Authors:
Matthew Rockloff Central Queensland University, Australia

Search for other papers by Matthew Rockloff in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-0080-2690
,
Matthew Browne Central Queensland University, Australia

Search for other papers by Matthew Browne in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-2668-6229
,
Alex M T Russell Central Queensland University, Australia

Search for other papers by Alex M T Russell in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-3685-7220
,
Nerilee Hing Central Queensland University, Australia

Search for other papers by Nerilee Hing in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-2150-9784
,
Tess Armstrong Central Queensland University, Australia

Search for other papers by Tess Armstrong in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-9683-7371
, and
Nancy Greer Central Queensland University, Australia

Search for other papers by Nancy Greer in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-6045-1984
Open access

Abstract

Background and aims

Legacy gambling harms are negative consequences of gambling that extend past periods of low risk, moderate risk and problem gambling. Gambling harm is typically measured within a 12-month timeframe and is often restricted to examining harm amongst active gamblers. The present research aimed to explore whether people experienced gambling harms 12 months or more after the resolution of at-risk or problem gambling, and how long these legacy harms lasted.

Methods

An online survey was conducted in New Zealand with past and current gamblers and concerned significant others (CSOs) of gamblers, N = 1,240 (50.8% female), that asked them about both past and current gambling harms.

Results

A majority of both gamblers and CSOs of gamblers indicated that they still suffered from gambling harm even after most of their behavioural issues with gambling had been resolved, 12+ months ago. Legacy gambling harms reduced over time, with harms diminishing most quickly in the early years, and having an average half-life of 4 years. Harms involving community-relationships, church involvement, and domestic and other violence resolved more quickly than others.

Discussion and conclusions

Legacy harms are common among ex-problem gamblers and should be considered in any full accounting of the impacts of gambling.

Conclusion

Understanding the time course and persistence of legacy harms from gambling can provide gamblers, treatment professionals and public health experts with insights into how to address gambling's long-term consequences.

Abstract

Background and aims

Legacy gambling harms are negative consequences of gambling that extend past periods of low risk, moderate risk and problem gambling. Gambling harm is typically measured within a 12-month timeframe and is often restricted to examining harm amongst active gamblers. The present research aimed to explore whether people experienced gambling harms 12 months or more after the resolution of at-risk or problem gambling, and how long these legacy harms lasted.

Methods

An online survey was conducted in New Zealand with past and current gamblers and concerned significant others (CSOs) of gamblers, N = 1,240 (50.8% female), that asked them about both past and current gambling harms.

Results

A majority of both gamblers and CSOs of gamblers indicated that they still suffered from gambling harm even after most of their behavioural issues with gambling had been resolved, 12+ months ago. Legacy gambling harms reduced over time, with harms diminishing most quickly in the early years, and having an average half-life of 4 years. Harms involving community-relationships, church involvement, and domestic and other violence resolved more quickly than others.

Discussion and conclusions

Legacy harms are common among ex-problem gamblers and should be considered in any full accounting of the impacts of gambling.

Conclusion

Understanding the time course and persistence of legacy harms from gambling can provide gamblers, treatment professionals and public health experts with insights into how to address gambling's long-term consequences.

Introduction

Legacy gambling harms are negative consequences of gambling that remain after a discrete episode of problematic gambling has been largely resolved (Langham et al., 2016). These residual impacts from gambling can remain after any level of problematic gambling involvement, including low risk, moderate risk and problem gambling (Ferris & Wynne, 2001). That is, people who are in recovery from gambling problems, no matter how minor, may still suffer some legacy harms that continue to damage their wellbeing and health. Some ongoing harms may be severe and affect people's life-course (Russell et al., 2021), such as long-term employment, and may also affect the next generation; for instance, by materially or emotionally depriving the children of gamblers (Langham et al., 2016).

Past research has focused on people suffering from severe gambling disorders (Baxter, Hilbrecht, & Wheaton, 2019; Price, Hilbrecht, & Billi, 2021). However, a more recent wave of research employs a public health approach that also recognises the broader effects of gambling on people without a mental health diagnosis of disordered gambling (Wardle, Reith, Langham, & Rogers, 2019) as well as people socially connected to the gambler who are often called concerned-significant others (CSOs) or affected others (AOs). People who have lesser problems with gambling may still suffer gambling harms and can still negatively affect those around them (Browne et al., 2016, 2017; Browne & Rockloff, 2018). The community as a whole can also be affected by gambling problems; for example, through the costs associated with treatment and the criminal justice system (Hing, Russell, Black, Rockloff, Browne, Tulloch, & Woo, 2022). Using this public health approach, gambling harm has been conceptualised as the adverse consequences of gambling that lead to a reduction in health and wellbeing in the population (Browne et al., 2016). The benefits of this approach to harm are that negative outcomes are comprehensively measured and validated against health metrics common to other disease states. This allows a comparison of gambling harm against the negative effects of other problems on similar bases, such as alcohol abuse. The commonality helps to ensure that money and effort is best spent to improve the health and wellbeing of the population by focusing appropriately on the issues yielding the best cost-benefit calculations (Johnstone & Regan, 2020; Latvala, Lintonen, & Konu, 2019; Wardle et al., 2019). Currently, attention is being directed beyond a focus on individual gamblers as the source of harm, and extending it to include considerations around product design, regulation, legal reform, and industry practices (e.g., Abbott et al., 2013; Hilbrecht et al., 2020; Latvala et al., 2019; Price et al., 2021).

Measurement instruments including the Harm Questionnaire (HQ, Blaszczynski et al., 2015) and the Short Gambling Harms Screen (SGHS, Browne, Goodwin, & Rockloff, 2018) have provided a means for quantifying gambling harm. In particular, the SGHS has been psychometrically validated, and has the benefit of being tied to health state utilities commonly used in public health (Browne et al., 2022; McLauchlan, Browne, Russell, & Rockloff, 2020; Murray Boyle, Browne, Rockloff, & Flenady, 2021). Browne et al. (in press n.d.) has provided further validation for the SGHS (renamed the Gambling Harm Scale-10 or GHS-10) and developed a new scale (GHS-20) that provides more detail on the domains of gambling-harm (e.g., financial, relationship, etc.). This work has provided a similar set of validated measures for gambling harm to affect others (GHS-10-AO, GHS-20-AO). The authors have recognised that these instruments are only measured within a specific timeframe (past 12 months), and do not consider harm to everyone, such as the children of gamblers. Nonetheless, these scales have been used in recent prevalence studies of gambling to find that 9–10% of adults report experiencing harms in the prior 12 months related to their own gambling (ACIL, 2017; Browne et al., 2017, 2019; Hing et al., 2021; Paterson, Leslie, & Taylor, 2019; Rockloff et al., 2020; Salonen, et al., 2019; Stevens, Gupta, & Flack, 2019; Woods, Sproston, Brook, & Delfabbro, 2018). Furthermore, an estimated six people are harmed by each person with gambling problems (Goodwin, Browne, Rockloff, & Rose, 2017), which amounts to 5–11% of adults being harmed as so-called affected others or likewise CSOs (Castrén, Lind, Hagfors, & Salonen, 2021; Hing et al., 2022; Rockloff et al., 2020; Salonen, Alho, & Castrén, 2016; Stevens et al., 2019).

