1. Estimating incidence of heroin use from treatment data presentation for TDI expert meeting Lucas Wiessing (EMCDDA), Lucilla Ravà and Carla Rossi (Univ Rome), EMCDDA, Lisbon, 23 June 2003

2. Background Incidence has been developed as part of the key indicator prevalence and patterns of problem drug use - one of the five EMCDDA key indicators Since 1997, EMCDDA (+ DG-Research-TSER / Pompidou Group) projects have resulted in estimated incidence curves for some cities and countries (Univ. Rome Tor Vergata - Prof. Carla Rossi) Guidelines have been prepared Recent project and expert meeting (May 2003) aims to obtain more data and estimates, final results expected by early 2004 -> led to request to TDI expert group

3. What is the difference between incidence and prevalence ? Prevalence is the total number of cases existing at a given moment in time (point prevalence), usually it is easier to estimate the cases that have existed during a one year period (one year period-prevalence) Incidence is the rate of NEW cases occurring over time, usually estimated for consecutive years (yearly incidence, either by calendar years in public health data, or by person- years-of-observation in a cohort study)

4. Why estimate incidence ? Incidence is much more sensitive to changes over time (trends) than prevalence It relates to the start of problem drug use careers and to prevention of initiation It can provide historical information regarding the epidemic of heroin use It can be used to forecast treatment needs in the near future Trends can be derived from some sentinel sites, it is not essential to have complete national coverage

5. How to estimate incidence of first heroin use from treatment data The principle is simple: an observed time series of cases entering first treatment shows a certain curve (incidence of first treatment). If we know the average duration since first heroin use, we can back-project this curve to obtain the unobserved incidence curve of first heroin use Average duration is called the latency period (LP) and its distribution needs to be estimated first

6. There are important limitations We cannot estimate total incidence from treatment data, only part of it, as a proportion of new heroin users will never enter treatment, we call this relative incidence which is a lower bound of total incidence – the shape of the curve should however be unbiased If we include cases who started heroin use before the first year of observed treatment, we have left- truncation of LP, i.e. Some short LPs are left out. The LP is also right-truncated, i.e. we can not observe LPs which are longer than our time series Changes in incidence can affect LP estimation depending on the way LP is estimated

7. Two methods proposed in EMCDDA Guidelines 1) Latency period (LP) analysis + Back-calculation method (BC) 2) Reporting delay adjustment (RDA) or lag- correction method (with or without separate LP analysis)

8. Latency period (LP) analysis (EMCDDA/Univ. Rome pilot project) This needs to be done on individual data records Can be done in one or few sentinel sites only, results can then be used by BC method at national level and with aggregate data Need to understand biases resulting from selection of cases into the observed data Analysis of covariates e.g.: age of first use, gender, route of administration (time dependent, see request on age at first IDU), which are important to take into account in incidence estimation LP results not only needed for incidence, also important for own sake (treatment careers)

10. Latency period distribution (EMCDDA /Univ. Rome pilot project) Rome metropol.:mean 6.5, median 5 yr Amsterdam:mean 7.1, median 5 yr London:mean 6.7, median 5 yr

11. Kaplan-Meyer curves Rome: no difference by gender (EMCDDA /Univ. Rome pilot project)

12. Kaplan-Meyer curves Rome: strong age effect (EMCDDA /Univ. Rome pilot project)

13. Back-calculation (BC) method (Brookmeyer and Gail, Lancet 1986; Heisterkamp et al, Biometrical Journal, 1999; Downs et al, AIDS 2000) Done with a specifically developed programme that applies a deconvolution function, to back-project observed curve of treatment incidence into the unobserved curve of onset incidence of first use No need for individual data records, aggregate time series is sufficient (but at least 8-10 years long, ideally much longer) Latency period distribution is treated as a separate input element and can come from other/ local data Very well suited to handle large regions /national data

14. Observed and projected treatment incidence, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)

15. Back-calculated incidence of first heroin use, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)

16. Observed and projected treatment incidence, Amsterdam, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)

17. Back-calculated incidence of first heroin use, Amsterdam, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted) EMCDDA 2001

18. Back-calculated incidence of first heroin use by region, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted) EMCDDA 2001

19. Estimated treatment incidence, Italy, BC method with age as covariate (Ravà et al submitted)

20. Back-calculated cumulative incidence of first heroin use by region, Italy, BC method (Ravà et al submitted)

21. Lag-Correction / Reporting Delay Adjustment (RDA) method (Brookmeyer and Liao, Am J Epidemiol 1990; Hickman et al, Am J Epidemiol 2001) Needs individual level data Therefore more appropriate for local level estimations (e.g. 1 large treatment centre) but complete local coverage is still important Easier to understand and no black-box BC programme needed LP analysis is implicit, but can also be done separately (analysis of covariates)

22. Relative incidence of opiate use, Belgium - French Community, Lisbon and Budapest (lag-correction method) EMCDDA /Univ. Rome pilot project Note: data and analyses were carried out by the national focal points of Portugal, Belgium and Hungary, in collaboration with EMCDDA and Univ. Rome

23. Request from incidence estimation expert group: can two items be added to core list ? Age at first injection (first priority) Compare with and validate age at first use Indicates age at first problem use or regular/ heavy use Essential for infectious diseases indicator as it allows distinguishing prevalence of HIV/hepatitis in new injectors Age at first treatment (second priority) To estimate LP among those users who have already had their first treatment, i.e. the prevalent treatment cases To understand treatment careers of your clients To validate information from first treatment demand data It would be important to add instructions on how to ask this retrospective information – always relate to important life stages / events (e.g. leaving school)

24. Conclusions Incidence estimation allows for potentially powerful use of treatment data by estimating heroin onset incidence and projecting future treatment needs Adding few items to the TDI protocol could much improve use of the data for incidence estimation (as is the case for infectious diseases surveillance) EMCDDA projects and guidelines have led to pilot estimates of incidence in the EU, but more effort and especially data are needed Join forces between TDI group and expert group on the key indicator prevalence of problem drug use at national/ EU level