Variation amid active surveillance data and administrative data in Canada
Published by: The Public Health Agency of Canada
Issue: Volume 48-1, January 2022: COVID-19 Mortality and Social Inequalities
Date published: January 2022
Volume 48-1, January 2022: COVID-19 Mortality and Social Inequalities
Divergences between healthcare-associated infection administrative data and active surveillance data in Canada
Virginie Boulanger1,2, Étienne Poirier1,2, Anne MacLaurin3, Caroline Quach1,2,4,5
1 Département de microbiologie, infectiologie et immunologie, Faculté de médecine, Université de Montréal, Montréal, QC
2 Centre de recherche - CHU Sainte-Justine, Montréal, QC
3 Healthcare Excellence Canada
4 Département clinique de médecine de laboratoire, CHU Sainte-Justine, Montréal, QC
5 Prévention et contrôle des infections, Département de pédiatrie, CHU Sainte-Justine, Montréal, QC
Boulanger V, Poirier E, MacLaurin A, Quach C. Divergences between healthcare-associated infection administrative data and active surveillance data in Canada. Can Commun Dis Rep 2022;48(1):4-16. https://doi.org/10.14745/ccdr.v48i01a02
Keywords: surveillance, healthcare-associated infection, administrative data
Background: Although Canada has both a national active surveillance system and administrative data for the passive surveillance of healthcare-associated infections (HAI), both have identified strengths and weaknesses in their data collection and reporting. Active and passive surveillance work independently, resulting in results that diverge at times. To understand the divergences between administrative health data and active surveillance data, a scoping review was performed.
Method: Medline, Embase and Cumulative Index to Nursing and Allied Health Literature along with grey literature were searched for studies in English and French that evaluated the use of administrative data, alone or in comparison with traditional surveillance, in Canada between 1995 and November 2, 2020. After extracting relevant information from selected articles, a descriptive summary of findings was provided with suggestions for the improvement of surveillance systems to optimize the overall data quality.
Results: Sixteen articles met the inclusion criteria, including twelve observational studies and four systematic reviews. Studies showed that using a single source of administrative data was not accurate for HAI surveillance when compared with traditional active surveillance; however, combining different sources of data or combining administrative with active surveillance data improved accuracy. Electronic surveillance systems can also enhance surveillance by improving the ability to detect potential HAIs.
Conclusion: Although active surveillance of HAIs produced the most accurate results and remains the gold-standard, the integration between active and passive surveillance data can be optimized. Administrative data can be used to enhance traditional active surveillance. Future studies are needed to evaluate the feasibility and benefits of potential solutions presented for the use of administrative data for HAI surveillance and reporting in Canada.
Each year, many Canadians acquire an infection during their hospital stay that increases morbidity and mortality, and that bears a financial cost to the healthcare systemFootnote 1. These healthcare-associated infections (HAI) are preventable, measurable, and are the most frequently reported adverse event in healthcare worldwide. Every year, it is estimated that 220,000 Canadian patients develop a HAIFootnote 2. Many HAIs are now caused by antimicrobial resistant organisms (AROs), which make them difficult to treat. The Public Health Agency of Canada (PHAC) estimates that approximately 2% of patients admitted to large, academic Canadian hospitals will acquire an infection with an ARO during their hospital stayFootnote 3. Surveillance, including monitoring and reporting of HAI, is a critical component of infection prevention and control and needs to be strengthened at the national level. Although coronavirus disease 2019 (COVID-19) did not originate as a HAI, the current pandemic has revealed how critical it is to have reliable and consistent data in order to formulate an effective response to infection. When asked to provide projections regarding the course of COVID-19 virus, Prime Minister Trudeau said that "….the inconsistency in the data from across Canada is part of the delay in offering a nationwide picture"Footnote 4.
In Canada, PHAC collects national data on multiple HAIs through the Canadian Nosocomial Infection Surveillance Program (CNISP); a program established in 1994 as a partnership between PHAC, the Association of Medical Microbiology and Infectious Disease Canada and sentinel hospitals from across CanadaFootnote 5. The objectives of CNISP are to provide national and regional benchmarks, identify trends on selected HAIs and AROs, and provide key information to help inform the development of federal, provincial and territorial infection prevention and control programs and policiesFootnote 5. At present, the CNISP network comprises 87 acute-care sentinel hospitals from ten provinces and one territory. The network's goal is to have all Canadian acute care hospitals adopt the CNISP HAI surveillance definitions and contribute data to the national surveillance systemFootnote 2. Despite the desire to expand the surveillance program, CNISP is limited 1) by funding capacity, 2) by lack of human resources available to participate in national surveillance reference 2 and 3) because most hospitals already report to their provincial government and are unwilling to enter data twice. As a result, CNISP HAI rates may not provide a complete picture and some segments of the Canadian hospital population are underrepresented-such as smaller, community hospitalsFootnote 6.