Although there is growing literature on the pervasive harms experienced by people with gambling problems, the estimates noted above have generally presumed that the gambling problems are concurrent with the harm. There is little work that attempts to address, quantitatively, and within a public health framework, how legacy harms might affect people with past gambling problems. Similarly, there is little work on how concerned significant others (CSOs) might be affected by legacy harms, despite theoretical expositions of such harms (Langham et al., 2016).

Langham et al.’s (2016) framework on harms does not specify the time course of gambling problems, stating that “The framework does not attempt to capture causal sequences or pathways of harms, this would only be possible using a prospective longitudinal methodology.” (p. 5). The Brain Disease Model of Addiction (BDMA) (Leshner, 1997), however, suggests that people experience addiction as a chronic, relapsing disorder. Consequently, it was our expectation that the negative consequences that people experience from problematic gambling should diminish in time with respect to the most recent episode (i.e., before a potential new relapse).

Although harms that are concurrent with gambling problems are well understood (Blackman, Browne, Rockloff, Hing, & Russell, 2019; Browne & Rockloff, 2018, 2020; Langham et al., 2016; Neal, Delfabbro, & O’Neil, 2005, 2018; Rockloff, Browne, Russell, Merkouris, & Dowling, 2019), the degree to which various issues persist after problems have ceased is less clear. Given the lack of attention to this question, the following basic research questions were directed at people with past gambling problems and CSOs with past problems from someone else's gambling:

  1. Are these harmful consequences still evident after the cessation of problematic gambling?

  2. Do legacy harms diminish over time, and at what rate?

  3. Assuming legacy harms do disappear over time, are there any differences between the types of harm in how quickly they resolve? (e.g., financial, relationship, etc.)

Per Langham et al.’s (2016) harms framework, Q1 explores the assumption that some legacy harms should be evident after problematic gambling cesses. The BDMA (Leshner, 1997) suggests that, as implicit in Q2, harms diminish with distance from an acute episode. Finally, Q3 is exploratory. We are not aware of any particular theory or strong reasoning to suggest that some legacy harms might last longer than others however, this is a question with practical significance in setting expectations for recovery.

Methods

Participants

Survey participants were recruited through Qualtrics, which is an aggregator of several New Zealand-based online panel providers. This was a purposive, non-probability sample aimed at gathering large numbers of gamblers and CSOs with past gambling-related problems. Qualtrics screened out duplicates and ineligible respondents who did not meet the qualifying criteria, as described below; as well as people who finished too quickly, had patterned responding, or showed other signs of inattention. Compensation was provided for participants by the panel, most often in the form of points that could be redeemed for small-value prizes, cash or gift certificates. The survey, as described below, was open between April 8th, 2020 and May 23rd, 2020. Although participants completed the survey during the COVID-19 pandemic, questions in the survey related to their legacy-harm experiences during 2019 and earlier to avoid complications around shutdowns of gambling venues in 2020. Moreover, questions on income also related to 2019, since some people's income was often affected by the pandemic in 2020 (Fletcher, Prickett, & Chapple, 2021).

The inclusion criteria for the study was being of age 18+, living in New Zealand, and answering “yes” to the following question: “Has there been a time in your life when your gambling caused issues in your life, no matter how minor?” For the purposes of this study, we assumed and defined these “issues” as synonymous with some degree of problematic gambling, inclusive of (but not necessarily showing the existence of) low risk, moderate risk and problem gambling in the respondent's past, as defined by the PGSI. The language of “issues” was intended to avoid potential bias, since respondents may not have been willing to admit to potentially stigmatising gambling problems.

A total of 2,460 participants started the survey. Data quality checks (e.g., speeding through the survey, keyboard mashing) resulted in the removal of 420 respondents, and another 590 were removed for incomplete survey responses. The final sample included 1,450 persons, including 735 gamblers and 505 Concerned Significant Others (CSOs). Gamblers were people who admitted to 12+ month past issues with gambling. CSOs were people who did not admit to issues with their own gambling, but nevertheless answered “yes” to the question: “Have you had a close relationship* with a person whose gambling has caused issues in your life, no matter how minor?” The footnote to this question (*) said, “When we talk about a close relationship, we are referring to a personal relationship with someone that you care about and have had regular communication with.”

Measures

Time since problematic gambling ceased

Participants were asked about the most recent time “…in your life where gambling caused issues for you, no matter how minor”, and provided dates, including the year and month (if known) when “most” of these issues started and ended. The time (year and month) when the problem ended was subtracted from the time when the survey was completed (Apr 8–May 23, 2021) to form the instrumental variable for subsequent analyses. For simplicity of exposition, this variable is hereafter referred to as “time.” Thus, for example, people who were experiencing current issues with gambling have time recorded as “0”.

Problem gambling severity index

The Problem Gambling Severity Index (PGSI) is a 9-item scale for measuring gambling problems. Each question, such as “have you ever bet more than you could afford to lose?” is rated on a scale ranging from 0 = never to 3 = almost always and referred only to symptoms experienced within the last 12 months from survey administration. Summed scores classify gamblers into four groups, including 0 = non-problem, 1–2 low-risk, 3-7 high-risk, and 8+ problem gambler (Ferris & Wynne, 2001).

Gambling harms

Respondents completed a checklist of 83 potential harms (y/n) that can occur as a result of gambling (Browne et al., 2017). By design, these harms used, “phrasing that was similar regardless of whether the source of the harms was one's own gambling, or someone else's gambling. This facilitated comparisons between the two groups.” (p. 130). Harms questions had minor wording variation to account for whether the harm originated from “your past gambling” (for gamblers) or “HIS or HER past gambling” (for CSOs). Harms were grouped into financial, relationship, emotional and psychological, health, work/study, and “other” harms inclusive of cultural harms and social deviance. Participants were asked to check each harm that they experienced during the most recent episode of gambling. For participants who had their last gambling issues at least 12 months distant from the time of the survey, they were subsequently asked to check off those same harms that “… still affected you at Sometime Within The Last 12 Months due to gambling that happened in the past.” Participants were also given the opportunity to add new harms that were not yet identified from gambling that took place more than 12 months ago. Nevertheless, the few responses gathered from this additional question only reiterated prior harms, often with some new details, and thus these responses were not included in the analyses below.

Murray Boyle et al. (2021) established that harms experienced at an individual level constitute a unitary construct as conceptualised by item-response theory. In other words, the presence or absence of specific symptomatology reflects both differing degrees of item severity, and also a latent degree of global harm experienced by individuals at a given time. This finding justifies our later analyses aggregating harms into larger constructs grouped along the functional domains as detailed above (i.e., financial, relationship, etc.).

