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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).
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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 

Administrative and surveillance data are used to inform many different types of healthcare epidemiology and antimicrobial stewardship (HE&AS) research, from rapid cycle process improvements (eg, internal to a specific healthcare setting to study quality and safety), to single-center observational studies, to large multicenter studies using data sources that are shared across institutions or at a regional or national level. An important characteristic of administrative and surveillance data is the ability to leverage existing data sources as opportunities for learning and continuous improvement. 1

Administrative data are traditionally defined as data collected for coding and billing. A broader discussion is warranted for studies related to HE&AS that includes any information collected primarily for nonresearch purposes, such as clinical or electronic health record (EHR) data, pharmacy data, billing/coding information, and other surveillance or registry data. Because these data are often already collected or are being collected in an ongoing fashion, such studies can often be conducted efficiently. National or regional level surveillance data collection, such as data collected by the Centers for Disease Control and Prevention (CDC) and partners through the National Healthcare Safety Network (NHSN), or other healthcare-associated infection (HAI) reporting that occurs because of Federal or state mandate, is also available for use in research studies. Data collected through NHSN, for example, is collected prospectively and is intended for use in HE&AS studies, which can be research or nonresearch in nature. Because these data are intended for wider use, the methodology, validation, and auditing documentation can provide assurance about data quality that are not available for most administrative data sources.

Administrative and surveillance data may also be used for clinical and quality improvement, to study utilization (eg, antimicrobials or laboratory tests), for comparative effectiveness research (eg, comparing different infection control or stewardship strategies), for monitoring for safety measures and outcomes, to study costs related to interventions or outcomes, or to study the effects of changes in health policies. Because they are available from most healthcare institutions, administrative data provide an opportunity for researchers to study “real-world” practices, in contrast to controlled research studies, and to include patients who otherwise may be excluded, thus potentially increasing the generalizability of the findings. In this review, we focus on the benefits and challenges of using administrative and surveillance data to study HE&AS.

ADVANTAGES AND DISADVANTAGES

The advantages and disadvantages of using administrative data for research vary by data source, but some common themes are evident (Table 1). Administrative data are widely available and are often previously collected; thus, they increase efficiency. Meaningful use has increased EHR use, with a goal of enhancing data sharing and standardization, patient engagement, and data transparency, and this approach supports data collection for multiple purposes, including evaluation of processes and outcomes. 2 In most cases, data collection has already occurred and does not disrupt patient care, and individual informed consent may be waived by the institutional review board (IRB) due to the minimal risk and impracticality of consent for these studies. Generalizability is often maximized through the inclusion of a larger proportion of the population of interest, although limitations remain (eg, EHR data from a university hospital consortium might not be generalizable to community hospitals). The major disadvantage of using administrative data is lack of control over data collected for other purposes, including which variables were collected, how they were defined, and whether they were standardized and/or complete. Electronically captured data may be discrete or textual—the latter requiring sophisticated techniques such as natural language processingReference Tan, Gao and Koch 3 to utilize the data for research purposes. A comprehensive understanding of which data will be used, the purpose(s) for which the data were originally collected, and the quality control of the data are essential. For data sources such as NHSN, extensive documentation is available regarding how data are collected, case definitions, and changes that have occurred over time, and this documentation should be carefully considered and fully understood when planning the analysis.

TABLE 1 Advantages, Disadvantages, and Potential Pitfalls of Using Administrative and Surveillance Data in Healthcare Epidemiology and Antimicrobial Stewardship Research

NOTE. EHR, electronic health record. ICD, International Classifications of Diseases. IRB, institutional review board. NHSN, National Healthcare Safety Network.

PITFALLS AND TIPS

Although studies using administrative data may seem relatively easy to perform compared to studies employing primary data collection methods, several pitfalls must be considered. During the study planning stages, close communication with those responsible for generating the data or with those who work with it regularly is key to enhancing the validity of the inferences derived from these studies.

