EMCDDA SCIENTIFIC REPORT. Study on incidence of problem drug use and latency time to treatment in the European Union

Transcription

1 EMCDDA SCIENTIFIC REPORT Study on incidence of problem drug use and latency time to treatment in the European Union EMCDDA/2000

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3 EMCDDA SCIENTIFIC REPORT Study on incidence of problem drug use and latency time to treatment in the European Union EMCDDA/EPI/CT. 99.EP.05/2000 3

4 This report was prepared by: Carla Rossi Un. of Rome Tor Vergata Dept. of Mathematics, Italy Lucilla Ravà Un. of Rome Tor Vergata Dept. of Mathematics, Italy Maria Grazia Calvani Un. of Rome Tor Vergata Dept. of Mathematics, Italy Katiuscia Berretta Un. of Rome Tor Vergata Dept. of Mathematics, Italy Erik van Ameijden Trimbos Institute, Amsterdam The Netherlands Luc Bils Comité de Concertation sur l Alcool et les autres Drogues (CCAD), Brussels, Belgium Marcel Buster GG&GD, Amsterdam The Netherlands C.Cominskey National Un. of Ireland, Mathematics Department, Ireland Marina Davoli Agenzia di Sanità Pubblica Regione Lazio, Italy Daniela De Angelis MRC Biostatistics Unit, Un. of Cambridge Forvie Site, Robinson Way, Cambridge CB2 2SR Siem Heisterkamp RIVM, Bilthoven The Netherlands Matthew Hickman Centre for Research on Drugs and Health Behaviour Social Science and Medicine Imperial College, London UK Fabio Mariani Istituto di fisiologia clinica CNR, Pisa Italy Jeorge Ribeiro Gabinate de Planeamento e de Coordenacao do Combate à Droga (GPCCD) Francis Sartor Scientific Institute of Public Health, Brussels Belgium Sean Seaman INSERM, Villejuif France Katalin Veress Institute of Hygiene and Epidemiology, Semmelweis University, Medical School, Budapest D. Walckiers Scientific Institute of Public Health Louis Pasteur, Brussels, Belgium Lucas Wiessing, EMCDDA, Lisbon Richard Hartnoll, EMCDDA, Lisbon European Monitoring Centre for Drugs and Drug Addiction, [2000] Quotation is authorised providing the source is acknowledged. European Monitoring Centre for Drugs and Drug Addiction Rua da Cruz de Santa Apolónia PT Lisboa Portugal Tel: Fax: info@emcdda.org 4

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6 List of contents LIST OF CONTENTS...6 EXECUTIVE SUMMARY...7 Introduction...7 Objectives and tasks...7 Materials and Methods and Results...8 Latency period analysis...8 Estimating the incidence of problem drug use through the Back-Calculation...12 INTRODUCTION...16 Background...16 Purpose of the project...16 Objectives...16 Tasks...16 THE STUDY OF THE LATENCY PERIOD...18 Introduction...18 Results for Amsterdam, French Community of Belgium, Budapest, Lisbon, London and Rome...18 Discussion for Amsterdam, French Community of Belgium, Budapest, Lisbon, London and Rome...23 Results for Dublin...25 ESTIMATING THE INCIDENCE OF PROBLEM DRUG USE THROUGH THE BACK-CALCULATION...28 Introduction...28 Method 28 Results...29 Amsterdam...29 Italy...32 Conclusive remarks and further developments...36 ALTERNATIVE INCIDENCE ESTIMATION METHODS...38 Introduction...38 The alternative method: applications...39 THE DESIGN OF THE EU DATABASE FOR THE LATENCY PERIOD ANALYSIS...41 REFERENCES...42 APPENDIX...44 ANNEXES

7 EXECUTIVE SUMMARY Introduction There are similarities between the spread of drug use, in particular for addictive drugs such as heroin, and infectious diseases. Use of drugs is communicated, obviously not as an organic agent, but as a kind of innovative social practice or custom, and not to everyone but only to those who, for whatever reason, are not immune (prone individuals). Once the basically contagious nature of drug use is accepted it becomes possible to study and to model the process of transmission. The epidemiological concepts of incidence (the rate of new cases occurring within a certain time period) and prevalence (the number of all existing cases at a certain moment in time) are thus operationally valuable in studying illegal drug use. Unfortunately the population of users cannot be properly studied by standard statistical (descriptive) methods as such users, due to the actual drug laws and policies implemented in the various countries, are engaged in illegal acts, thus they resist identification and constitute a hidden population. It is therefore necessary to use inferential methods and mathematical models in studying problem drug use. Such models allow, on the basis of indirect indicators, such as therapy presentations, to estimate interesting quantities such as incidence of first use, which retains a special significance as the most suitable measure of the tendency of new individuals to become involved with addictive drugs. In particular, incidence figures may provide an indication of whether the number of problem drug users is growing (epidemic phase), falling or stable (endemic phase). New techniques, recently developed for understanding the epidemiology of infectious diseases, which utilise surveillance data to estimate incidence and prevalence, are presently available and can be properly adapted in order to obtain more accurate results and estimates, in particular the class of Back-Calculation methods appears quite convenient. In order to adapt and apply Back-Calculation, the estimation of the latency period distribution, i.e. the period between first heroin use and first treatment demand, is a prerequisite. A multi-site project to estimate incidence and time trends of problem drug use by means of survival analysis and Back-Calculation models, financed by the EMCDDA, was conducted, in the period September 1998-March 1999, at the University of Rome Tor Vergata. This has resulted in the use and further development of the Empirical Bayesian Back-Calculation model to estimate incidence from treatment data, and in a preliminary analysis of the latency time between initiation and first treatment by standard methods of survival analysis. The analyses were based on therapy data from Amsterdam, London, Lisbon and various Italian cities. In the present project the same analyses have been methodologically improved and extended to more sites, in order to simultaneously increase insights into the European epidemic of problem drug use. Objectives and tasks 1. To reanalyse the data from the previous pilot study with special attention to comparability between sites, sensitivity analyses of covariates, external biases and problems inherent to Back-Calculation. Particular attention will be addressed to the comparability of the effect due to the covariate age at first use. 2. To study the latency time and estimating the incidence in at least three more sites 3. To analyse the data production process in each site and to give detailed descriptions of potential sources of bias and error per site. If possible, to give a detailed (quantitative) description of treatment availability over time. 7

