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Non-Clinical Medicine - Science Policy


Coauthorship and Institutional Collaborations on Cost-Effectiveness Analyses: A Systematic Network Analysis
Published: Tuesday, May 29, 2012
Author: Ferrán Catalá-López et al.

by Ferrán Catalá-López, Adolfo Alonso-Arroyo, Rafael Aleixandre-Benavent, Manuel Ridao, Máxima Bolaños, Anna García-Altés, Gabriel Sanfélix-Gimeno, Salvador Peiró

Background

Cost-Effectiveness Analysis (CEA) has been promoted as an important research methodology for determining the efficiency of healthcare technology and guiding medical decision-making. Our aim was to characterize the collaborative patterns of CEA conducted over the past two decades in Spain.

Methods and Findings

A systematic analysis was carried out with the information obtained through an updated comprehensive literature review and from reports of health technology assessment agencies. We identified CEAs with outcomes expressed as a time-based summary measure of population health (e.g. quality-adjusted life-years or disability-adjusted life-years), conducted in Spain and published between 1989 and 2011. Networks of coauthorship and institutional collaboration were produced using PAJEK software. One-hundred and thirty-one papers were analyzed, in which 526 authors and 230 institutions participated. The overall signatures per paper index was 5.4. Six major groups (one with 14 members, three with 7 members and two with 6 members) were identified. The most prolific authors were generally affiliated with the private-for-profit sector (e.g. consulting firms and the pharmaceutical industry). The private-for-profit sector mantains profuse collaborative networks including public hospitals and academia. Collaboration within the public sector (e.g. healthcare administration and primary care) was weak and fragmented.

Conclusions

This empirical analysis reflects critical practices among collaborative networks that contributed substantially to the production of CEA, raises challenges for redesigning future policies and provides a framework for similar analyses in other regions.

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