Opioid overdose risk during and after drug treatment for heroin dependence: An incidence density case-control study nested in the VEdeTTE cohort.

INTRODUCTION AND AIMS: To corroborate protective effects of a range of drug treatment modalities against overdose mortality risk. DESIGN AND METHODS: Nested case-control study, with incidence density sampling, selecting controls retrospectively at each case event. Cases and controls came from a sub-cohort of opioid-dependent patients (n = 4444) from two Italian regions (Lazio and Piedmont). From 1998 to 2005, there were 91 overdose deaths (cases) matched to 352 controls. The primary outcome was overdose mortality and the primary exposure was drug treatment: opioid agonist treatment (OAT), opioid detoxification, residential community, psychosocial and other pharmacological treatment. Conditional logistic regression models generated intervention effects comparing mortality risk in and out of treatment, adjusting for confounding variables. RESULTS: Overall, drug treatment reduced overdose mortality risk by 80% [adjusted odds ratio (AOR) 0.18, 95% confidence interval (CI) 0.10-0.33, P < 0.001] compared to being out of treatment. There was a particularly strong protective effect of OAT on overdose mortality (AOR 0.08, 95% CI 0.03-0.23, P < 0.001) compared to being out of treatment. There was evidence of a substantially elevated risk of overdose in the first month of leaving treatment (AOR 23.50, 95% CI 7.84-70.19, P < 0.001) compared to being in treatment. DISCUSSION AND CONCLUSIONS: The nested case-control design strengthened earlier findings that OAT in Italy has strong protective effects on overdose mortality risk, much stronger than has been previously seen in other Western European settings.

Identifying Relapses in Multiple Sclerosis Patients through Administrative Data: A Validation Study in the Lazio Region, Italy.

BACKGROUND: Relapse is frequently considered an outcome measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objectives of this study were to identify relapse episodes in patients with MS in the Lazio region using health administrative databases and to evaluate the validity of the algorithm using patients enrolled at MS treatment centers. METHODS: MS cases were identified in the period between January 1, 2006 and December 31, 2009 using data from regional Health Information Systems (HIS). An algorithm based on HIS was used to identify relapse episodes, and patients recruited at MS centers were used to validate the algorithm. Positive and negative predictive values (PPV, NPV) and the Cohen's kappa coefficient were calculated. RESULTS: The overall MS population identified through HIS consisted of 6,094 patients, of whom 67.1% were female and the mean age was 41.5. Among the MS patients identified by the algorithm, 2,242 attended the centers and 3,852 did not. The PPV was 58.9%, the NPV was 76.3%, and the kappa was 0.36. CONCLUSIONS: The proposed algorithm based on health administrative databases does not seem to be able to reliably detect relapses; however, it may be a helpful tool to detect healthcare utilization, and therefore to identify the worsening condition of a patient's health.