8 4. To give an in-depth epidemiological interpretation of differences between sites in latency time, incidence, and covariates in terms of access to treatment and characteristics of the epidemic (e.g. changes in patterns of use). In order to achieve the objectives a work group, comprising representatives of various EU countries, has been established. Materials and Methods and Results Latency period analysis The latency period (LP) between first use of opiates and first treatment demand can be seen as the period in which problems develop that will require treatment, for those drug users who eventually present for treatment. The analysis of LP is important, not only as a prerequisite in order to apply Back-Calculation methods, but also for several other reasons. In particular, it helps in understanding the natural history of opiate use, it can be used for planning and monitoring interventions directed at improving the attraction or appropriateness of treatment services, it is important for understanding factors influencing the progression to treatment. Individual data from drugs treatment services was obtained from the various sites. The combined data set contained the following variables: age at first drug use, year of first registration at the treatment service, gender, ethnicity, route of administration. Other variables such as marital status, work activity, educational level were not considered as they relate to time of entry in the service rather than time at first drug use. For the analysis of LP standard methods were used. Kaplan-Meier analysis was used to assess the univariate non-parametric survival function. Cox proportional hazard regression was used to determine the influence of covariates on LP by estimating the corresponding regression parameters. In the Cox model, the covariate age at first heroin use has been coded as a continuous variable in order to obtain comparability of the corresponding ratio s between the sites, given that the age distributions differed strongly between countries. The latency period analyses were performed for Amsterdam, the French Community of Belgium, Budapest, Dublin, Lisbon, London and Rome. Univariate analysis by Kaplan Meier shows that mean LP is 6.7 years for Rome, 7.0 years for London and 7.1 years for Amsterdam and for the French Community of Belgium. For Lisbon a longer LP is observed of 8.7 years, which is, however, probably due to the short existence of the services. Budapest has the shorter mean LP, 3.1 years. This result appears to be similar to that obtained for Dublin (3.1 years). Both situations can be due to biases related to the recent fast increase in incidence which causes higher and higher proportions of recent addicts to appear in the therapy centres (see below). The median values are equal in Rome, London and Amsterdam with 5.0 years, while it is 6 years for the French Community of Belgium and 2.5 years for Budapest and 2.4 for Dublin. The stratified Kaplan-Meier analysis was then performed to determine the covariates which were to be introduced in the Cox regression model (see Table 1). Age at first heroin use is analysed only by Cox regression as it was coded as a continuous variable. The variable ethnicity was disregarded from further analysis for Amsterdam and London, since resulted that the differences in LP for the sites were due to differences in onset of the heroin epidemics for different ethnic groups. The results of the analysis of prognostic factors, the Cox model analysis, are reported in Table 2. The LP analysis for Dublin was referred to the year of 1 st treatment Since only one year was available, comparison with the other sites was not possible. Kaplan-Meier analyses results are showed in Table 3, while output 8

