gen Intern Med 2001;16:376–383. 12. Nicolle LE, Mayhew WJ, Bryan L. Prospective randomized comparison of therapy and no therapy for asymptomatic bacteriuria in institutionalized elderly women. Am J Med 1987;83:27–33. 13. Nicolle LE, Bjornson J, Harding GK et al. Bacteriuria in elderly institutionalized men. N Engl J Med 1983;309:1420–1425. 14. National Center for Health Statistics. Health, United States, 2011 With Special Feature on Socioeconomic Status and Health [on-line]. Available at http://www.cdc.gov/nchs/data/hus/hus11.pdf Accessed December 27, 2012. 15. Mody L. Optimizing antimicrobial use in nursing homes: No longer optional. J Am Geriatr Soc 2007;55:1301–1302. 16. Linnebur SA, Fish DN, Ruscin JR et al. Impact of a multidisciplinary intervention on antibiotic use for nursing home-acquired pneumonia. Am J Geriatr Pharmacother 2011;9:442–450.
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17. Majerovitz SD, Mollott RJ, Rudder C. We’re on the same side: Improving communication between nursing home and family. Health Commun 2009;24:12–20. 18. Zimmerman S, Gruber-Baldini AL, Hebel R et al. Nursing home facility risk factors for infection and hospitalization: Importance of RN turnover, administration and social factors. J Am Geriatr Soc 2002;50:1987–1995. 19. Loeb M, Bentley DW, Bradley S et al. Development of minimum criteria for the initiation of antibiotics in residents of long-term-care facilities: Results of a consensus conference. Infect Control Hosp Epidemiol 2001;22:120–124. 20. Khandelwal C, Lathren C, Sloane P. Ten clinical situations in long-term care for which antibiotics are often prescribed but rarely necessary. Ann Long-Term Care 2012;20:23–29. 21. Centers for Medicare and Medicaid Services. Nursing Home Data Compendium, 2010 Ed. [on-line]. Available at https://www.cms.gov/Medicare/
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22.
23. 24.
25. 26.
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Provider-Enrollment-and-Certification/CertificationandComplianc/downloads/ nursinghomedatacompendium_508.pdf Accessed December 27, 2012. Schurr J, Bertrand R, Hurd D. Using Nursing Home Antibiograms to Improve Antibiotic Prescribing and Delivery [on-line]. Available at http:// www.ahrq.gov/news/events/conference/2012/track_a/62_garfinkel_et-al/hirshon.html Accessed February 13, 2014. Levy C, Palat SI, Kramer AM. Physician practice patterns in nursing homes. J Am Med Dir Assoc 2007;8:558–567. Katz PR, Karuza J, Intrator O et al. Nursing home physician specialists: A response to the workforce crisis in long-term care. Ann Intern Med 2009;150:411–413. Katz PR, Karuzna J. Physician practice in the nursing home: Missing in action or misunderstood. J Am Geriatr Soc 2005;53:1826–1288. Ouslander JG, Berenson RA. Reducing unnecessary hospitalizations of nursing home residents. N Engl J Med 2011;365:1165–1167.
