Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

BACKGROUND: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. AIM: This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. METHODS: A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. RESULTS: According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. CONCLUSION: The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.

Do workers negative self-select when they commute? Evidence for the Chilean case of long-distance commuting.

Long-distance commuting (LDC) as a strategy of labor factor mobility has become relevant in recent decades, mainly in those economies characterized by a significant relative weight of extractive activities. The phenomenon is key to understanding the current structure and dynamics of these labor markets, although little is known about self-selection in LDC. This document addresses this knowledge gap by analyzing the case of Chile using functional areas. Chile is a country where LDC has become the principal strategy of labor mobility and is closely linked to the mining and construction sectors. The results obtained show a pattern of negative self-selection, meaning that it is the least qualified who have the highest probability of commuting between functional areas. Commuting could therefore be more than just a mechanism for accessing qualified labor, allowing less qualified individuals access job opportunities when the labor market where they come from is more qualified.