9 from Cox regression model in Table 4. For the variable age at first heroin use, each year of increased age corresponds with an increase in the risk of entering first treatment of 6,7% (ranging from 6.2% in Rome to 7.5% for the French Community of Belgium). This cumulates to an increased risk of entering first treatment of about 91% per 10 years increase of age at first use. The results suggest that LP from first heroin use to first treatment is similar across the participating sites, except Dublin and Budapest. Despite differences in the magnitude of effects of the other covariates, the effects of age at first heroin use were strikingly similar between sites (except Dublin and Budapest), and the risk of entering first treatment strongly increased with age at first heroin use. Females in general show a higher risk of entering first treatment than males, while some consistent differences are observed between starting heroin use by different route of administration. Ethnicity was not significant in most sites except Lisbon. The results can be used as an evidence base on which other work could be built. For planning interventions such as needle exchange or HIV testing and counselling it is important to have information on the size and dynamics of the population of problem drug users (in some countries such as Italy and Portugal this is almost equal to injecting drug users). It is known that drug users in treatment are relatively protected from infection or overdose death by having less instances of risk. If young drug users take longer to get into treatment this implies that they are longer at risk for infection or overdose, and this would be similar for the other subgroups of users identified by the analysis of covariates. Several limitations have to be taken into account when interpreting our results. First, the year of implementation of treatment services could profoundly influence LP, if the analysis would include data from the first years that treatment was available. For heroin users entering first treatment in those years, LP could never be shorter than the period since the year of their first heroin use and the year of implementation of services, resulting in an artificially longer LP (left-truncation). Second, changes in policy implementation and in drug laws could artificially change LP, e.g. if police activity is increased during a certain period and arrests are referred to treatment. Third, the stage of the heroin use epidemic could strongly influence LP. During the first years of an epidemic all observed LP would by definition be shorter, and with decreasing incidence the longer LP would be over-represented. Thus, in order to properly interpret the results related to the latency analysis, the information about the incidence is also necessary. In particular, as mentioned above, a recent and fast increasing epidemic can cause an underestimate of the latency period and, viceversa, and old decreasing epidemic can result in an overestimate of the latency period. Thus, further external information is needed to properly interpret the results. Let us consider, for example, the results obtained for Dublin, where the latency period appears very short and the influence of age at first use seems to be negligible, and let us try to use external information from the national report provided by the Irish Focal Point to better evaluate possible biases due possibly to a fast increasing incidence of use. In the following (Table 5), some statistics regarding indirect measures of heroin use are reported and clearly indicate a general recent increase in heroin use in Ireland. Similar trends can also be obtained on the basis of survey data or drug related deaths. This can cause various biases in the latency period analysis such as underestimation and the negligible effect of some covariate, in particular age at first use. 9

10 Table 1 Kaplan-Meyer summaries for the latency period analysis. Site Group (size) Mean 1 quart. Median 3 quart. Amsterdam Total (1078) Analysis for years of treatment data Males (888) Females (190) Surinam/Dutch Antillians (233) Dutch (744) Turkish/Moroc (71) Route of administration is not available French Community of Belgium Analysis for years of treatment data Total (2608) Males (2019) Females (578) Injectors (513) Smokers (1415) Injectors (main)+smokers (secondary) (142) Injectors (secondary)+smokers (main) (130) Oral (208) Sniffers (25) Injectors+Smokers (134) Ethnicity is not available Budapest Total (400) Analysis for years of treatment data Males (316) Females (84) Injectors (311) Non injectors (89) Ethnicity is not available Lisbon Total (918) Analysis for years Males (761) of treatment data Females (157) White (813) Black (105) Injectors (213) Smokers (687) Sniffers (18) London Total (9892) Analysis for years Males (7429) of treatment data Females (2463) White, Black, Other (9417) Chinese, Asian other (126) Indian, Pakistan, Bangladesh (349) Injectors (5381) Smokers (4511) Rome (inner city) Analysis for year of treatment data 96 Total (4646) Males (3890) Females (756) Route of administration and ethnicity are not available 10

11 Table 2 Cox multivariate model: in the last column is reported the increment in the risk of the group indicate with respect to the baseline group when the other covariates are fixed. Site Group (size) Baseline group AOR (C.I.) Amsterdam Males (888) Males 1 Analysis for years of treatment data Females (190) ( ) Age 1 st heroin use (baseline 11) 1 Each year ( ) Route of administration is not available French Community of Belgium Analysis for years of treatment data Males (2019) Males 1 Females (578) ( ) Injectors (513) Injectors 1 Smokers (1415) ( ) Injectors (main)+smokers (secondary) (142) ( ) Injectors (secondary)+smokers (main) (130) ( ) Oral (208) ( ) Sniffers (25) ( ) Injectors+Smokers (134) ( ) Age 1 st heroin use (baseline 5) 1 Each year ( ) Ethnicity is not available Budapest Sex, route of administration and age at 1 st heroin use are not significant Analysis for Ethnicity is not available years of treatment data Lisbon Males (761) Males 1 Analysis for Females (157) ( ) years of Injectors (213) Injectors 1 treatment data Smokers (687) ( ) Sniffers (18) ( ) White (813) White 1 Black (105) ( ) Age 1 st heroin use (baseline 11) 1 Each year ( ) London Males (7429) Males 1 Analysis for Females (2463) ( ) years of Injectors (5381) Injectors 1 treatment data Smokers (4511) ( ) Age 1 st heroin use (baseline 12) 1 Each year ( ) Rome (inner city) Analysis for year of treatment data 96 Age 1 st heroin use (baseline 11) 1 Each year ( ) Sex is not significant Route of administration and ethnicity are not available Table 3 Kaplan-Meyer summaries for the latency period analysis (Dublin data-set). Site Group (size) Mean 1 quart. Median 3 quart. Dublin Total (1166) Analysis for year of Males (801) treatment data 96 Females (365) Injectors (441) Non injectors (725) Ethnicity is not available 11

