Dataset Information

Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging.

ABSTRACT:

Background

Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.

Objective

The aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes.

Methods

Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.

Results

Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).

Conclusions

This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.

International registered report identifier (irrid)

DERR1-10.2196/24887.

SUBMITTER: van Allen Z

PROVIDER: S-EPMC8235290 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeThe Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA.ParticipantsA total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit).Findings to dateOf the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease.Future plansThe CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

Project description:BACKGROUND:Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient's current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied. RESULTS:We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups. CONCLUSIONS:The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.

Project description:BackgroundLoneliness is a public health concern and its influence on morbidity and mortality are well documented. The association between loneliness and emergency department visits is less clear. Further, while sex and gender-related factors are known to be associated with loneliness and health services use, little research looks at the relationship by gender. Our study aimed to estimate the association between loneliness and emergency department use in the previous 12 months. We aimed to determine if this association differed based on gender identity and gender-related characteristics.MethodsWe used a retrospective cohort study design to analyze population-based survey data from the Canadian Longitudinal Study on Aging (CLSA). We analysed data from the baseline and follow-up 1 survey respondents (2015-2018) from both the tracking (telephone interviews) and comprehensive (in-home data collection) cohorts (n=44816). Loneliness was assessed using a dichotomous measure (lonely/not lonely) from a validated scale. Emergency department visits were dichotomous (yes/no) by self-reported emergency department use in the 12 months prior to the survey date. Multivariable logistic regression analyses using analytic weights examined the association between loneliness and emergency department visit, controlling for other demographic, social, and health related factors.ResultsWe identified 44,413 respondents to the baseline and follow-up 1 survey. The prevalence of loneliness in our sample was 23.1% (n=10263). Of those who had been to the emergency department in the previous year, 27.2% (n=2793) were lonely. Lonely respondents had higher odds of an emergency department visit (aOR: 1.13, 95% CI: 1.05-1.21), adjusted for various demographic and health factors. Loneliness was associated with emergency department visits more so in women (aOR: 1.15, 95% CI: 1.05-1.25) than in men (aOR: 1.10, 95% CI: 0.99-1.22).ConclusionsIn our study, loneliness was associated with emergency department visits in the previous 12 months. When our analysis was disaggregated by gender, we found differences in the odds of emergency department visit for men, women, and gender-diverse respondents. The odds of ED visit were higher in women than men. These findings highlight the general importance of identifying loneliness in both primary care and hospital. Care providers in ED need resources to refer patients who present in this setting with health issues complicated by social conditions such as loneliness.