Inequality in Social Support Associated With Mild Cognitive Impairment: A Cross-Sectional Study of Older (≥60 Years) Residents in Shanghai, China

Social support plays a critical role in the detection and management of mild cognitive impairment (MCI). However, socioeconomic inequalities exist in both social support and health care services. Our study aimed to compare the level of social support received by MCI patients in comparison with those without MCI and to determine its link with income. Secondary data analyses were performed. Social support was measured using the Duke Social Support Index (DSSI) and satisfaction ratings. Multivariate logistic regression models were constructed to determine the associations of personal income and MCI with social support after adjustment for variations in the sociodemographic and health characteristics of the respondents. The multiplicative and additive interaction effects of income and MCI were further examined through introducing the MCI Income variable to the regression models and using the relative excess risk due to interaction (RERI) analysis, respectively. The logistic regression models showed that the respondents with MCI had significantly lower social support as measured by the DSSI scores (AOR = 33.03, < 0.001) and satisfaction ratings (AOR = 7.48, < 0.001) compared with those without MCI. Similarly, social support decreased with lower personal income ( < 0.001). There existed a significant multiplicative interaction effect between personal income and MCI on social support (AOR = 0.30-0.32, < 0.01). The gap in social support between those with and without MCI was higher in the higher income group compared with the lower income group ( < 0.001). No significant additive interaction effects on social support were found between MCI and income. There are significant disparities in social support between people living with and without MCI. Such a gap is more profound in people with higher income. The inequality in social support associated with MCI may present a significant challenge to the successful implementation of community MCI detection and management.