12 Table 4 Cox multivariate model: in the last column is reported the increment in the risk of the group indicate with respect to the baseline group when the other covariates are fixed. Site Group (size) Baseline group AOR (C.I.) Dublin Males (801) Males 1 Analysis for year of Females (365) ( ) treatment data 96 Non injectors (725) Non injectors 1 Injectors (441) ( ) Ethnicity is not available Age at 1 st heroin use is not significant Table 5 Official statistics related to law enforcement data (Source: Annual Reports of An Garda Síochána). Ireland Number of Drug Charges/Prosecutions by Type of Drug Type of Drug Heroin Ireland Number and quantity (kg) of Seizures by Type of Drug Type of Drug Number Quantity Number Quantity Number Quantity Heroin Estimating the incidence of problem drug use through the Back- Calculation The Back-Calculation (BC), a methodology which was first proposed by Brookmeyer and Gail (1986) to estimate the HIV infection curve on the basis of AIDS incidence data, has been used in this project to back-calculate the onset incidence of problem drug use, for those who will eventually be observed, from observed data on drug users presenting to treatment, or firstly recorded by some agency for any reason, and from an estimate of the latency period,. A particular BC method proposed by Heisterkamp et coll. (1995, 1999), the Empirical Bayesian Back-Calculation (EB-BC), has been adopted in this context. The software for the EB-BC is written in S-plus language and has been recently updated. An user friendly interface has been produced by Siem Heisterkamp. The BC has been applied by using aggregated treatment data collected in Amsterdam (NL) in Italy, and in UK, provided by different sources depending on the local availability, and classified according to year of registration, age class and gender, and region or ethnicity when applicable. The latency period was estimated, by using different local datasets (Amsterdam (NL), Latina (Italy), UK) comprising individual records, provided by the different regional health authorities. The method was not applied to the other sites because of too short time series and/or biases in the data (French Community of Belgium, Hungary, Ireland, and Lisbon). In figures 1-4 the results of the application of the Back-calculation procedure for Amsterdam and Italy are shown. Figures 1 and 2 reports the estimated incidence curve of DUs, with 95%CI, with the age at first treatment covariate included in the model. This covariate has a significant impact on the latency period distribution as previously reported, thus the best choice is to use the estimates obtained including the age-covariate. Figures 3 and 4 report the estimated incidence curve of DUs under treatment, with the age at first treatment covariate, with 95%CI. From the graph it appears that for Amsterdam the trend of the onset incidence curve is decreasing or stabilizing (endemic phase) and that the population involved in the problem is also ageing. For Italy the incidence curve shows two peaks respectively in 1985 (the lowest one) and 1991 (the highest one). Nevertheless, this is not necessarily an evidence of two different epidemic in time, since the two peaks could be determined 12

13 by different local sub-epidemics developing differently in time. Preliminary results obtained applying the method at local level (using the same latency period distribution used for the national data) show different incidence curves (not shown), even when an apparently homogeneous territory, namely the three biggest regions in the southern part of Italy (Puglia, Campania and Sicilia), is considered. The situation is consistent with external knowledge about the local peculiarities of the regions for what concerns, for example, traffic routes and socio-economic framework. The regional curves are much more similar to regular epidemic curves, such as that obtained for Amsterdam. Therapy incidence data provided by the partners from countries different from Italy and the Netherlands did not allow to apply reliably the Back-Calculation method to estimate the incidence curve. The problem was mainly related to the number of years of therapy incidence made available. To better understand the problem we can consider the application of the EB-BC to data provided for London. In Figure 5 the estimated curve of DUs incidence by age-class, with 95% CI, as estimated through the EB-BC with the age-covariate included in the model. The low level of the tails appears quite unreal and very likely is due to biases in the data and to a too short series of observed data ( ). Moreover the incidence data of people under treatment are rather sparse (not shown) and this can affect the estimate considerably. A possible alternative method was then considered and first applied by the local work group in London. The method and the application to the London data-set is described in the paper by Seaman, Hickman and De Angelis presented as Country Report for UK. The same method was then applied to the other data-sets presenting problems for the application of BC, namely Lisbon, Budapest and the French Community of Belgium. The results are summarized in the following Figure 6. The different phases of the epidemics can be easily appreciated. Comparing the incidence curves estimated in the various cities it is possible to conclude that for Budapest similar biases as those observed for Dublin may be caused by the phase of the heroin epidemic. These can result in the underestimation of the latency period and an apparent negligible effect of otherwise important covariates, such as age at first use. Further investigations might help to better understand the space-time behaviour of the heroin epidemic in the European countries. Several remarks and further developments could be suggested on the basis of the results presented above. Both the latency period and the subsequent BC estimates are highly sensitive to the quality and completeness of data, thus it is quite crucial that routine data collection is improved in the various countries, in particular with regards to the completeness and accuracy. The incidence curves provided by the EB-BC estimation procedure are dependent on the latency period model chosen, but the location of the peaks of the epidemic seems to be a robust parameter. Also the qualitative trends seem to be robustly estimated. It is important to note that the BC underestimates the size of the epidemic among the total population due to the hidden part of the drug users population who will never present for treatment. Methodological developments should be aimed at reducing the uncertainties of the estimate of onset incidence in recent years, which constitute the main difficulties in applying any BC estimation procedures. For example a dynamic (compartmental) model of the drug user career could be used to estimate the size of the drug epidemic by a different approach and gather further information on the behaviour of the incidence curve in recent years. 13