National statistics reported by PHAC relating to HAIs only include data from hospitals that participate in CNISP as they all follow standardized case definitions, methods and case reporting. Currently HAI rates reported by provinces and territories or posted by individual hospitals cannot be combined as case definitions, methods of data collections and calculation of rates vary from hospital to hospital and between provinces and territoriesFootnote 2. Active surveillance is done by Infection Prevention and Control (IPC) practitioners and each province, territory, administrative region or hospital can determine their own surveillance protocols based on local epidemiology and resources, making it difficult to evaluate improvement efforts and compare HAI rates in Canadian hospitalsFootnote 7.
On the other hand, Canada has a wealth of administrative health data including insurance registries, inpatient hospital care, vital statistics, prescription medications and electronic health record systemFootnote 8. Exploring the potential of integrating these diverse administrative health data sets could provide a more robust picture of HAIs across Canada.
The hospital discharge abstract database (DAD), housed at the Canadian Institute for Health Information (CIHI), collects demographic and clinical information from patient discharge summaries from all acute care facilities in Canada, except in Québec (Québec has its own discharge abstract database-Maintenance et exploitation des données pour l'étude de la clientele hospitalière (MED-ÉCHO)-that reports to CIHI's Hospital Morbidity Database)Footnote 9. Information is entered in the database by professional coders from all hospitals and is used by CIHI to produce data and analytic reports. The CIHI's Data and Information Quality Program is recognized internationally for its high standardFootnote 10. However, discharge summaries are not standardized across the country and reflect only what is entered into the summary by the attending physician. The CIHI could, however, be a potential partner to support data collection and reporting of HAIs for acute care hospital. We conducted a scoping review to identify existing gaps between administrative data and active surveillance data for healthcare-associated infection surveillance and to propose possible integration strategies to optimize data.
The main research question was "What are the discrepancies between HAI administrative data and active surveillance data in Canada?". The research sub-questions were: Are administrative data valid for HAI surveillance? For each type of HAI, what are the discrepancies between administrative data and hospital surveillance data? We performed this scoping review following the PRISMA extension for scoping reviewFootnote 11.
For this review, HAI included Clostridioides difficile (C. difficile; CDI), catheter-associated bloodstream infection (CLABSI) or catheter-associated urinary tract infection (CAUTI) or urinary tract infection (UTI) (CAUTI), methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococci (VRE), carbapenem-resistant Enterobacteriaceae (CPE), antimicrobial resistant organism (ARO or AMR), bloodstream infection (BSI), surgical site infections (SSI) and ventilator-associated pneumonia (VAP).
We performed a search developed in collaboration with a medical research librarian. The inclusion criteria consisted of articles evaluating passive surveillance of specific or various HAIs in Canada. We included articles (qualitative, quantitative and mixed-method studies) published between 1995 and November 2, 2020 in Canada. The search strategy contained terms relative to location (Canada), surveillance, data source and HAI. In addition, we performed a second search with the same terms (except for the location) and only including systematic reviews.
A pilot selection process was carried out to identify databases with relevant studies and three electronic databases were searched: MEDLINE, EMBASE and Cumulative Index to Nursing and Allied Health Literature (CINAHL), in English and French with no date restriction. The search strategies were created on MEDLINE then adapted for the other databases (Supplemental Data S1). After deduplication, two reviewers independently screened citations by title and abstract. Selected articles were evaluated for eligibility at the full-text level. The first reviewer also performed a hand search of the grey literature and reviewed the references list of all eligible and published studies to identify any articles that were not initially captured through electronic search. Conflicts were resolved through discussion until consensus was reached.
Data extraction and quality assessment
An electronic data form was developed on Distiller SR (Evidence Partners, Ottawa, Canada) for this scoping review. The following data were extracted from each article: general information; study details; types of HAI and surveillance; source of data; outcomes and results.
Both reviewers assessed each study's quality/risk of bias of each study using ROBINS-I for non-randomized studiesFootnote 12 and AMSTAR-2 tool for systematic reviewFootnote 13. Overall, studies were ranked at low, moderate or high risk of bias. Any disagreement or inconsistency between the reviewers were resolved through discussion. The complete data collection and quality assessment items are shown in Supplemental Data S2 .
A qualitative descriptive approach was used to synthesize the data collected. Principal studies characteristics, summary of performance statistics and quality assessment scores were summarized into tables. We presented a summary of findings for each study grouping into categories depending on the type of administrative data used and the scope of the study. We focused on how the administrative data were used for HAI surveillance, the divergence in results with traditional surveillance and if author recommended administrative data to enhance surveillance. A synthesis of systematic reviews was also presented with studies categorized as review assessing validity of administrative data or review assessing validity of electronic surveillance system.
Overall, 1,316 studies were identified through the electronic search and 12 from hand searches. After deduplication, 1,102 studies remained, of which 104 were selected for a full-text review. Finally, 16 studies were included in the scoping review from electronic search. Twelve studies were observational studiesFootnote 14Footnote 15Footnote 16Footnote 17Footnote 18Footnote 19Footnote 20Footnote 21Footnote 22Footnote 23Footnote 24Footnote 25, and four were systematic reviewsFootnote 26 Footnote 27Footnote 27Footnote 28 (Figure 1).