Procedure

Rockloff et al. (2022) published the full survey instrument, inclusive of the information sheet and consent form. The survey took about 20 min to complete. Participants were preferentially classified as gamblers if they reported some past issues with their own gambling. People who did not report past issues with their gambling were asked if they had issues that occurred due to someone else's gambling, and these participants are identified below as concerned significant others (CSOs).

Statistical analyses

Of the 1,450 valid completes for the survey, 210 persons indicated that their gambling problems were ongoing within the last 12 months. To address the specific problem of the present study, the analyses were restricted to the 1,240 remaining cases, which included 735 persons reporting on harm from their own past gambling, and 505 CSOs who had reported harm from someone else's past gambling. All statistical analyses were conducted at an alpha level of significance of P < 0.01.

Linear mixed effect modelling (LME) was used to estimate the rate of decay in the likelihood of selecting harms from the past issue with gambling (i.e., a last-12-month continuing harm) based on the time elapsed between the issues and survey completion. LME modelling is applicable to the present data given its utility in tracking individual-level changes in outcomes over time. As noted by Ghisletta, Renaud, Jacot, and Courvoisier (2016), life course researchers often collect longitudinal data by repeatedly assessing the same individuals over time. Such data are inherently dependent, that is, errors are correlated between observations within the same individual. Thus, they cannot be analysed with standard classical models, like ordinary least squares regression, because these assume independent (uncorrelated) errors for all observations, which would lead to incorrect statistical inference about the estimated parameters. The underlying assumption of these analyses was that increased distance in time from the gambling issue would lower the likelihood of the harm continuing to occur. The half-life of each harm was calculated from this model to give estimates of when, in year-distance, a randomly chosen harm from each category, financial, relationship, etc., is just 50% likely to still affect a gambler.

The half-life of a process refers to the time it takes for a quantity to reduce to half of its initial value. When the decline is occurring at a constant rate, i.e., an exponential decay as modelled in this analysis, then the half-life is constant over the lifetime of the process. For example, if it takes 10 years for 50% of a radioactive isotope to decay, then after another 10 years, then a further 50% of the remainder will have decayed, leading to 25% of the initial quantity. The half-life (of 10 years in this example) is an intuitive way to describe the rate of decay, with shorter half-lives corresponding to faster rates of decay. In the present analysis, the quantity of interest is the probability of experiencing a given gambling harm, and the half-life is the time required for the likelihood of reporting that harm to halve.

Time-decay trajectories were not calculable for individual harms as opposed to categories of harm. The diverse nature of specific gambling harms entails that positive observations of the 83 harms were relatively sparse. Instead, a simpler solution was employed to estimate specific harms that were relatively short- vs. long-lived. For each legacy harm that was identified as ongoing within the last 12 months, the mean number of years since the most recent gambling issue was calculated. Using this metric, gambling harms that are relatively short-lived should have lower means, and correspondingly, gambling harms that are relatively longer-lived should have a high mean score.

Ethics

Ethics: Conduct of the research was carried out in accordance with the Declaration of Helsinki. The study was approved by Central Queensland University's Human Research Ethics Committee (HREC # 22,278), which operates in accordance with the National Statement on Ethical Conduct in Research published by the National Health and Medical Research Council of Australia

Results

Demographic profile of sample

The demographic characteristics of each sample, including gamblers and CSOs with past gambling-related issues, 12+ months distant, are shown in Table 1. Most characteristics were somewhat similar, although there was a higher proportion of female respondents who were CSOs.

Table 1.

Demographic profile of 1,240 case sample used for analyses

Variable Gamblers (n = 735) CSOs (n = 505)
Age 42.4 SD = 15.6 40.9 SD = 16.7
Gender 42.7% female 62.6% female
Annual Household Income $78–104K Median $78–104K Median
Urban location (not rural) 48.4% 46.9%
Ethnicity 71.3% European/Other 71.5% European/Other
14.1% Māori 14.3% Māori
10.7% Asian 10.3% Asian
3.8% Pacific 4.0% Pacific
Problem Gambling Severity Index (last 12 mo) 34.1% Non-prob. 77.6% Non-prob.
14.1% Low risk 11.1% Low risk
20.5% Mod. risk 6.7% Mod. risk
31.2% Problem 4.6% Problem
CSO type N/A 27.1% Close friend
26.1% Partner/spouse
15.6% Parent
15.2% Other whānau or family member
6.1% Sibling (brother or sister)
6.1% Co-worker/colleague
2.2% Child
1.4% Other

Are harmful consequences ongoing?

A slight majority of gamblers (417, 56.7%) experienced at least some continuing harm from past gambling, having a median of three legacy harms (IQR 2,7). Similarly, most CSOs also reported having some legacy gambling harm (291 or 57.6%), also with a median of three harms chosen (IQR 3,6). In the sample, the median time since gambling problems had ceased was 1.8 years (IQR 0.25,9.1). These findings answered the first question posed in the introduction by verifying that people believe at least some gambling harm is ongoing, even after suggesting that “most of their “issues” with gambling are no longer relevant (as of 2 years ago, on average).

Do legacy harms reduce over time?

Bivariate correlations were used to determine whether legacy harms demonstrated a reliable reduction with distance from the end of the most recent issues with gambling; that is, with more time past problem-resolution. As expected, the number of reported legacy harms decreased with respect to the distance in time from when the most recent gambling issue was resolved, r = −0.15, P < 0.01. However, for CSOs there was no significant decrease in the report of gambling harm with respect to time, r = −0.05, P > 0.01, ns. Consequently, subsequent analyses using LME were only run using gamblers and not CSOs, since the significant reduction in harm over time was not evident for the latter.

Table 2 shows the LME models for each of the six domains of harm outlined by Langham et al. (2016). The probability of reporting harm within each domain was predicted as a function of time, expressed as log (days +1) distant from a past issue with gambling. As shown, presence of harm significantly decreased for all domains except for work/study harms. Work/study harms, however, had lower power due to the relative rarity of harms within this domain. In addition, the beta coefficient for work/study was comparable and slightly higher than the health domain; the latter of which proved significant owing to higher power.

Table 2.