14 Fig 1-2: Incidence of DUs estimated through the Gamma latency period distribution, with the age at first treatment covariate with 95%CI Total population by age class - (Amsterdam Italy) Onset incidence * se Onset incidence * se years years Fig 3-4: Incidence of DUs under treatment estimated through the Gamma latency period distribution, with the age at first treatment covariate with 95%CI Total population by age class- (Amsterdam Italy) 6-monthly DrugsHelp incidence * se 6-monthly DrugsHelp incidence * se years years Fig 5: Incidence of DUs estimated with the age at first treatment covariate with 95%CI Total population by age class UK bcfut1t3.aem Fig 6: Incidence curves estimated by the Brookmeyer and Liao method for French Community of Belgium, Lisbon and Budapest. Adjusted incidence curves Onset incidence * se Adjusted onset incidence Years years Lisbon Belgium Budapest 14

15 It must also be stressed that the alternative method used to estimate the incidence curves for Lisbon, the French Community of Belgium and Budapest requires different data with respect to the Back-Calculation. In particular, as it is just a calibration method to adjust observed onset incidence, it is necessary to collect individual data which include age or year at first use. The observed onset incidence is calculated on the basis of individual records of drug users in therapy and these are usually self reported, thus they are possibly biased and subject to underreporting generating missing data. As a consequence, the method is much less reliable and more costly, and it is suitable only for local incidence estimation exercises. These can be local in the sense of the French Community of Belgium (just related to a well defined sub-set of the drug user population: the French community), in the sense of London or Amsterdam (related to a special area) or in the sense of Lisbon or Budapest (even a more special area or service). In any case the estimated trends just refer to the special target populations taken into account; thus, unless these can be considered as representative samples of the global population, nothing can be inferred about the general qualitative trends. It may well be that the trend is increasing in a particular area but not everywhere, possibly due to space-time diffusion. Nothing can be inferred about the quantitative trends and the sizes, unless the analyses are based on the individual records of all the drug users assisted in the area (country, region, city.). On the other hand, the application of the Back-Calculation method only requires aggregated therapy incidence data, which are routinely collected by the surveillance systems, and results much less costly and much more reliable and robust as it is not based on self reporting of any kind. 15

16 Background Introduction A multi-site project to estimate incidence and time trends of problem drug use by means of survival analysis and Back-Calculation models was conducted, in the period September 1998-March 1999, at the University of Rome 'Tor Vergata', in collaboration with and financed by the EMCDDA. This has resulted in the further development and use of the Empirical Bayesian Back-Calculation model to estimate incidence from treatment data, and a preliminary extended analysis of the latency time between initiation and first treatment by standard methods of survival analysis. The analyses were based on therapy data from Amsterdam, London, Lisbon and various Italian cities. The present project was aimed at extending these analyses including more sites and providing a wider interpretation of the results both from a methodological point of view and from an epidemiological point of view. Purpose of the project The objectives of the project are reported below. Objectives To understand the epidemiological factors related to the different incidence patterns observed including their relation to availability and need for treatment of young drug users To extend the analyses to at least three more sites. To understand the potential sources of bias and error in the data production processes of the participating sites. To coordinate a multi-disciplinary and multi-national working group of experts and disseminate the methodology among them. In order to achieve the above reported objectives the following tasks were considered. Tasks 1. To reanalyse the data from the previous pilot study with special attention to comparability between sites, sensitivity analyses of covariates, external biases and problems inherent to Back-Calculation. Particular attention will be addressed to the comparability of the effect due to the covariate age at first use by introducing, in the latency period analysis, a standard stratification of the variable and applying the Cox regression model using such variable as a continous covariate. 2. To study the latency time and estimating incidence in at least three more sites 3. To analyse the data production process in each site and to give detailed descriptions of potential sources of bias and error per site. If possible, to give a detailed (quantitative) description of treatment availability over time. 16

17 4. To give an in-depth epidemiological interpretation of differences between sites in latency time, covariates and incidence in terms of access to treatment and characteristics of the epidemic (e.g. changes in patterns of use). A work group comprising representatives of various EU countries has been established. The work group had a first general meeting in Munich at the beginning of February 2000 in order to define the workplan (agenda in appendix) and several small meetings with the different partners to discuss the results and plan the format of the country reports (May: Belgium; June: UK, the Netherlands; July: Portugal; October: Hungary). The representatives from Germany (Christoph Kroeger and Ludwig Kraus) could not provide suitable data for the analysis. Only partial data were provided for Dublin. The various analyses are described in the following. The local results are interpreted and commented in depth from an epidemiological point of view mainly in the country reports reported as annexes. In fact, the country reports have been set up in order to provide the basis for papers and local publications aimed at dissemination of the results directed to policy makers and authorities specifically at country or local level. Thus, some special local analyses are only included in the country reports (see for example the French Community of Belgium case) as they comprise variables which cannot be used for general and interesting cross-country comparisons but which can useful and of interest for policy makers at local level. The country report for Italy, for example, was the basis for 2 publications written in Italian directed to health and social workers. 17

