Overview of Pharmacoepidemiological Databases in the Assessment of Medicines Under Real-Life Conditions

Torre, Carla; Martins, Ana Paula

doi:10.5772/35318

Cited by 6 publications

(6 citation statements)

References 90 publications

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“…IM systems operated either in countries with non-existing or weak monitoring SR schemes, such as sub-Saharan African countries (23, 139), or in countries that have the most widely used record-linkage databases in the world for drug research, such as the UK (e.g., Clinical Practice Research Datalink) (140) or the Netherlands (e.g., PHARMO) (141)—picturing the contribution of IM systems in the real-world evidence generation data. Regardless the differences found within the methodologies used, these schemes were developed with the purpose of filling the gap between RCT (high internal validity and low external validity) (142, 143), SR data (limited by under and selective reporting) (25, 144) and automated database studies (their large size and their longer follow-up times and representativeness make it possible to study real-world effectiveness and safety, but they are usually poor in detailed covariate data) (145, 146). Based on event monitoring and by tracking patients and drug use in a life-cycle based fashion, the results originating from IM studies encompasses the identification/quantification of factors that possibly negatively affect the benefit/risk balance, including (new) adverse events (identification and strengthening of signals), increase of knowledge of drug utilization patterns, identification of off-label use, among others.…”

Section: Discussionmentioning

confidence: 99%

Intensive Monitoring Studies for Assessing Medicines: A Systematic Review

et al. 2019

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Introduction: Intensive monitoring (IM) is one of the methods of post-marketing active surveillance based upon event monitoring, which has received interest in the current medicines regulatory landscape. For a specific period of time, IM involves primary data collection and is actively focused on gathering longitudinal information, mainly safety, since the first day of drug use. Objectives: To describe IM systems and studies' data published over 11-years period (2006–2016). Specifically, we reviewed study population/event surveillance, methodological approaches, limitations, and its applications in the real-world evidence generation data. Methods: We completed a systematic search of MEDLINE and EMBASE to identify studies published from 2006 to 2016, that used IM methodology. We extracted data using a standardized form and results were analyzed descriptively. The methodological quality of selected studies was assessed using the modified Downs and Black checklist. Results: From 1,400 screened citations, we identified 86 papers, corresponding to 69 different studies. Seventy percent of reviewed studies corresponded to established IM systems, of which, more than half were prescription event monitoring (PEM) and modified-PEM. Among non-established IM systems, vaccines were the most common studied drugs ( n = 14). The median cohort size ranged from 488 (hospitals) to 10,479 (PEM) patients. Patients and caregivers were the event data source in 39.1% of studies. The mean overall quality score was similar between established and non-established IM. Conclusions: Over the study period, IM studies were implemented in 26 countries with different maturity levels of post-marketing surveillance systems. We identified two major limitations: only 20% of studies were conducted at hospital-level, which is a matter of concern, insofar as healthcare systems are facing a lack of access to new medicines at ambulatory care level. Additionally, IM access to data of drug exposure cohorts, either at identification or at follow-up stages, could somehow constitute a barrier, given the complexity of managerial, linkable, and privacy data issues.

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Section: Discussionmentioning

confidence: 99%

Intensive Monitoring Studies for Assessing Medicines: A Systematic Review

et al. 2019

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“…Developed sequentially ordered case by case rules were presented mathematically. To the best of our knowledge, no robust algorithmic approach has yet been reported to evaluate treatment duration with individual medications in multiple treatment scenario [22,27].…”

Section: Discussionmentioning

confidence: 99%

“…Representative examples include the UK Clinical Practice Research Database and Centricity TM EMR (CEMR) database of USA [27,28]. The extraction, quality control and management of such voluminous longitudinal data under individual study protocols is highly methodologically and computationally involved, and challenging from data mining and analytical viewpoints [22,29].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Montvida¹,

Arandjelović²,

Reiner³

et al. 2017

TOBIOIJ

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Background:Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research. Objectives:To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Methods:Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment ("chaining"), while second disregards chaining fields and relies on the chronology of records ("continuous"). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagonlike peptide-1 receptor agonists. Results:At individual patient level the "chaining" approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the "continuous" method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level. Conclusion:The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.

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“…The use of computerised databases has led to a significant reduction in adverse events and prescription errors [19,20], shorter hospital stays and lower mortality [21], along with better patient tracking, information exchange, efficient handling of information, and real-time data provision [16,22]. Large New Insights into the Future of Pharmacoepidemiology and Drug Safety pharmacoepidemiology data bases facilitate research, but they require well trained personnel to produce and handle big data [17,23]. The use of electronic data has led to a significant reduction in the manual effort of data collection, easy incorporation of regional data into a study, minimal need for recalls, and removal of interviewer bias [24].…”

Section: Computational and Statistical Models In Pharmacoepidemiologymentioning

confidence: 99%

Computer-Aided Pharmacoepidemiology in Drug Use and Safety: Examining the Intersection between Data Science and Medicines Research

Chikowe¹,

Mwakilama²

2021

New Insights Into the Future of Pharmacoepidemiology and Drug Safety

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Pharmacoepidemiology is a relatively new area of study that focuses on research aimed at producing data about drugs’ usage and safety in well-defined populations. Its significant impact on patient safety has translated into improving health care systems worldwide, where it has been widely adopted. This field has developed to an extent that policy and guidelines makers have started using its evidence alongside that produced from randomised controlled clinical trials. Although this significant improvement has been partly attributed to the adoption of statistics and computer-aided models into the way pharmacoepidemiology studies are designed and conducted, certain gaps still exist. This chapter reports some of the significant developments made, along with the gaps observed so far, in the adoption of statistics and computing into pharmacoepidemiology research. The goal is to highlight efforts that have led to the new pharmacoepidemiology developments, while examining the intersection between data science and pharmacology through research narrative reviews of computer-aided pharmacology. The chapter shows the significant number of initiatives that have been applied/adopted to improve pharmacoepidemiology research. Nonetheless, further developments in integrating pharmacoepidemiology with computers and statistics are needed in order to enhance the research agenda.

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Overview of Pharmacoepidemiological Databases in the Assessment of Medicines Under Real-Life Conditions

Cited by 6 publications

References 90 publications

Intensive Monitoring Studies for Assessing Medicines: A Systematic Review

Intensive Monitoring Studies for Assessing Medicines: A Systematic Review

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Computer-Aided Pharmacoepidemiology in Drug Use and Safety: Examining the Intersection between Data Science and Medicines Research

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