Of the 12 observational studies included, six focused on SSI, three on CDI, two on MRSA and one on BSI. Studies were performed from 2009 to 2020 and eight were from Alberta. Seven studies compared administrative data with hospital surveillance data and seven studies used data linkage. All studies used DAD as the source of administrative data (alone or combined with other sources). The main characteristics of all included studies are summarized in Table 1.
|First author, year||Study design||Study population and sample size (n=)||Administrative data source||Condition(s)||Province(s)||Risk of bias|
|Cohort study||All index laminectomy and spinal fusion procedure cases in Alberta from 2008 to 2015 (n=21,222)||DAD + NACRSTable 1 footnote a||Surgical site infection||Alberta||Low|
|Ramirez-Mendoza, 2016||Cohort study||All acute-care patients in Alberta and Ontario from April 2012 to March 2013 (n=217)Table 1 footnote b||DADTable 1 footnote a||Methicillin resistant Staphylococcus aureus||Alberta and Ontario||Low|
|Pfister, 2020||Cohort study||All acute-care patients in Alberta from April 2015 to March 2019 (IPC n=9,557, DAD n=8,617)||DADTable 1 footnote a||Clostridioides difficile||Alberta||Low|
|Rennert-May, 2018||Cohort study||All primary hip or knee arthroplasty cases in Alberta from April 2012 to March 2015 (n=24,512)||DADTable 1 footnote a||Surgical site infection||Alberta||Low|
|Almond, 2019||Cohort study||All acute-care patients in Alberta from April 2015 to March 2017 (n=4,737)||DAD + laboratory dataTable 1 footnote a||Clostridioides difficile||Alberta||Low|
|Rusk, 2016||Cohort study||All primary hip or knee arthroplasty cases in Alberta from April 2013 to June 2014 (n=11,774)||DAD + NACRSTable 1 footnote a||Surgical site infection||Alberta||Low|
|Daneman, 2011||Cohort study||All cesarean delivery cases at Sunnybrook Health Science Centre from January 2008 to December 2009 (n=2,532)||DAD + NACRS + physician claimsTable 1 footnote a||Surgical site infection||Ontario||Low|
|Lethbridge 2019||Cohort study||All hip or knee replacement surgery cases in Nova Scotia from 2001 to 2015 (n=36,140)||DAD + NACRS + physician claims||Surgical site infection||Nova Scotia||Low|
|Leal, 2010||Cohort study||All adult patient in Calgary Health Region in 2005 (sample of n=2,281)||Cerner's PathNet laboratory + OracleTable 1 footnote c||Bloodstream infection||Alberta||Low|
|Lee, 2019||Cohort study||All adult patients in four adult acute-care facilities in Calgary region from April 2011 to March 2017 (n=2,430)||DAD||Methicillin resistant Staphylococcus aureus||Alberta||Low|
|Daneman, 2009||Cohort study||All elderly patients hospitalized for elective surgery in Ontario from April 1992 to March 2006 (n=469,349)||DAD + Ontario Health Insurance Plan + Ontario Drug Benefits database||Surgical site infection||Ontario||Low|
|Daneman, 2012||Cohort study||All patients (older than one year old) admitted to an acute-care hospital in Ontario from April 2002 to March 2010 (n=180)Table 1 footnote b||DADTable 1 footnote a||Clostridioides difficile||Ontario||Low|
Four systematic reviews were also included, three on the use of electronic surveillance system (ESS) and one on the use of administrative data for HAI surveillance. All reviews included at least one article from Canada. The study characteristics are summarized in Table 2.
|First author, year||Number of included studies, year||Objective||Databases||Conclusion||Other information||Risk of bias|
|Van Mourik, 2015||57 studies from 1995 to 2013||Accuracy of administrative data used for HAI surveillance||Medline, Embase, CINAHL, Cochrane||Administrative data had limited and highly variable accuracy||n=1/3 studies included had important methodological limitation||Moderate|
|Leal, 2008||24 studies from 1980 to 2007||Identify and appraise published literature assessing validity of ESS compared with conventional surveillance||Medline||Electronic surveillance has good utility compared to conventional surveillance||No assessment of quality of studies included||High|
|Freeman, 2013||24 studies from 2000 to 2011||Assess utility of ESS for monitoring and detecting HAI||Medline, Cochrane, Ovid, Embase, Web of science, Scopus, JSTOR, Wiley Online Library, BIOSIS Preview||Hospital should develop and employ ESS for HAI||Majority of studies have emphasis on linkage of electronic database||High|
|Streefkerk, 2020||78 studies up to January 2018||Give insight in the current status of ESS, evaluating performance and quality||Embase, Medline, Cochrane, Web of Science, Scopus, CINAHL, Google Scholar||With a sensitivity generally high but variable specificity, ESS as yet to reach a mature stage, need further work||Authors selected 10 best studies that may constitute a reference for ESS development||High|
Within-study risk of bias
Observational studies were assessed for risk of bias using the ROBIN-1 tool (Table 1). Most of these studies used similar methodology but lacked information on missing data (Supplemental Table S3). However, they were all assessed as low risk of bias.