18 Introduction The study of the latency period Latency period (LP) is the period between first heroin use and first treatment demand. For the analysis of LP standard methods were used. Kaplan-Meier analysis was used to assess the univariate non-parametric survival function. P-Plot were used to estimate the best parametric model to apply the Back-Calculation procedure (see below). Cox proportional hazard regression was used to determine the influence of covariates on LP by estimating the corresponding regression parameters. In the Cox model, the covariate 'age at first heroin use' has been coded as a continuous variable in order to obtain comparability of the corresponding ratio's between the sites given that the age distributions differed strongly between countries. We analysed data from Amsterdam, French Community of Belgium, Budapest, Dublin (separately discussed, see 2.3), Lisbon, London and Rome. Results for Amsterdam, French Community of Belgium, Budapest, Lisbon, London and Rome Univariate analysis by Kaplan Meier method shows that mean LP is 6.7 years for Rome, 7.0 years for London and 7.1 years for Amsterdam and for the French Community of Belgium. For Lisbon a longer LP is observed of 8.7 years, which is however probably due to the short existence of the services. Budapest shows the shortest LP, with a mean of 3.1 years, same as Dublin. The median values are equal in Rome, London and Amsterdam with 5.0 years, while it is 6 years for the French Community of Belgium and 2.5 years for Budapest, 2.4 for Dublin. It must be noted that the estimate of the latency period can be influenced by the phase of the epidemic. During the first years of an epidemic all observed LP would by definition be shorter, and with decreasing incidence the longer LP would be over-represented. This can cause various biases in the latency period analysis such as underestimation and the negligible effect of some covariate, in particular age at first use. This is possibly the case for Budapest and Dublin as it is better discussed in the following (see 4.1). We performed the stratified Kaplan-Meier analysis to determine the covariates to introduce in the Cox regression model (see Table 1). Age at first heroin use is analysed only by Cox regression as it was coded as a continuous variable. The variable ethnicity was disregarded from further analysis for Amsterdam and London. The reason was that differences in LP for the sites were mainly due to differences in onset of the heroin epidemics for different ethnic groups, as evidenced by the tabulation of year of first use (see Figures 1a and 1b). For Amsterdam, this bias was illustrated by the prospective sub-analysis based on the selection of drug users that started using heroin in 1985/86. In the prospective analysis the apparent effect of ethnicity disappeared and all ethnic groups showed equal LP. For London the prospective analysis based on drug users starting in 1991 did not give different results, however the prospective analysis was not very different from the retrospective one due to the limited number of treatment years available. Gender remained significant in most sites. The risk of entering first treatment was about 22% higher for females than for males for the French Community of Belgium, for Amsterdam and London it was 28% higher, for Lisbon it was 47% higher and in Rome and Budapest it was not significant (see Table 2). 18

19 Table 1 Kaplan-Meyer summaries for the latency period analysis. Site Group (size) Mean 1 quart. Median 3 quart. Amsterdam Total (1078) Analysis for years of treatment data Males (888) Females (190) Surinam/Dutch Antillians (233) Dutch (744) Turkish/Moroc (71) Route of administration is not available French Community of Belgium Analysis for years of treatment data Total (2608) Males (2019) Females (578) Injectors (513) Smokers (1415) Injectors (main)+smokers (secondary) (142) Injectors (secondary)+smokers (main) (130) Oral (208) Sniffers (25) Injectors+Smokers (134) Ethnicity is not available Budapest Total (400) Analysis for years of treatment data Males (316) Females (84) Injectors (311) Non injectors (89) Ethnicity is not available Lisbon Total (918) Analysis for years Males (761) of treatment data Females (157) White (813) Black (105) Injectors (213) Smokers (687) Sniffers (18) London Total (9892) Analysis for years Males (7429) of treatment data Females (2463) White, Black, Other (9417) Chinese, Asian other (126) Indian, Pakistan, Bangladesh (349) Injectors (5381) Smokers (4511) Rome (inner city) Analysis for year of treatment data 96 Total (4646) Males (3890) Females (756) Route of administration and ethnicity are not available 19

20 Figure 1a: Ethnicity-Amsterdam Figure 1b: Ethnicity-Lisbon Country group Turkye and Morocco Dutch 20 Ethnicity Count Surinam/Dutch Antill es Count 0 Back White Year of 1st heroin use Year of 1st heroin use Figure 1c: Ethnicity-London Ethnicity Indian, Pakistani, B 200 angladeshi Count Chinese, Asian Other White, Black, Other Year of 1st heroin use Figure 1d: Route-Lisbon Figure 1e: Route-London Route 20 injecting 200 Route sniffing Smoke Count 0 smoking Count 0 Inject Year of 1st heroin use Year of 1st heroin use Route of administration was available for the French Community of Belgium, Budapest, Lisbon and London, and remained significant after adjustment for the other covariates in all these sites, except for Budapest. In Lisbon, drug users who started heroin use by smoking the substance showed a more than double risk of entering treatment than those who started use by injecting (AOR %CI ) 1. Sniffers had a lower progression to treatment as compared to injectors (OR %CI ), however the number of sniffers was very small (n=18). 1 Note that the risk function is the reciprocal of the mean LP, thus an increasing risk implies a decreasing LP 20

