Patterns of health care use related to respiratory conditions in early life: A birth cohort study with linked administrative data

Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.

Ascertaining asthma status in epidemiologic studies: a comparison between administrative health data and self-report

BACKGROUND

Studies have suggested that agreement between administrative health data and self-report for asthma status ranges from fair to good, but few studies benefited from administrative health data over a long period. We aimed to (1) evaluate agreement between asthma status ascertained in administrative health data covering a period of 30 years and from self-report, and (2) identify determinants of agreement between the two sources.

METHODS

We used administrative health data (1983-2012) from the Quebec Birth Cohort on Immunity and Health, which included 81,496 individuals born in the province of Quebec, Canada, in 1974. Additional information, including self-reported asthma, was collected by telephone interview with 1643 participants in 2012. By design, half of them had childhood asthma based on health services utilization. Results were weighted according to the inverse of the sampling probabilities. Five algorithms were applied to administrative health data (having ≥ 2 physician claims over a 1-, 2-, 3-, 5-, or 30-year interval or ≥ 1 hospitalization), to enable comparisons with previous studies. We estimated the proportion of overall agreement and Kappa, between asthma status derived from algorithms and self-reports. We used logistic regression to identify factors associated with agreement.

RESULTS

Applying the five algorithms, the prevalence of asthma ranged from 49 to 55% among the 1643 participants. At interview (mean age = 37 years), 49% and 47% of participants respectively reported ever having asthma and asthma diagnosed by a physician. Proportions of agreement between administrative health data and self-report ranged from 88 to 91%, with Kappas ranging from 0.57 (95% CI: 0.52-0.63) to 0.67 (95% CI: 0.62-0.72); the highest values were obtained with the [≥ 2 physician claims over a 30-year interval or ≥ 1 hospitalization] algorithm. Having sought health services for allergic diseases other than asthma was related to lower agreement (Odds ratio = 0.41; 95% CI: 0.25-0.65 comparing ≥ 1 health services to none).

CONCLUSIONS

These findings indicate good agreement between asthma status defined from administrative health data and self-report. Agreement was higher than previously observed, which may be due to the 30-year lookback window in administrative data. Our findings support using both administrative health data and self-report in population-based epidemiological studies.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.

Adenotonsillectomy, bronchoscopy and bronchoalveolar lavage in the management of preschool children with severe asthma: pilot study

PURPOSE

This is a pilot study that describes the feasibility and clinical course of a cohort of preschool children with severe asthma undergoing a combined adenotonillectomy (TA), bronchoscopy (B), and bronchoalveolar lavage (BAL) procedure.

METHODS

A retrospective cohort study of preschool patients with severe asthma who underwent a combined TA-B-BAL procedure between 2012 and 2019. Subjects were treated at a tertiary care asthma clinic and had a diagnosis of preschool asthma according to the Canadian Thoracic Society Guidelines. Data on demographics, clinical characteristics, medication use, virology and microbiology from bronchoalveolar lavage, and asthma control questionnaires were collected. Variables were analyzed using paired t test.

RESULTS

Eighteen preschool subjects (mean age 3.19 ± 1.13 years) with severe asthma were identified through the asthma clinic. Patients treated with standard asthma care and a combined  TA-B-BAL procedure  experienced a decrease in the number of oral steroid courses (p = 0.017), emergency department visits (p = 0.03) and wheezing exacerbations (p = 0.026) following the procedure. Ten patients experienced clinically meaningful improvements in TRACK scores after the procedure (p < 0.001).

CONCLUSION

This pilot study provides early evidence that a combined TA-B-BAL procedure is feasible in preschool children with severe asthma and that the procedure may reduce asthma medication use and hospital visits.

Risk of asthma in children diagnosed with bronchiolitis during infancy: protocol of a longitudinal cohort study linking emergency department-based clinical data to provincial health administrative databases

INTRODUCTION

The Canadian Bronchiolitis Epinephrine Steroid Trial (CanBEST) and the Bronchiolitis Severity Cohort (BSC) study enrolled infants with bronchiolitis during the first year of life. The CanBEST trial suggested that treatment of infants with a combined therapy of high-dose corticosteroids and nebulised epinephrine reduced the risk of admission to hospital. Our study aims to-(1) quantify the risk of developing asthma by age 5 and 10 years in children treated with high-dose corticosteroid and epinephrine for bronchiolitis during infancy, (2) identify risk factors associated with development of asthma in children with bronchiolitis during infancy, (3) develop asthma prediction models for children diagnosed with bronchiolitis during infancy.

METHODS AND ANALYSIS

We propose a longitudinal cohort study in which we will link data from the CanBEST and BSC study with routinely collected data from provincial health administrative databases. Our outcome is asthma incidence measured using a validated health administrative data algorithm. Primary exposure will be treatment with a combined therapy of high-dose corticosteroids and nebulised epinephrine for bronchiolitis. Covariates will include type of viral pathogen, disease severity, medication use, maternal, prenatal, postnatal and demographic factors and variables related to health service utilisation for acute lower respiratory tract infection. The risk associated with development of asthma in children treated with high-dose corticosteroid and epinephrine for bronchiolitis will be assessed using multivariable Cox proportional hazards regression models. Prediction models will be developed using multivariable logistic regression analysis and internally validated using a bootstrap approach.

ETHICS AND DISSEMINATION

Our study has been approved by the ethics board of all four participating sites of the CanBEST and BSC study. Finding of the study will be disseminated to the academic community and relevant stakeholders through conferences and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ISRCTN56745572; Post-results.