OBJECTIVES: To determine whether antibiotic prescribing can be reduced in nursing homes using a quality improvement (QI) program that involves providers, staff, residents, and families. DESIGN: A 9-month quasi-experimental trial of a QI program in 12 nursing homes (6 comparison, 6 intervention) conducted from March to November 2011. SETTING: Nursing homes in two regions of North Carolina, roughly half of whose residents received care from a single practice of long-term care providers. PARTICIPANTS: All residents, including 1,497 who were prescribed antibiotics. INTERVENTION: In the intervention sites, providers in the single practice and nursing home nurses received training related to prescribing guidelines, including situations for which antibiotics are generally not indicated, and nursing home residents and their families were sensitized to matters related to antibiotic prescribing. Feedback on prescribing was shared with providers and nursing home staff monthly. MEASUREMENTS: Rates of antibiotic prescribing for presumed urinary tract, skin and soft tissue, and respiratory infections. RESULTS: The QI program reduced the number of prescriptions ordered between baseline and follow-up more in intervention than in comparison nursing homes (adjusted incidence rate ratio = 0.86, 95% confidence interval = 0.79–0.95). Based on baseline prescribing rates of From the aCecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, bSchool of Social Work, University of North Carolina at Chapel Hill, cDepartment of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; d Abt Associates, Inc., Cambridge, Massachusetts; eSchool of Nursing, University of North Carolina at Chapel Hill, and fDepartment of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. Address correspondence to Sheryl Zimmerman, Kenan Distinguished Professor, Associate Dean, and Co-Director, Program on Aging, Disability, and Long-Term Care, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, 725 Martin Luther King Jr. Boulevard, Chapel Hill, NC 27599. E-mail: [email protected] DOI: 10.1111/jgs.12784
JAGS 62:907–912, 2014 © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society
12.95 prescriptions per 1,000 resident-days, this estimated adjusted incidence rate ratio implies 1.8 prescriptions avoided per 1,000 resident-days. CONCLUSION: This magnitude of effect is unusual in efforts to reduce antibiotic use in nursing homes. Outcomes could be attributed to the commitment of the providers; outreach to providers and staff; and a focus on common clinical situations in which antibiotics are generally not indicated; and suggest that similar results can be achieved on a wider scale if similar commitment is obtained and education provided. J Am Geriatr Soc 62:907–912, 2014.
Key words: antibiotic quality improvement
prescribing;
nursing
homes;
A
ntibiotic resistance is one of the three biggest threats to health,1 and nursing home (NH) populations are an important setting within which to reduce overuse.2 Antibiotics are among the most commonly prescribed pharmaceuticals in NHs, with a recent study finding that, over 6 months, 42% of NH residents received antibiotics.3 Evidence further suggests that the high use of antibiotics contributes to antimicrobial resistance, with a point-prevalence survey of 117 NH residents finding that 43% were culture positive for antimicrobial-resistant pathogens, including methicillin-resistant staphylococcus aureus, extended-spectrum b-lactamase–producing Klebsiella pneumoniae or Escherichia coli, and vancomycin-resistant enterococci.4 Not only does overuse facilitate resistance, but also a notable proportion of antibiotic use may be unnecessary. (Studies indicate that 25–75% of prescriptions do not meet clinical guidelines for appropriate prescribing.)5–8 The most common clinical situation leading to inappropriate antibiotic therapy is suspected urinary tract infection,9 which accounts for 30% to 50% of antibiotics prescribed in NHs.10 One study found that one-third of residents who received antibiotics for urinary tract infection were being treated for asymptomatic bacteriuria—a condition
0002-8614/14/$15.00
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ZIMMERMAN ET AL.
for which antibiotics do not improve outcomes—rather than symptomatic disease.11 Furthermore, the inappropriate prescribing of antibiotics has been demonstrated in multiple randomized controlled trials to harm rather than benefit NH residents.11–13 Nationwide, 1.4 million people reside in 15,690 NHs.14 It is no surprise, then, that an editorial in the Journal of the American Geriatrics Society asserted that “with the growing body of evidence demonstrating the effectiveness of simple educational interventions, a proactive approach to curbing and eventually eliminating inappropriate antibiotic usage in NHs is no longer optional.”15 The challenge is to better define, refine, and test interventions that will optimize antibiotic use. To be successful, these interventions must address multiple risk factors and be acceptable to primary care providers, NH staff who oversee care and communicate signs and symptoms, and residents and their families.16,17
