Can «big data» be useful to Public Health?

Transcription

1 Can «big data» be useful to Public Health? 1 Jean-Michel Costes Summer Interactive Symposium Research 2.0, Concordia University - Montréal June 10, 2015

2 Outline : The Legal Framework of e-game in The e-game in in 2014 Prevention of Problematic e-game (epg) International studies on the use of gamblers data Project of a predictive model of epg based on the gamblers data, collected in «Massive data» on e-game in The ODJ-ARJEL Study Methodological, operational and political issues s

3 Use of digital data, a sensitive topic in 3 The bill on digital The bill on intelligence services

4 Gambling : legal framework before Gambling legal framework in before 2010 Casinos (Tables games, Slot machines) Poker Lotteries Sports betting Horse racing Off line Authorization/ Concession FDJ (Française des jeux) FDJ (Française des jeux) PMU (Pari mutuel urbain) On line FDJ (Française des jeux) = State monopolies = Private operators = Prohibited 8/10/2014

5 Gambling : legal framework since Gambling legal framework in after 2010 Casinos (Tables games, Slot machines) Poker Lotteries Sports betting Horse racing Off line Authorization/ Concession FDJ (Française des jeux) FDJ (Française des jeux) PMU (Pari mutuel urbain) On line Licensed operators FDJ (Française des jeux) Licensed operators Licensed operators = State monopolies = Private operators = Prohibited 8/10/2014

6 Online gambling in 6 National survey on online gambling 2012; OFDT/ODJ (N = 4,000 online gamblers) (Tovar, Costes, and Eroukmanoff, 2013) National survey on gambling 2014; INPES/ODJ (N = 16,000 French, aged of years old) (Costes and al. 2015) In 2014, in, online gambling prevalence is: 4.1 % in general population aged from 15 to 75 (IC 95 % : 3.8% 4.4%) 7.3 % among gamblers 16/04/2015

7 Factors associated with the use of internet to gamble univariate approach (1) 7 % of of gamblers players who use OR bruts IC-95% internet to play Socio-demographics Gender Female Male Age years years years years years Occupation Employed Students Unemployed Other inactives Socio-professional categorie csp Csp-m csp Diploma < High school diploma High school diploma > High school diploma /04/2015

8 Factors associated with the use of internet to gamble univariate approach (3) 8 Game behaviour Frequency % of gamblers players who use OR bruts IC-95% internet to play {1;24} {24;52} {52;104} {104;+} Spendings < {250;500} {500 ;1000} > = Nb of games practiced (7 classes) and Practice (ref= 1; do not practice) Draw Games Scratch Card Games Horserace Betting Sports bets Poker Casino Games Problematic Game (ICJE >=5) No Yes /04/2015

9 Factors associated with the use of internet to gamble - multivariate approach (2) 9 Logistic Regression Variable to explain : online gambling Explanatory variables: Socio-demographics : sex, age, spc, diploma Gambling behavior : frequency, spending, nb of games, type of game practiced, problematic gambling (CPGI >=5) Socio-demographics Gender OR ajusted IC-95% Female 1 Male Age years years Socio-professional categorie csp- 1 csp Diploma < High school diploma 1 > =High school diploma OR ajusted IC-95% Game behaviour Frequency < 52 times per year 1 > =52 times per year Spendings < 500 per year 1 > =500 per year Nb of games practiced (7 classes) 1 or and Practice (ref= 1; do not practice) Draw Games Scratch Card Games Horserace Betting Sports bets Poker Casino Games Problematic Game (ICJE >=5) No 1 Yes /04/2015

10 Outline : The Legal Framework of e-game in The e-game in in 2014 Prevention of Problematic e-game (epg) International studies on the use of gamblers data Project of a predictive model of epg based on the gamblers data, collected in «Massive data» on e-game in The ODJ-ARJEL Study Methodological, operational and political issues s

11 Behavior marker and DSM criterion IJIRE% International%Journal%of%Internet%Research%Ethics% 11 Vol.%3%(12/2010)% * number of behaviours that are engaged in by online problem gamblers. This was devised by the first author and based on conversations with members of online gaming industry. Table 1: Summary of problem gambling criteria (DSM-IV) and likelihood of identification of problem gambling behaviour online DSM-IV Criterion Likelihood of online identification Experiencing salience/preoccupation Experiencing tolerance Experiencing relapse Experiencing withdrawal symptoms Escaping from reality Chasing losses Concealing involvement Engaging in unsociable behaviour Ruining a relationship/opportunity Other people providing a bail-out Very good possibility Reasonable possibility Slight possibility Unlikely Unlikely Definitely Unlikely Unlikely Unlikely Slight possibility (Griffiths 2013) These are additional to those identified above (i.e., chasing losses, spending high amounts of time and money, and increasing the amount of gambling over time). As a general rule of thumb, it is assumed that the more of these online behaviours that are detected, the more likely the person is to be a problem gambler. Playing a variety of stakes Playing a variety of different stakes (in games like online poker) indicates

