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

BACKGROUND: Both 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 utili...

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Autores principales: Browne, Matthew, Russell, Alex M. T., Begg, Stephen, Rockloff, Matthew J., Li, En, Rawat, Vijay, Hing, Nerilee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044680/
https://www.ncbi.nlm.nih.gov/pubmed/35473621
http://dx.doi.org/10.1186/s12889-022-13243-4
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author Browne, Matthew
Russell, Alex M. T.
Begg, Stephen
Rockloff, Matthew J.
Li, En
Rawat, Vijay
Hing, Nerilee
author_facet Browne, Matthew
Russell, Alex M. T.
Begg, Stephen
Rockloff, Matthew J.
Li, En
Rawat, Vijay
Hing, Nerilee
author_sort Browne, Matthew
collection PubMed
description BACKGROUND: Both 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Gambling 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13243-4.
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spelling pubmed-90446802022-04-28 Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm Browne, Matthew Russell, Alex M. T. Begg, Stephen Rockloff, Matthew J. Li, En Rawat, Vijay Hing, Nerilee BMC Public Health Research BACKGROUND: Both 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Gambling 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13243-4. BioMed Central 2022-04-27 /pmc/articles/PMC9044680/ /pubmed/35473621 http://dx.doi.org/10.1186/s12889-022-13243-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Browne, Matthew
Russell, Alex M. T.
Begg, Stephen
Rockloff, Matthew J.
Li, En
Rawat, Vijay
Hing, Nerilee
Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title_full Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title_fullStr Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title_full_unstemmed Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title_short Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm
title_sort benchmarking gambling screens to health-state utility: the pgsi and the sghs estimate similar levels of population gambling-harm
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044680/
https://www.ncbi.nlm.nih.gov/pubmed/35473621
http://dx.doi.org/10.1186/s12889-022-13243-4
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