BMC public health · 2025
Towards more reliable gambling cost estimates, a population-based study with register-linkage.
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Latvala Tiina A, Rockloff Matthew, Browne Matthew, Roukka Tomi, Lintonen Tomi P, Salonen Anne H
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.