Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-25T01:20:55.300Z Has data issue: false hasContentIssue false
  • English
  • Français

Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship: Use of Administrative and Surveillance Databases

Part of: Research and Methodology in Infection Control

Published online by Cambridge University Press:  30 August 2016

Marci Drees ,
Jeffrey S. Gerber ,
Daniel J. Morgan  and
Grace M. Lee
Marci Drees*
Affiliation:
Christiana Care Health System, Wilmington, Delaware
Jeffrey S. Gerber
Affiliation:
The Children’s Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
Daniel J. Morgan
Affiliation:
University of Maryland School of Medicine, and VA Maryland Healthcare System, Baltimore, Maryland
Grace M. Lee
Affiliation:
Harvard Pilgrim Health Care Institute, Harvard Medical School and Boston Children’s Hospital, Boston, Massachusetts
*
Address correspondence to Marci Drees, MD, MS, Hospital Epidemiologist Christiana Care Health System, 501 W. 14th Street, Wilmington, DE 19801 (mdrees@christianacare.org).
Get access Rights & Permissions [Opens in a new window]

Abstract

Administrative and surveillance data are used frequently in healthcare epidemiology and antimicrobial stewardship (HE&AS) research because of their wide availability and efficiency. However, data quality issues exist, requiring careful consideration and potential validation of data. This methods paper presents key considerations for using administrative and surveillance data in HE&AS, including types of data available and potential use, data limitations, and the importance of validation. After discussing these issues, we review examples of HE&AS research using administrative data with a focus on scenarios when their use may be advantageous. A checklist is provided to help aid study development in HE&AS using administrative data.

Infect Control Hosp Epidemiol 2016;1–10

Type
SHEA White Papers
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 11 , November 2016 , pp. 1278 - 1287
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Observational Studies in a Learning Health System. Washington, DC: National Academies Press; 2013. doi:10.17226/18438.Google Scholar
2. Meaningful use definition and objectives. US Department of Health and Human Services Office of the National Coordinator for Health Information Technology, National Learning Consortium website. https://www.healthit.gov/providers-professionals/meaningful-use-definition-objectives. Published 2015.Accessed December 15, 2015.Google Scholar
3. Tan, SS-L, Gao, G, Koch, S. Big data and analytics in healthcare. Methods Inf Med 2015;54:546–547.Google Scholar
4. Dalton, BR, Sabuda, DM, Bresee, LC, Conly, JM. Assessment of antimicrobial utilization metrics: days of therapy versus defined daily doses and pharmacy dispensing records versus nursing administration data. Infect Control Hosp Epidemiol 2015;36(6):688–694.Google Scholar
5. Schweizer, ML, Eber, MR, Laxminarayan, R, et al. Validity of ICD-9-CM coding for identifying incident methicillin-resistant Staphylococcus aureus (MRSA) infections: is MRSA infection coded as a chronic disease? Infect Control Hosp Epidemiol 2011;32:148–154.Google Scholar
6. Gehrich, AP, Lustik, MB, Mehr, AA, Patzwald, JR. Risk of postoperative urinary tract infections following midurethral sling operations in women undergoing hysterectomy. Int Urogynecol J 2015: Epub ahead of print. doi:10.1007/s00192-015-2861-z.Google ScholarPubMed
