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A systematic review of decision analytic modeling techniques for the economic evaluation of dental caries interventions

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PLOS ONE
卷 14, 期 5, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0216921

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  1. Health and Family Planning Commission of the Haidian Beijing Fund [72]
  2. Peking University 'Medical-Informatics' Seed Fund [BMU20160599]

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Objectives Dental caries occur through a multifactorial process that may influence all tooth surfaces throughout an individual's life. The application of decision analytical modeling (DAM) has gained an increasing level of attention in long-term outcome assessment and economic evaluation of interventions on caries in recent years. The objective of this study was to systematically review the application of DAM and assess their methodological quality in the context of dental caries. Methods A systematic review of the literature published to 31 st December 2018 was conducted in Medline, EMBASE, NHSEED, and Web of Science electronic databases. The main information and model characteristics of studies was extracted with the methodological quality of included studies reviewed and assessed using the Philips' checklist. Results Twenty five studies from different settings were included. Modeling techniques mainly comprised main type of modeling including Markov models (n = 12), Markov/microsimulation mixed model (n = 7), systematic dynamic models (n = 3), microsimulation models (n = 2) and decision tree (n = 1). The mean number of criteria fulfilled was 25.1 out of 60 items, which varied between year of study and research groups. The percentage of criteria fulfilled for data dimension was lower than for the structure and consistency dimension. Critical issues were data quality, incorporation of utility values, and uncertainty analysis in modeling. Conclusion The current review revealed that the methodological quality of DAM in dental caries economic evaluations is unsatisfied. Future modeling studies should adhere more closely to good practice guidelines, especially with respect to data quality evaluation, utility values incorporation, and uncertainty analysis in DAM based studies.

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