Evidence map›Paper›PMID 35473621›Full record

ArticleBMC public health2022

Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm.

Matthew Browne, Alex M T Russell, Stephen Begg, Matthew J Rockloff, En Li, Vijay Rawat, Nerilee Hing

Abstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Developing a utility value set for the Gambling Quality of Life Scale-Brief (GQoLS-Brief) using a discrete choice experiment.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
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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

7 authors.

Matthew BrowneSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia. m.browne@cqu.edu.au.
Alex M T RussellSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia.
Stephen BeggLa Trobe Rural Health School, College of Science, Health and Engineering, La Trobe University, Melbourne, Australia.
Matthew J RockloffSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia.
En LiSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia.
Vijay RawatSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia.
Nerilee HingSchool of Medical, Health & Applied Sciences, Central Queensland University, Bundaberg B8 G.47 University Dr, Branyan, QLD, 4670, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBoth the Problem Gambling Severity Index (PGSI) and the Short Gambling Harms Screen (SGHS) purport to identify individuals harmed by gambling. However, there is dispute as to how much individuals are harmed, conditional on their scores from these instruments. We used an experienced utility framework to estimate the magnitude of implied impacts on health and wellbeing.

methodsWe measured health utility using the Short Form Six-Dimension (SF-6D), and used this as a benchmark. All 2603 cases were propensity score weighted, to balance the affected group (i.e., SGHS 1+ or PGSI 1+ vs 0) with a reference group of gamblers with respect to risk factors for gambling harm. Weighted regression models estimated decrements to health utility scores attributable to gambling, whilst controlling for key comorbidities.

resultsWe found significant attributable decrements to health utility for all non-zero SGHS scores, as well as moderate-risk and problem gamblers, but not for PGSI low-risk gamblers. Applying these coefficients to population data, we find a similar total burden for both instruments, although the SGHS more specifically identified the subpopulation of harmed individuals. For both screens, outcomes on the SF-6D implies that about two-thirds of the 'burden of harm' is attributable to gamblers outside of the most severe categories.

conclusionsGambling screens have hitherto provided nominal category membership, it has been unclear whether moderate or 'at-risk' scores imply meaningful impact, and accordingly, population surveys have typically focused on problem gambling prevalence. These results quantify the health utility decrement for each category, allowing for tracking of the aggregate population impact based on all affected gamblers.

Indexed as

GamblingBenchmarkingHumansOrganizationsPrevalenceRiskGambling harmsGambling problemsHealth utilityProblem gambling severity indexSF-6DShort gambling harms screen

Identifiers

PMID35473621
PMCPMC9044680

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Registered trials

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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.