Title |
A systematic review of modelling approaches in economic evaluations of health interventions for drug and alcohol problems
|
---|---|
Published in |
BMC Health Services Research, April 2016
|
DOI | 10.1186/s12913-016-1368-8 |
Pubmed ID | |
Authors |
Van Phuong Hoang, Marian Shanahan, Nagesh Shukla, Pascal Perez, Michael Farrell, Alison Ritter |
Abstract |
The overarching goal of health policies is to maximize health and societal benefits. Economic evaluations can play a vital role in assessing whether or not such benefits occur. This paper reviews the application of modelling techniques in economic evaluations of drug and alcohol interventions with regard to (i) modelling paradigms themselves; (ii) perspectives of costs and benefits and (iii) time frame. Papers that use modelling approaches for economic evaluations of drug and alcohol interventions were identified by carrying out searches of major databases. Thirty eight papers met the inclusion criteria. Overall, the cohort Markov models remain the most popular approach, followed by decision trees, Individual based model and System dynamics model (SD). Most of the papers adopted a long term time frame to reflect the long term costs and benefits of health interventions. However, it was fairly common among the reviewed papers to adopt a narrow perspective that only takes into account costs and benefits borne by the health care sector. This review paper informs policy makers about the availability of modelling techniques that can be used to enhance the quality of economic evaluations for drug and alcohol treatment interventions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 37% |
United Kingdom | 7 | 37% |
Australia | 2 | 11% |
Unknown | 3 | 16% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 17 | 89% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Scientists | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 2% |
Spain | 1 | <1% |
United States | 1 | <1% |
Unknown | 139 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 27 | 19% |
Student > Ph. D. Student | 18 | 13% |
Researcher | 15 | 10% |
Student > Bachelor | 14 | 10% |
Student > Doctoral Student | 11 | 8% |
Other | 26 | 18% |
Unknown | 33 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 28 | 19% |
Nursing and Health Professions | 16 | 11% |
Economics, Econometrics and Finance | 16 | 11% |
Engineering | 8 | 6% |
Social Sciences | 7 | 5% |
Other | 26 | 18% |
Unknown | 43 | 30% |