Harm-reduction interventions in injection drug use.
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Heroin↗
Biomedical subjects
Daniela De Angelis
Publications and source records attributed to Daniela De Angelis.
Estimated progression rates in three United Kingdom hepatitis C cohorts differed according to method of recruitment.
OBJECTIVES: To estimate hepatitis C virus (HCV) progression rates between disease stages prior to cirrhosis, using data from liver biopsies in three observational cohorts. To demonstrate how the method of cohort recruitment can influence the estimation of HCV-progression rates. STUDY DESIGN AND SETTING: Data came from three United Kingdom observational cohorts, assembled from different referral sources. In total, 987 HCV-infected patients with an estimated (or known) date of infection and at least one histologically scored liver biopsy were eligible for inclusion in the analysis. Liver biopsy scores were used to determine the stage of HCV-related liver disease. A three-state continuous time Markov model was used to estimate covariate-specific average probabilities of progression of disease. RESULTS: After adjusting for confounders, considerably different rates of disease progression were estimated in the three cohorts. For a group of patients with the same demographics, the estimated 20-year probability of progression to cirrhosis was 12% (95% confidence interval CI = 6-22) in a hospital-based cohort, 6% (95% CI = 3-13) in a posttransfusion cohort, and 23% (95% CI = 14-37) in a cohort recruited from a tertiary referral center. CONCLUSION: Researchers using estimates of disease progression should be aware that the method of cohort recruitment has considerable influence on the progression rates that are derived.
Cohort Studies↗
Bayesian back-calculation using a multi-state model with application to HIV.
Back-calculation is a method of obtaining estimates of the number of infections of a disease over time. Data on an endpoint of the disease, together with knowledge of the time from infection to endpoint, allows reconstruction of the incidence of infection. The technique has had much success when applied to the HIV epidemic, using incidence of AIDS diagnoses to inform past HIV infections. In recent years, the period from infection to AIDS has changed considerably due to new regimes of anti-viral therapies. This has led to attempts to use incidence of first positive HIV test as an alternative basis for back-calculation. Developing on earlier work, this paper explores the feasibility of a multi-state formulation of the back-calculation method that models the disease and diagnosis processes and uses HIV diagnoses as an endpoint. Estimation is carried out in a Bayesian framework, which naturally allows incorporation of external information to inform the diagnosis probabilities. The idea is illustrated on data from the HIV epidemic in homosexuals in England and Wales.
Bayes Theorem↗
Estimating long-term trends in the incidence and prevalence of opiate use/injecting drug use and the number of former users: back-calculation methods and opiate overdose deaths.
The authors explored an age-specific back-calculation approach to estimating long-term trends in the incidence and prevalence of opiate use/injecting drug use (IDU) in England for 1968-2000. The incidence of opiate use/IDU was estimated by combining information on the observed opiate overdose deaths of persons aged 15-44 years with knowledge on the distribution of the time between starting opiate use/IDU and death by overdose (incubation time distribution). The resulting incidence, together with the incubation time distribution, other drug-related mortality, and the general age-specific mortality rate, was then used to estimate the prevalence of current and former users. Provisional estimates suggested two major increases in incidence in the late 1970s and early 1990s, with models including information on age at death suggesting a recent decline since 1997 and that prevalence of opiate use/IDU increased substantially in the 1990s. Results were crucially dependent on assumptions about key parameters of the back-calculation framework. In theory, the approach is a valuable addition to the portfolio of indirect methods for estimating incidence and prevalence of dependent opiate use/IDU. In practice, its full potential will be realized only once better information on the process of stopping opiate use/IDU becomes available and more precise estimates of current and historical overdose mortality are obtained.
Adolescent↗
Modeling the percolation of annotation errors in a database of protein sequences.
Public sequence databases contain information on the sequence, structure and function of proteins. Genome sequencing projects have led to a rapid increase in protein sequence information, but reliable, experimentally verified, information on protein function lags a long way behind. To address this deficit, functional annotation in protein databases is often inferred by sequence similarity to homologous, annotated proteins, with the attendant possibility of error. Now, the functional annotation in these homologous proteins may itself have been acquired through sequence similarity to yet other proteins, and it is generally not possible to determine how the functional annotation of any given protein has been acquired. Thus the possibility of chains of misannotation arises, a process we term 'error percolation'. With some simple assumptions, we develop a dynamical probabilistic model for these misannotation chains. By exploring the consequences of the model for annotation quality it is evident that this iterative approach leads to a systematic deterioration of database quality.
Animals↗
Changes over calendar time in the risk of specific first AIDS-defining events following HIV seroconversion, adjusting for competing risks.
BACKGROUND: Although studies have reported large reductions in the risks of AIDS and death since the introduction of potent anti-retroviral therapies, few have evaluated whether this has been similar for all AIDS-defining diseases. We wished to evaluate changes over time in the risk of specific AIDS-defining diseases, as first events, using data from individuals with known dates of HIV seroconversion. METHODS: Using a competing risks proportional hazards model on pooled data from 20 cohorts (CASCADE), we evaluated time from HIV seroconversion to each first AIDS-defining disease (16 groups) and to death without AIDS for four calendar periods, adjusting for exposure category, age, sex, acute infection, and stratifying by cohort. We compared results to those obtained from a cause-specific hazards model. RESULTS: Of 6,941, 2,021 (29%) developed AIDS and 437 (6%) died without AIDS. The risk of AIDS or death remained constant to 1996 then reduced; relative hazard = 0.89 (95% CI: 0.77-1.03); 0.90 (95% CI: 0.81-1.01); and 0.32 (95% CI: 0.28-0.37) for 1979-1990, 1991-1993, and 1997-2001, respectively, compared to 1994-1996. Significant risk reductions in 1997-2001 were observed in all but two AIDS-defining groups and death without AIDS in a competing risks model (with similar results from a cause-specific model). There was significant heterogeneity in the risk reduction across events; from 96% for cryptosporidiosis, to 17% for death without AIDS (P < 0.0001). CONCLUSION: These findings suggest that studies reporting a stable trend for particular AIDS diseases over the period 1979-2001 may not have accounted for the competing risks among other events or lack the power to detect smaller trends.
AIDS-Related Opportunistic Infections↗