Assumptions about data quality are a common misstep. Understanding whether and to what extent data validation occurs is critical. In the case of billing/coding data, submission of data to payors requires some standardization/common methodology; however, the coding goal may be to maximize reimbursement to the extent that can be supported by clinical documentation rather than to accurately reflect a clinically valid case definition for the diagnosis of interest. For inpatient pharmacy data, researchers may observe very different utilization depending on whether they use pharmacy inventory data, patient-level orders for medications, charges for medications, or actual medication administration.Reference Dalton, Sabuda, Bresee and Conly 4 For outpatient encounters, one may track prescriptions written (which may never be filled) versus pharmacy claims (which requires the medication to be filled at a pharmacy); however, neither of these approaches precisely reflects whether the patient actually took the medication. Most other administrative data are subject to a similar limitation—that what is documented may or may not reflect reality. Furthermore, identification of antibiotic-resistant bacteria has been particularly difficult with administrative data. One study found that >50% of patients with ICD-9 codes for methicillin-resistant Staphylococcus aureus (MRSA) did not actually have evidence of this organism.Reference Schweizer, Eber and Laxminarayan 5

Validation thus becomes an important component of studies using administrative data. Large surveillance datasets, such as the CDC’s NHSN and the American College of Surgery’s National Surgery Quality Improvement Program (NSQIP), have clearly defined definitions (which may differ from clinical definitions) and established protocols for data collection, and these undergo regular validation. Furthermore, while these programs are frequently used by the sites submitting data to measure their own outcomes and benchmark with comparator facilities, these databases also provide national-level data and have been used to study postoperative infection rates,Reference Gehrich, Lustik, Mehr and Patzwald 6 geographic variability in antibiotic resistance,Reference Soe, Edwards, Sievert, Ricks, Magill and Fridkin 7 and national healthcare personnel influenza vaccination rates,Reference Lindley, Bridges and Strikas 8 among other topics. Even a single-center study using a hospital EHR may benefit from internal validation via chart review to measure the extent to which particular variables capture the items of interest.

Knowing the data limitations is fundamental to informing the study plan and to conducting proper sensitivity analyses. The goal may not be 100% accuracy, but as long as the case definition and how it is being identified and reported does not change over time, validity may be maintained. The effect of possible misclassification bias on the potential results should be examined prior to study initiation. This requires some knowledge of the system(s) under study; for example, researchers studying antimicrobial use in sepsis may need to rely on ICD-9/ICD-10 codes for “sepsis” to identify cases. These codes are unlikely to be assigned 100% accurately, but they may be reasonably valid if coding procedures have remained constant. However, if an unrelated sepsis-related initiative increased the coders’ attention to sepsis diagnoses during part of the study period, the cases prior to the sepsis initiative may have been coded quite differently than later cases, and the protocol may need to be modified accordingly.

PUBLISHED EXAMPLES IN HEALTHCARE EPIDEMIOLOGY AND ANTIMICROBIAL STEWARDSHIP

Administrative data may be used for multiple purposes to describe a wide variety of HE&AS-related outcomes. One of the fundamental uses of “traditional” administrative data (ie, coding) is for surveillance, particularly for HAIs that are not easily monitored in other ways. For example, until recently, the NHSN did not include surveillance for Clostridium difficile infections (CDI), but several investigators used administrative data to estimate CDI incidence.Reference Dubberke, Butler and Yokoe 9 , Reference Young-Xu, Kuntz and Gerding 10 Surgical site infections (SSIs) can be particularly difficult to capture via traditional surveillance; thus, coding data may serve as an important method of ascertainment, particularly of post-discharge SSIs.Reference Olsen, Nickel and Fox 11 , Reference Perencevich, Sands, Cosgrove, Guadagnoli, Meara and Platt 12 However, numerous studies have evaluated the accuracy of administrative data in generalReference O’Malley, Cook, Price, Wildes, Hurdle and Ashton 13 – Reference van Walraven, Bennett and Forster 15 or have compared administrative (mostly coding) data to other forms of surveillance for infections such as CDI,Reference Dubberke, Reske, McDonald and Fraser 16 – Reference Wen, Barber and Ananthakrishnan 19 central-line–associated bloodstream infection,Reference Furuno, Harris and Wright 20 – Reference Patrick, Davis and Sedman 23 SSI,Reference Curtis, Graves and Birrell 24 ventilator-associated pneumonia,Reference Drees, Hausman, Rogers, Freeman, Frosch and Wroten 25 and overall HAIs.Reference Sherman, Heydon and St John 26 – Reference Jhung and Banerjee 30 In general, these studies have found administrative data to lack sensitivity and specificity for the infections under study.