7. Soe, MM, Edwards, JR, Sievert, DM, Ricks, PM, Magill, SS, Fridkin, SK. Evaluating state-specific antibiotic resistance measures derived from central line-associated bloodstream infections, National Healthcare Safety Network, 2011. Infect Control Hosp Epidemiol 2015;36:54–64.Google Scholar
8. Lindley, MC, Bridges, CB, Strikas, RA, et al. Influenza vaccination performance measurement among acute care hospital-based health care personnel—United States, 2013–14 influenza season. MMWR 2014;63:812–815.Google ScholarPubMed
9. Dubberke, ER, Butler, AM, Yokoe, DS, et al. Multicenter study of surveillance for hospital-onset Clostridium difficile infection by the use of ICD-9-CM diagnosis codes. Infect Control Hosp Epidemiol 2010;31:262–268.Google Scholar
10. Young-Xu, Y, Kuntz, JL, Gerding, DN, et al. Clostridium difficile infection among Veterans Health Administration patients. Infect Control Hosp Epidemiol 2015;36:1038–1045.Google Scholar
11. Olsen, MA, Nickel, KB, Fox, IK, et al. Incidence of surgical site infection following mastectomy with and without immediate reconstruction using private insurer claims data. Infect Control Hosp Epidemiol 2015;36:907–914.Google Scholar
12. Perencevich, EN, Sands, KE, Cosgrove, SE, Guadagnoli, E, Meara, E, Platt, R. Health and economic impact of surgical site infections diagnosed after hospital discharge. Emerg Infect Dis 2003;9:196–203.Google Scholar
13. O’Malley, KJ, Cook, KF, Price, MD, Wildes, KR, Hurdle, JF, Ashton, CM. Measuring diagnoses: ICD code accuracy. Health Serv Res 2005;40(5 Pt 2):1620–1639.Google Scholar
14. De Coster, C, Quan, H, Finlayson, A, et al. Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium. BMC Health Serv Res 2006;6:77.Google Scholar
15. van Walraven, C, Bennett, C, Forster, AJ. Administrative database research infrequently used validated diagnostic or procedural codes. J Clin Epidemiol 2011;64:1054–1059.Google Scholar
16. Dubberke, ER, Reske, KA, McDonald, LC, Fraser, VJ. ICD-9 codes and surveillance for Clostridium difficile-associated disease. Emerg Infect Dis 2006;12:1576–1579.CrossRefGoogle ScholarPubMed
17. Scheurer, DB, Hicks, LS, Cook, EF, Schnipper, JL. Accuracy of ICD-9 coding for Clostridium difficile infections: a retrospective cohort. Epidemiol Infect 2007;135:1010–1013.Google Scholar
18. Pakyz, AL, Patterson, JA, Motzkus-Feagans, C, Hohmann, SF, Edmond, MB, Lapane, KL. Performance of the present-on-admission indicator for Clostridium difficile infection. Infect Control Hosp Epidemiol 2015;36:838–840.Google Scholar
19. Wen, J, Barber, GE, Ananthakrishnan, AN. Identification of recurrent Clostridium difficile infection using administrative codes: accuracy and implications for surveillance. Infect Control Hosp Epidemiol 2015;36:893–898.Google Scholar
20. Furuno, JP, Harris, AD, Wright, MO, et al. Prediction rules to identify patients with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci upon hospital admission. Am J Infect Control 2004;32:436–440.Google Scholar
21. Wright, SB, Huskins, WC, Dokholyan, RS, Goldmann, DA, Platt, R. Administrative databases provide inaccurate data for surveillance of long-term central venous catheter-associated infections. Infect Control Hosp Epidemiol 2003;24:946–949.Google Scholar
22. Tehrani, DM, Russell, D, Brown, J, et al. Discord among performance measures for central line-associated bloodstream infection. Infect Control Hosp Epidemiol 2013;34:176–183.CrossRefGoogle ScholarPubMed