Summary of GLME models predicting probability of reporting harm within each domain as a function of time (gamblers only)

Link function: Binomial (logit)

Dependent variable: p(harm)
Financial Relat. Emotional/Psych. Health Work/Stud. Other
Fixed effects B (SE)
log (time +1) −0.862* −0.962* −0.969* −0.779* −0.797 −0.839*
(0.092) (0.149) (0.126) (0.167) (0.342) (0.323)
Constant −2.953* −3.966* −2.882* −5.544* −9.046* −8.535*
(0.356) (0.306) (0.255) (0.700) (0.716) (0.650)
Random effects SD
Response IDa 1.584 2.481 2.189 3.222 6.246 5.790
Harm IDb 1.302 0.305 0.514 0.537 0.739 0.565
Observations 10,448 8,489 7,183 10,448 7,836 10,448
Log Likelihood −2,128.14 −1,305.03 −1,603.56 −1,318.07 −533.36 −658.40
Akaike Inf. Crit. 4,264.28 2,618.07 3,215.13 2,644.13 1,074.72 1,324.80
Bayes. Inf. Crit. 4,293.29 2,646.25 3,242.64 2,673.15 1,102.58 1,353.81

Note: * P < 0.01, a The standard deviation of random intercepts across individuals, b The standard deviation of random intercepts across specific harms within each domain.

Are there domain differences in the rate of harm reduction with time?

The expected probabilities for these fitted models are illustrated in Fig. 1. Some harm domains, such as emotional and psychological harms, are generally more frequent and thus the probability curves are higher across time periods. The log gradient in the reduction of harm across time for each domain is relatively constant. This observation is a property of the similar beta-weights for time shown in Table 2. The similarities are validated by a test employing the standard errors, t(1) = 0.817, P = 0.56, ns.

Fig. 1.
Fig. 1.

Average estimated probability of gamblers reporting harms within each domain as a function of time

Citation: Journal of Behavioral Addictions 2022; 10.1556/2006.2022.00073

Half-life of legacy harms

To lend greater interpretability to Table 2, the probability model was used to calculate the half-life of legacy gambling harms in each domain. The half-life was calculated as the time when it is likely that a randomly chosen legacy harm, once suffered by a gambler, is still likely to remain as a continuing harmful consequence. Similarly, probabilities for 75% and 25% likelihoods for legacy harms remaining are also shown. As illustrated in Table 3, the average half-life of legacy harms was 4.0 years. Financial harms had the longest half-life at 5.0 years, and the “Other” domain of harms had the shortest half-life at 2.2 years.

Table 3.

Half–life of legacy gambling harms

Likelihood that a single harm, chosen at random[2], remains…
75% 50% 25%
Financial 1.4 yrs 5.0 yrs 16.0 yrs
Relationship 1.0 3.4 11.4
Emotional/Psych. 1.0 3.8 13.4
Health 1.2 4.6 15.8
Work/Study 0.6 2.4 9.6
Other 0.6 2.2 6.8
Average[1] 1.1 4.0 13.5

[1] The average-calculation is weighted by the prevalence of harms in each category.

[2] Figures shown are an estimate of the half-life for one specific harm chosen at random within each domain, including Financial, Relationship, etc.

Long- and short-lived legacy harms

The LME models, however, could not be employed to explore whether individual harms (e.g., late payment of bills due to gambling) were relatively long- or short-lived, since many of the 83 harms were relatively rare (cf., Browne et al., 2017). There were too few observations of each harm to make stable estimates via LME for the mean length of all 83 harms. Instead, the mean number of years since the most recent gambling issue was calculated for each legacy harm. Relatively short-lived harms should have a lower mean than other harms, whereas long-lived harms should have a high mean. The mean scores are only relative measures that are valid for this sample, however, and the half-life calculations shown in Table 3 should be used for estimates of the typical length of legacy harms. Table 4 shows legacy harms that depart significantly from the grand mean score for all harms (M = 2.75) after applying a Holm-correction for multiple tests (83). All harms identified as significant in Table 4 are more short-lived than the average.

Table 4.

For gamblers who experienced each legacy harm (see count), a calculation of the mean number of years since the most recent gambling issue (only significant findings shown^)

Mean Years Std Dev Count 1-sample t^ P-value
Outcast from community due to involvement with gambling 0.44 0.23 11 −33.40 <0.01*
Outcast from church due to involvement with gambling 0.55 0.36 12 −21.22 <0.01*
Had experiences with violence (include family/domestic violence) 0.94 1.36 19 −5.79 <0.01*

^ Note: differences from grand mean 2.75 years. * P < 0.01 with Holm-correction applied.

Discussion

People who lose a job due to gambling may still be unemployed even after they resolve their issue with gambling. Legacy harms are residual effects of gambling problems that have been resolved in the past but still affect the individual, their close contacts or the community at large.

Gambling harm for practical reasons of recall accuracy, and for consistency, are often measured referencing the past 6–12 months (Browne et al., 2018; Shannon, Anjoul, & Blaszczynski, 2017). Consequently, gambling harm is usually measured only in relation to people who gambled within the last 6–12 months (or their contacts), and thus such measurement can exclude the possibility of continuing harms amongst people who no longer actively gamble. Legacy gambling harm can be obscured or ignored using dominant methods and may underestimate the negative effects of gambling in the community.

This current study aimed to see if legacy harms are nominated by people who have resolved most of their issues with gambling behaviour in the past, at least 12+ months distant. The results revealed that a majority of gamblers with past problems, as well as CSOs with someone who had caused them issues, still suffered from lingering legacy harms. A better understanding of these legacy harms can provide insight for gamblers in recovery. It can set expectations for how their lives might improve in recovery.

Reductions in gambling harms over time

Legacy gambling harms should reduce with greater distance from past problems with gambling. The results showed that legacy harms amongst gamblers decreased with time from past issues. The same decrease was not evident for CSOs. It is not clear why this is the case, although it may be that CSOs' problems were less severe. It is worth noting that the correlations between harm and time for CSOs was negative (r = −0.05), as predicted, but just not at a significant level (i.e., α = 0.01).

The rate of reduction in harm was well represented by a logistic log function of time, meaning that the underlying propensity for harm (i.e., the linear predictor in the GLM) declines at a constant proportional rate. The probability of reporting a specific harm was a logistic function of this underlying propensity (see Fig. 1). There was no substantial or significant difference in the rate of logistic decay in harms based on the broad domains of harm, such as financial, relationship, etc., as shown by the similar beta weights for Time in Table 2. Importantly, however, the more common legacy harms tended to persist for a longer period by virtue of an initially higher base-rate. Consequently, financial and emotional legacy-harms were the most persistent overall, since so many people had these harms in the first instance (i.e., during the period of their gambling problems).

The LME modelling allowed for a calculation of the half-life of legacy harms, which provided important practical insights into how long legacy harms might be expected to last past the end of gambling-related behavioural problems. On average, legacy harms had a half-life of 4 years, meaning that people with particular legacy harm were at least 50% likely to be free of that harm after 4 years in recovery. Financial harms had the longest half-lives at 5 years, whereas “other” harms, including cultural and social deviance harms, had the shortest half-lives at 2.2 years. It is important for gamblers to be aware, therefore, that the financial consequences of gambling problems can be long-lasting. Treatment approaches, including financial counselling, should incorporate means to address these long-term financial problems, rather than only addressing immediate financial crises caused by problematic gambling.

The diversity and relative rarity of individual types of harm made the LME modelling Impractical for use in determining which of the 83 specific harms were short or long-lived. Instead, the mean number of years since the most recent gambling issue was calculated for each legacy harm. Low mean values should be associated with relative short-lived harms, whereas high means should be associated with long-lived harms. Each harm was compared with a t-test to the grand mean for legacy harms. Legacy harms related to community and church involvement were relatively more short-lived than other harms. In addition, the legacy harm of experiences of domestic and other violence related to gambling was also more short-lived. These were exploratory findings. It may be that people's relationship problems are more affected by immediate gambling problems than the aftermath of gambling involvement, although the reasons for these findings could be the subject of future research. These short-lived harms can give gamblers in recovery hopeful indications for how quickly these issues can be resolved. It should be noted that only 3 of 83 harms had resolutions that were different (and faster) than the mean time for resolution of gambling-harms. This relative rarity, however, is partly explained by the high-bar of adjusting the probabilities for the effects of multiple comparisons.

Implications

Gamblers in recovery should be prepared to understand that some harm will continue after problematic gambling engagement ends. These long-term harms, if broadly understood by the public, may have a prophylactic benefit. Communities can develop strategies to reduce gambling involvement by promotion of its long-term negative consequences. More positively, counselling services can better inform clients about their path to recovery and help them to develop strategies to reduce ongoing harms. Governments and regulators should incorporate the costs of legacy harms into their considerations for possible gambling expansion, since prior methods tend to underestimate the total social costs.

Limitations

An important limitation to the present results include the common issues involving recall biases. Rockloff et al. (2022) partially addressed this issue by noting that ongoing legacy harms were reliably related to decrements in wellbeing, as measured by the Australian Unity Wellbeing Index (Cummins, Eckersley, Pallant, van Vugt, & Misajon, 2003) and SF-6D (Brazier, Roberts, & Deverill, 2002). Nevertheless, some recall bias in the absolute quantity of past harms identified may remain. The current methodology also relies on the identification of a discrete episode of problem gambling behaviour that had ended at least 12+ months before survey administration. The reality of gambling problems may be somewhat more nuanced, where problem gambling behaviour cycles more quantitatively rather than episodically. Although the survey included participants from each of the main ethnic backgrounds common in New Zealand, the sparsity of gambling-harms precluded a comparison between these groups. Lastly, the survey asked about harms being present or absent, whereas there may have been some unmeasured reduction in the seriousness of each harmful outcome during the period of recovery. Limiting assumptions may be explored in future research.

Conclusion

Legacy gambling harms are an understudied and often overlooked consequence of excessive gambling involvement. Typical measurement paradigms for gambling harm use a timeframe of the past 6 or 12-months, and may not consider harms to people who are not actively involved in gambling. The present study found that lingering harms from gambling that occur more than 12 months after the resolution of low risk, moderate risk and problem gambling are common. Legacy harms reduce over time, with the most rapid declines occurring in early years. A specific legacy harm, chosen at random, is about 50% likely to still be affecting a gambler after 4 years in recovery. Harms related to community and church involvement and to domestic and other violence are some of the earliest to resolve. This research provides valuable information for gamblers regarding their expectations for recovery and should help inform protective efforts aided by treatment professionals and public health experts.

Funding sources

The Ministry of Health, New Zealand. The Ministry works across the health sector to deliver better health outcomes for New Zealanders (award number: 20649434).

Authors’ contribution

MR conceptualised the research program, authored the draft and contributed to statistical analyses. MB assisted with research design and conducted the statistical analysis. AMTR assisted with research design, survey programming and participant recruitment. NH, TA and NG assisted with the research design and with refining the manuscript.

Conflicts of interest

The authors declare no conflict of interest in relation to this manuscript.

References

  • Abbott, M. , Binde, P. , Hodgins, D. , Korn, D. , Pereira, A. , … Williams, R. (2013). Conceptual framework of harmful gambling: An international collaboration. Ontario Problem Gambling Research Centre (OPGRC).

    • Search Google Scholar
    • Export Citation
  • ACIL ALLEN Consulting (2017). Fourth social and economic impact study of gambling in Tasmania. https://www.treasury.tas.gov.au/liquor-and-gaming/publications-and-research/social-and-economic-impact-studies

    • Search Google Scholar
    • Export Citation
  • Baxter, D. G. , Hilbrecht, M. , & Wheaton, C. T. J. (2019). A mapping review of research on gambling harm in three regulatory environments. Harm Reduction Journal, 16(1), 12. https://doi.org/10.1186/s12954-018-0265-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blackman, A. , Browne, M. , Rockloff, M. , Hing, N. , & Russell, A. M. T. (2019). Contrasting effects of gambling consumption and gambling problems on subjective wellbeing. Journal of Gambling Studies, 35(3), 773792. https://doi.org/10.1007/s10899-019-09862-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blaszczynski, A. , Anjoul, F. , Shannon, K. , Dylan, P. , & Wieczorek, M. (2015). Gambling harm minimisation report. NSW Office of Liquor, Gambling and Racing, 1186. https://www.responsiblegambling.nsw.gov.au/__data/assets/pdf_file/0011/878303/gambling-harm-minimisation-report.pdf.

    • Search Google Scholar
    • Export Citation
  • Brazier, J. , Roberts, J. , & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271292. https://doi.org/10.1016/s0167-6296(01)00130-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Bellringer, M. , Greer, N. , Kolandai-Matchett, K. , Rawat, V. , Langham, E. , … Abbott, M. (2017). Measuring the Burden of gambling Harm in New Zealand (No. 978-1-98-850273-1). Wellington: New Zealand Ministry of Health. http://www.health.govt.nz/publication/measuring-burden-gambling-harm-new-zealand.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , Goodwin, B. C. , & Rockloff, M. J. (2018). Validation of the short gambling harm screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies, 34, 499512. https://doi.org/10.1007/s10899-017-9698-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Langham, E. , Rawat, V. , Greer, N. , Li, E. , Rose, J. , … Best, T. (2016). Assessing gambling-related harm in Victoria: A public health perspective. Victorian Responsible Gambling Foundation. http://prism.ucalgary.ca/handle/1880/51519.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , Newall, P. , Rawat, V. , Tulloch, C. , Rockloff, M. , Li, E. , … Begg, S. (n.d.). The Gambling Harms Scales: Instruments to assess impact to gamblers and affected others that are benchmarked to health utility. Victorian Responsible Gambling Foundation.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , & Rockloff, M. J. (2018). Prevalence of gambling-related harm provides evidence for the prevention paradox. Journal of Behavioral Addictions, 7(2), 410422. https://doi.org/10.1556/2006.7.2018.41.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , & Rockloff, M. (2020). Measuring behavioural dependence in gambling: A case for removing harmful consequences from the assessment of problem gambling pathology. Journal of Gambling Studies, 36(4), 10271044. https://doi.org/10.1007/s10899-019-09916-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Rockloff, M. , Hing, N. , Russell, A. , Boyle, C. M. , Rawat, V. , & Sproston, K. (2019). NSW gambling survey 2019. Sydney: NSW Office of Responsible Gambling.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Russell, A. M. T. , Begg, S. , Rockloff, M. J. , Li, E. , Rawat, V. , & Hing, N. (2022). Benchmarking gambling screens to health-state utility: The PGSI and the SGHS estimate similar levels of population gambling-harm. BMC Public Health, 22(1), 839. https://doi.org/10.1186/s12889-022-13243-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Castrén, S. , Lind, K. , Hagfors, H. , & Salonen, A. H. (2021). Gambling-related harms for affected others: A Finnish population-based survey. International Journal of Environmental Research and Public Health, 18(18). https://doi.org/10.3390/ijerph18189564.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cummins, R. A. , Eckersley, R. , Pallant, J. , van Vugt, J. , & Misajon, R. (2003). Developing a national Index of subjective wellbeing: The Australian unity wellbeing Index. Social Indicators Research, 64(2), 159190. https://doi.org/10.1023/A:1024704320683.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferris, J. , & Wynne, H. (2001). The Canadian problem gambling index: Final report. Ottawa: Canadian Centre on Substance Abuse. https://www.greo.ca/Modules/EvidenceCentre/files/Ferris%20et%20al(2001)The_Canadian_Problem_Gambling_Index.pdf.

