Grant: Travel Awards - Institute Community Support

Many epidemiological studies have addressed factors characterizing working older adults. However, these studies are mostly based on hypothesis-driven data analyses wherein a-priori hypotheses on the possible factors are formulated and statistical analyses are used to verify the proposed hypotheses. In the present work, we proposed new analysis approaches based on data mining to better understand specific factors that describe working aged population. Data from the Canadian Community Health Survey (CCHS)-Healthy Aging have been analysed. We targeted men and women aged 65 and over, knowing that retirement has often started at this age. However, some older people decide to continue working after 65 years and we are interested in understanding the various factors that specifically characterize this particular population. Our sample include n= 8529 men and women and a total of 47 variables related to social and family environment, general and mental health, mobility, social participation, nutrition, history of falls, amount of savings and educational level. First, we used a k-means clustering analysis to gain a comprehensive understanding of the characteristics of the working aged persons. Secondly, we used logistic regression, a method widely used in epidemiological studies, to find key relevant factors that characterise the working aged population. Lastly, to refine our understanding, we used a decision tree method to find the main predictors of work maintainability among older adults. The tree analysis methods gave the same key factors characterising elders in the workforce, related to age, living arrangement, general and mental health, emotional support, educational level, and saving amount. These results might help policy makers implement with confidence targeted actions to maintain older adults in the workforce.