12 Behavior Indicators 12 Data «gambler account» : gender, postal code Game data : type of game, bets, amount wagered, winnings Financial data : deposits, withdrawals Moderators data : limits

13 The baseline - PG 13 Screening tool : CPGI An indirect indicator : Request of self-exclusion Evolution of the moderators' limits Interventions of operators

14 The Transparency Project 14 Division on Addiction, The Cambridge Health Alliance, Harvard Medical School and Bwin Links between PG and : Level and frequency of bet, gambling intensity after opening an account (LaBrie and Shaffer 2011) Frequency, intensity, variability and evolution of bets in the 1 st month (Braverman and Shaffer 2012) Level, frequency and accumulation of stakes, losses, number of days of gambling (Gray, LaPlante, and Shaffer 2012)

15 The research program «gambling machines» in the UK 15 PREDICTING PROBLEM GAMBLERS: Analysis of industry data (Wardle, Parke, and Excell 2014) (Wardle and al. 2014) (Excell and al., 2014) 19 potential predictive indicators have been identified : frequency of play duration of play net expenditure levels of play engagement (interactions between expenditure and time) number of activities undertaken chasing behaviour number and type of games played within a session of play debit card reload/cash and debit card switching debit card payment declines variability in staking behaviour use of repeat bets functions play of multiple machines simultaneously stake size game volatility cash-out behaviour.

16 The research program «gambling machines» in the UK Survey on a sample of 4700 gamblers Gambling data of the loyalty card 16 Questionnaire (DSM and CPGI) Number of Playing Days Maximum Weekly Total Winnings Average Daily Player Loss Average Session Total Win Player Loss Average Daily Player Total Stake Average Weekly Net Position Average Daily Player Loss Number of Losing Sessions Average Player Loss (Session) Maximum Session Different Games Maximum Daily Total Win Number of Sessions Per Week Mean Decrease in Model Accuracy Figure 14 - Input variable importance for Player Model (Between Session Markers)

17 The research program «gambling machines» in the UK 17 1 Player Model using Between Session Markers True Positive Rate False Positive Rate Player Model (Between Session Marker, AUC=0.69) BaseLine (AUC=0.62) Random Figure 13 - Player Predictive Model (Between Session markers)

18 Outline : The Legal Framework of e-game in The e-game in in 2014 Prevention of Problematic e-game (epg) International studies on the use of gamblers data Project of a predictive model of epg based on the gamblers data collected in «Massive data» on egame in The ODJ-ARJEL Study Methodological, operational and political issues s

19 Gamblers data - ARJEL 19 Gambling legal framework in after 2010 Casinos (Tables games, Slot machines) Poker Lotteries Sports betting Horse racing Off line Authorization/ Concession FDJ (Française des jeux) FDJ (Française des jeux) PMU (Pari mutuel urbain) On line Licensed operators FDJ (Française des jeux) Licensed operators Licensed operators = State monopolies = Private operators = Prohibited ARJEL Data 8/10/2014

20 ARJEL Data 20

21 ARJEL Data Amount of stakes per trimester (million ) Montant des mises par trimestre T3 T4 T Paris spor fs Paris hippiques Poker Ensemble T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

22 ARJEL Data Number Nombre of active de comptes gamblers joueurs accounts ac fs per par trimester trimestre (in (en thousands) milliers) Paris spor fs Paris hippiques Poker Ensemble T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

23 ARJEL Data 23 Share Part des of connexions connections sur on support mobile mobile devices (en(in %) %) 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% PARIS SPORTIFS PARIS HIPPIQUES POKER T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

24 ARJEL Data Montant Bonus amounts des bonus per par trimester trimestre T3 T4 T Paris spor fs Paris hippiques Poker Ensemble T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

25 ARJEL Data % Cumulation of the stakes per trimester per gambler account > 1,000 Cumul des mises par trimestre par compte joueur > 1000 (en %) (in %) 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% PARIS SPORTIFS PARIS HIPPIQUES POKER T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

26 ARJEL Data % Cumulation des of mises the stakes des comptes of gamblers joueurs accounts du premier of the cen1 st lle centile ayant le who bet plus the misé most (en (in%) %) 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% T3 T4 T PARIS SPORTIFS PARIS HIPPIQUES POKER T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T T2 T3 T4 T1 2015

27 The ODJ-ARJEL Study 27 Goal To elaborate a predictive model of problematic gambling based on regulation data. General approach The CPGI score is the variable to explain; the indicators of activity, the explanatory variables of the model Joueur Compte joueur Mois Jour The reference screening tool: CPGI Inclusion criterion in the study Critères d'inclusion des individus dans l'étude et effectifs de joueurs correspondant compte compte ouvert compte ouvert ouvert de 1 à + 12 M de - de 1 mois 12 mois actif /30 DJ Compte actif / 12 DM inactif / 30 DJ Un compte actif : sur une période x est un compte sur lequel on observe au moins une activité de jeu (mise) sur cette En ligne : On considère qu'un individu à un compte actif au cours d'une période (12DM ou 30 DJ) si au moins un compte a été actif au cours de cette période. En colonne : Pour la durée d'ouverture, on prend en compte la durée du plus ancien compte ouvert (non fermé). Inclus dans l'échantillon Inclus dans le modèle 16/04/2015