21 Figure 2a: Ethnicity (prospective Amsterdam) Figure 2b Ethnicity (retrospective Amsterdam) Survival Functions Survival Functions 1,2 1,2 1,0 1,0,8,8,6,6,4,4,2 Ethnicity,2 Ethnicity Cum Survival 0,0 -,2 Turkye/Morocco Dutch Surinam/Dutch Antill Cum Survival 0,0 -,2 Turkye and Morocco Dutch Surinam/Dutch Antill Latency time (years) Latency time (years) Figure 2c: Ethnicity (prospective London) Figure 2d: Ethnicity (retrospective London) 1,2 Survival Functions 1,2 Survival Functions 1,0 1,0,8,8,6,6,4,4,2 Ethnicity,2 Ethnicity Cum Survival 0,0 -, India,Pakist,Banglad Chinese, Asian Other White, Black, Other Cum Survival 0,0 -, India,Pakist,Banglad Chinese,Asian Other White,Black, ther Latency time (years) Latency time (years) In London, the adjusted risk was about 40% higher for smokers as compared to injectors (AOR %CI ). For the French Community of Belgium, drug users with injecting as secondary route and smoking as main route showed a risk of entering treatment 24% higher than those who started use by injecting (AOR %CI ), injectors and smokers (with unknown main or secondary route) 22% higher than injectors (AOR %CI ), sniffers 9.5% higher than injectors (AOR %CI ), injectors 15% higher than drug users with injecting as main route and smoking as secondary route (AOR %CI ) and 10% higher than drug users by oral route (AOR %CI ), smokers 0.3% higher than injectors (AOR %CI ). For Lisbon, ethnicity remained significant after adjustment, with black drug users having a 29% higher risk of entering first treatment (AOR %CI ). In this analysis age at first heroin use was included as well. For this variable, each year of increased age corresponds to an increase in the risk of entering first treatment of 6,7% (ranging from 6.2% in Rome to 7.5% for the French Community of Belgium). This cumulates to an increased risk of entering first treatment of about 91% per 10 years increase of age at first use. 21

22 Table 2 Cox multivariate model: in the last column is reported the increment in the risk of the group indicate with respect to the baseline group when the other covariates are fixed. Site Group (size) Baseline group AOR (C.I.) Amsterdam Males (888) Males 1 Analysis for years of treatment data Females (190) ( ) Age 1 st heroin use (baseline 11) 1 Each year ( ) Route of administration is not available French Community of Belgium Analysis for years of treatment data Males (2019) Males 1 Females (578) ( ) Injectors (513) Injectors 1 Smokers (1415) ( ) Injectors (main)+smokers (secondary) (142) ( ) Injectors (secondary)+smokers (main) (130) ( ) Oral (208) ( ) Sniffers (25) ( ) Injectors+Smokers (134) ( ) Age 1 st heroin use (baseline 5) 1 Each year ( ) Ethnicity is not available Budapest Sex, route of administration and age at 1 st heroin use are not significant Analysis for Ethnicity is not available years of treatment data Lisbon Males (761) Males 1 Analysis for Females (157) ( ) years of Injectors (213) Injectors 1 treatment data Smokers (687) ( ) Sniffers (18) ( ) White (813) White 1 Black (105) ( ) Age 1 st heroin use (baseline 11) 1 Each year ( ) London Males (7429) Males 1 Analysis for Females (2463) ( ) years of Injectors (5381) Injectors 1 treatment data Smokers (4511) ( ) Age 1 st heroin use (baseline 12) 1 Each year ( ) Rome (inner city) Analysis for year of treatment data 96 Age 1 st heroin use (baseline 11) 1 Each year ( ) Sex is not significant Route of administration and ethnicity are not available 22

