Association of cognitive impairment and elderly mortality: differences between two cohorts ascertained 6-years apart in China

The Chinese Longitudinal Healthy Longevity Survey (CLHLS) was a nationwide survey that randomly selected half of the cities and counties in 23 provinces of China, and recruited participants aged 65 years and older. A more detailed description of the CLHLS has been published elsewhere [19].

The protection of human subjects for the CLHLS was approved by the Ethics Committees. All participants or their legal representatives signed written consent forms to participate in the baseline and follow-up surveys.

The present study evaluated baseline cognitive status of two cohorts, ascertained in 2002 and 2008, using the same scale of cognitive function. Cognitive impairment was evaluated using the Mini-Mental State Examination (MMSE), a widely used cognitive test [20] and adapted into the Chinese language based on the international standard of MMSE questionnaire, and carefully tested by previous pilot survey interviews [21]. The total MMSE score ranges from 0 to 30 within 6 dimensions: orientation, registration, attention, language, memory, and visual construction skills. Three methods were used to define cognitive impairment: (1) ≥24, 18–23, and < 18 were used to define normal cognition (reference), mild cognitive impairment and serious cognitive impairment [22, 23]; (2) < 18 was used to define cognitive impairment for participants who didn’t receive any formal education, < 21 for participants who received 6 years of education or less, and < 25 for participants who received more than 6 years of education [16, 24]; (3)≥24 and < 23 were used to define normal cognition (reference) and cognitive impairment [22, 23].

The main outcome was all-cause mortality occurring during the follow-up survey in 2002–2008 and 2008–2014, with followed up every 3 years respectively. Each cohort was followed for 6 years to quantify mortality and the mortality date. Mortality date was ascertained from interviews with family members or relatives of participants [18]. The cause-specific mortality was not involved in this study because (1) many of the elderly died at home rather than in medical institutions where cause of mortality might be recorded, and (2) mortality surveillance systems are uncertain in many survey fields.

A number of variables were collected through a face-to-face standardized questionnaire, including demographic characteristics, economic status, lifestyles, health conditions and medical services.

Marital status was classified into unmarried and married. Education level was classified as no formal education, elementary school graduate (1–6 years of education), and high school graduate (>6 years of education). Region was defined as: urban, rural and suburban. Exercise was categorized into yes or no. Housework and reading were divided into 3 categories: never, sometimes, and often. Binary variables were defined to assess current status of smoking, drinking, depression and disability in six activities of daily living (ADL) including dressing, bathing, using the toilet, getting in/out of a bed or chair and feeding. An ADL impairment was defined as a elder’ s response of “needs help” to at least one or more of activities associated with one of the six items. Participants with systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg were considered hypertensive. Self-reported history of heart disease and stroke were also collected. According to the survey, we assessed the level of medical care by whether the participant was receiving adequate medical care at present? (Yes or No)” and on the basis of the payor of the medical costs (public medicare or not)”. We assessed economic status by asking, “are all financial sources enough for your life?”, “do you have a pension?” and “how is your life compared with other local people? (richer, similar and poorer)”.

We divided the elderly into 3 groups by the conventional MMSE cut-off points for 2002–2008 and 2008–2014. Mean and standard deviation were summarized for continuous variables, frequency and percentage for categorical variables. Comparisons between the elderly were conducted using the chi-square test for categorical variables, Kruskal-Wallis test for continuous variables.

Kaplan–Meier analysis was used to draw the survival curves stratified by MMSE score, compared by the log-rank test. We used the Cox proportional hazards models and the important confounders were identified by previous studies [13, 17]. Less than 1.3% for all independent variables had missing values, and due to such low missing rates, we deleted the cases with missing values, and the results have no significant difference compared to those with imputation [18].

Hazards ratio (HR) and 95% confidence intervals (CIs) were estimated with the construction of Cox proportional hazards models: the crude model was model 1; age and sex were adjusted in model 2; marital status, living alone, exercise, alcohol consumption, and smoking status were further adjusted in model 3; ADL and depression were further adjusted in model 4; and medical care and economic status were further adjusted in model 5. We pooled data from the 2002 cohort and the 2008 cohort and a variable that was assigned a value of 0 in the 2002 cohort and 1 in the 2008 cohort was included in the Cox proportional hazards model [25]. We tested the interaction between each cohort and cognitive impairment and explored whether the influence of cognitive impairment on mortality decreases over time. For subgroup analyses, the elderly were stratified by age group (65–79, 80–89, 90–99, and ≥ 100) and sex (male and female) in model 5. Interactions of baseline cognitive impairment with age group and sex on mortality risk were explored.