Systematic reviews were assessed using the AMSTAR-2 tool (Table 2, Supplemental Table S4). One article was considered at moderate risk of bias as it did not report its protocol or describe included studies in adequate details. Three articles were considered at high risk of bias as some did not report their protocol or assess the risk of bias, quality or heterogeneity of included studies.
Summary of findings
Studies using one administrative database compared with active surveillance
Validation studies showed that DAD used alone for capturing HAI cases is not valid in comparison with IPC traditional active hospital surveillance. For example, Rennert-May et al.Footnote 17 assessed the validity of using the ICD-10 code administrative database (DAD) to identify complex SSIs within three months of hip or knee arthroplasty. The study found that the ICD codes in DAD were highly specific (99.5%) but had a sensitivity of 85.3% and a predictive positive value of only 63.6%. They concluded that DAD was not able to accurately determine if someone had an SSI according to surveillance definition (Table 3). Pfister et al. Footnote 15 came to the same conclusion with a validation study on DAD capturing CDI cases. The CDI rate was 28% higher in the DAD compared to IPC surveillance, showing that DAD seems inadequate to capture true infection incidence. Findings show that the DAD includes recurrent CDI and cannot distinguish symptomatic from asymptomatic cases. In fact, DAD had only a moderate sensitivity of 85% and a positive predictive value of 80% (Table 3).
|First author, year||Comparator||Results||Conclusion|
|Infection rate||TP, FP, FN, TN, Total||Sensitivity||Specificity||Positive predictive value||Negative predictive value|
|DAD + NACRS compared with published traditional surveillance data||
2.7 per 100 procedures of laminectomy
3.2 per 100 procedures of spinal fusion
Rate reported by administrative data was similar to published rate from traditional surveillance
Need validation study to verified results
|DAD compared with IPC data||
Alberta (cases per 10,000 patient-days)
Ontario (cases per 10,000 patient-days)
|N/A||N/A||N/A||N/A||N/A||Using Pearson correlation there was good evidence of the comparability of administrative and IPC surveillance data|
|DAD compared with IPC data||
DAD: 6.49 per 1,000 admissions
IPC: 5.06 per 1,000 admissions
|85%||N/A||80%||N/A||DAD was moderately sensitive, but likely inadequate to capture true incidence|
|Rennert-May, 2018||DAD compared with IPC data||N/A||220 TP
|85.3%||99.5%||63.6%||99.8%||Administrative data had reasonable testing characteristics, but IPC surveillance was superior|
|Almond, 2019||DAD + laboratory data compared with IPC data||
DAD/lab (per 10,000 patient-days)
4.96 for HAI
IPC (per 10,000 patient-days)
3.46 for HAI
|96.6%||65.7%||74.3%||94.9%||Laboratory surveillance method was highly sensitive, but not overly specific|
|Rusk, 2016||DAD + NACRS + IPC compared with IPC data alone||
DAD/NACRS/IPC: 1.7 per 100 procedures
IPC: 1.3 per 100 procedures
|N/A||89.9%||99%||N/A||N/A||Medical chart review for cases identified through administrative data was an efficient strategy to enhance IPC surveillance|
|Daneman, 2011||DAD + NACRS + physician claims compared with IPC data||N/A||N/A||77.3%||87%||17.4%||99.1%||Administrative data had poor sensitivity and positive predictive value and were inadequate as a quality indicator|
|Lethbridge, 2019||DAD or NACRS compared with DAD + NACRS + physician claim||Difference of 0.44 between DAD or NACRS alone and all data together||N/A||N/A||N/A||N/A||N/A||Rates were underestimated using single-source administrative data|
On the other hand, Daneman et al. evaluated if mandatory public reporting by hospital was associated with reduction in hospitals CDI rates in OntarioFootnote 23. Aside from the main analysis, they performed a cross-validation of CDI rates from administrative data against rates reported by single institutions via the mandatory public reporting system. They used Pearson correlation coefficients weighted for hospital bed-days and found an excellent concordance across the institutionsFootnote 23.
The same coefficient was used in the study by Ramirez Mendoza et al.Footnote 18 that compared DAD with surveillance data for hospital-acquired MRSA in Alberta and Ontario. The results showed strong correlation between DAD and IPC surveillance data. The study concluded that there was good evidence of comparability between these datasets; however, rate or denominator diverged widely between administrative data and active surveillance data (Table 3). Some authors did not agree with the study conclusion or methodology, notably with the choice of Pearson correlation using hospital-level data and the difference of rates or denominators between administrative and surveillance dataFootnote 30.