    • Search Google Scholar
    • Export Citation
  • Fletcher, M. , Prickett, K. C. , & Chapple, S. (2021). Immediate employment and income impacts of covid-19 in New Zealand: Evidence from a survey conducted during the alert level 4 lockdown. New Zealand Economic Papers, 18. https://doi.org/10.1080/00779954.2020.1870537.

    • Search Google Scholar
    • Export Citation
  • Ghisletta, P. , Renaud, O. , Jacot, N. , & Courvoisier, D. (2016). Linear mixed-effects and latent curve models for longitudinal life course analyses. In C. Burton-Jeangros , S. Cullati , A. Sacker , & D. Blane (Eds.), A life course perspective on health trajectories and transitions. Springer. https://doi.org/10.1007/978-3-319-20484-0.

    • Search Google Scholar
    • Export Citation
  • Goodwin, B. C. , Browne, M. , Rockloff, M. , & Rose, J. (2017). A typical problem gambler affects six others. International Gambling Studies, 17(2), 276289. https://doi.org/10.1080/14459795.2017.1331252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilbrecht, M. , Baxter, D. , Abbott, M. , Binde, P. , Clark, L. , Hodgins, D. C. , … Williams, R. J. (2020). The conceptual framework of harmful gambling: A revised framework for understanding gambling harm. Journal of Behavioural Addictions, 9(2), 190205. https://doi.org/10.1556/2006.2020.00024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hing, N. , Russell, A. M. T. , Black, A. , Rockloff, M. , Browne, M. , Tulloch, C. , … Woo, L. (2022). Gambling-related harms to concerned significant others: A national Australian prevalence study. Journal of Behavioural Addictions, 11(2), 361372. https://doi.org/10.1556/2006.2022.00045.

    • Search Google Scholar
    • Export Citation
  • Hing, N. , Russell, A. M. T. , Browne, M. , Rockloff, M. , Greer, N. , Rawat, V. , … Woo, L. (2021). The second national study of interactive gambling in Australia (2019–20) (No. 2). Melbourne: Gambling Research Australia.

    • Search Google Scholar
    • Export Citation
  • Johnstone, P. , & Regan, M. (2020). Gambling harm is everybody’s business: A public health approach and call to action. Public Health, 184, 6366. https://doi.org/10.1016/j.puhe.2020.06.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langham, E. , Thorne, H. , Browne, M. , Donaldson, P. , Rose, J. , & Rockloff, M. (2016). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16, 80. https://doi.org/10.1186/s12889-016-2747-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latvala, T. , Lintonen, T. , & Konu, A. (2019). Public health effects of gambling–debate on a conceptual model. BMC Public Health, 19(1), 1077. https://doi.org/10.1186/s12889-019-7391-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leshner, A. I. (1997). Addiction is a brain disease, and it matters. Science (New York, N.Y.), 278(5335), 4547. https://doi.org/10.1126/science.278.5335.45.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLauchlan, J. , Browne, M. , Russell, A. M. T. , & Rockloff, M. (2020). Evaluating the reliability and validity of the short gambling harm screen: Are binary scales worse than likert scales at capturing gambling harm? JGI, 44. https://cdspress.ca/wp-content/uploads/2022/09/James-McLauchlan-Matthew-Browne-Alex-M-T-Russell-Matthew-Rockloff.pdf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray Boyle, C. , Browne, M. , Rockloff, M. , & Flenady, T. (2021). Opportunity costs or not? Validating the short gambling harm screen against a set of “Unimpeachable” negative impacts. Journal of Clinical Medicine Research, 10(3), 549. https://doi.org/10.3390/jcm10030549.

    • Search Google Scholar
    • Export Citation
  • Neal, P. N. , Delfabbro, P. H. , & O’Neil, M. G. (2005). Problem gambling and harm: Towards a national definition. https://hekyll.services.adelaide.edu.au/dspace/handle/2440/40558.

    • Search Google Scholar
    • Export Citation
  • Paterson, M. , Leslie, P. , & Taylor, M. (2019). 2019 ACT gambling survey. https://papers.ssrn.com/abstract=3729048.

  • Price, A. , Hilbrecht, M. , & Billi, R. (2021). Charting a path towards a public health approach for gambling harm prevention. Zeitschrift Fur Gesundheitswissenschaften = Journal of Public Health, 29(1), 117. https://doi.org/10.1007/s10389-020-01437-2.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. , Bellringer, M. , Lowe, G. , Armstrong, T. , Browne, M. , Palmer_du_Preez, K. , … Greer, N. (2022). Life course and legacy gambling harms in New Zealand. New Zealand: New Zealand Ministry of Health. https://www.health.govt.nz/publication/life-course-and-legacy-gambling-harms-new-zealand.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. , Browne, M. , Hing, N. , Thorne, H. , Russell, A. , Greer, N. , … Sproston, K. (2020). Victorian population gambling and health study 2018–2019. Melbourne: Victorian Responsible Gambling Foundation. https://responsiblegambling.vic.gov.au/documents/759/Population_study_2018_2019_PUBLISHED_REPORT_March_2020.pdf.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Rockloff, M. J. , Browne, M. , Russell, A. M. T. , Merkouris, S. S. , & Dowling, N. A. (2019). A quantification of the net consumer surplus from gambling participation. Journal of Gambling Studies, 35, 11471162. https://doi.org/10.1007/s10899-019-09845-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T. , Browne, M. , Hing, N. , Visintin, T. , Begg, S. , Rawat, V. , & Rockloff, M. (2021). Stressful life events precede gambling problems, and continued gambling problems exacerbate stressful life events; A life course calendar study. Journal of Gambling Studies. Advance online publication. https://doi.org/10.1007/s10899-021-10090-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, A. H. , Alho, H. , & Castrén, S. (2016). The extent and type of gambling harms for concerned significant others: A cross-sectional population study in Finland. Scandinavian Journal of Public Health, 44(8), 799804. https://doi.org/10.1177/1403494816673529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, A. , Lind, K. , Castren, S. , Lahdenkari, M. , Selin, J. , & Hellman, M. (2019). Gambling harms survey 2016–2017. Helsinki, Finland: National Institute for Health and Welfare.

