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Estimating incidence of heroin use from treatment data presentation for TDI expert meeting. Lucas Wiessing (EMCDDA), Lucilla Rav à and Carla Rossi (Univ Rome), EMCDDA, Lisbon, 23 June 2003. Background.
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Estimating incidence of heroin use from treatment datapresentation for TDI expert meeting Lucas Wiessing (EMCDDA), Lucilla Ravà andCarla Rossi (Univ Rome), EMCDDA, Lisbon, 23 June 2003
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
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)
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
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
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
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)
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)
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
Kaplan-Meyer curves Rome: no difference by gender (EMCDDA /Univ. Rome pilot project)
Kaplan-Meyer curves Rome: strong age effect (EMCDDA /Univ. Rome pilot project)
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
Observed and projected treatment incidence, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)
Back-calculated incidence of first heroin use, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)
Observed and projected treatment incidence, Amsterdam, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted)
Back-calculated incidence of first heroin use, Amsterdam, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted) EMCDDA 2001
Back-calculated incidence of first heroin use by region, Italy, BC method (EMCDDA /Univ. Rome pilot project; Ravà et al submitted) EMCDDA 2001
Estimated treatment incidence, Italy, BC method with age as covariate (Ravà et al submitted)
Back-calculated cumulative incidence of first heroin use by region, Italy, BC method (Ravà et al submitted)
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)
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
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)
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