Evidence map›Paper›PMID 39217345›Full record

ArticleInternational journal of health geographics2024

Accessibility, neighborhood socioeconomic disadvantage and expenditures on electronic gambling machines: a spatial analysis based on player account data.

Jani Selin, Pasi Okkonen, Susanna Raisamo

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Article in International journal of health geographics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jani SelinFinnish Institute for Health and Welfare, P. O. Box 30, 00271, Helsinki, Finland. jani.selin@thl.fi.
Pasi OkkonenFinnish Institute for Health and Welfare, P. O. Box 30, 00271, Helsinki, Finland.
Susanna RaisamoFinnish Institute for Health and Welfare, P. O. Box 30, 00271, Helsinki, Finland.

Funding

Sosiaali- ja Terveysministeriö Appropriation under section 52 of the Lotteries Act
6 · The paper itself

Abstract

backgroundGambling and its harmful effects on human health and well-being represent a significant public health concern in many countries, with electronic gambling machines (EGMs) recognized as one of the most detrimental forms of gambling. Previous research has established an association between EGM accessibility, expenditure, gambling harm, and the socioeconomic status (SES) of neighborhoods. However, there is limited understanding of the direct impact of SES and EGM accessibility on individual player expenditures. Prior estimations of expenditure often rely on self-reported data or venue-level revenue statistics. This study uses high spatial resolution socioeconomic data together with individual-level account-based location and expenditure (point of sales) data (71,669 players, 745 EGM venues) to explore the association between EGM accessibility and neighborhood SES and to examine whether the EGM expenditure of neighborhood residents is associated with EGM accessibility and neighborhood SES. DATA AND

methodsPlayer account data include information on the home location and expenditure of the entire EGM gambling population across every EGM venue located in the Helsinki region, Finland. High-resolution (250 × 250 m) grid-level data on socioeconomic variables were used to obtain the local socioeconomic conditions of the players. EGM accessibility was estimated for every grid cell using a calibrated gravity model derived from the player account data. Statistical analyses included correlation analysis, spatial autocorrelation analysis, and regression models.

resultsFirst, significantly higher levels of EGM accessibility were found in areas with lower local SES. Second, regression analysis revealed that both higher EGM accessibility and lower local SES were associated with higher annual losses per adult. These results, in combination with visual and spatial autocorrelation analyses, revealed that accessibility to EGM gambling is highly concentrated, especially in lower socioeconomic neighborhoods with higher levels of EGM expenditure.

conclusionsThe results lay the groundwork for future spatial research on gambling harm, expenditure, accessibility, and SES utilizing detailed account data on the interaction between players and venues. The results underscore the importance of spatial restrictions when regulating EGM accessibility, particularly in areas with vulnerable populations, as a crucial measure for public health and harm prevention. The results also enable targeted gambling harm prevention actions at the local level.

Indexed as

GamblingSpatial AnalysisAdultFemaleFinlandHumansMaleMiddle AgedNeighborhood CharacteristicsResidence CharacteristicsSocial ClassSocioeconomic Disparities in HealthSocioeconomic FactorsYoung AdultAccessibilityAccount-based gambling dataElectronic gambling machinesExpenditureSocioeconomic disadvantage

Identifiers

PMID39217345
PMCPMC11365189

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.