    • Search Google Scholar
    • Export Citation
  • Shannon, K. , Anjoul, F. , & Blaszczynski, A. (2017). Mapping the proportional distribution of gambling-related harms in a clinical and community sample. International Gambling Studies, 17(3), 366385. https://doi.org/10.1080/14459795.2017.1333131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, M. 1st , Gupta, H. 2nd , & Flack, M. 3rd. (2019). Northern territory gambling prevalence and wellbeing survey report 2018. Menzies School of Health Research. http://dx.doi.org/10.11575/PRISM/31751.

    • Search Google Scholar
    • Export Citation
  • Wardle, H. , Reith, G. , Langham, E. , & Rogers, R. D. (2019). Gambling and public health: We need policy action to prevent harm. BMJ, 365, l1807. https://doi.org/10.1136/bmj.l1807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woods , Sproston , Brook , & Delfabbro (2018). Gambling prevalence in south Australia (2018). South Australia: Services. https://www.problemgambling.sa.gov.au/about/research.

    • Search Google Scholar
    • Export Citation
  • Abbott, M. , Binde, P. , Hodgins, D. , Korn, D. , Pereira, A. , … Williams, R. (2013). Conceptual framework of harmful gambling: An international collaboration. Ontario Problem Gambling Research Centre (OPGRC).

    • Search Google Scholar
    • Export Citation
  • ACIL ALLEN Consulting (2017). Fourth social and economic impact study of gambling in Tasmania. https://www.treasury.tas.gov.au/liquor-and-gaming/publications-and-research/social-and-economic-impact-studies

    • Search Google Scholar
    • Export Citation
  • Baxter, D. G. , Hilbrecht, M. , & Wheaton, C. T. J. (2019). A mapping review of research on gambling harm in three regulatory environments. Harm Reduction Journal, 16(1), 12. https://doi.org/10.1186/s12954-018-0265-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blackman, A. , Browne, M. , Rockloff, M. , Hing, N. , & Russell, A. M. T. (2019). Contrasting effects of gambling consumption and gambling problems on subjective wellbeing. Journal of Gambling Studies, 35(3), 773792. https://doi.org/10.1007/s10899-019-09862-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blaszczynski, A. , Anjoul, F. , Shannon, K. , Dylan, P. , & Wieczorek, M. (2015). Gambling harm minimisation report. NSW Office of Liquor, Gambling and Racing, 1186. https://www.responsiblegambling.nsw.gov.au/__data/assets/pdf_file/0011/878303/gambling-harm-minimisation-report.pdf.

    • Search Google Scholar
    • Export Citation
  • Brazier, J. , Roberts, J. , & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271292. https://doi.org/10.1016/s0167-6296(01)00130-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Bellringer, M. , Greer, N. , Kolandai-Matchett, K. , Rawat, V. , Langham, E. , … Abbott, M. (2017). Measuring the Burden of gambling Harm in New Zealand (No. 978-1-98-850273-1). Wellington: New Zealand Ministry of Health. http://www.health.govt.nz/publication/measuring-burden-gambling-harm-new-zealand.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , Goodwin, B. C. , & Rockloff, M. J. (2018). Validation of the short gambling harm screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies, 34, 499512. https://doi.org/10.1007/s10899-017-9698-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Langham, E. , Rawat, V. , Greer, N. , Li, E. , Rose, J. , … Best, T. (2016). Assessing gambling-related harm in Victoria: A public health perspective. Victorian Responsible Gambling Foundation. http://prism.ucalgary.ca/handle/1880/51519.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , Newall, P. , Rawat, V. , Tulloch, C. , Rockloff, M. , Li, E. , … Begg, S. (n.d.). The Gambling Harms Scales: Instruments to assess impact to gamblers and affected others that are benchmarked to health utility. Victorian Responsible Gambling Foundation.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , & Rockloff, M. J. (2018). Prevalence of gambling-related harm provides evidence for the prevention paradox. Journal of Behavioral Addictions, 7(2), 410422. https://doi.org/10.1556/2006.7.2018.41.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , & Rockloff, M. (2020). Measuring behavioural dependence in gambling: A case for removing harmful consequences from the assessment of problem gambling pathology. Journal of Gambling Studies, 36(4), 10271044. https://doi.org/10.1007/s10899-019-09916-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browne, M. , Rockloff, M. , Hing, N. , Russell, A. , Boyle, C. M. , Rawat, V. , & Sproston, K. (2019). NSW gambling survey 2019. Sydney: NSW Office of Responsible Gambling.

    • Search Google Scholar
    • Export Citation
  • Browne, M. , Russell, A. M. T. , Begg, S. , Rockloff, M. J. , Li, E. , Rawat, V. , & Hing, N. (2022). Benchmarking gambling screens to health-state utility: The PGSI and the SGHS estimate similar levels of population gambling-harm. BMC Public Health, 22(1), 839. https://doi.org/10.1186/s12889-022-13243-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Castrén, S. , Lind, K. , Hagfors, H. , & Salonen, A. H. (2021). Gambling-related harms for affected others: A Finnish population-based survey. International Journal of Environmental Research and Public Health, 18(18). https://doi.org/10.3390/ijerph18189564.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cummins, R. A. , Eckersley, R. , Pallant, J. , van Vugt, J. , & Misajon, R. (2003). Developing a national Index of subjective wellbeing: The Australian unity wellbeing Index. Social Indicators Research, 64(2), 159190. https://doi.org/10.1023/A:1024704320683.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferris, J. , & Wynne, H. (2001). The Canadian problem gambling index: Final report. Ottawa: Canadian Centre on Substance Abuse. https://www.greo.ca/Modules/EvidenceCentre/files/Ferris%20et%20al(2001)The_Canadian_Problem_Gambling_Index.pdf.

    • Search Google Scholar
    • Export Citation
  • Fletcher, M. , Prickett, K. C. , & Chapple, S. (2021). Immediate employment and income impacts of covid-19 in New Zealand: Evidence from a survey conducted during the alert level 4 lockdown. New Zealand Economic Papers, 18. https://doi.org/10.1080/00779954.2020.1870537.

