Latvala Tiina A, Rockloff Matthew, Browne Matthew, Roukka Tomi, Lintonen Tomi P, Salonen Anne H
Department of Promotional and Preventive Work, Finnish Institute for Health and Welfare, Mannerheimintie 166, Helsinki, 00300, Finland.
Experimental Gambling Research Laboratory, Central Queensland University, Queensland, Australia.
BMC Public Health. 2025 Aug 19;25(1):2839. doi: 10.1186/s12889-025-24043-x.
Although past research has shown a strong association between gambling participation and harms, relatively few studies have attempted to quantify the cost of these harms to society. The need to quantify costs has been identified in several countries, however, no consensus exists in the field of gambling studies on how one should estimate them.
Three methods were selected for costs calculations: Causality adjustment factors (with two variations: CAF 80%/ CAF 50%), Excess costs, and a method based on Bayes theorem. Our purpose was not to examine the overall costs of gambling, but rather to evaluate different approaches for one specific outcome. Our focus was on indirect costs relating to productivity losses associated with long-term work disability in those aged 18-64 years who had experienced gambling problems before long-term work disability had started. Work disability was operationalized as the net days of sickness absence and disability pension. The study used population-based Gambling Harms survey and the survey data were linked with the register data.
These three methods gave very different estimates on costs relating to productivity losses associated past problem gambling. The Excess cost method gave the highest estimate of 127.04 (59.26 Int$/adult) followed by the Causality adjustment method (CAF80%) of 123.61 (57.66 Int$/adult) and CAF 50% with 77.26 (36.04 Int$/adult). The method based on Bayes theorem gave the lowest estimate of the cost at 18.28 million euros (8.52 Int$/adult).
Methods commonly used in gambling cost studies yield higher estimates of gambling costs when arbitrary causality adjustment methods are used. Bayes theorem allows leveraging data on temporal patterns of gambling problems to estimate the plausible proportion where gambling is the precipitating factor for the experienced harm, rather than the other way around. Additionally, costs could be presented as Int$ per adult using the PPP exchange rate to facilitate the comparison of gambling costs between countries.
尽管过去的研究表明赌博参与和危害之间存在紧密联系,但相对较少的研究试图量化这些危害对社会造成的成本。一些国家已经认识到量化成本的必要性,然而,在赌博研究领域,对于如何估算这些成本尚未达成共识。
选择了三种方法来计算成本:因果关系调整因子(有两种变体:CAF 80%/CAF 50%)、超额成本法以及基于贝叶斯定理的方法。我们的目的不是研究赌博的总体成本,而是评估针对一个特定结果的不同方法。我们关注的是与18 - 64岁在长期工作残疾开始前就有赌博问题的人群中与生产力损失相关的间接成本。工作残疾被定义为病假缺勤和残疾抚恤金的净天数。该研究使用了基于人群的赌博危害调查,并且调查数据与登记数据相链接。
这三种方法对与过去问题赌博相关的生产力损失成本给出了非常不同的估计。超额成本法给出的最高估计值为127.04(59.26国际美元/成年人),其次是因果关系调整法(CAF80%),为123.61(57.66国际美元/成年人),CAF 50%为77.26(36.04国际美元/成年人)。基于贝叶斯定理的方法给出的成本估计值最低,为1828万欧元(8.52国际美元/成年人)。
当使用任意因果关系调整方法时,赌博成本研究中常用的方法会得出更高的赌博成本估计值。贝叶斯定理允许利用关于赌博问题时间模式的数据来估计赌博是所经历危害的促成因素的合理比例,而不是相反。此外,使用购买力平价汇率以国际美元/成年人的形式呈现成本,便于比较各国之间的赌博成本。