Studies combining multiple administrative databases
Results show that combining databases increases the accuracy, yet still not as accurate as traditional active surveillance. Lethbridge et al.Footnote 24 combined multiple types of administrative data and compared them with a single source administrative data to identify SSIs following hip and knee replacement in Nova Scotia. Used alone, DAD and National Ambulatory Care Reporting System (NACRS) had higher rates than physician billing but underestimated the infection rate with a percentage difference of 44% compared with the combination of the three databases. This implies that approximately 17% of infected cases would have been missed with DAD or NACRS alone. The authors concluded that combining databases enhanced SSI surveillance.
Daneman et al.Footnote 20 validated the accuracy of DAD, NACRS and physician claim database against traditional surveillance for the detection of cesarean delivery SSI within 30 days of surgery in Ontario. They found a sensitivity of only 16.7% for DAD used alone, 37.9% for DAD combined with NACRS and 77.3% for DAD combined with NACRS and physician claims database. All had a high specificity (87%-98.3%) but a very low predictive positive value (17.4%-27.4%) (Table 3). The authors recommended that the administrative data not be used as a quality indicator for interhospital comparison.
In contrast, Crocker et al.Footnote 14 compared infection rates calculated using a combination of DAD and NACRS to identify spinal procedure and SSIs. They showed that these rates were comparable with postoperative SSI rate published using traditional surveillance (Table 3). However, the validity of the results was not verified in this study.
Studies combining administrative database with laboratory database
Studies showed that laboratory records could be used to enhance administrative data. For example, Almond et al.Footnote 25 assessed the validity of a laboratory-based surveillance method to identify hospital-acquired CDI (HA-CDI). Laboratory data alone can result in overestimation of CDI rates, with positive laboratory result not meeting the case definitions for HA-CDI (e.g. asymptomatic colonization, recurrent CDI). However, this study assessed the alternative of linking positive CDI laboratory records to DAD. The study demonstrated a very high sensitivity but a specificity of 65.7% and a positive predictive value of 74.3% (Table 3). These results indicated that 26% of cases classified as HAI were not true HAI cases, resulting in a higher rate observed with this method. In addition, authors completed a receiver operator characteristic (ROC) analysis to see if using a time from admission (collection date−admission date) of ≥4 days was the appropriate algorithm to use for classifying hospital-acquired cases in the laboratory dataset. The ROC analysis indicated that more cases were classified correctly five days after admission. Thus, a simple change in the laboratory detection using longer time from admission to classify cases as healthcare-associated may increase the specificity with a small cost to sensitivity.
Another study from Leal et al. pushed one step further by developing an electronic surveillance system (ESS) for monitoring BSI by linking laboratory and administrative databases Footnote 21. The ESS included definitions for classifying BSI and their location; nosocomial, healthcare-associated-community onset or community-acquired infection. The system was compared with chart review done by a research assistant and an infectious diseases physician. Chart review and ESS identified 329 and 327 BSI episodes respectively. The authors found high concordance regarding acquisition location of infection (Kappa=0.78) and they were able to improve definitions after post hoc revision. Surveillance data obtained through ESS identified and classified BSI with a high degree of agreement with manual chart review.
Studies using administrative data to enhance active surveillance
Studies showed that administrative data can be used to enhance IPC surveillance. Lee et al.Footnote 16 assessed the benefits of linking population-based IPC surveillance with DAD for hospital-acquired (HA) and community-acquired (CA) MRSA cases in Alberta. This enabled IPC surveillance to have more relevant information available in a timely manner. The authors were able to successfully link 94.6% of the total surveillance records and identify key differences between patients with HA and CA-MRSA, showing that administrative data could be used to enhance hospital surveillance.
Through a retrospective cohort study, Rusk et al.Footnote 19 evaluated a new strategy to improve traditional IPC surveillance by using administrative data to trigger medical chart review. Eligible patients followed by the IPC team were linked to DAD and NACRS and these administrative databases provided diagnosis and procedure codes for each visit and/or readmission. The strategy using administrative data captured 87% of cases identified by IPC surveillance, with a sensitivity of 90% and specificity of 99%. This confirmed that the administrative data-triggered medical chart review is an efficient strategy to improve SSI surveillance.
Study to improve hospital comparison using administrative data
Daneman et al.Footnote 22 demonstrated that administrative data (DAD + physician claims) can be used to create a modified Nosocomial Infections Surveillance surgical risk stratification index comparable with the one used for clinical surveillance. This index allowed for the adjustment of infection rate when comparing with other facilities. The study concluded that both administrative and clinical sources can contribute to infection surveillance, with administrative data used to identify patients with possible infections or improving detection of post-discharge diagnoses.