23. Patrick, SW, Davis, MM, Sedman, AB, et al. Accuracy of hospital administrative data in reporting central line-associated bloodstream infections in newborns. Pediatrics 2013;131:S75–S80.CrossRefGoogle ScholarPubMed
24. Curtis, M, Graves, N, Birrell, F, et al. A comparison of competing methods for the detection of surgical-site infections in patients undergoing total arthroplasty of the knee, partial and total arthroplasty of hip and femoral or similar vascular bypass. J Hosp Infect 2004;57:189–193.Google Scholar
25. Drees, M, Hausman, S, Rogers, A, Freeman, L, Frosch, K, Wroten, K. Underestimating the impact of ventilator-associated pneumonia by use of surveillance data. Infect Control Hosp Epidemiol 2010;31:650–652.Google Scholar
26. Sherman, ER, Heydon, KH, St John, KH, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27:332–337.CrossRefGoogle ScholarPubMed
27. Stevenson, KB, Khan, Y, Dickman, J, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. Am J Infect Control 2008;36:155–164.Google Scholar
28. Stamm, AM, Bettacchi, CJ. A comparison of 3 metrics to identify health care-associated infections. Am J Infect Control 2012;40:688–691.Google Scholar
29. Goto, M, Ohl, ME, Schweizer, ML, Perencevich, EN. Accuracy of administrative code data for the surveillance of healthcare-associated infections: a systematic review and meta-analysis. Clin Infect Dis 2014;58:688–696.Google Scholar
30. Jhung, MA, Banerjee, SN. Administrative coding data and health care-associated infections. Clin Infect Dis 2009;49:949–955.Google Scholar
31. Surveillance for C. difficile, MRSA, and other drug-resistant infections. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/acute-care-hospital/cdiff-mrsa/index.html. Published 2015. Accessed December 15, 2015.Google Scholar
32. Durkin, MJ, Baker, AW, Dicks, KV, et al. A comparison between National Healthcare Safety Network laboratory-identified event reporting versus traditional surveillance for Clostridium difficile infection. Infect Control Hosp Epidemiol 2015;36:125–131.Google Scholar
33. Baker, AW, Durkin, MJ, Dicks, K V, et al. Methicillin-resistant Staphylococcus aureus bloodstream infection surveillance: National Healthcare Safety Network’s laboratory-identified event reporting versus traditional laboratory-confirmed bloodstream infection surveillance. Infect Control Hosp Epidemiol 2014;35:1286–1289.CrossRefGoogle ScholarPubMed
34. Dubberke, ER, Reske, KA, Yan, Y, Olsen, MA, McDonald, LC, Fraser, VJ. Clostridium difficile–associated disease in a setting of endemicity: identification of novel risk factors. Clin Infect Dis 2007;45:1543–1549.Google Scholar
35. Khong, CJ, Baggs, J, Kleinbaum, D, Cochran, R, Jernigan, JA. The likelihood of hospital readmission among patients with hospital-onset central line–associated bloodstream infections. Infect Control Hosp Epidemiol 2015;36:886–892.Google Scholar
36. McGregor, JC, Perencevich, EN, Furuno, JP, et al. Comorbidity risk-adjustment measures were developed and validated for studies of antibiotic-resistant infections. J Clin Epidemiol 2006;59:1266–1273.Google Scholar
37. McGregor, JC, Kim, PW, Perencevich, EN, et al. Utility of the Chronic Disease Score and Charlson Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms. Am J Epidemiol 2005:161483–161493.Google Scholar
38. Daneman, N, Simor, AE, Redelmeier, DA. Validation of a modified version of the national nosocomial infections surveillance system risk index for health services research. Infect Control Hosp Epidemiol 2009;30:563–569.Google Scholar