23 Discussion for Amsterdam, French Community of Belgium, Budapest, Lisbon, London and Rome Our results suggest that LP from first heroin use to first treatment is similar across the participating sites. Despite differences in the magnitude of effects of the other covariates, the effects of age at first heroin use were strikingly similar between sites, and the risk of entering first treatment strongly increased with age at first heroin use. Females in general show an higher risk of entering first treatment than males, while some consistent differences are observed between starting heroin use by different route of administration. Ethnicity was not significant in most sites except Lisbon. The similarities between sites and over time suggest that LP is not strongly related to external factors, such as treatment availability, but that it may be part of the natural history of problematic heroin use. The apparently anomalous case of Budapest can be better interpreted considering the shape of the estimated incidence curve (see below 4.1) showing that the onset of the epidemic is so recent that only individuals with short LP have been already observed. Several limitations have to be taken into account when interpreting our results. First, the year of implementation of treatment services could profoundly influence LP, if the analysis would include data from the first years that treatment was available. For heroin users entering first treatment in those years, LP could never be shorter than the period since the year of their first heroin use and the year of implementation of services, resulting in an artificially longer LP (left-truncation).for the sites Amsterdam, London and Rome we are confident that this problem has been limited by using data well after local implementation of services. In Amsterdam, services exist since 1980 and we have analysed data starting from Also, we observe an increasing LP with calendar time which is opposite to what one would observe if lefttruncation were present. However it can not be excluded that this effect is still present in our results. In London, services exist since 1989/90, while we have analysed data since The risk of entering treatment in 1998 is higher than that of 1995, consistent with the existence of such a left-truncation bias. If one analyses the full time series from London ( ) a constantly increasing risk, or decreasing LP, is observed (not shown). We assume that the LP from the (more recent) years analysed is less biased, but cannot exclude residual bias. The results from Lisbon, suggesting a longer LP than in the other sites, are clearly biased, given that our analysis is based on the years while the services which provided the data existed only since Of the Lisbon results we therefore believe that only the direction of effects of covariates can be meaningfully interpreted, not the magnitude of these effects or the length of the LP itself. Second, changes in policy implementation and in drug laws could artificially change LP, e.g. if police activity is increased during a certain period and arrests are referred to treatment. We have no data to test this possibility, however the effect on LP is in general smoothed out when analysing a series of treatment years. For London, a slight extra increase in risk is observed in 1996, which could have been caused by extra referrals, although the effect could as well be caused by some reporting artefact (it is known that underreporting temporarily increased in 1997). Third, the stage of the heroin use epidemic could strongly influence LP. During the first years of an epidemic all observed LP would by definition be shorter, and with decreasing incidence the longer LP would be overrepresented. In Amsterdam and London the epidemics have probably not been stable in the years of analysis and the resulting time trends in the relative risks are consistent with the phase of the epidemic (not shown). In Amsterdam it is believed that the heroin epidemic was already decreasing in 1985 and continued to decrease for some years after stabilising at very low incidence levels, while in London other studies do indicate a possible increasing heroin epidemic during the late 1990s. It is however impossible to 23

24 separate out the bias resulting from non-stable epidemics from the other potential biases. Fourth, data quality might have influenced our results. For London, the data are subject to underreporting and delayed reporting, with unknown but presumably minor effects on LP. For Lisbon, the data refer to a small but high-risk neighbourhood, meaning that drug users could have visited treatment services in other areas prior to appearing in our data. For Amsterdam this is not the case, as the central methadone registry covers all methadone provisions in a large area. For Rome the analyses of time trends applied to therapy years (Rossi, 1999b) show that year of first treatment is not significant, this possibly means that the epidemic has stabilised in the area. Such results are confirmed by recent estimation of onset incidence curves at local level in the same area (see below the analysis for the Lazio region). The covariates that appear to influence LP may give important information for planning / targeting interventions. Young heroin users appear to progress much slower to treatment than older ones. This may either be because young heroin users do not need treatment as soon as the older ones but the effect could also be due to a lesser appropriateness or 'attraction' of treatment services for young drug users. Young drug users may not know how to find their way to services as well as older users. Our results may therefore indicate that specific services for young heroin users are needed or that existing services should target young users with special programmes. These aspects should be further investigated properly interviewing drug users in therapy and not in therapy. It might be valuable for treatment services to monitor changes over time in the effects of covariates on LP, and to use this information as evidence for improved coverage of subgroups of heroin users. For this it would be necessary to perform the present analyses e.g. every two years, and if possible on a larger geographical region. Contrary to the above reasoning, it would appear that the gender differences that we found would be difficult to attribute to a higher appropriateness of treatment services for females. It seems more plausible to interpret the higher risk of females to enter first treatment as indicating that those who eventually enter treatment progress to problematic use sooner than males. Besides factors relating to the progression to problems or to characteristics of treatment services, the risk of entering first treatment might be influenced by the social network of a heroin user, if their social contacts would convince them to seek treatment. That the strength of social networks might influence entering treatment is also suggested by other analyses showing that LP is longer in inner-city than in more provincial areas (Rossi 1999b for preliminary analyses). Route of administration shows consistent effects on LP. In both London and Lisbon, smokers have an higher risk of entering first treatment than injectors (similar results can be extrapolated from the findings for French Community of Belgium). This may be due to a faster progression to problematic use of smokers for both London and Lisbon. Although it is known that injecting is more efficient than smoking, anecdotal evidence from Lisbon indicates that smoking may produce a faster effect than injecting. It can not be excluded that, despite injecting being more efficient, smoking leads sooner to problematic use, similar to e.g. the more deleterious effects of crack smoking vs. cocaine sniffing. In Lisbon, the difference by route of administration may also be due to a difference in the time of onset of the sub-epidemics of smoking vs. injecting (see Figure 1d). In many cities smoking seems to have followed the injecting epidemic with several years. Our data on year of onset do not show this for London or Lisbon. However one must take note that we are only analysing data from heroin users who eventually entered first treatment in the years observed. It might be possible that despite our data indicating almost parallel sub-epidemics of injecting and smoking in London and Lisbon, in reality the smoking epidemic was larger and later than the injecting one, but smokers entered first treatment to a lesser extent. 24