Because of the challenges faced when conducting HAI surveillance, the CDC recently developed a surveillance module (known as LabID) 31 that is based solely on administratively collected data (admission/discharge and microbiology result dates and patient location) for CDI and MRSA bacteremia surveillance. These modules have been adopted by the Centers for Medicare and Medicaid Services (CMS) as conditions of participation. Several studies have compared this “administrative” surveillance to traditional surveillance, and generally higher rates of hospital-onset infections with administrative data were reported, due primarily to differences in definition.Reference Durkin, Baker and Dicks 32 , Reference Baker, Durkin and Dicks 33 Administrative data can also be used to expand HAI-related knowledge, such as risk factors,Reference Dubberke, Reske, Yan, Olsen, McDonald and Fraser 34 outcomes such as readmissions,Reference Khong, Baggs, Kleinbaum, Cochran and Jernigan 35 or to provide risk-adjustment for studies of antimicrobial-resistant organismsReference McGregor, Perencevich and Furuno 36 , Reference McGregor, Kim and Perencevich 37 or other HAIs.Reference Daneman, Simor and Redelmeier 38 , Reference Kanerva, Ollgren and Lyytikainen 39

Several scenarios make the use of administrative data particularly compelling. One is antimicrobial utilization surveillance, where the primary objective is to capture all antimicrobial use, not just use in particular settings or by practitioners enrolled in a study. Several such ecologic studies have been published, in which researchers were able to access databases of antimicrobial prescriptions (some from entire countries) and compare these data to antimicrobial resistance rates across the same geographic areas.Reference Elseviers, Ferech, Vander Stichele and Goossens 40 – Reference Hicks, Chien, Taylor, Haber and Klugman 43 While these studies are subject to the limitation of ecologic fallacy and inability to prove causation, this high-level view of antimicrobial use and resistance would be extremely difficult without administrative data. At the level of individual practice, both billing/claims and EHR data have been used to study antimicrobial use at the point of care during outpatient visits, in long-term care settings, or even with telephone-based contact.Reference Maselli and Gonzales 44 – Reference Rummukainen, Mäkelä, Noro, Finne-Soveri and Lyytikäinen 46

Administrative and surveillance data can also be used to conduct “natural experiments,” for example, when there is a change in state or federal policy related to HAI. One prominent example is the increase in laws and regulations related to public reporting of HAI. Several researchers have studied the impact of these laws on blood culture and antibiotic utilizationReference Flett, Ozonoff, Graham, Sandora and Priebe 47 and on central-line–associated bloodstream infection (CLABSI) rates. Interestingly, while hospitals in states with mandatory public reporting were more likely to participate in a national CLABSI prevention program and these participants trended toward greater CLABSI reduction,Reference Marsteller, Hsu and Weeks 48 other investigators have found no difference in HAI reduction in states with mandatory reporting.Reference Pakyz and Edmond 49 , Reference Lee, Kleinman and Soumerai 50 To some extent, this discrepancy may be due to the different data sources used, and researchers must be cognizant that the same outcome as measured in different administrative data sets may lead to different inferences. Others have used administrative data to study whether CMS non-reimbursement policies related to HAI have changed provider behavior related to test ordering,Reference Morgan, Meddings and Saint 51 billing,Reference Kawai, Calderwood and Jin 52 or infection rates.Reference Waters, Daniels and Bazzoli 53 These questions are particularly applicable to administrative data; whatever data inaccuracies may exist, they are likely to be stable over time, other than the effect of the new policy (provided other significant changes in practice patterns have not simultaneously occurred).

