Recapture or precapture? Fallibility of standard capture-recapture methods in the presence of referrals between sources.

Capture-recapture methods, largely developed in ecology, are now commonly used in epidemiology to adjust for incomplete registries and to estimate the size of difficult-to-reach populations such as problem drug users. Overlapping lists of individuals in the target population, taken from administrative data sources, are considered analogous to overlapping "captures" of animals. Log-linear models, incorporating interaction terms to account for dependencies between sources, are used to predict the number of unobserved individuals and, hence, the total population size. A standard assumption to ensure parameter identifiability is that the highest-order interaction term is 0. We demonstrate that, when individuals are referred directly between sources, this assumption will often be violated, and the standard modeling approach may lead to seriously biased estimates. We refer to such individuals as having been "precaptured," rather than truly recaptured. Although sometimes an alternative identifiable log-linear model could accommodate the referral structure, this will not always be the case. Further, multiple plausible models may fit the data equally well but provide widely varying estimates of the population size. We demonstrate an alternative modeling approach, based on an interpretable parameterization and driven by careful consideration of the relationships between the sources, and we make recommendations for capture-recapture in practice

Problem drug use prevalence estimation revisited:heterogeneity in capture-recapture and the role of external evidence

BACKGROUND AND AIMS: Capture–recapture (CRC) analysis is recommended for estimating the prevalence of problem drug use or people who inject drugs (PWID). We aim to demonstrate how naive application of CRC can lead to highly misleading results, and to suggest how the problems might be overcome. METHODS: We present a case study of estimating the prevalence of PWID in Bristol, UK, applying CRC to lists in contact with three services. We assess: (i) sensitivity of results to different versions of the dominant (treatment) list: specifically, to inclusion of non‐incident cases and of those who were referred directly from one of the other services; (ii) the impact of accounting for a novel covariate, housing instability; and (iii) consistency of CRC estimates with drug‐related mortality data. We then incorporate formally the drug‐related mortality data and lower bounds for prevalence alongside the CRC into a single coherent model. RESULTS: Five of 11 models fitted the full data equally well but generated widely varying prevalence estimates, from 2740 [95% confidence interval (CI) = 2670, 2840] to 6890 (95% CI = 3740, 17680). Results were highly sensitive to inclusion of non‐incident cases, demonstrating the presence of considerable heterogeneity, and were sensitive to a lesser extent to inclusion of direct referrals. A reduced data set including only incident cases and excluding referrals could be fitted by simpler models, and led to much greater consistency in estimates. Accounting for housing stability improved model fit considerably more than did the standard covariates of age and gender. External data provided validation of results and aided model selection, generating a final estimate of the number of PWID in Bristol in 2011 of 2770 [95% credible interval (Cr‐I) = 2570, 3110] or 0.9% (95% Cr‐I = 0.9, 1.0%) of the population aged 15–64 years. CONCLUSIONS: Steps can be taken to reduce bias in capture–recapture analysis, including: careful consideration of data sources, reduction of lists to less heterogeneous subsamples, use of covariates and formal incorporation of external data

Medication storage in Emergency Medical Services : temperature ranges from a South African sample

CITATION: Wylie, C. A., et al. 2021. Medication storage in Emergency Medical Services : temperature ranges from a South African sample. South African Journal of Pre-hospital Emergency Care, 2(1):2-6, doi:10.24213/2-1-4480.The original publication is available at https://www.journals.ac.za/index.php/sajpec/Background: Pre-hospital emergency care providers working in emergency medical services (EMS) are licenced to administer medication to the acutely ill and injured. In South Africa, there are significant seasonal variations in temperature, sometimes far exceeding the recommended medication storage temperature. The aim of this study was to determine the summer temperature ranges inside select emergency vehicles and storage facilities in four provinces in South Africa. Methods: A prospective, observational study was conducted in four (Cape Town, Western Cape; Johannesburg, Gauteng; Durban, KwaZulu-Natal; Potchefstroom, North West) provinces during the summer (February – March) months of 2019. A continuous temperature monitoring device was placed in the medication storage room, the response vehicle drug bags, and an ambulance at a single private EMS base in each of the provinces. Temperature data were recorded in fifteen-minute intervals. The data were extracted after six weeks and subjected to descriptive analysis. Data were also analysed in six-hourly strata to account for daily temperature variations. Results: A total of 36 002 temperature readings were recorded during the study period. The mean (range) temperature across the four bases was 25.4°C (13.1–56.8) for ambulances, 25.7°C (13.3–49.1) for primary response vehicles, and 24.4°C (17.3–33.9) for medication storage facilities. The highest mean (range) temperatures, of 33.7°C (20.4–47.9), were recorded in a Johannesburg-based primary response vehicle between 12h00 and 18h00. Conclusion: Current medication storing and transporting practices not maintain temperatures according to the recommended storage conditions. Further investigation should address the implication of temperature fluctuations on medication degradation, and a sustainable, cost-effective solution should be developed to store medication in the pre-hospital setting.https://www.journals.ac.za/index.php/sajpec/article/view/4480Publisher's versio

