Evidence map›Paper›PMID 31470843›Full record

ArticleBMC public health2019

The density of electronic gambling machines and area-level socioeconomic status in Finland: a country with a legal monopoly on gambling and a decentralised system of EGMs.

Susanna Raisamo, Arho Toikka, Jani Selin, Maria Heiskanen

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  11. Indigenous Gambling and Problem Gambling in Canada.Journal of gambling studies · 2022
    Article
  12. Article
  13. On the efficiency of Nordic state-controlled gambling companies.Nordisk alkohol- & narkotikatidskrift : NAT · 2021
    Article
  14. Article
  15. [The gambling industry: a public health perspective.]Revista espanola de salud publica · 2020
    Article
  16. Can gambling-funded research be non-biased? The case of section 52.Nordisk alkohol- & narkotikatidskrift : NAT · 2019
    Article
  17. From Clicks to Bets: How Social Media Engagement Influences Gambling Severity-Cross-Sectional Research.Inquiry : a journal of medical care organization, provision and financing
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Susanna RaisamoDepartment of Public Health Solutions, Alcohol, Drugs and Addictions Unit, National Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland. susanna.raisamo@thl.fi.
Arho ToikkaFaculty of Social Science, University of Helsinki, P. O. Box 54, FI-00014, Helsinki, Finland.
Jani SelinDepartment of Public Health Solutions, Alcohol, Drugs and Addictions Unit, National Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland.
Maria HeiskanenFaculty of Social Science, University of Helsinki, P. O. Box 54, FI-00014, Helsinki, Finland.

Funding

Ministry of Social Affairs and Health under section 52 of the Appropriation of the Lotteries Act.
6 · The paper itself

Abstract

backgroundElectronic gambling machines (EGMs) are considered a risky form of gambling. Internationally, studies have reported that the density of EGMs tends to be higher in socioeconomically disadvantaged areas than in more advantaged ones. We examined whether this holds true in the Finnish context where a decentralised system of EGMs guarantees wide accessibility to this form of gambling. More precisely, we investigated the association between the density of EGMs and area-level socio-economic status (SES).

methodsThe primary measure was the EGM density, referring to the number of EGMs per 1000 adults. The area-level SES was defined on the basis of the median income of inhabitants, the proportion of unemployment in the area and educational attainment (% of those beyond primary education). Three additional area characteristics were used as control variables in the analyses; the overall population density, economic activity (the number of jobs in the area per employed inhabitant), and the mean age of the inhabitants. Analyses were based on linear regression.

resultsThe EGM density was 3.68 per 1000 inhabitants (SD = 2.63). A lower area-level SES was correlated with a higher EGM density. In further analyses, this effect was mostly explained by the income of the inhabitants. Of the control variables, the population density had no detectable effect on the EGM density while areas with a higher mean age of the inhabitants, as well a higher density of jobs, had more EGMs.

conclusionsEGMs are unequally located in Finland, with more EGMs located in socio-economically less advantaged areas. The higher machine density in areas of social disadvantage is not in line with the aim of the Finnish gambling policy, which is to prevent and reduce harm caused by gambling. Changes in policy are required, especially with regard to the decisions on the placement of EGMs. This should not be made solely by gaming operators and/or from fiscal perspectives.

Indexed as

Poverty AreasAdultFinlandGamblingHumansMiddle AgedPolicyPoliticsSocioeconomic FactorsDensityElectronic gambling machine (EGM)GamblingSocioeconomic status (SES)

Identifiers

PMID31470843
PMCPMC6717347

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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.