Systematic review and administrative data
Only one studyFootnote 26 assessed the accuracy of administrative data for surveillance of HAI. Others reviewed articles on ESS using electronic medical records for HAI surveillance compared to traditional surveillance, but included many articles that used a combination of administrative data and ESS Footnote 27Footnote 28Footnote 29. Administrative data was found to have very heterogeneous sensitivity and positive predictive value, generally low to modest with a particularly poor accuracy for the identification of device-associated HAI (e.g. CLABSI, CAUTI) (Table 4)Footnote 26Footnote 28. In general, the highly variable accuracy for administrative data was mainly due to the amount of different diagnostic codes used between studies Footnote 26. Van Mourik et al. assessed the accuracy of administrative data. One-third of included study had important methodological limitations and ones with higher risk of bias were associated with a more optimistic picture than those employing robust methodologiesFootnote 26. On the other hand, Leal et al. found a good sensitivity and excellent specificity for administrative data (Table 5)Footnote 29. However, populations and methodologies were very heterogeneous, and the quality of the studies included in the review was not assessed. All four reviews found that combining administrative data sources with other sources for surveillance, in particular with microbiology data, improved the accuracy. Studies also found that microbiology data had a good sensitivity Footnote 28Footnote 29; however, Freeman et al. concluded that ESS using microbiology data alone tended to overestimate HAIFootnote 27. Streefkerk et al.Footnote 28 also found that microbiology data combined with antibiotic prescription and laboratory (biochemistry, hematology, etc.) data were more accurate than microbiology alone (Table 5). Finally, most studies concluded that administrative data were advantageous to track HAI requiring post-discharge surveillance (e.g. SSI).
|First author, year||Number of articles included||Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive value (NPV)||Other information|
|Three studies used single-source data, 37 used multi-source data including laboratory, four used multi-source data excluding laboratory|
- Sensitivity below 40% for all but one study
- SE higher for BSI/sepsis
SE and PPV around 40%
|SE below 60%
PPV below 25%
SE higher in UTI than CLAUTI
|Gain in sensitivity of almost 10% when combining database
Studies with higher risk of bias were more optimistic
|Sensitivity was generally high, but specificity very variable|
|First author, year||Number of articles included||Sensitivity (SE), Specificity (SP) (range or average)||Other information|
|Administrative data||Laboratory data||Administrative data + laboratory data||Other|
|Microbiology||Microbiology + antibiotic prescription||Microbiology + antibiotic prescription + chemistry|
(AD=7, L=61, O=10)
|SE=30%Table 1 footnote a
SP=94.5%Table 1 footnote a
|In general, good sensitivity for studies using microbiology data|
AD + L=6, O=5)
|SE=59%–96%Table 1 footnote b
SP=95%–99%Table 1 footnote b
|-||AD + L combined had higher SE but lower SP than for either alone|
Systematic review and electronic surveillance system
Results showed that electronic surveillance using algorithms for HAI detection from electronic medical records had not yet reached a mature stage but presented good opportunities and potential. Most concluded that ESS should be developed and used in hospitals, recognizing that these methods can reduce burden associated with traditional manual surveillanceFootnote 27Footnote 28Footnote 29. In fact, sensitivity was generally high and specificity variable for most ESS compared with traditional active surveillance (Tables 4 and 5). Freeman et al. found that a lot of computer algorithms for electronic surveillance outperformed manual chart review methodFootnote 27. A majority of studies in this review emphasized the linkage of electronic databases with "in-house" surveillance system rather than commercial softwareFootnote 27. Streefkerk et al. demonstrated that the best ESS used a two-step procedure with cases selection using ESS was followed by confirmatory assessment of selected cases by the IPC teamFootnote 28. In the same review, seven studies tried to develop an ESS that could find all HAIs, with a sensitivity ranging from 0.78 to 0.99. Leal et al. demonstrated that ESS were potentially inexpensive, efficient and could reach a sensitivity of 100% when the infection of interest is defined by the presence of a positive cultureFootnote 29. However, ESS were less efficient when the infection is diagnosed based on clinical evaluation of symptoms or tests other than a positive microbiology culture. Moreover, the quality of data and linkage may influence the quality of the ESSFootnote 29. Freeman et al. also concluded that in some studies, the lack of clinical data in an electronic format reduced the ability of ESS to detect HAIFootnote 27.
Canada has a great wealth of administrative health data collected at the provincial/territorial level from diverse parts of the healthcare system. However, these data are not used to their full potential and their increased use could enhance HAI surveillance efforts and decrease the workload associated with traditional active surveillance. This scoping review explored the use and validity of administrative data used alone or combined with other data sources for HAI surveillance in Canada. Overall, studies showed that using one source of administrative data alone for surveillance of HAI is not sufficiently accurate in comparison with traditional active surveillance. However, combining different sources of data improved accuracy. Moreover, combining administrative data with active surveillance was shown to be an effective strategy to enhance active surveillance and decrease work burden for IPC teams.
Advantage and inconvenience of administrative data
Administrative data are not collected for surveillance purposes. However, they have a lot of attractive characteristics that make them interesting for the enhancement of HAI surveillance. They are inexpensive, available from nearly all healthcare facilities, collected in a consistent manner, subjected to quality check and do not add an administrative burden to clinicians or patientsFootnote 31. Deterministic linkages can also be performed between databases that collect healthcare number, as each Canadian has a unique identifying health number.