METHODS This 9-month quasi-experimental difference-in-differences trial of a quality improvement (QI) program was implemented in 12 NHs participating in the Collaborative Studies of Long-Term Care consortium based at the University of North Carolina at Chapel Hill (UNC). Six NHs in the Piedmont region of North Carolina served by one longterm care provider practice (Extended Care Physicians) were matched to six NHs in the mountain region of North Carolina according to bed size and profit status; the same practice group, but different providers, served the comparison sites. The two regions were adjacent and similar in terms of cultural and sociodemographic factors but were geographically separated, which avoided spillover effects. The sample size was determined based on a power calculation specifying a minimum detectable effect size of 15 percentage points for genitourinary and upper respiratory infections, because it was deemed that such a rate reduction would be significant based on evidence from prior work that approximately 86% of infections are treated with antibiotics,18 and 30% are not clinically indicated. Assuming an intraclass correlation of 0.03, an average cluster size of 116 residents per NH, and a two-sided alpha of 0.05, power to detect a 15-percentage-point change was estimated to be 0.87 for genitourinary infections and 0.86 for upper respiratory infections. Research nurses who were blind to study aims and condition obtained information related to antibiotic prescribing. They reviewed infection control logs and recorded information on prescriber, antibiotic name, start date, and suspected clinical indication for each resident who received an antibiotic between March and November 2011. Data were also obtained from staff regarding NH characteristics (e.g., estimates of resident case-mix, nurse staffing, proportion of residents served by the long-term care practice group). The institutional review boards of UNC and Abt Associates, Inc. approved all aspects of the research design, including a waiver of informed consent. The QI program was initiated in June 2011. The QI program targeted providers, NH staff, and residents and their families. Providers and NH nurses received on-site training from clinical and health services
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researchers from UNC (including a physician, nurse, infectious disease specialist, and experts in long-term care organization) on prescribing guidelines (e.g., components of the Loeb Minimum Criteria for initiation of antibiotics) and 12 common situations in which systemic antibiotics are generally not indicated: positive urine culture in an asymptomatic individual; urine culture ordered solely because of change in urine appearance; nonspecific symptoms or signs not referable to the urinary tract (with or without a positive urine culture); upper respiratory infection (common cold); bronchitis or asthma in an individual who does not have chronic obstructive pulmonary disease; infiltrate on chest X-ray in the absence of clinically significant symptoms; suspected or proven influenza in the absence of a secondary infection; respiratory symptoms in an individual with advanced dementia receiving palliative care or at the end of life; skin wound without cellulitis, sepsis, or osteomyelitis (regardless of culture result); small (.99 >.99 >.99 >.99 .08 .23 .62
1.2 15.9 9.9 50.2
0.2 14.1 4.4 12.1
1.3 9.1 13.6 38.9
0.4 5.3 3.4 8.6
.49 .29 .14 .09
0.2 1.8 1.8 7.8
0.4 2.1 1.5 4.0
0.5 3.2 6.0 17.2
0.6 2.8 7.2 16.5
.27 .34 .19 .20
21.6 21.5 30.5 24.2 2.3 67.4
6.2 9.3 5.2 10.6 0.9 9.1
8.0 14.9 27.9 42.7 6.3 78.1
6.0 14.5 5.5 20.2 4.7 14.0
.003 .38 .42 .07 .06 .15
84.3 10.1 0.3 0.1 0.2 70.0 48.0 1.22
10.8 4.6 0.5 0.2 0.3 6.3 16.9 0.08
77.7 8.2 0.0 0.4 0.9 54.9 34.9 1.14
18.0 13.7 0.0 0.9 1.7 24.3 27.7 0.13
.46 .76 .18 .50 .36 .17 .35 .23
69.0 10.0 55.3 28.4
.10 .31
58.1 10.5 41.4 14.7
Fisher exact test (two-sided) for categorical outcomes, two-sided t-test for continuous outcomes. Extended Care Physicians, LLC, the long-term care practice group participating in the project. SD = standard deviation; FTE = full-time equivalent.
b
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to 11.80 (pooled difference-in-differences = 2.75 prescriptions per 1,000 resident-days, P = .05). There were no statistically significant difference-in-differences according to specific indication at the P < .05 level, but prescribing for urinary and skin and soft tissue indications fell more in intervention sites than comparison sites (both P = .09). Adjusting for NH-level confounders (Table 2), there was a statistically significant reduction in prescribing rates for all indications combined in intervention NHs (IRR = 0.86, 95% confidence interval (CI) = 0.79–0.95; P = .002) but not for urinary or skin and soft tissue indications considered separately. In these multivariate models, the QI program reduced the prescribing rate for respiratory indications (IRR = 0.71, 95% CI = 0.56–0.90; P = .005). Statistical power considerations may explain why the effect on urinary and skin and soft tissue prescribing was not statistically significant, because point estimates for these indications indicate greater prescribing declines in intervention sites than in comparison sites after the QI program. Based on the baseline prescribing rate of 12.95 prescriptions per 1,000 resident-days, the estimated adjusted IRR of 0.86 implies a reduction to 11.1 prescriptions per 1,000 resident-days attributable to the QI program, or approximately 1.8 prescriptions avoided per 1,000 resident-days.
DISCUSSION Programs to address inappropriate prescribing in NHs have ranged from establishing prescribing guidelines19 to designing interventions to improve prescribing practices.22 To be effective, NH antibiotic stewardship programs must consider challenges related to NH understaffing (which limits the time available to complete additional paperwork), absence of electronic records, and prescribers who
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are not directly employed by the NH. Nursing homes vary in the number and intensity of providers who are involved in care.23 The most common pattern is for a NH to have a medical director (or medical group) who manages the majority of residents but whose main work is office-based and who employs a nurse practitioner or physician assistant because the physician visits the nursing home for at most a half-day a week. Long-term care specialty practices, in which a few providers (usually a mix of physicians, nurse practitioners, and physician assistants) manage the majority of (or all) residents, often visiting the NH several times a week, serve a growing minority of NHs.24 In both cases, a large number of additional community physicians who treat a few residents each supplement these main providers.25 “Closed” provider models in which all physicians are actively involved in numerous facets of care, such as participating in regular medical staff meetings, are uncommon.23 The procedures employed in this QI program were sensitive to these concerns. The magnitude of effect achieved in this study is unusual for efforts to reduce antibiotic use in NHs. Although the study design did not allow the contribution of separate components of the QI program to be discerned, the data give clues as to their effectiveness. During postproject interviews, prescribers indicated that training related to situations in which antibiotics are generally not indicated changed their thinking and influenced their prescribing, and NH staff reported that the medical care referral form increased communication regarding relevant signs and symptoms. Antibiotic prescribing was not differentially reduced between the providers who attended the training and other providers, suggesting that NH staff communication was in part operative in achieving the lower antibiotic prescribing rates. That said, successful reduction in prescribing can nonetheless be attributed to working with a long-term
Table 2. Effect of Quality Improvement Program on Antibiotic Prescribing Rates: Total and According to Indication All Indications Characteristic
Effect of quality improvement program Intervention site Nurse staffing ratio Director of nursing turnover Nursing staff turnovera Case-mix Age group Herfindahl–Hirschman Index Proportion white Proportion Medicaid Proportion dementia Case-mix index Proportion physician prescribersb Proportion ECPc
Urinary Tract Infection
Respiratory Infection
Skin and Soft Tissue Infection
Incidence Rate Ratio (95% Confidence Interval)
0.86 1.15 0.99 0.91 1.20
(0.79–0.95)d (0.76–1.75) (0.98–1.00)d (0.69–1.20) (0.38–3.77)
0.84 1.67 0.98 0.92 1.23
(0.66–1.05) (0.96–2.89) (0.97–1.00)d (0.61–1.38) (0.26–5.77)
0.71 1.14 0.98 0.52 2.32
(0.56–0.90)d (0.57–2.25) (0.97–1.00)d (0.31–0.86)d (0.35–15.17)
0.89 1.25 0.99 0.79 1.66
(0.62–1.28) (0.52–3.01) (0.97–1.01) (0.45–1.38) (0.16–17.10)
1.00 0.99 0.99 1.01 1.58 0.99 1.01
(1.00–1.00) (0.98–1.00) (0.98–0.99)d (1.01–1.02)d (0.16–15.94) (0.97–1.01) (0.99–1.02)
1.00 0.99 0.99 1.02 1.90 1.01 1.01
(1.00–1.00) (0.98–1.01) (0.98–1.00) (1.00–1.03)d (0.09–41.02) (0.98–1.04) (0.99–1.03)
1.00 0.98 0.99 1.02 1.34 0.98 1.00
(1.00–1.00) (0.96–1.00) (0.97–1.00) (1.01–1.04)d (0.03–55.89) (0.94–1.01) (0.98–1.02)
1.00 0.99 0.98 1.02 0.56 0.98 1.01
(1.00–1.00) (0.97–1.02) (0.96–1.00) (0.99–1.04) (0.01–55.94) (0.93–1.02) (0.98–1.04)
All results adjusted for clustering of prescriptions within residents within nursing homes. a Includes registered nurses, licensed practical nurses, licensed vocational nurses, and certified nursing assistants. b Includes Doctor of Dental Surgery, Doctor of Dental Medicine, Doctor of Medicine, Doctor of Osteopathy, and Doctor of Podiatry. c The long-term care practice group participating in the project. Statistic refers to the proportion of Extended Care Physicians (ECP) providers in the nursing home setting. d P < .05.
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care–only medical group. The advantages of working with such a group included the need to train fewer providers (i.e., seven providers wrote 41–55% of prescriptions), night and weekend coverage by providers who knew the residents and procedures of the practice and of the NH, and a greater interest in long-term care medicine than is typical. Therefore, one challenge for replicating these findings is determining whether similar results can be obtained in settings with more-diffuse medical coverage and lessinvested providers. Another element of successful prescribing is related to the matter that even comparison sites reduced their prescribing, suggesting that the act of reviewing infections may itself increase attention to prescribing. In addition, although the data indicate that reduction was significant only in prescribing for respiratory infections, prescribing for all indications fell, suggesting that not achieving statistical significance for other infection types might well have been an artifact of their denominator. The size, design, and length of this study were not adequate to address questions about the intervention’s effects on hospitalization or death rates. These are timely issues, especially with the current healthcare emphasis on decreasing hospitalizations—an emphasis that could conceivably increase prescribing in an attempt to avoid hospitalization.26 Furthermore, data were not available to identify infections that were not treated with antibiotics. In addition, it was not possible to control for differences in case-mix and processes of care between conditions (e.g., the intervention group had younger residents, proportionately more CNAs, and fewer physicians, although the difference-in-differences analytical procedure tempers concern about this limitation), and the accuracy of the infection logs was not ascertained (although there is no reason to expect bias between the intervention and comparison sites in their recording practices). In addition, although the intervention and comparison sites were geographically separate, it is possible that changes in practice occurring in one geographic region might have been communicated to another region—the results of which would have tempered the intervention effect. Furthermore, the extent to which the prescription of an antibiotic is the proper course of treatment is one that is best determined on an individual basis. Despite these potential limitations, this study’s success in lowering prescription rates is impressive and stands in contrast to other efforts.13 Whether such results can be replicated, sustained, and improved upon remains to be seen, as does whether achieving prescribing reductions can slow the development of antibiotic resistance and help maintain the effectiveness of existing antibiotics.
ACKNOWLEDGMENTS The authors thank the staff of the NHs participating in the Collaborative Studies of Long-Term Care for their ongoing efforts to promote the quality of care in nursing homes and other residential long-term care settings. Thanks also are extended to Nancy Siebens, RN, and Mary Lou Vanrite, RN, for their expert data collection and Sarah Shoemaker, PharmD, for her critical review of the analysis. Finally, the authors thank the members of Extended Care Physicians, PA, for their involvement in
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this project, most especially Steven Corder, MD, and Douglas Nelson, MD. Funded by Agency for Healthcare Research and Quality (AHRQ) Contract HHSA290200600019i, Task Order No. 11. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Study concept and design: Zimmerman, Sloane, Bertrand. Acquisition of data: Mitchell. Analysis and interpretation of data: Zimmerman, Sloane, Bertrand, Olsho, Beeber, Kistler, Hadden, Edwards, Webber, Mitchell. Preparation of manuscript: Zimmerman, Sloane, Bertrand, Olsho, Beeber, Kistler, Hadden, Edwards, Webber, Mitchell. Sponsor’s Role: This project was funded by AHRQ. Staff at AHRQ participated in the development of the scope of work. Approval from AHRQ was required before the manuscript could be submitted for publication, but the authors are solely responsible for the content and the decision to submit it for publication.
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