Lastly, because billing is such an important component of many administrative datasets, studies attempting to determine costs or cost-effectiveness usually rely on administrative data. In healthcare epidemiology, many studies have measured increased or attributable costs related to HAI,Reference Perencevich, Sands, Cosgrove, Guadagnoli, Meara and Platt 12 , Reference Goudie, Dynan, Brady and Rettiganti 54 – Reference Mauldin, Salgado, Hansen, Durup and Bosso 60 while others have evaluated the cost or cost-effectiveness of interventions.Reference Bejko, Tarzia and Carrozzini 61 – Reference Brilli, McClead and Crandall 65 A recent study utilized several different administrative datasets to estimate the potential cost-benefit of CDI reduction.Reference Slayton, Scott, Baggs, Lessa, McDonald and Jernigan 66 In any attempt to analyze costs of infections or of their prevention, an understanding of charges versus actual costs is critical.Reference Finkler 67

MAJOR TAKE-HOME POINTS

In light of the important pitfalls and limitations raised here, researchers planning a study in HE&AS using administrative data must consider several important points (Table 2). Most importantly, these points underscore the need for thorough understanding of the data source(s) prior to the initiation of a study. This awareness must include the development of an appropriate research question, detailed understanding of the datasets available (including working with those who generate or work with the data regularly), and recognition of the validation (or lack thereof) and limitations of the data related to the particular research question under investigation. Table 3 provides examples of research publications using different types of administrative data.

TABLE 2 Checklist of Key Considerations When Developing a Study in Healthcare Epidemiology or Antimicrobial Stewardship Using Administrative or Surveillance Data

NOTE. EHR, electronic health record; IRB, institutional review board. IT, information technology; NHSN, National Healthcare Safety Network.

TABLE 3 Examples of Different Types of Administrative and Surveillance Data and Their Use in Hospital Epidemiology and Antimicrobial Stewardship Research

CONCLUSIONS

Administrative and surveillance data can inform many types of HE&AS research. As the use of administrative data becomes more common (uptake of EHRs) and has greater impact (ie, CMS pay for performance), scrutiny of the specific data collection methodology and data validity will only increase. Ideally, harmonization and standardization of these data will increase accordingly. More advanced techniques such as natural language processing will further increase the richness of data available from administrative sources. Compared to traditional controlled trials, studies using administrative data are efficient, can include vastly larger numbers of patients (increasing power to detect differences), may have broader applicability and generalizability, and have the potential to improve healthcare in a wide variety of settings. As hospital epidemiology, infection prevention and control, and antimicrobial stewardship increasingly attract attention, researchers in this field have an obligation to ensure that the data being generated are utilized, analyzed, and interpreted appropriately.

ACKNOWLEDGMENTS

The authors would like to thank SHEA and members of the SHEA Research Committee for their support and review of this manuscript.

Financial support: No financial support was provided relevant to this article.

Potential conflicts of interest: All authors report no conflicts of interest relevant to this article.

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
Figure 0

TABLE 1 Advantages, Disadvantages, and Potential Pitfalls of Using Administrative and Surveillance Data in Healthcare Epidemiology and Antimicrobial Stewardship Research

Figure 1

TABLE 2 Checklist of Key Considerations When Developing a Study in Healthcare Epidemiology or Antimicrobial Stewardship Using Administrative or Surveillance Data

Figure 2

TABLE 3 Examples of Different Types of Administrative and Surveillance Data and Their Use in Hospital Epidemiology and Antimicrobial Stewardship Research