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. 2020 Feb 12;17(4):1171.
doi: 10.3390/ijerph17041171.

Economic Evaluations Informed Exclusively by Real World Data: A Systematic Review

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Economic Evaluations Informed Exclusively by Real World Data: A Systematic Review

Elizabeth Parody-Rúa et al. Int J Environ Res Public Health. .

Abstract

Economic evaluations using Real World Data (RWD) has been increasing in the very recent years, however, this source of information has several advantages and limitations. The aim of this review was to assess the quality of full economic evaluations (EE) developed using RWD. A systematic review was carried out through articles from the following databases: PubMed, Embase, Web of Science and Centre for Reviews and Dissemination. Included were studies that employed RWD for both costs and effectiveness. Methodological quality of the studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Of the 14,011 studies identified, 93 were included. Roughly half of the studies were carried out in a hospital setting. The most frequently assessed illnesses were neoplasms while the most evaluated interventions were pharmacological. The main source of costs and effects of RWD were information systems. The most frequent clinical outcome was survival. Some 47% of studies met at least 80% of CHEERS criteria. Studies were conducted with samples of 100-1000 patients or more, were randomized, and those that reported bias controls were those that fulfilled most CHEERS criteria. In conclusion, fewer than half the studies met 80% of the CHEERS checklist criteria.

Keywords: economic evaluation; electronic health records; real world data; real world evidence; systematic review.

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Conflict of interest statement

The authors declare that they have no conflict of interest related to this systematic review.

Figures

Figure 1
Figure 1
Flow chart of paper selection.
Figure 2
Figure 2
Quality assessment according CHEERS check list. Items 15 (Choice of model) and 16 (assumptions) only apply to mathematical models and were not scored. NA: Not apply

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