SynthesisBMJ open2015
Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
Synthesis in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- Predictive performance of automated surveillance algorithms for intravascular catheter bloodstream infections: a systematic review and meta-analysis.Antimicrobial resistance and infection control · 2023Pooled it
- Beyond the operating room: do hospital characteristics have an impact on surgical site infections after colorectal surgery? A systematic review.Antimicrobial resistance and infection control · 2021Pooled it
- Pooled it
- Validity and Reliability of Administrative Coded Data for the Identification of Hospital-Acquired Infections: An Updated Systematic Review with Meta-Analysis and Meta-Regression Analysis.Health services research · 2018Pooled it
- Nonlinear Association of Length of Stay with In-Hospital Mortality in Alzheimer's Disease Hospitalizations: Admission-Only vs. Inpatient-Course Prediction Using Explainable Machine Learning.Geriatrics (Basel, Switzerland) · 2026Article
- Three Decades of Use of the Minimum Basic Data Set in Infectious Disease Research in Spain: A Scoping Review with an Evidence-Mapping Approach.Tropical medicine and infectious disease · 2026Review
- Healthcare-Associated Infections Impact Mortality in Patients Admitted to the Acute Care Hospital from the Emergency Department.Journal of clinical medicine · 2026Article
- Canadian Metadata Catalogue for Health Science Research.International journal of population data science · 2026Article
- Impact of community-acquired invasive Escherichia coli disease on mortality and readmissions in elderly patients: a multi-center study in Germany.BMC infectious diseases · 2025Article
- Small-Area Lung Cancer Incidence and Mortality: Cross-Sectional Population-Based Study Using Hospital Discharge and Death Registration Data.JMIR public health and surveillance · 2025Article
- Association Between Nurse Staffing Coverage and Patient Outcomes in a Context of Prepandemic Structural Understaffing: A Patient-Unit-Level Analysis.Journal of nursing management · 2025Article
- Use of ICD-9-CM coding for identifying antibiotic prescriptions during hospitalization: a Delphi consensus model.Infection prevention in practice · 2024Article
- Interaction of Insurance and Neighborhood Income on Operative Colorectal Cancer Outcomes Within a National Database.The Journal of surgical research · 2024Article
- Measuring the Direct Medical Costs of Hospital-Onset Infections Using an Analogy Costing Framework.PharmacoEconomics · 2024Article
- Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology.NPJ digital medicine · 2024Article
- Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea.BMC health services research · 2024Article
- A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
- Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study.Frontiers in digital health · 2024Article
- Challenges in defining surgical-site infections following hydrocephalus surgeries.Infection control and hospital epidemiology · 2023Article
- Validation of the first computerized indicator for orthopaedic surgical site infections in France: ISO-ORTHO.Antimicrobial resistance and infection control · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveMeasuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.
methodsSystematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.
results57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.
conclusionsAdministrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.