ABSTRACT
Objectives
This study sought to investigate the impact of gambling habits on HSUVs and health-related quality of life (HRQoL) using SF-36 measure in the Australian general population.
Methods
Using the 2015 wave of Household Income and Labour Dynamics in Australian survey (n = 17,606) age- and gender-specific HSUVs were estimated according to the severity of gambling problem (measured by the Problem Gambling Severity Index). OLS and Tobit regression models were used to control for demographic and other confounding factors. Marginal effects of the gambling statuses for the expected value of HSUV were estimated to calculate quality-adjusted life year (QALY) loss attributed to gambling.
Results
The predicted HSUVs on Australian weights for low- and moderate-risk and problem gamblers were −0.030 (95%CI −0.060 to −0.000), −0.057 (95%CI −0.089 to −0.025) and −0.181 (95%CI −0.239 to −0.123) less than non-gamblers &/or non-problem gamblers. Low HSUVs related to gambling behavior were predicted by age, gender, education, and employment. Gambling was responsible for 443.44 (95%CI −695.16 to −188.13) QALY losses in 2015 in the Australian general population.
Conclusion
Gambling is significantly and negatively associated with HRQoL and HSUVs and the magnitude of this association is determined by the severity of the gambling problem.
Article highlights
The impact of gambling on different aspects of quality of life and health state utility value (HSUV) has not been investigated in the general population and limited information is available about the effect of socioeconomic indicators on HSUV loss.
The growth of economic evaluations and importance of value for money interventions justifies the need for recent, high-quality evaluations of cost-effectiveness in the area of gambling.
The results of this study demonstrated the significant effect of gambling habit on quality of life and utility loss comparing with other mental health problems in the general population.
The current study provides HSUV sets by gambling problem severity which can be used in the future healthcare evaluation and health economic modeling.
Acknowledgments
This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS or the Melbourne Institute.
Author contributions
The author interpreted data, read and approved the final manuscript.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Supplementary material
Supplemental data for this article can be accessed here.