“…For example, combinations of hospital name + admission + discharge date or unique personal identifiers (eg, social security number in the United States) in combination with nonunique identifiers (eg, date of birth) can serve as a confirmation of successful linkage . Reference standards may be externally derived, for example, from an existing data source that can be linked to the individual data files and/or crosswalk . External data sources may include vital statistics, census records, or institutional records (eg, medical records and discharge data).…”
Purpose: The purpose of this paper is to provide guidance on the evaluation of data linkage quality through the development of a checklist for reporting key elements of the linkage process.
Methods:Responding to a call for manuscripts from the International Society for Pharmacoepidemiology (ISPE), a working group including international representation from the academic, industry, and contract research, and regulatory sectors was formed to develop a checklist for evaluation of data linkage performance and reporting data linkage specifically for pharmacoepidemiologic research. This checklist expands on the reporting of studies conducted using observational routinely collected health data specific to pharmacoepidemiology (RECORD-PE) guidelines.
Results:A key aspect of data linkage evaluation for pharmacoepidemiology is to articulate how a linkage process was performed and its accuracy in terms of validation and verification of the resulting linked data. This study generates a checklist, which covers domains including data sources, linkage variables, linkage methods, linkage results, and linkage evaluation. For each domain, specific recommendations provide a clear and transparent assessment of the linkage process.
Conclusions:Linking data sources can help to enrich analytic databases to more accurately define study populations, enable adjustment for confounding, and improve the capture of health outcomes. Clear and transparent reporting of data linkage processes will help to increase confidence in the evidence generated from these data by allowing researchers and end users to critically assess the potential for bias owing to the data linkage process.
“…For example, combinations of hospital name + admission + discharge date or unique personal identifiers (eg, social security number in the United States) in combination with nonunique identifiers (eg, date of birth) can serve as a confirmation of successful linkage . Reference standards may be externally derived, for example, from an existing data source that can be linked to the individual data files and/or crosswalk . External data sources may include vital statistics, census records, or institutional records (eg, medical records and discharge data).…”
Purpose: The purpose of this paper is to provide guidance on the evaluation of data linkage quality through the development of a checklist for reporting key elements of the linkage process.
Methods:Responding to a call for manuscripts from the International Society for Pharmacoepidemiology (ISPE), a working group including international representation from the academic, industry, and contract research, and regulatory sectors was formed to develop a checklist for evaluation of data linkage performance and reporting data linkage specifically for pharmacoepidemiologic research. This checklist expands on the reporting of studies conducted using observational routinely collected health data specific to pharmacoepidemiology (RECORD-PE) guidelines.
Results:A key aspect of data linkage evaluation for pharmacoepidemiology is to articulate how a linkage process was performed and its accuracy in terms of validation and verification of the resulting linked data. This study generates a checklist, which covers domains including data sources, linkage variables, linkage methods, linkage results, and linkage evaluation. For each domain, specific recommendations provide a clear and transparent assessment of the linkage process.
Conclusions:Linking data sources can help to enrich analytic databases to more accurately define study populations, enable adjustment for confounding, and improve the capture of health outcomes. Clear and transparent reporting of data linkage processes will help to increase confidence in the evidence generated from these data by allowing researchers and end users to critically assess the potential for bias owing to the data linkage process.
“…An assessment of HIV drug resistance early warning indicators has led to changes in recordkeeping and the strengthening of adherence and monitoring 24 . The MoHSS and researchers have used EDT data to investigate adverse reactions associated with zidovudine, tenofovir, and nevirapine 23 – 25 . The findings from the nevirapine study contributed to the MoHSS revising its treatment guidelines and stopping the use of nevirapine-containing ART to initiate treatment of pregnant women with high baseline CD4 cell counts 25 …”
Integrating patient and commodity data into one system while maintaining specialized functionality has allowed managers to monitor and mitigate stock-out risks more effectively, as well as provide earlier warning for HIV drug resistance.
“…Other clinical care information at ART sites is routinely recorded in paper medical records. The medical records have been shown to have high quality information on therapy, clinical progress notes, and laboratory values …”
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