39. Kanerva, M, Ollgren, J, Lyytikainen, O, Group FPSS. Interhospital differences and case-mix a nationwide prevalence survey. J Hosp Infect 2010;76:135–138.Google Scholar
40. Elseviers, MM, Ferech, M, Vander Stichele, RH, Goossens, H, ESAC project group. Antibiotic use in ambulatory care in Europe (ESAC data 1997–2002): trends, regional differences and seasonal fluctuations. Pharmacoepidemiol Drug Saf 2007;16:115–123.Google Scholar
41. MacKenzie, FM, Bruce, J, Struelens, MJ, et al. Antimicrobial drug use and infection control practices associated with the prevalence of methicillin-resistant Staphylococcus aureus in European hospitals. Clin Microbiol Infect 2007;13:269–276.Google Scholar
42. Adriaenssens, N, Coenen, S, Versporten, A, et al. European Surveillance of Antimicrobial Consumption (ESAC): outpatient antibiotic use in Europe (1997–2009). J Antimicrob Chemother 2011;66(Suppl 6):vi3–12.Google Scholar
43. Hicks, LA, Chien, Y-W, Taylor, TH, Haber, M, Klugman, KP. Outpatient antibiotic prescribing and nonsusceptible Streptococcus pneumoniae in the United States, 1996–2003. Farley MM, Thomas S, Holst A, et al., eds. Clin Infect Dis 2011;53:631–639.Google Scholar
44. Maselli, JH, Gonzales, R, Colorado Medical Society. Measuring antibiotic prescribing practices among ambulatory physicians: accuracy of administrative claims data. J Clin Epidemiol 2001;54:196–201.Google Scholar
45. Ewen, E, Willey, VJ, Kolm, P, McGhan, WF, Drees, M. Antibiotic prescribing by telephone in primary care. Pharmacoepidemiol Drug Saf 2014. doi:10.1002/pds.3686.Google Scholar
46. Rummukainen, M-L, Mäkelä, M, Noro, A, Finne-Soveri, H, Lyytikäinen, O. Assessing prevalence of antimicrobial use and infections using the minimal data set in Finnish long-term care facilities. Am J Infect Control 2013;41:e35–e37.Google Scholar
47. Flett, KB, Ozonoff, A, Graham, D a, Sandora, TJ, Priebe, GP. Impact of mandatory public reporting of central line-associated bloodstream infections on blood culture and antibiotic utilization in pediatric and neonatal intensive care units. Infect Control Hosp Epidemiol 2015;36:878–885.Google Scholar
48. Marsteller, JA, Hsu, Y-J, Weeks, K. Evaluating the impact of mandatory public reporting on participation and performance in a program to reduce central line-associated bloodstream infections: evidence from a national patient safety collaborative. Am J Infect Control 2014;42:S209–S215.Google Scholar
49. Pakyz, AL, Edmond, MB. Influence of state laws mandating reporting of healthcare-associated infections: the case of central line-associated bloodstream infections. Infect Control Hosp Epidemiol 2013;34:780–784.Google Scholar
50. Lee, GM, Kleinman, K, Soumerai, SB, et al. Effect of nonpayment for preventable infections in US hospitals. N Engl J Med 2012;367:1428–1437.Google Scholar
51. Morgan, DJ, Meddings, J, Saint, S, et al. Does nonpayment for hospital-acquired catheter-associated urinary tract infections lead to overtesting and increased antimicrobial prescribing? Clin Infect Dis 2012;55:923–929.Google Scholar
52. Kawai, AT, Calderwood, MS, Jin, R, et al. Impact of the Centers for Medicare and Medicaid Services Hospital-Acquired Conditions policy on billing rates for 2 targeted healthcare-associated infections. Infect Control Hosp Epidemiol 2015;36:871–877.Google Scholar
53. Waters, TM, Daniels, MJ, Bazzoli, GJ, et al. Effect of Medicare’s nonpayment for Hospital-Acquired Conditions: lessons for future policy. JAMA Intern Med 2015;175:347–354.CrossRefGoogle ScholarPubMed