    • Search Google Scholar
    • Export Citation
  • Ghisletta, P. , Renaud, O. , Jacot, N. , & Courvoisier, D. (2016). Linear mixed-effects and latent curve models for longitudinal life course analyses. In C. Burton-Jeangros , S. Cullati , A. Sacker , & D. Blane (Eds.), A life course perspective on health trajectories and transitions. Springer. https://doi.org/10.1007/978-3-319-20484-0.

    • Search Google Scholar
    • Export Citation
  • Goodwin, B. C. , Browne, M. , Rockloff, M. , & Rose, J. (2017). A typical problem gambler affects six others. International Gambling Studies, 17(2), 276289. https://doi.org/10.1080/14459795.2017.1331252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilbrecht, M. , Baxter, D. , Abbott, M. , Binde, P. , Clark, L. , Hodgins, D. C. , … Williams, R. J. (2020). The conceptual framework of harmful gambling: A revised framework for understanding gambling harm. Journal of Behavioural Addictions, 9(2), 190205. https://doi.org/10.1556/2006.2020.00024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hing, N. , Russell, A. M. T. , Black, A. , Rockloff, M. , Browne, M. , Tulloch, C. , … Woo, L. (2022). Gambling-related harms to concerned significant others: A national Australian prevalence study. Journal of Behavioural Addictions, 11(2), 361372. https://doi.org/10.1556/2006.2022.00045.

    • Search Google Scholar
    • Export Citation
  • Hing, N. , Russell, A. M. T. , Browne, M. , Rockloff, M. , Greer, N. , Rawat, V. , … Woo, L. (2021). The second national study of interactive gambling in Australia (2019–20) (No. 2). Melbourne: Gambling Research Australia.

    • Search Google Scholar
    • Export Citation
  • Johnstone, P. , & Regan, M. (2020). Gambling harm is everybody’s business: A public health approach and call to action. Public Health, 184, 6366. https://doi.org/10.1016/j.puhe.2020.06.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langham, E. , Thorne, H. , Browne, M. , Donaldson, P. , Rose, J. , & Rockloff, M. (2016). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16, 80. https://doi.org/10.1186/s12889-016-2747-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latvala, T. , Lintonen, T. , & Konu, A. (2019). Public health effects of gambling–debate on a conceptual model. BMC Public Health, 19(1), 1077. https://doi.org/10.1186/s12889-019-7391-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leshner, A. I. (1997). Addiction is a brain disease, and it matters. Science (New York, N.Y.), 278(5335), 4547. https://doi.org/10.1126/science.278.5335.45.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLauchlan, J. , Browne, M. , Russell, A. M. T. , & Rockloff, M. (2020). Evaluating the reliability and validity of the short gambling harm screen: Are binary scales worse than likert scales at capturing gambling harm? JGI, 44. https://cdspress.ca/wp-content/uploads/2022/09/James-McLauchlan-Matthew-Browne-Alex-M-T-Russell-Matthew-Rockloff.pdf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray Boyle, C. , Browne, M. , Rockloff, M. , & Flenady, T. (2021). Opportunity costs or not? Validating the short gambling harm screen against a set of “Unimpeachable” negative impacts. Journal of Clinical Medicine Research, 10(3), 549. https://doi.org/10.3390/jcm10030549.

    • Search Google Scholar
    • Export Citation
  • Neal, P. N. , Delfabbro, P. H. , & O’Neil, M. G. (2005). Problem gambling and harm: Towards a national definition. https://hekyll.services.adelaide.edu.au/dspace/handle/2440/40558.

    • Search Google Scholar
    • Export Citation
  • Paterson, M. , Leslie, P. , & Taylor, M. (2019). 2019 ACT gambling survey. https://papers.ssrn.com/abstract=3729048.

  • Price, A. , Hilbrecht, M. , & Billi, R. (2021). Charting a path towards a public health approach for gambling harm prevention. Zeitschrift Fur Gesundheitswissenschaften = Journal of Public Health, 29(1), 117. https://doi.org/10.1007/s10389-020-01437-2.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. , Bellringer, M. , Lowe, G. , Armstrong, T. , Browne, M. , Palmer_du_Preez, K. , … Greer, N. (2022). Life course and legacy gambling harms in New Zealand. New Zealand: New Zealand Ministry of Health. https://www.health.govt.nz/publication/life-course-and-legacy-gambling-harms-new-zealand.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. , Browne, M. , Hing, N. , Thorne, H. , Russell, A. , Greer, N. , … Sproston, K. (2020). Victorian population gambling and health study 2018–2019. Melbourne: Victorian Responsible Gambling Foundation. https://responsiblegambling.vic.gov.au/documents/759/Population_study_2018_2019_PUBLISHED_REPORT_March_2020.pdf.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. J. , Browne, M. , Russell, A. M. T. , Merkouris, S. S. , & Dowling, N. A. (2019). A quantification of the net consumer surplus from gambling participation. Journal of Gambling Studies, 35, 11471162. https://doi.org/10.1007/s10899-019-09845-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T. , Browne, M. , Hing, N. , Visintin, T. , Begg, S. , Rawat, V. , & Rockloff, M. (2021). Stressful life events precede gambling problems, and continued gambling problems exacerbate stressful life events; A life course calendar study. Journal of Gambling Studies. Advance online publication. https://doi.org/10.1007/s10899-021-10090-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, A. H. , Alho, H. , & Castrén, S. (2016). The extent and type of gambling harms for concerned significant others: A cross-sectional population study in Finland. Scandinavian Journal of Public Health, 44(8), 799804. https://doi.org/10.1177/1403494816673529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, A. , Lind, K. , Castren, S. , Lahdenkari, M. , Selin, J. , & Hellman, M. (2019). Gambling harms survey 2016–2017. Helsinki, Finland: National Institute for Health and Welfare.

    • Search Google Scholar
    • Export Citation
  • Shannon, K. , Anjoul, F. , & Blaszczynski, A. (2017). Mapping the proportional distribution of gambling-related harms in a clinical and community sample. International Gambling Studies, 17(3), 366385. https://doi.org/10.1080/14459795.2017.1333131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, M. 1st , Gupta, H. 2nd , & Flack, M. 3rd. (2019). Northern territory gambling prevalence and wellbeing survey report 2018. Menzies School of Health Research. http://dx.doi.org/10.11575/PRISM/31751.

    • Search Google Scholar
    • Export Citation
  • Wardle, H. , Reith, G. , Langham, E. , & Rogers, R. D. (2019). Gambling and public health: We need policy action to prevent harm. BMJ, 365, l1807. https://doi.org/10.1136/bmj.l1807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woods , Sproston , Brook , & Delfabbro (2018). Gambling prevalence in south Australia (2018). South Australia: Services. https://www.problemgambling.sa.gov.au/about/research.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI

2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jun 2022 0 0 0
Jul 2022 0 0 0
Aug 2022 0 0 0
Sep 2022 0 0 0
Oct 2022 0 330 203
Nov 2022 0 223 115
Dec 2022 0 8 7