Objective: To examine the relationship between social trust and depressive symptoms among China's elderly, placing special emphasis on the disparities between urban and rural settings.
Design: We employed latent profile analysis to categorise individual patterns of social trust. Subsequently, multiple linear regression analysis was employed to determine if there was an association between these identified social trust patterns and depressive symptoms. Additionally, we examined the potential interactive effects between urban-rural differences and patterns of social trust on depressive symptoms.
Setting: The China Family Panel Studies (CFPS) database.
Participants: The data was sourced from the CFPS for the years 2018 and 2020, encompassing a total of 5645 participants aged 60 and above.
Outcome Measures: Depressive symptoms were evaluated employing an eight-item adaptation of the Centre for Epidemiologic Studies Depression Scale. The scores from these eight items were aggregated to create an index of depressive symptoms, which was used to quantify the severity of depressive symptoms.
Results: The findings demonstrate a significant link between patterns of social trust and depressive symptoms, with individuals manifesting high social trust (HST) showing a lower propensity for depressive symptoms (Beta=-2.26, 95% CI=-2.62, -1.92). Furthermore, a marked association is apparent between social trust patterns and the changes in depressive symptoms. Additionally, urban dwellers (Beta=-0.67, 95% CI=-1.23, -0.11) demonstrate a more pronounced correlation between patterns of social trust and depressive symptoms, particularly within the HST group.
Conclusion: Our findings highlight a strong link between social trust patterns and depressive symptoms, particularly regarding their changes. Urbanites, notably within the HST group, show a lower risk of experiencing depressive symptoms. There is an urgent requirement to establish social trust-specific interventions to decrease susceptibility to depressive symptoms among the rural populace.
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| http://dx.doi.org/10.1136/bmjopen-2024-086508 | DOI Listing |
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