54. Goudie, A, Dynan, L, Brady, PW, Rettiganti, M. Attributable cost and length of stay for central line-associated bloodstream infections. Pediatrics 2014;133:e1525–e1532.Google Scholar
55. Hubner, C, Hubner, N-O, Muhr, M, et al. Cost analysis of hospitalized Clostridium difficile-associated diarrhea (CDAD). GMS Hyg Infect Control 2015;10:Doc13. doi:10.3205/dgkh000256.Google ScholarPubMed
56. Magee, G, Strauss, ME, Thomas, SM, Brown, H, Baumer, D, Broderick, KC. Impact of Clostridium difficile-associated diarrhea on acute care length of stay, hospital costs, and readmission: a multicenter retrospective study of inpatients, 2009–2011. Am J Infect Control 2015;43:1148–1153.Google Scholar
57. Nelson, RE, Jones, M, Liu, C-F, et al. The impact of healthcare-associated methicillin-resistant Staphylococcus aureus infections on post-discharge healthcare costs and utilization. Infect Control Hosp Epidemiol 2015;36:534–542.Google Scholar
58. Thakore, R V, Greenberg, SE, Shi, H, et al. Surgical site infection in orthopedic trauma: A case-control study evaluating risk factors and cost. J Clin Orthop Trauma 2015;6:220–226.Google Scholar
59. Wilson, MZ, Rafferty, C, Deeter, D, Comito, MA, Hollenbeak, CS. Attributable costs of central line-associated bloodstream infections in a pediatric hematology/oncology population. Am J Infect Control 2014;42:1157–1160.Google Scholar
60. Mauldin, PD, Salgado, CD, Hansen, IS, Durup, DT, Bosso, JA. Attributable hospital cost and length of stay associated with health care-associated infections caused by antibiotic-resistant Gram-negative bacteria. Antimicrob Agents Chemother 2010;54:109–115.Google Scholar
61. Bejko, J, Tarzia, V, Carrozzini, M, et al. Comparison of efficacy and cost of iodine impregnated drape vs. standard drape in cardiac surgery: study in 5100 patients. J Cardiovasc Transl Res 2015;8:431–437.Google Scholar
62. Bessesen, MT, Lopez, K, Guerin, K, et al. Comparison of control strategies for methicillin-resistant Staphylococcus aureus . Am J Infect Control 2013;41:1048–1052.Google Scholar
63. Ghosh, A, Jiao, L, Al-Mutawa, F, O’Neill, C, Mertz, D. Value of an active surveillance policy to document clearance of meticillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci amongst inpatients with prolonged admissions. J Hosp Infect 2014;88:230–233.Google Scholar
64. Goldsack, JC, DeRitter, C, Power, M, et al. Clinical, patient experience and cost impacts of performing active surveillance on known methicillin-resistant Staphylococcus aureus positive patients admitted to medical-surgical units. Am J Infect Control 2014;42:1039–1043.Google Scholar
65. Brilli, RJ, McClead, REJ, Crandall, W V, et al. A comprehensive patient safety program can significantly reduce preventable harm, associated costs, and hospital mortality. J Pediatr 2013;163:1638–1645.Google Scholar
66. Slayton, RB, Scott, RD, Baggs, J, Lessa, FC, McDonald, LC, Jernigan, JA. The cost-benefit of federal investment in preventing Clostridium difficile infections through the use of a multifaceted infection control and antimicrobial stewardship program. Infect Control Hosp Epidemiol 2015;36:681–687.Google Scholar
67. Finkler, SA. The distinction between cost and charges. Ann Intern Med 1982;96:102–109.CrossRefGoogle ScholarPubMed
68. Federal Policy for the Protection of Human Subjects, Docket ID number HHS–OPHS–2015–0008. United States Government website. http://federalregister.gov/a/2015-21756. Published 2015. Accessed December 23, 2015.Google Scholar
