title: A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Health Products Regulation Group, Health Sciences Authority, 11 Biopolis Way #11-01 Helios, Singapore, 138667, Singapore. chan_cheng_leng@hsa.gov.sg.|School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia. chan_cheng_leng@hsa.gov.sg.
authors.forename: Cheng Leng
authors.initials: CL
authors.lastname: Chan
authors.affiliation: Health Products Regulation Group, Health Sciences Authority, 11 Biopolis Way #11-01 Helios, Singapore, 138667, Singapore.
authors.forename: Pei San
authors.initials: PS
authors.lastname: Ang
authors.affiliation: School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Shu Chuen
authors.initials: SC
authors.lastname: Li
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D044466:Asian People; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology*; D006282:Health Personnel; D006801:Humans; D060735:Pharmacovigilance; D011795:Surveys and Questionnaires