28 The ODJ-ARJEL Sex Study 28 Regulation data selected for the study For Player Type of data Player number Age For Account Player number Account number Opening date Validation date Type of data Goal To elaborate a predictive model of problematic gambling based on regulation data. General approach Bets limit Deposits limit Withdrawal limit Self-exclusion For Months Player number Account number Month number Winnings The CPGI score is the variable to explain; the Losses indicators of activity, the explanatory variables of the model The reference screening tool: Chasing 1 CPGI For Day Inclusion criterion in the study Choosing variables of activity entering the model Stakes Simple horserace betting Complex horserace betting Complex sports bets Sports live betting Sports bet type of sports Chasing 2 (definition: from noon to noon) Player number Account number Month number Day number Stakes Buy-in Buy-in Deposits limit Withdrawals Bonus History since opening (amount date) History since opening (amount date) History since opening (amount date) Number and history since opening Type of data Cumulation per type of game Cumulation per type of game Stakes cumulation Stakes cumulation Stakes cumulation Stakes cumulation Stakes cumulation Number of 3 consecutive deposits on a rolling period of 12h Number of deposit in the hour following the stake Type of data Number and history since opening Cumulation per type of game Number and history since opening Cumulation Nb and amounts cumulation Nb and amounts cumulation Nb and amounts cumulation per type of game Color code: specific variables for each type of game Poker Horserace betting Sports Bets Bets 16/04/2015

29 The ODJ-ARJEL Study 29 Goal To elaborate a predictive model of problematic gambling based in regulation data. General approach The CPGI score is the variable to explain; the indicators of activity, the explanatory variables of the model The reference screening tool: CPGI Inclusion criterion in the study Choosing variables of activity entering the model Recruitment of gamblers to complete CPGI. Sample of 10,000 gamblers 16/04/2015

30 Issues of the study 30 Methodological Issues : Thee referential, CPGI, risk threshold Multi-activity Operational Issues : Survey gamblers Choice of statistical tools Ethical Issues : Use of personal data Political Issues : Will, legal developments

31 31 The achievement and utility of such an approach lead us to : Face methodological challenges Reflect on the purpose of such a model What is the optimal threshold for identifying problem gambling? Take into account the ethical issues The most salient problems that online researchers in the gambling studies field are likely to face concern ethical issues (informed consent, deception, public versus private spaces, lurking). Despite such ethical dilemmas, these are not insurmountable and can be remedied if careful thought and rationale is provided. (M. D. Griffiths and Whitty 2010)

32 References Braverman, J., et H. J. Shaffer «How Do Gamblers Start Gambling: Identifying Behavioural Markers for High-Risk Internet Gambling». The European Journal of Public Health 22 (2): doi: /eurpub/ckp232. Costes, Jean-Michel, Vincent Eroukmanoff, Jean-Baptiste Richard, et Marie-Line Tovar «Les jeux d argent et de hasard en en 2014». ODJ, Les notes de l observatoire des jeux,, no 4: 9. Excell, David, Georgiu Bobashev, Daniel Gonzales-Ordonez, Heather Wardle, Tom Whitehead, Robert J. Morris, et Paul Ruddle «report 3 - predicting problem gambling - final.pdf». Responsible Gambling Trust. Gray, Heather M., Debi A. LaPlante, et Howard J. Shaffer «Behavioral Characteristics of Internet Gamblers Who Trigger Corporate Responsible Gambling Interventions.» Psychology of Addictive Behaviors 26 (3): doi: /a Griffiths, Mark D «Behavioral tracking in gambling: new empirical data and the development of a new social responsibility tool». Griffiths, M. D., et M. W. Whitty «Online behavioural tracking in Internet gambling research: Ethical and methodological issues». International Journal of Internet Research Ethics 3 (12): LaBrie, Richard, et Howard J. Shaffer «Identifying Behavioral Markers of Disordered Internet Sports Gambling». Addiction Research & Theory 19 (1): doi: / Philander, Kahlil S «Identifying High-Risk Online Gamblers: A Comparison of Data Mining Procedures». International Gambling Studies 14 (1): doi: / Tovar, Marie-Line, Jean-Michel Costes, et Vincent Eroukmanoff «Les jeux d argent et de hasard sur Internet en en 2012». OFDT, Tendances,, no 85: 6. Wardle, Heather, David Excell, Eleanor Ireland, Nevena Ilic, et Stephen Sharman «report 2 identifying problem gambling - findings from a survey of loyalty card customers». Responsible Gambling Trust. Wardle, Heather, Jonathan Parke, et Excell «report 1 theoretical markers of harm for machine play in a bookmakers - a rapid scoping review». Responsible Gambling Trust. 32

33 34 Thank you for your attention To find out more: