Predictors and consequences of visual trajectories in Chinese older population: A growth mixture model

Abstract Background Given the relatively high prevalence of vision impairment and the heterogeneity of visual changes among the elderly population, we aimed to identify the visual trajectories and to examine the predictors and consequences associated with each trajectory class. Methods We analysed data from 2235 participants involved in the 5th, 6th, 7th, and 8th waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), where vision impairment was evaluated using an adapted Landolt-C chart during each wave. We employed a growth mixture model (GMM) to identify distinct visual trajectories and logistic regression analysis to examine the predictors associated with each trajectory class. Furthermore, we investigated the effect of visual trajectories on distal consequences, including cognitive function, activities of daily living (ADL), instrumental activities of daily living (IADL), depression, anxiety, and fall risk. Within the CLHLS study, cognitive function was assessed using the Chinese version of the Mini-Mental State Examination (CMMSE), ADL via the Katz index, and IADL through a modified version of Lawton's scale. Lastly, depression was assessed using the 10-item version of the Centre for Epidemiologic Studies (CES-D-10), while anxiety was measured using the Generalized Anxiety Disorder scale (GAD-7). Fall risk was determined by asking the question: 'Have you experienced any falls within the past year?' Results We identified two distinct visual trajectories in our analysis. Most older adults (n = 1830, 81.9%) initially had a good vision level that diminished ('high-baseline decline' group). Conversely, the remaining participants (n = 405, 18.1%) initially had a lower vision level that improved over time ('low-baseline improvement' group). The 'high-baseline decline' group was more likely to include older adults with relatively higher body mass index (BMI) (odds ratio (OR) = 1.086; 95% confidence interval (CI) = 1.046, 1.127), individuals with higher formal educational qualifications (OR = 1.411; 95% CI = 1.068, 1.864), those current engaging in exercise (OR = 1.376; 95% CI = 1.046, 1.811), and individuals reporting more frequent consumption of fruit (OR = 1.357; 95% CI = 1.053, 1.749). Conversely, the 'low-baseline improvement' group had a higher likelihood of including older individuals (OR = 0.947; 95% CI = 0.934, 0.961), residents of nursing homes (OR = 0.340; 95% CI = 0.116, 0.993) and those self-reporting cataracts (OR = 0.268; 95% CI = 0.183, 0.391) and glaucoma (OR = 0.157; 95% CI = 0.079, 0.315). Furthermore, the 'high-baseline decline' group showed a positive impact on distal consequences, adjusting for sex, birthplace, residence, main occupation, education, economic status, and marital status. This impact included cognitive function (correlation coefficient (β) = 2.092; 95% CI = 1.272, 2.912), ADL (β = −0.362; 95% CI = −0.615, −0.108), IADL (β = −1.712; 95% CI = −2.304, −1.121), and reported lower levels of depression (β = 0.649; 95% CI = 0.013, 1.285). We observed no significant influence on fall risk and anxiety within the identified visual trajectories in the adjusted model. Conclusions Vision in older adults with ocular disease could potentially be improved. Having formal education, maintaining an appropriate BMI, engaging in exercise, and consuming fruit more frequently appear to be beneficial for the visual health of the elderly. Considering the negative impact of visual impairment experience on distal cognition, self-care ability, and depression symptoms, stakeholder should prioritise long-term monitoring and management of vision impairment among older adults.