Furthermore, many studies demonstrated that administrative data are advantageous for tracking HAIs requiring post-discharge surveillanceFootnote 19Footnote 20Footnote 22Footnote 26. This is very important for infections like SSIs, where the majority are developed after discharge Footnote 19Footnote 32Footnote 33Footnote 34. For example, in the study by Rusk et al., 96% of SSI cases were identified after discharge and 43% of confirmed SSI cases were identified at a facility other than where the procedure was performedFootnote 19. These results show that conducting active SSI surveillance only at the operative hospital limits SSI detection. The best practices for surveillance of healthcare-associated infection published by Public Health Ontario state that "to date there is no generally accepted method for conducting post-discharge surveillance for SSIs outside the hospital setting…Infection Prevention and Control Professional are encouraged to develop innovative approaches for the detection of post-discharge SSIs that do not interfere with the time spent on other components of their surveillance system"Footnote 35. Examples of solutions proposed included the use of administrative databases and electronic screening of patients' records post-discharge for symptoms and signs of infectionFootnote 35.
Barriers in accurate administrative data for hospital-acquired infection surveillance in Canada
In Canada, CIHI collects clinical data through the Clinical Administrative Databases that consists of two separate databases: The Discharge Abstract Database-Hospital Morbidity Database; and NACRS Footnote 36. At this time, CIHI publicly reports on some HAIs such as in-hospital sepsis, UTIs and ARO, most at the national level only, using data collected from DAD. The CIHI has a comprehensive data quality program and any known quality issues are addressed by the data provider or documented in data limitations documentation available to all usersFootnote 36. However, there are still many barriers to be overcome before accurate administrative data for HAI surveillance could be produced. Studies show that the lack of accuracy is an important limitation in using administrative data as a quality indicator for hospital comparison. For instance, the variability of medical practice, the documentation and discharge coding amongst facilities, the interpretation of medical coders, the fact that data collection relies on primary care provider and that information is based on their capacity to detect and report a HAI (possible misclassification errors, human errors)Footnote 15Footnote 19Footnote 37Footnote 38. Essentially, information is limited by what is reported in the medical chart and depends mainly on adequate clinician documentation.
For example, reporting to the DAD database requires the physician to adequately fill the discharge summary, including HAIs if known. HAIs are usually not detected in real time and may likely be assessed differently by a clinician and the infection prevention and control team, the latter following standardized definitions. The health records department's professional coding specialist then translates charts and discharge summaries into standard codes. A study conducted in 2015-2016 in Alberta interviewed coders on physician-related barriers to producing high-quality administrative dataFootnote 39. These barriers included incomplete and nonspecific documentation by physicians, physicians and coders using different terminology (e.g. physician diagnostic not in ICD-10 list), lack of communication between coders and physicians (mainly in urban settings) and the fact that coders are limited in their ability to add, modify or interpret physician documentation. Finally, coders are not allowed to use supporting documentation that could increase specificity of diagnostic codes (e.g. laboratory reports)Footnote 39. In fact, an important limitation for CIHI is that in general, the physician documentation takes priority over all other documentation, even if laboratory reports or other documentation indicate a different diagnosis. Yet there are multiple studies demonstrating that laboratory data could be used to enhance administrative dataFootnote 13Footnote 21Footnote 29Footnote 37. Hence, allowing coders to use laboratory data could be a feasible solution to improve coding accuracy.
Integration of administrative data in infection prevention and control surveillance
Studies also demonstrated that the use of administrative data by IPC team can enhance HAI surveillance and reduce the workload for IPC professionals. Lee et al. demonstrated that linking surveillance data with administrative data allows to have detailed information in a timely manner and they urged jurisdictions and healthcare systems to consider adopting this type of data linkage for surveillance practicesFootnote 16. Rusk et al. demonstrated an efficient strategy to identify potential SSI cases for further IPC review using administrative data codes, improving case-finding consistency and reducing time and resources neededFootnote 19. All these studies showed that administrative data can be used to enhance traditional surveillance by IPC team. The reverse could also be true. As noted previously, coders can only use physician documentations to report diagnoses. On the other hand, traditional surveillance by IPC professional is considered the gold-standard of surveillance and results in accurate data. If coders could access IPC surveillance outcome, this may enhance the validity of physician documentation and interpretation by coders.
