Frontiers in Public Health (Sep 2023)
Decomposition and comparative analysis of depressive symptoms between older adults living alone and with others in China
Abstract
ObjectiveThis research dealt with investigating and measuring the contribution of the factors that impact depression in older adults living alone vs. those living with others (hereafter referred to as “not alone”) in China.DesignThis investigation adopts a cross-sectional research design. The dataset employed for this study comprises data from 2018 the Chinese Longitudinal Health Longevity Survey (CLHLS).SettingThe research involved data sourced from China, specifically from 23 of its provinces. From the 8th CLHLS, 12,197 older adults were selected who met the study requirements.MeasuresBinary logistic regression models were established to delve into the primary factors impacting the depressive symptoms of the individuals. Furthermore, Fairlie models were employed to assess these factors between older adults living alone and those not living alone. This approach facilitated an in-depth analysis of their respective contributions.ResultsIt was observed that the demographic of Chinese older adults exhibited depressive symptoms at a rate of 11.92%. Older adults who resided alone (15.76%) exhibited a higher prevalence of depressive symptoms in comparison to their counterparts living in not-alone settings (11.15%). Employing Fairlie decomposition analysis, it was determined that this observed disparity in depressive symptoms, amounting to 55.33% of the overall difference, could be primarily attributed to distinct factors. This encompassed variance in marital status (20.55%), years of school (4.63%), self-reported local income status (7.25%), self-reported sleep status (17.56%), and self-reported health status (4.24%).ConclusionThe resulting data indicated that depressive symptoms exhibited an elevated prevalence in older adults living alone than in those living not alone. This discrepancy was predominantly attributed to variance in socioeconomic marital status, years of school, self-reported local income status, self-reported sleep status, and self-reported health status by living alone vs. not alone. Mitigating these influential factors could help develop targeted and meticulous intervention strategies, precisely tailored to improve the mental well-being of older adults at high risk.
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