Validity of routine surveillance data: a case study on Swedish notifications of methicillin-resistant Staphylococcus aureus

Abstract

The members of the group are listed at the end of the article Surveillance of communicable diseases is a public health corner stone. Routine notification data on communicable diseases are used as a basis for public health action as well as for policy making. While there are agreed standards for evaluating the performance of surveillance systems, it is rarely possible to analyse the validity of the data entered into these systems. In this study we compared data on all Swedish cases of methicillin-resistant Staphylococcus aureus (MRSA) routinely notified between 2000 and 2003 with follow-up information collected for each of these cases as part of a public health project. The variables Reason for testing (clinical sample, contact tracing, screening of risk group), Clinical presentation (disease, colonisation), Transmission setting (healthcare-acquired, community-acquired), Country of acquisition (Sweden, abroad) and Risk-occupation (yes, no) were analysed for sensitivity, positive predictive value and completeness of answers. The sensitivity varied between 23% and 83%, the positive predictive values were generally higher (55% to 97%), while missing answers varied from 11% to 59%. The proportion of community-acquired cases was markedly higher when excluding either cases of MRSA colonisation or cases found through public health-initiated activities (contact tracing or screening of risk groups). We conclude that the quality of routine surveillance data may be inadequate for in-depth epidemiological analyses. This should be taken into account when interpreting routine surveillance figures. Whether or not the case definition includes cases of MRSA colonisation may have a significant impact on population-wide estimates of MRSA occurrence.

Estimation of the incidence of MRSA patients: evaluation of a surveillance system using health insurance claim data

“…Similarly, more appropriate diagnostic criteria for MRSA patients should be identified by considering the length of stay, the amounts of antibiotics given for MRSA infection, and the diagnosis. Differences in the sensitivity and specificity of various diagnostic criteria [ 29 , 30 ] must be considered in order to appropriately evaluate MRSA surveillance using data from HICs.…”

“…Our finding that notification to JANIS about MRSA patients was not affected by patients' age suggests the usefulness of information from HICs for the evaluation of the infection surveillance system. Previous surveys have studied the reliability of the MRSA surveillance system by asking medical facilities to report additional information [ 29 , 31 ]. However, the reliability of such investigations are affected by response rates, because data are not obtained from medical institutions that do not report to the surveillance system.…”

Quality and Utility of Information Captured by Surveillance Systems Relevant to Antimicrobial Resistance (AMR): A Systematic Review

“…Thirteen studies [13,15,17,20,22,26,[31][32][33][34][35][36]54] investigated the second interpretation of the term "completeness" (the extent to which fields are completed in forms). All studies used retrospective analysis of routinely collected data to determine completeness.…”

“…This attribute was examined in five studies [13,14,24,35,36], using retrospective analysis of routinely collected data. There was wide variation, with the lowest value (1.1%) being reported for one variable in the TB surveillance system in California [36], whereas the TB Notification System of the United States Military scored 100% [14].…”

“…There was wide variation, with the lowest value (1.1%) being reported for one variable in the TB surveillance system in California [36], whereas the TB Notification System of the United States Military scored 100% [14]. Stenhem et al [35] observed that variation in the PPV (calculated by comparing the information in the first notification with the information in the study database) depended on the variable collected, Sprinson et al [36] observed wider variations within their dataset when they compared the data on a sample of cases from a TB registry with medical records.…”