Descriptive results

Data on memory, verbal fluency and letter search were available for 28,356 participants.

Participants with missing data for cognitive function were younger (p<0.001), more likely to be male (p<0.001), had lower educational attainment (p<0.001), owned fewer assets (p<0.001), and had higher values for parental education (p<0.001) and childhood amenities (p<0.001) than those participants who had cognitive function data. The number of participants with complete data after listwise deletion on all variables used in the structural equation model of life course SEP and cognition was 25,127. Summary statistics and frequency distributions of the study variables used in analysis on life course SEP and cognition, based on complete cases, are presented in Table 4.2.

Average age of participants was 60.0 years in the sample as a whole, and was similar across centres with somewhat greater dispersion of the distribution in Kaunas (age range at Kaunas baseline was 45-72 years). Women made up 53.9% of the sample. Lower mean values were observed in Novosibirsk for measures of childhood and current material circumstances (all two sample t-tests p<0.001) than in the other study centres, consistent with generally poorer socioeconomic conditions in Russia. Participants in these cohorts had relatively high levels of education. Educational level was lower in Czech towns than in the other centres, most likely

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as a result of differences in the degree of urbanisation (six smaller towns made up the Czech sample, whereas Novosibirsk, Krakow and Kaunas are important regional urban centres).

Participants' average educational level was markedly higher than that of their mothers’ (and fathers’), undoubtedly a consequence of educational expansion which occurred in the post-war period. In the Czech sample participants' mothers were less likely than mothers in the other centres to have no formal education but the proportion with university education was also comparably smaller. There were significant differences in educational level between men and women across centres, with higher proportions of men having university education; the difference was greatest in the Czech sample (chi square p<0.001), and smallest in Kaunas (chi square p=0.014). Men in all centres also reported owning a higher mean number of household assets than women (two sample t-tests p<0.001 for all centres), perhaps partly a result of a higher proportion of women in the sample being widowed, single or living alone.

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Table 4.2. Descriptive characteristics of study sample for life course SEP and cognition (based on listwise deletion, n=25,127)

Men ___ Women

Figures are means with standard deviations in parentheses or proportions, as appropriate.

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4.2.2. Preliminary regression analysis

Preliminary regression analyses indicated significant heterogeneity between centres in associations of SEP measures and cognitive outcomes, with interactions generally significant at 1% level. In addition, statistically significant heterogeneity was observed between genders for some combinations of SEP measures and cognitive function, usually involving own education or household assets and verbal fluency or letter search. Subsequently, analyses were performed stratified by centre and gender.

Detailed results from preliminary regression analyses are shown in Appendix III, starting on pg. 250. Estimates of direct effects of SEP measures on cognition from regression analyses should correspond to results from structural equation modelling, save for minor discrepancies resulting from differences in assumptions and model specification. Therefore, only structural equation modelling results are discussed in detail in this section. However, some additional findings resulting from regression analyses are worth noting and are thus presented briefly in this section.

For regression analyses of associations between life course SEP measures and cognition there were 23,888 participants with complete data on all variables used in the models: cognitive function (z-transformed cognitive test scores and global cognition), life course SEP measures, health-related behaviours and health status measures. In age-adjusted models all SEP measures were significantly associated with all cognitive tests and global cognition across centres and genders, with the exception of non-significant associations between childhood amenities with verbal fluency and letter search as outcomes in Novosibirsk men. In these

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Table 4.3. Regression results for life course SEP and global cognition, before and after adjusting for alcohol intake and smoking

Mutually-adjusted Health behaviours Mutually-adjusted Health behaviours

b SE b SE b SE b SE

Model 1: Adjusted for age and measurement wave.

Model 2: Adjusted for age, alcohol intake, smoking and measurement wave.

Reference group is “primary level or less” for own education and “less than primary level” for mother’s education.

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models both own education and mother’s (and father’s) education showed a graded positive relationship with cognitive performance.

Results from mutually adjusted models and models further adjusted for two core health behaviours, alcohol intake and smoking, with global cognition (averaged z-scores across all four cognitive tests) as the outcome are shown in Table 4.3. Results for the other cognitive tests were similar and are shown in Appendix III in Table III-3 and Table III-4 for men and women, respectively. In mutually adjusted models the coefficients for SEP measures were generally significantly reduced compared to models adjusted only for age, most notably for childhood SEP measures and least notably for own education. In contrast, associations between life course SEP measures and cognitive function were largely unaffected by further adjustments for the two core health-related behaviours; across centres coefficients for all SEP measures remained largely unchanged or were attenuated only slightly. This suggests that both alcohol consumption and smoking are unlikely to be important mediators of the associations between SEP and cognition in these populations.

Further adjustments for health measures resulted in only a slight attenuation in the associations between mother’s education, education and household assets, and cognitive performance (see Appendix III). Similar results were obtained in additional analyses also controlling for physical activity and depressive symptoms. Among health measures, only self-rated health and history of stroke were consistently associated with cognitive function, in positive and inverse directions, respectively. Since adjustments for health behaviours as well as health measures had only a very limited impact on the associations between SEP measures from across the life course and cognitive function, they were not included as potential mediators or confounders in structural equation analysis.

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