69. Stringham, J, Young, N. Using MedPAR data as a measure of urinary tract infection rates: implications for the Medicare inpatient DRG payment system. Perspect Heal Inf Manag 2005;2:12.Google ScholarPubMed
70. McHugh, M, Martin, TC, Orwat, J, Dyke, K Van. Medicare’s policy to limit payment for hospital-acquired conditions: the impact on safety net providers. J Health Care Poor Underserved 2011;22:638–647.CrossRefGoogle ScholarPubMed
71. Calderwood, MS, Kleinman, K, Soumerai, SB, et al. Impact of Medicare’s payment policy on mediastinitis following coronary artery bypass graft surgery in US hospitals. Infect Control Hosp Epidemiol 2014;35:144–151.Google Scholar
72. Calderwood, MS, Kleinman, K, Bratzler, DW, et al. Medicare claims can be used to identify US hospitals with higher rates of surgical site infection following vascular surgery. Med Care 2014;52:918–925.Google Scholar
73. Owens, PL, Barrett, ML, Raetzman, S, Maggard-Gibbons, M, Steiner, CA. Surgical site infections following ambulatory surgery procedures. JAMA 2014;311:709–716.Google Scholar
74. Aparasu, RR, Chatterjee, S, Chen, H. Risk of pneumonia in elderly nursing home residents using typical versus atypical antipsychotics. Ann Pharmacother 2013;47:464–474.Google Scholar
75. Krishnarajah, G, Landsman-Blumberg, P, Eynullayeva, E. Rotavirus vaccination compliance and completion in a Medicaid infant population. Vaccine 2015;33:479–486.Google Scholar
76. Landrum, ML, Neumann, C, Cook, C, et al. Epidemiology of Staphylococcus aureus blood and skin and soft tissue infections in the US military health system, 2005–2010. JAMA 2012;308:50–59.Google Scholar
77. Romley, JA, Chen, AY, Goldman, DP, Williams, R. Hospital costs and inpatient mortality among children undergoing surgery for congenital heart disease. Health Serv Res 2014;49:588–608.CrossRefGoogle ScholarPubMed
78. Poeran, J, Mazumdar, M, Rasul, R, et al. Antibiotic prophylaxis and risk of Clostridium difficile infection after coronary artery bypass graft surgery. J Thorac Cardiovasc Surg 2015; epub ahead. doi:10.1016/j.jtcvs.2015.09.090.Google Scholar
79. Parker, SL, McGirt, MJ, Murphy, JA, Megerian, JT, Stout, M, Engelhart, L. Comparative effectiveness of antibiotic-impregnated shunt catheters in the treatment of adult and pediatric hydrocephalus: analysis of 12,589 consecutive cases from 287 US hospital systems. J Neurosurg 2015;122:443–448.CrossRefGoogle Scholar
80. Baker, M, Yokoe, DS, Stelling, J, et al. Automated outbreak detection of hospital-associated infections. ID Week, San Diego, CA. Infectious Diseases Society of America website. https://idsa.confex.com/idsa/2015/webprogram/Paper52912.html. Published 2015. Accessed December 23, 2015.Google Scholar
81. Gulliford, MC, Dregan, A, Moore, M V, et al. Continued high rates of antibiotic prescribing to adults with respiratory tract infection: survey of 568 UK general practices. BMJ Open 2014;4(10):e006245. doi:10.1136/bmjopen-2014-006245.Google Scholar
82. Currie, CJ, Berni, E, Jenkins-Jones, S, et al. Antibiotic treatment failure in four common infections in UK primary care 1991-2012: longitudinal analysis. BMJ 2014;349:g5493.Google Scholar
83. Hicks, LA, Taylor, TH, Hunkler, RJ. U.S. outpatient antibiotic prescribing, 2010. N Engl J Med 2013;368:1461–1462.Google Scholar
84. Fleurence, RL, Curtis, LH, Califf, RM, Platt, R, Selby, J V, Brown, JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc. 21:578–582.Google Scholar
85. McNeil, MM, Gee, J, Weintraub, ES, et al. The Vaccine Safety Datalink: successes and challenges monitoring vaccine safety. Vaccine 2014;32:5390–5398.Google Scholar