Integration of administrative data in electronic surveillance systems
Another potential approach to make surveillance less labor-intensive is to use electronic surveillance systems. In the current review, seven observational studies used data linkage of electronic databases and three systematic reviews assessed electronic surveillance systems. Leal et al. developed a complete ESS to identify and classify BSI with a high degree of agreement with manual chart reviewFootnote 21. Results from the systematic review by Freeman et al. suggested that ESS implementation is feasible in many settings and should be developed by hospitals Footnote 27. The ESS can also be developed to detect more than one HAI. Moreover, the systematic review by Steefkerk et al. on ESS presented the 10 best studies selected based on the overall quality and performance score, and the majority used a two-step procedure using administrative, electronic medical records or microbiology data followed by a confirmatory assessment by the IPC professional Footnote 28. In this case, ESS could be designed to favor sensitivity over specificity, knowing that manual review will exclude false positivesFootnote 31. Streefkerk et al. presented seven studies with ESS that could detect all HAIsFootnote 28. Their review even included one study describing an excellent performing algorithm to detect HAI in real time with a sensitivity of 0.99 and a specificity of 0.93; HAIs included UTI, BSI, respiratory tract infection, gastrointestinal tract infection, skin and soft tissue infection and other infections (parotitis, chickenpox, neurological infections, etc.)Footnote 40. However, these seven studies were not performed in Canada. In fact, other countries already have electronic data in place in their facilities and implementation of ESS for HAI surveillance is thus feasible. In Canada, not all hospitals have access to a good electronic health record system.
Some provinces are good models for surveillance using electronic data. For example, most studies included in this scoping review were from provinces that have electronic systems (e.g. Alberta, Ontario). Alberta is a good example for HAI surveillance as all acute-care sites conduct traditional surveillance using a single surveillance protocol and a centralized online data entry systemFootnote 41. This system allows administrative information to be shared between all its facilities. Québec also has a centralized electronic system created for the Surveillance Provinciale des Infections Nosocomiales program using uniform definitions to detect HAIFootnote 42; however, no study from Québec met our inclusion criteria. One study by Gilca et al. is worth considering: this study included 83 acute-care hospitals participating in CDI surveillance in the province of QuébecFootnote 43. Authors compared administrative and surveillance data and found an excellent agreement between rates obtained from MED-ÉCHO (hospital discharge database) and CDI incidence according to provincial surveillance. However, the origin of acquisition for CDI cases was not indicated in the administrative database. Thus, it was not possible to separate nosocomial from community-acquired cases with only the use of administrative data.
A study conducted in three states in the United States and in the province of Ontario, Canada assessed the information technology challenges and strategies of developing and implementing a multihospital electronic system to prevent MRSAFootnote 44. They included 11 hospitals, all with an understaffed information technology group, and with seven different systems having unique information technology structure and unique data system. They found innovative strategies to enable automated collection, sharing, analysis and reporting of data in a compatible format for all hospitals. The study was published in 2013, and authors are currently applying the same strategies to develop ESS for other HAIs. This study is a good example of the feasibility of implementing ESS using different hospital systems.
Strengths and limitations
We used standardized and robust methods to identify, review and assess quality of the published literature with all steps performed by two independent reviewers. Two different search strategies were used to ensure that all Canadian studies were included as well as systematic reviews that included at least one study in Canada. Our review included a small number of studies; however, we are confident that our search strategies combined with hand-search captured all relevant available articles. This is the first review to report on divergences between administrative data and surveillance data for HAI surveillance in Canada.
This review has several limitations. We included only studies that were published in French or English; however, as French and English are the two official languages in Canada, we do not expect to have missed important studies. Observational studies identified represent only three Canadian provinces, with two-thirds of the studies from Alberta. Alberta has a province-wide integrated healthcare system that is easily queried, which is not the case with the systems in the remaining provinces. While our review included both articles published in English or French, our search was conducted using only English terms. We searched only three databases and we may have missed relevant articles included in other databases. This study was conducted on Canadian data only and may not be generalizable to other countries.
This scoping review identified numerous divergences between administrative data and active surveillance data for HAI surveillance in Canadian hospitals. However, it also identified possible solutions, depending on the HAI under surveillance, and demonstrated that administrative data can be used to enhance HAI surveillance. Electronic surveillance systems have the potential to save time and human resources and combining multiple administrative datasets may also improve data accuracy. The IPC team who used administrative data or electronic surveillance systems were able to reduce their workload in active surveillance. Although active surveillance of HAIs produced the more accurate results and remains the gold-standard, further studies on HAI surveillance in Canada should focus on the feasibility of data sharing between provinces through electronic systems, the feasibility for medical coders to have access to documentation other than physician documentation, and the feasibility of using administrative data to help reduce the burden of active surveillance.
- VB - Conceptualization, methodology, investigation, validation, formal analysis, writing-original draft
- EP - Investigation, validation, writing-review
- AM - Conceptualization, resources, writing-review and editing, funding acquisition
- CQ - Conceptualization, writing-review & editing, supervision, funding acquisition
The content and view expressed in this article are those of the authors and do not necessarily reflect those of the Government of Canada.
None of the authors had any conflicts of interest to disclose.
V Boulanger and E Poirier are supported through a MITACS Accelerate/ Healthcare Excellence Canada internship. We thank M Clar for her assistance in the literature search strategy. L Prelude and A Chapman for their support and D Diallo for her assistance with English translation. C Quach is the Tier-1 Canada Research Chair in Infection Prevention: from hospital to the community.
This work was funding by Healthcare Excellence Canada and MITACS Accelerate and Healthcare Excellence Canada.
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