Comparative effectiveness of urine drug testing schedules alongside opioid agonist treatment: Emulation of a population-based target trial in British Columbia, Canada.

Background And Aim: Urine drug testing is often utilized alongside opioid agonist treatment to assess client progress by validating self-reported substance use, monitoring for diversion and supporting clinical decisions for take-home dosing. However, there is a paucity of evidence to support the practice of urine drug testing. We aimed to determine the association of alternative urine drug testing frequencies with opioid agonist treatment discontinuation, compared with no monitoring, among individuals receiving methadone or buprenorphine/naloxone treatment.

Comparative effectiveness of missed dose protocols of opioid agonist treatment in British Columbia, Canada: protocol for a population-based target trial emulation.

Introduction: Methadone and buprenorphine/naloxone are effective medications for people with opioid use disorder; however, interruptions in daily dosing are common and diminish the benefits of these medications. While clinical guidelines in most North American jurisdictions, including British Columbia (BC), recommend dose adjustment after treatment interruptions to varying levels of specificity, the evidence to support these recommendations is limited. We aim to estimate the comparative effectiveness of alternative dose adjustment strategies on subsequent overdose-related acute care visits and discontinuation of opioid agonist treatment in BC, Canada.

Heterogeneity in health care pathways preceding the classical recognition of adult-onset multiple sclerosis: A multichannel state sequence analysis.

Introduction: Higher healthcare use before recognition of adult-onset multiple sclerosis (MS) raises the possibility of earlier disease detection.

Objective: To describe common clinical pathways before a first recorded demyelinating event or MS symptom onset.

Methods: We applied multichannel state sequence analyses to generate typologies of clinical pathways using linked clinical and population-based health administrative data in British Columbia, Canada (1991-2020).

Can patient-reported data improve predictions about who will be a high-need, high-cost patient in British Columbia?

Purpose: Improving the outcomes for high-need, high-cost (HNHC) patients requires accurately predicting who will become an HNHC patient. The objectives of this study are to: (1) develop models to predict individuals at risk of becoming future HNHC patients, and (2) compare the performance of predictive models with and without patient-reported data.

Methods: We used data from two patient-reported surveys datasets from British Columbia, Canada (inpatient and emergency department (ED) surveys) and linked administrative data.

High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.

Purpose: Health administrative databases often contain no information on some important confounders, leading to residual confounding in the effect estimate. We aimed to explore the performance of high-dimensional disease risk score (hdDRS) to deal with residual confounding bias for estimating causal effects with survival outcomes.

Methods: We used health administrative data of 49 197 individuals in British Columbia to examine the relationship between tuberculosis infection and time-to-development of cardiovascular disease (CVD).

Simultaneously Dealing With Immortal Time Bias and Residual Confounding: A Case Study of a High-Dimensional Propensity Score Approach With a Nested Case-Control Framework in Multiple Sclerosis Research.

Background: Observational studies of time-dependent treatments often face immortal time bias and residual confounding, complicating treatment effect estimation. We implemented a high-dimensional propensity score (hdPS) analysis within a nested case-control (NCC) framework to address both biases simultaneously.

Methods: We used a retrospective cohort of 19 360 individuals with multiple sclerosis (MS) in British Columbia, Canada, to examine the relationship between disease-modifying drugs (DMDs) and all-cause mortality.

Informative patterns of health care utilization preceding the recognition of adult-onset multiple sclerosis: A population-based study.

Background: Early recognition of multiple sclerosis (MS) remains a pivotal challenge. Little is understood about the trajectories of health care use before recognition of adult-onset MS, and the relationship of the trajectories with subsequent disability.

Methods: We accessed linked clinical and population-based health administrative data in British Columbia, Canada (1991-2020).

Direct-Acting Antivirals and Risk of Hepatitis C Extrahepatic Manifestations.

Importance: Hepatitis C virus (HCV) infection is associated with various extrahepatic manifestations (EHMs) that can significantly impact patients' quality of life and overall health outcomes.

Objective: To assess the association between successful direct-acting antiviral (DAA) treatment and the risk of EHMs in individuals with chronic HCV infection.

Design, Setting, And Participants: This population-based retrospective cohort study used data from 1990 to 2021, with a median follow-up of 2.

Examining the Role of Race/Ethnicity and Sex in Modifying the Association Between Early Smoking Initiation and Mortality: A 20-Year NHANES Analysis.

Introduction: The authors aim to examine the modifications of the relationship between early smoking initiation and overall mortality by race/ethnicity and sex using data from U.S. adults in the 1999-2018 National Health and Nutrition Examination Surveys.

Enhancing risk prediction base on health administrative data using high-dimensional prediction model.

Objectives: Health administrative datasets often do not contain important clinical variables for predicting the risk of medical outcomes. However, they often contain a wide range of health-care variables that can be used to develop a high-dimensional prediction model (hdPM) that compensates for the lack of clinical predictors. We aimed to compare the predictive performance of an hdPM with a conventional model that relies only on investigator-specified clinical predictors.

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation?

Purpose: We aim to evaluate various proxy selection methods within the context of high-dimensional propensity score (hdPS) analysis. This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios.

