Dataset Information

The validity of self-initiated, event-driven infectious disease reporting in general population cohorts.

ABSTRACT:

Background

The 2009/2010 pandemic influenza highlighted the need for valid and timely incidence data. In 2007 we started the development of a passive surveillance scheme based on passive follow-up of representative general population cohorts. Cohort members are asked to spontaneously report all instances of colds and fevers as soon as they occur for up to 9 months. Suspecting that compliance might be poor, we aimed to assess the validity of self-initiated, event-driven outcome reporting over long periods.

Methods

During two 8 week periods in 2008 and 2009, 2376 and 2514 cohort members in Stockholm County were sent one-week recall questionnaires, which served as reference method.

Results

The questionnaires were completed by 88% and 86% of the cohort members. Whilst the false positive proportion (1-specificity) in the reporting was low (upper bound of the 95% confidence interval [CI] ? 2% in each season), the false negative proportion (failure to report, 1-sensitivity) was considerable (60% [95% CI 52%-67%] in each season). Still, the resulting epidemic curves for influenza-like illness compared well with those from existing General Practitioner-based sentinel surveillance in terms of shape, timing of peak, and year-to-year variation. This suggested that the error was fairly constant.

Conclusions

Passive long-term surveillance through self-initiated, event-driven outcome reporting underestimates incidence rates of common upper respiratory tract infections. However, because underreporting appears predictable, simple corrections could potentially restore validity.

SUBMITTER: Merk H

PROVIDER: S-EPMC3629155 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

The validity of self-initiated, event-driven infectious disease reporting in general population cohorts.

Merk Hanna H Kühlmann-Berenzon Sharon S Bexelius Christin C Sandin Sven S Litton Jan-Eric JE Linde Annika A Nyrén Olof O

PloS one 20130417 4

<h4>Background</h4>The 2009/2010 pandemic influenza highlighted the need for valid and timely incidence data. In 2007 we started the development of a passive surveillance scheme based on passive follow-up of representative general population cohorts. Cohort members are asked to spontaneously report all instances of colds and fevers as soon as they occur for up to 9 months. Suspecting that compliance might be poor, we aimed to assess the validity of self-initiated, event-driven outcome reporting ...[more]

PMID: 23613891

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Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.

Dataset Information

The validity of self-initiated, event-driven infectious disease reporting in general population cohorts.

Background

Methods

Results

Conclusions

Publications

The validity of self-initiated, event-driven infectious disease reporting in general population cohorts.

Similar Datasets

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