Estimating the prevalence of problem drug use from drug-related mortality data.

BACKGROUND AND AIMS: Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'. METHODS: Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. RESULTS: We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74-0.94%) of 15-64-year-olds, which is similar to a published estimate based on capture-recapture analysis. CONCLUSIONS: Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach

Impact of opioid substitution therapy on antiretroviral therapy outcomes:a systematic review and meta-analysis

BACKGROUND: Human immunodeficiency virus (HIV)-infected people who inject drugs (PWID) frequently encounter barriers accessing and remaining on antiretroviral therapy (ART). Some studies have suggested that opioid substitution therapy (OST) could facilitate PWID's engagement with HIV services. We conducted a systematic review and meta-analysis to evaluate the impact of concurrent OST use on ART-related outcomes among HIV-infected PWID. METHODS: We searched Medline, PsycInfo, Embase, Global Health, Cochrane, Web of Science, and Social Policy and Practice databases for studies between 1996 to November 2014 documenting the impact of OST, compared to no OST, on ART outcomes. Outcomes considered were coverage and recruitment onto ART, adherence, viral suppression, attrition from ART, and mortality. Meta-analyses were conducted using random-effects modeling, and heterogeneity assessed using Cochran Q test and I2 statistic. RESULTS: We identified 4685 articles, and 32 studies conducted in North America, Europe, Indonesia, and China were included. OST was associated with a 69% increase in recruitment onto ART (hazard ratio [HR], 1.69; 95% confidence interval [CI], 1.32-2.15), a 54% increase in ART coverage (odds ratio [OR], 1.54; 95% CI, 1.17-2.03), a 2-fold increase in adherence (OR, 2.14; 95% CI, 1.41-3.26), and a 23% decrease in the odds of attrition (OR, 0.77; 95% CI, .63-.95). OST was associated with a 45% increase in odds of viral suppression (OR, 1.45; 95% CI, 1.21-1.73), but there was limited evidence from 6 studies for OST decreasing mortality for PWID on ART (HR, 0.91; 95% CI, .65-1.25). CONCLUSIONS: These findings support the use of OST, and its integration with HIV services, to improve the HIV treatment and care continuum among HIV-infected PWID

How modeling helps in developing self-sovereign identity governance framework : An experience report

Digital Identity has become a topic that attracts the attention of researchers due to the enormous number of services that have been provided online recently. Researchers face many obstacles regarding the security, privacy, and utility of digital identity. Self-Sovereign Identity (SSI) ecosystems provide a solution for digital identity, in addition to providing a decentralized human-centric paradigm that enables users to own and control their identity. Governance framework (GF) is a key challenge in building SSI ecosystems for two reasons. First, the GF needs to address various aspects such as user needs, standards, laws, and business requirements in an ecosystem. Second, the ecosystem consists of diverse, dynamic, and distributed groups of stakeholders. This research work adopts a new trend in developing GF by providing a visual view of the SSI ecosystem. In addition, it seeks to highlight the importance of domain-specific modeling in developing GF. It also addresses lessons learned from a real case study and a modeling journey that supported the creation of a GF. We will discuss the advantages and challenges of modeling and the used modeling tool based on the evaluation from the case feedback and conclude with future workpeerReviewe

Estimating the prevalence of problem drug use from drug-related mortality data.

BACKGROUND AND AIMS: Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'. METHODS: Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. RESULTS: We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74-0.94%) of 15-64-year-olds, which is similar to a published estimate based on capture-recapture analysis. CONCLUSIONS: Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach