Literature DB >> 28247278

A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms.

Cheng Leng Chan1,2, Pei San Ang3, Shu Chuen Li4.   

Abstract

INTRODUCTION: Most Countries have pharmacovigilance (PV) systems in place to monitor the safe use of health products. The process involves the detection and assessment of safety issues from various sources of information, communicating the risk to stakeholders and taking other relevant risk minimization measures.
OBJECTIVES: This study aimed to assess the PV status in Association of Southeast Asian Nation (ASEAN) countries, sources for postmarket safety monitoring, methods used for signal detection and the need for a quantitative signal detection algorithm (QSDA). Comparisons were conducted with centres outside ASEAN.
METHODS: A questionnaire was sent to all PV centres in ASEAN countries, as well as seven other countries, from November 2015 to June 2016. The questionnaire was designed to collect information on the status of PV, with a focus on the use of a QSDA.
RESULTS: Data were collected from nine ASEAN countries and seven other countries. PV activities were conducted in all these countries, which were at different stages of development. In terms of adverse drug reaction (ADR) reports, the average number received per year ranged from 3 to 50,000 reports for ASEAN countries and from 7000 to 1,103,200 for non-ASEAN countries. Thirty-three percent of ASEAN countries utilized statistical methods to help detect signals from ADR reports compared with 100% in the other non-ASEAN countries. Eighty percent agreed that the development of a QSDA would help in drug signal detection. The main limitation identified was the lack of knowledge and/or lack of resources.
CONCLUSION: Spontaneous ADR reports from healthcare professionals remains the most frequently used source for safety monitoring. The traditional method of case-by-case review of ADR reports prevailed for signal detection in ASEAN countries. As the reports continue to grow, the development of a QSDA would be useful in helping detect safety signals.

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Mesh:

Year:  2017        PMID: 28247278     DOI: 10.1007/s40264-017-0510-9

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  19 in total

1.  Use of measures of disproportionality in pharmacovigilance: three Dutch examples.

Authors:  Antoine C G Egberts; Ronald H B Meyboom; Eugène P van Puijenbroek
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

2.  Design considerations, architecture, and use of the Mini-Sentinel distributed data system.

Authors:  Lesley H Curtis; Mark G Weiner; Denise M Boudreau; William O Cooper; Gregory W Daniel; Vinit P Nair; Marsha A Raebel; Nicolas U Beaulieu; Robert Rosofsky; Tiffany S Woodworth; Jeffrey S Brown
Journal:  Pharmacoepidemiol Drug Saf       Date:  2012-01       Impact factor: 2.890

3.  Data mining in pharmacovigilance: the need for a balanced perspective.

Authors:  Manfred Hauben; Vaishali Patadia; Charles Gerrits; Louisa Walsh; Lester Reich
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

4.  Adverse drug event surveillance and drug withdrawals in the United States, 1969-2002: the importance of reporting suspected reactions.

Authors:  Diane K Wysowski; Lynette Swartz
Journal:  Arch Intern Med       Date:  2005-06-27

Review 5.  Quantitative signal detection using spontaneous ADR reporting.

Authors:  A Bate; S J W Evans
Journal:  Pharmacoepidemiol Drug Saf       Date:  2009-06       Impact factor: 2.890

6.  Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures.

Authors:  Pei San Ang; Zhaojin Chen; Cheng Leng Chan; Bee Choo Tai
Journal:  Expert Opin Drug Saf       Date:  2016-04-07       Impact factor: 4.250

7.  Pharmacovigilance activities in ASEAN countries.

Authors:  Wimon Suwankesawong; Teerapon Dhippayom; Wei-Chuen Tan-Koi; Chuenjid Kongkaew
Journal:  Pharmacoepidemiol Drug Saf       Date:  2016-05-12       Impact factor: 2.890

8.  A statistical methodology for postmarketing surveillance of adverse drug reaction reports.

Authors:  P K Norwood; A R Sampson
Journal:  Stat Med       Date:  1988-10       Impact factor: 2.373

9.  Pharmacovigilance activities in 55 low- and middle-income countries: a questionnaire-based analysis.

Authors:  Sten Olsson; Shanthi N Pal; Andy Stergachis; Mary Couper
Journal:  Drug Saf       Date:  2010-08-01       Impact factor: 5.606

Review 10.  Pharmacovigilance in Asia.

Authors:  Pipasha Biswas
Journal:  J Pharmacol Pharmacother       Date:  2013-12
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  3 in total

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Journal:  Int J Appl Basic Med Res       Date:  2018 Jul-Sep

Review 2.  Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System.

Authors:  Quan-Hoang Vuong; Manh-Tung Ho; Thu-Trang Vuong; Viet-Phuong La; Manh-Toan Ho; Kien-Cuong P Nghiem; Bach Xuan Tran; Hai-Ha Giang; Thu-Vu Giang; Carl Latkin; Hong-Kong T Nguyen; Cyrus S H Ho; Roger C M Ho
Journal:  J Clin Med       Date:  2019-02-01       Impact factor: 4.241

Review 3.  A Systematic Review of Pharmacovigilance Systems in Developing Countries Using the WHO Pharmacovigilance Indicators.

Authors:  Hamza Y Garashi; Douglas T Steinke; Ellen I Schafheutle
Journal:  Ther Innov Regul Sci       Date:  2022-06-03       Impact factor: 1.337

  3 in total

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