We cannot measure long-term progression about cognitive function on mortality. Previous studies demonstrated that both cognitive impairment and disability were independent of adverse impact on mortality [26, 27]. Therefore, the interaction synergistic effects between ADLs and cognitive impairment were tested in the models.

The following sensitivity analyses were conducted to check the robustness of the primary results: (1) removed the participants lost to follow-up to examine possible attrition bias; (2) excluded survival time less than 1 year due to the possibility that disease status in the last year of life could have affected the risk effects; (3) additionally adjusting for place of residence, dietary habits, hypertension, self-reported heart disease and stroke.

A total of 13,906 and 13,873 elderly aged 65 and older participated in the baseline survey in 2002 and 2008. The mean (SD) age of the elderly were 86.52 (11.63) and 87.22 (11.33) years in the two cohorts. A total of 55,277 person-years were documented in the 2002–2008 cohort, and 53,267 in the 2008–2014 cohort. Characteristics of the elderly who survived, died or lost to follow-up are displayed in Fig. 1. Participants with cognitive impairment tended to be older; female; unmarried; less likely to smoke, drink and live alone; have lower baseline ADL and depression; have higher rates of inadequate medical care and no pension. The baseline conditions of the two cohorts were similar (Table 1).

Additional file 1: Figure S1 shows the Kaplan-Meier survival curve by different categories of MMSE-based cognitive impairment. Significant differences were revealed by the log-rank test in the 3 groups (cognitive normal, mild cognitive impairment and serious cognitive impairment; P < 0.001) among the two cohorts. Table 2 shows that risk of mortality increased in parallel with a decrease in MMSE scores. In the crude model, the elderly with cognitive impairment (MMSE < 18) had an elevated risk of all-cause mortality compared to the other MMSE categories in 2002–2008 (MMSE < 18 [crude HR, 3.56; 95% CI, 3.39–3.74]) and 2008–2014 ([crude HR, 3.25; 95% CI, 3.10–3.41]). After adjusting for sex and age (model 2), demographic characteristics (model 3), functional status (model 4), and medical care and economic status (model 5), similar associations were found between cognitive impairment and mortality in the two cohorts. In the fully adjusted model, participants with MMSE scores indicative of cognitive impairment had increased all-cause mortality risk compared with the participants with normal cognition: for 2002–2008 mild cognitive impairment HR 1.28 (95% CI 1.20–1.37), severe cognitive impairment HR 1.48 (95% CI 1.39–1.57); for 2008–2014 mild cognitive impairment HR 1.20 (95% CI 1.12–1.28), severe cognitive impairment HR 1.32 (95% CI 1.25–1.41) (Table 2). When cognitive impairment was stratified by educational levels, cognitive impairment was independently associated with higher mortality risk, with hazard ratios (HRs) of 1.32 (95% confidence interval [CI], 1.25–1.39) in 2002–2008 cohort and 1.26 (95% CI, 1.19–1.32) in 2008–2014 cohort, compared to normal cognition. Similar results were obtained when cognitive impairment was defined by MMSE<24 or considering education level in both cohorts.

The association of cognitive impairment with all-cause mortality over the time was decreased comparing the 2002–2008 cohort to the 2008–2014 cohort. However, no significant interaction of cognitive impairment for all-cause mortality among two cohorts was significant (Table 3, P > 0.05 for interaction).

Lower MMSE score was consistently associated with elevated risk of mortality both in 2002–2008 and 2008–2014. The analysis stratified by sex revealed that cognitive impairment was associated with increased risk of all-cause mortality in females compared to males in 2002–2008 with HR 1.35 (95% CI 1.24–1.46) and in 2008–2014 HR 1.20 (95% CI 1.10–1.32). However, an interaction of cognitive impairment with sex for all-cause mortality was not observed (Table 3, P > 0.05 for interaction).

Compared to those with normal cognition, MSSE scores > 24, the younger elderly (65–79 years old) with cognitive impairment had higher risk of all-cause mortality, respectively in the two cohorts (Table 3).

Both cognitive impairment and ADL impairment have been considered important risk factors associated with elderly mortality. During the aging process, cognitive impairment and ADL impairment often co-exist and closely interact (Additional file 1: Table S1, S2).

Among two cohorts, there was almost no change in the association between cognitive impairment and all-cause mortality after excluding participants lost to follow-up or with survival time less than 1 year. The association was still robust after further adjustment for potential confounders (Additional file 1: Table S3, S4).