86. Messina, JA, Cober, E, Richter, SS, et al. Hospital readmissions in patients with carbapenem-resistant Klebsiella pneumoniae . Infect Control Hosp Epidemiol 2015:1–8.Google Scholar
87. Gerber, JS, Prasad, PA, Russell Localio, A, et al. Variation in antibiotic prescribing across a pediatric primary care network. J Pediatric Infect Dis Soc 2015;4:297–304.Google Scholar
88. Tartof, SY, Rieg, GK, Wei, R, Tseng, HF, Jacobsen, SJ, Yu, KC. A comprehensive assessment across the healthcare continuum: risk of hospital-associated Clostridium difficile infection due to outpatient and inpatient antibiotic exposure. Infect Control Hosp Epidemiol 2015;36:1409–1416.Google Scholar
89. Padia, R, Olsen, G, Henrichsen, J, et al. Hospital and surgeon adherence to pediatric tonsillectomy guidelines regarding perioperative dexamethasone and antibiotic administration. Otolaryngol Neck Surg 2015;153:275–280.Google Scholar
90. Ju, MH, Ko, CY, Hall, BL, Bosk, CL, Bilimoria, KY, Wick, EC. A comparison of 2 surgical site infection monitoring systems. JAMA Surg 2015;150:51–57.Google Scholar
91. Furuya, EY, Dick, A, Perencevich, EN, Pogorzelska, M, Goldmann, D, Stone, PW. Central line bundle implementation in US intensive care units and impact on bloodstream infections. PLoS One 2011;6(1):e15452. doi:10.1371/journal.pone.0015452.CrossRefGoogle ScholarPubMed
92. Nayar, V, Kennedy, A, Pappas, J, et al. Improving cardiac surgical site infection reporting and prevention by using registry data for case ascertainment. Ann Thorac Surg 2016;101:190–199.Google Scholar
93. Salmon, D, Yih, WK, Lee, G, et al. Success of program linking data sources to monitor H1N1 vaccine safety points to potential for even broader safety surveillance. Health Aff (Millwood) 2012;31:2518–2527.CrossRefGoogle ScholarPubMed
94. Yih, WK, Lee, GM, Lieu, TA, et al. Surveillance for adverse events following receipt of pandemic 2009 H1N1 vaccine in the Post-Licensure Rapid Immunization Safety Monitoring (PRISM) System, 2009–2010. Am J Epidemiol 2012;175:1120–1128.CrossRefGoogle ScholarPubMed
95. McGregor, JC, Bearden, DT, Townes, JM, et al. Comparison of antibiograms developed for inpatients and primary care outpatients. Diagn Microbiol Infect Dis 2013;76:73–79.CrossRefGoogle ScholarPubMed
96. Logan, LK, Braykov, NP, Weinstein, RA, Laxminarayan, R, CDC Epicenters Prevention Program. Extended-spectrum β-lactamase-producing and third-generation cephalosporin-resistant Enterobacteriaceae in children: trends in the United States, 1999–2011. J Pediatric Infect Dis Soc 2014;3:320–328.CrossRefGoogle ScholarPubMed
97. McDonald, LT, Clark, AM, Landauer, AK, Kuxhaus, L. Winning the war on surgical site infection: evidence-based preoperative interventions for total joint arthroplasty. AORN J 2015;102:182.e1–182.e11.Google Scholar
98. Toltzis, P, O’Riordan, M, Cunningham, DJ, et al. A statewide collaborative to reduce pediatric surgical site infections. Pediatrics 2014;134:e1174–e1180.Google Scholar
99. Saving lives and saving money: hospital-acquired conditions update; interim data from national efforts to make care safer, 2010–2014. Agency for Healthcare Research and Quality website http://www.ahrq.gov/professionals/quality-patient-safety/pfp/interimhacrate2014.html. Published 2014. Accessed December 29, 2015.Google Scholar
100. Morris, MS, Deierhoi, RJ, Richman, JS, Altom, LK, Hawn, MT. The relationship between timing of surgical complications and hospital readmission. JAMA Surg 2014;149:348–354.Google Scholar