Guidelines for good database selection and use in pharmacoepidemiology research

Hall, Gillian; Sauer, Brian C.; Bourke, Alison; Brown, Jeffrey S.; Reynolds, Matthew W.; Casale, Robert Lo

doi:10.1002/pds.2229

Cited by 125 publications

(46 citation statements)

References 9 publications

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“…Hall et al (2012) 37 set out guidelines for good pharmacoepidemiologic practice for database selection and use, and included several recommendations for single-site and multi-site studies. 37 They provide suggestions for data checking, including assessment of the completeness and accuracy of key study variables, check of external validity ( e.g ., is the rate of some metric consistent with external estimates), logic and plausibility checks ( e.g ., assessment of age ranges, missingness, and clinical plausibility), and trending assessments. 37 …”

Section: Data Quality Checking Approaches Used By Selected Distributementioning

confidence: 99%

“…37 They provide suggestions for data checking, including assessment of the completeness and accuracy of key study variables, check of external validity ( e.g ., is the rate of some metric consistent with external estimates), logic and plausibility checks ( e.g ., assessment of age ranges, missingness, and clinical plausibility), and trending assessments. 37 …”

Section: Data Quality Checking Approaches Used By Selected Distributementioning

confidence: 99%

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Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks

2013

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Background Electronic health information routinely collected during healthcare delivery and reimbursement can help address the need for evidence about the real-world effectiveness, safety, and quality of medical care. Often, distributed networks that combine information from multiple sources are needed to generate this real-world evidence. Objective We provide a set of field-tested best practices and a set of recommendations for data quality checking for comparative effectiveness research (CER) in distributed data networks. Methods Explore the requirements for data quality checking and describe data quality approaches undertaken by several existing multi-site networks. Results There are no established standards regarding how to evaluate the quality of electronic health data for CER within distributed networks. Data checks of increasing complexity are often employed, ranging from consistency with syntactic rules to evaluation of semantics and consistency within and across sites. Temporal trends within and across sites are widely used, as are checks of each data refresh or update. Rates of specific events and exposures by age group, sex, and month are also common. Discussion Secondary use of electronic health data for CER holds promise but is complex, especially in distributed data networks that incorporate periodic data refreshes. The viability of a learning health system is dependent on a robust understanding of the quality, validity, and optimal secondary uses of routinely collected electronic health data within distributed health data networks. Robust data quality checking can strengthen confidence in findings based on distributed data network.

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Section: Data Quality Checking Approaches Used By Selected Distributementioning

confidence: 99%

Section: Data Quality Checking Approaches Used By Selected Distributementioning

confidence: 99%

Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks

2013

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“…Finally, our results may not be generalizable to all settings. However, our analyses have expanded the populations to which these findings can be generalized by examining results from four different data sources within the US and UK [ 48 ], which contain claims and medical records data from both private and public health insurance plans.…”

Section: Discussionmentioning

confidence: 99%

Determinants of saxagliptin use among patients with type 2 diabetes mellitus treated with oral anti-diabetic drugs

Saine

Carbonari

Newcomb

et al. 2015

BMC Pharmacol Toxicol

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BackgroundThe patterns and determinants of saxagliptin use among patients with type 2 diabetes mellitus (T2DM) are unknown in real-world settings. We compared the characteristics of T2DM patients who were new initiators of saxagliptin to those who were new initiators of non-dipeptidyl peptidase-4 (DPP-4) inhibitor oral anti-diabetic drugs (OADs) and identified factors associated with saxagliptin use.MethodsWe conducted a cross-sectional study within the Clinical Practice Research Datalink (CPRD), The Health Improvement Network (THIN), US Medicare, and the HealthCore Integrated Research Database (HIRDSM) across the first 36 months of saxagliptin availability (29 months for US Medicare). Patients were included if they were: 1) ≥18 years old, 2) newly prescribed saxagliptin or a non-DPP-4 inhibitor OAD, and 3) enrolled in their respective database for 180 days. For each saxagliptin initiator, we randomly selected up to ten non-DPP-4 inhibitor OAD initiators matched on age, sex, and geographic region. Conditional logistic regression was used to identify determinants of saxagliptin use.ResultsWe identified 64,079 saxagliptin initiators (CPRD: 1,962; THIN: 2,084; US Medicare: 51,976; HIRDSM: 8,057) and 610,660 non-DPP-4 inhibitor OAD initiators (CPRD: 19,484; THIN: 19,936; US Medicare: 493,432; HIRDSM: 77,808). Across all four data sources, prior OAD use, hypertension, and hyperlipidemia were associated with saxagliptin use. Saxagliptin initiation was also associated with hemoglobin A1c results >8% within the UK data sources, and a greater number of hemoglobin A1c measurements in the US data sources.ConclusionsIn these UK and US data sources, initiation of saxagliptin was associated with prior poor glycemic control, prior OAD use, and diagnoses of hypertension and hyperlipidemia.Trial registrationClinicalTrials.gov identifiers NCT01086280, NCT01086293, NCT01086319, NCT01086306, and NCT01377935

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“…Because each database has different characteristics in terms of clinical and administrative records, researchers performing pharmacoepidemiological studies need to clearly recognize the strengths and weaknesses of each database [ 16 ]. According to the Ethical Guidelines on Biomedical Research Involving Human Subjects by Ministry of Education, Culture, Sports, Science and Technology and Ministry of Health, Labour and Welfare [ 17 ], pharmacoepidemiological studies of medical databases would be classified as research based on pre-existing material and information without any invasions and interventions.…”

Section: Reviewmentioning

confidence: 99%

Pharmacoepidemiology in Japan: medical databases and research achievements

Tanaka

Seto

Kawakami

2015

J Pharm Health Care Sci

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Pharmacoepidemiology involves development of new models to predict safety in the development stages of pharmaceutical products, development of various guidelines and policy related to clinical trials, pharmacovigilance, establishment and implementation of risk management in postmarketing studies, and cost-effectiveness research in medical and social welfare sectors. Evaluations of safety, efficacy, and costs of pharmaceutical products must be developed in a different way. More recently, “big data” in medicine have become the driving force behind epidemiological studies that attempt to solve questions in the clinical setting. Furthermore, it is important to pursue cost-effectiveness considering the government’s financial condition. Epidemiologic and economic research utilizing epidemiological data linked to cost data will provide scientific evidence for appropriate distribution of health resources.

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Guidelines for good database selection and use in pharmacoepidemiology research

Cited by 125 publications

References 9 publications

Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks

Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks

Determinants of saxagliptin use among patients with type 2 diabetes mellitus treated with oral anti-diabetic drugs

Pharmacoepidemiology in Japan: medical databases and research achievements

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