Methods: We conducted a plasmode simulation study using data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2013 to 2018.

How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?

Background: Residual confounding presents a persistent challenge in observational studies, particularly in high-dimensional settings. High-dimensional proxy adjustment methods, such as the high-dimensional propensity score (hdPS), are widely used to address confounding bias by incorporating proxies for unmeasured confounders. Extensions of hdPS have integrated machine learning, such as LASSO and super learner (SL), and doubly robust estimators, such as targeted maximum likelihood estimation (TMLE).

Bayesian adaptive enrichment design in multi-arm clinical trials: The BayesAET package for R users.

Background: Randomized controlled trials seldom assess treatment effect heterogeneity across subpopulations, potentially leading to suboptimal treatment recommendations and inefficient use of healthcare resources. Adaptive enrichment designs seek to identify patient subpopulations most likely to benefit from the treatment. This manuscript introduces BayesAET, an R package developed to support Bayesian adaptive enrichment trial designs.

Longitudinal trajectories of healthcare costs among high-need high-cost patients: a population-based retrospective cohort study in British Columbia, Canada.

Objectives: We sought to identify groups of high-need high-cost (HNHC) patients with distinct cost trajectories and describe the sociodemographic and clinical characteristics associated with group membership.

Design: A population-based retrospective cohort study, using administrative health data.

Setting: British Columbia, Canada.

Comparative effectiveness of methadone take-home dose initiation in British Columbia, Canada: protocol for a population-based retrospective cohort study using target trial guidelines.

Introduction: Due to inferior safety profile and higher risk of diversion than buprenorphine/naloxone, guidelines typically recommend stringent eligibility criteria such as daily witnessed ingestion of methadone for at least 12 weeks before considering take-home doses. Recent research has focused on whether or not to initiate take-home methadone doses, often using pandemic-era data when temporary prescribing changes provided a natural experiment on the impact of access to take-home doses. However, none of these studies adequately examined the optimal timing and criteria for safely starting take-home doses to enhance treatment outcomes.

Problem of pain in the USA: evaluating the generalisability of high-impact chronic pain models over time using National Health Interview Survey (NHIS) data.

Introduction: High-impact chronic pain (HICP) significantly affects the quality of life for millions of U.S. adults, imposing substantial economic/healthcare burdens.

Maternal age at first birth and uterine cancer risk: A comprehensive analysis using NHANES data (2003-2018).

Background: Several reproductive factors, including parity and age at menarche, have been identified as risk factors for uterine cancers. However, the association between maternal age at first birth and uterine cancer remains conflicting.

Methods: This cross-sectional study included females aged 20 years and older with at least one live birth across eight National Health and Nutrition Examination Survey (NHANES) cycles (2003-2018).

Evaluating the Role of High-Dimensional Proxy Data in Confounding Adjustment in Multiple Sclerosis Research: A Case Study.

Purpose: Given the historical use of limited confounders in multiple sclerosis (MS) studies utilizing administrative health data, this brief report evaluates the impact of incorporating high-dimensional proxy information on confounder adjustment in MS research. We have implemented high-dimensional propensity score (hdPS) and high-dimensional disease risk score (hdDRS) methods to assess changes in effect estimates for the association between disease-modifying drugs (DMDs) and all-cause mortality in an MS cohort from British Columbia (BC), Canada.

Methods: We conducted a population-based retrospective study using linked administrative databases from BC, including health insurance registries, demographics, physician visits, hospitalizations, prescriptions, and vital statistics.

Immunogenicity of bivalent versus monovalent mRNA booster vaccination among adult paramedics in Canada who had received three prior mRNA wild-type doses.

Comparative immunogenicity from different mRNA booster vaccines (directed at WT, BA.1 or BA.4/5 antigens) remains unclear.

The misclassification of depression and anxiety disorders in the multiple sclerosis prodrome: A probabilistic bias analysis.

Background: Studies suggest that depression/anxiety form part of the multiple sclerosis (MS) prodrome. However, several biases have not been addressed. We re-examined this association after correcting for: (i) misclassification of individuals not seeking healthcare, (ii) differential surveillance of depression/anxiety in the health system, and (iii) misclassified person-time from using the date of the first MS-related diagnostic claim (i.

Towards Robust Causal Inference in Epidemiological Research: Employing Double Cross-fit TMLE in Right Heart Catheterization Data.

Within epidemiological research, estimating treatment effects from observational data presents notable challenges. Targeted Maximum Likelihood Estimation (TMLE) emerges as a robust method, addressing these challenges by accurately modeling treatment effects. This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms.

Childhood poverty, social support, immigration background and adolescent health and life satisfaction: A population-based longitudinal study.

Objectives: This study examined whether poverty (neighborhood and household) was associated with future health or life satisfaction outcomes and whether the association operated through social support (adult support at home, adult support at school, peer belonging), or differed by the immigration background (nonimmigrant family or immigrant family) of the family.

Methods: This study utilized a retrospective, longitudinal, population-based cohort that included self-reported survey data from the Middle Years Development Instrument (MDI) completed by children at age 9 and age 12, linked to administrative records. Participants included 5906 children in British Columbia, Canada.