The Role of Cognitive Performance in Older Europeans’ General Health: Insights from Relative Importance Analysis

Abstract

This study explores the role of cognitive performance in the general health of older Europeans aged 50 and over, focusing on gender differences, using data from 336,500 respondents in the sixth wave of the Survey of Health, Aging, and Retirement in Europe (SHARE). Cognitive functioning was assessed through self-rated reading and writing skills, orientation in time, numeracy, memory, verbal fluency, and word-list learning. General health status was estimated by constructing a composite index of physical and mental health-related measures, including chronic diseases, mobility limitations, depressive symptoms, self-perceived health, and the Global Activity Limitation Indicator. Participants were classified into good or poor health status, and logistic regression models assessed the predictive significance of cognitive variables on general health, supplemented by a relative importance analysis to estimate relative effect sizes. The results indicated that males had a 51.1% lower risk of reporting poor health than females, and older age was associated with a 4.0% increase in the odds of reporting worse health for both genders. Memory was the strongest predictor of health status (26% of the model $R^2$), with a greater relative contribution than the other cognitive variables. No significant gender differences were found. While this study estimates the odds of reporting poorer health in relation to gender and various cognitive characteristics, adopting a lifespan approach could provide valuable insights into the longitudinal associations between cognitive functioning and health outcomes.

1. Introduction

Cognition is defined as the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses [1]. These internal mental processes explain how people perceive, remember, communicate, think, make decisions, and solve problems. Often characterized as the brain’s ultimate function, cognition is linked to neuropsychiatric disorders, such as schizophrenia, depression, and anxiety [2]. Cognitive function, a broader concept, encompasses both subjective and objective measures of general cognitive operations, including memory, attention, concentration, numeracy, and verbal fluency [3,4]. Thus, cognition plays a role in all mental processes involved in acquiring knowledge, manipulating information, and reasoning [5]. Age is associated with changes in cognitive function, particularly in processing speed, attention, and certain memory and language abilities [6]. Moreover, the variability in cognitive decline across individuals [7] highlights the importance of understanding gender-related changes in brain function in the aging population.

1.1. Association Between Cognitive Function and Health

A vast body of literature provides evidence of the interrelation between cognitive impairment and health. Authors conclude that, while cognitive training is an important factor negatively associated with cognitive decline, other predictors, including physical activity and healthy lifestyle patterns, may also protect against cognitive dysfunction, especially in the elderly [8]. The study by Roberts et al. [9] demonstrated that individuals in middle and late life who engage in artistic, craft, and social activities have a significantly reduced risk of mild cognitive impairment. This also applies to those who use computers in later life. Blankevoort et al. [10] explained that muscle strength, balance, and walking ability contribute to cognitive performance, particularly in memory, verbal, and visual attention. Additionally, Anstey and Christensen [11] found that hypertension, objective health indices, and cardiovascular disease are associated with cognitive decline.

Furthermore, recent studies suggest that cognitive frailty may be associated with a higher risk of functional disability and a greater disability burden compared to robust individuals [12]. Additionally, Dotchin et al. [13] and Shimada et al. [14] found that cognitive decline is a significant predictor of disability burden in the oldest individuals. Chaves et al. [15] and Samuel et al. [16] also suggested that cognitive impairment directly affects the quality of life in older adults by impairing their ability to perform activities of daily living (ADL), especially when these activities are related to painful medical symptoms and emotional disorders. Furthermore, Keramat et al. [17] concluded that older people with moderate or severe cognitive impairment are more likely to experience poorer physical health and mental performance. In this context, Giri et al. [18] supported the idea that cognitive dysfunction predicts not only physical health issues but also depression, while Ma [19] provided evidence that depression and anxiety could be risk factors for cognitive impairment.

A recent study using large-scale longitudinal data for European adults aged 50 and older found that physical activity mediates the relationship between cognitive function and depressive symptoms, suggesting that higher cognitive scores positively influence physical activity, thereby contributing to reduced depressive symptoms [20]. Moreover, Panghal et al. [21] demonstrated that cognitive impairment affects older adults’ well-being and has a diverse impact on ADL. Additionally, Scanlan et al. [22] found that the impact of cognitive decline on limitations in ADL deterioration was over four times greater compared to that of disease burden. Taylor et al. [23] observed that chronic conditions and cognitive disorders often co-occur, increasing the risk of cognitive decline due to the presence of chronic diseases. Similarly, Kim et al. [24] identified a strong association between chronic illnesses, such as hypertension and diabetes mellitus, and poorer memory and executive function. Finally, many authors link cognitive impairment with increased mortality risk, particularly in very old ages and individuals with a medical history [25,26,27].

1.2. Association Between Cognitive Function and Gender

Regarding gender differences in cognitive decline, descriptive studies indicate that a higher proportion of older women report cognitive impairment compared to their male peers [28]. This trend also applies to the oldest old, with women being more vulnerable than men [29]. Other research across European countries shows that irrespective of gender, being less engaged in mental activities is a significant predictor of memory decline, and thus cognitive impairment [30]. In contrast, more years of education and higher wealth levels serve as protective factors against cognitive decline for both genders in older ages [28,31]. Zheng and Jia [32] agree with de Maio Nascimento et al. [29] on the higher prevalence of cognitive impairment in females and further found that depression is a significant predictor of cognitive dysfunction for both genders. However, Calatayud et al. [33] revealed that depression was associated with lower cognitive levels only in men. Moreover, Scheel-Hincke et al. [34] reported that women are at a higher risk of ADL and instrumental activities of daily living (IADL) limitations than men, while Wu et al. [35] showed that worse midlife functional limitations, including ADL, physical function, and self-reported health, increase the likelihood of cognitive impairment for both genders. Furthermore, Formanek et al. [36], after adjusting for age and gender, found that Mediterranean countries and Central and Eastern Europe exhibited lower overall cognitive performance compared to Western Europe, while Scandinavia showed higher cognitive performance. When sociodemographic and clinical factors were incorporated into the model, these associations remained statistically significant.

1.3. The Present Study

The findings above clearly indicate a strong link between health status and cognitive function in both men and women. Therefore, it is crucial to examine various physical health measures—such as chronic diseases, mobility limitations, and long-standing activity limitations—as well as mental health-related metrics, and explore their interplay with cognitive characteristics that reflect cognitive performance. Given that genders often experience and respond differently in their daily lives, it is important to identify both the similarities and differences in the health–cognition relationship within the aging population.

In this context, it is important to integrate physical and mental health-related measures holistically to provide a comprehensive assessment of an individual’s overall health status and then evaluate the relative importance of cognitive variables in determining the general health of the aging population. Serafetinidou and Parpoula [37], in their investigation of the role of the Big Five personality traits on the physical health status of older Europeans, constructed a single index based on various physical health measures. This study builds on their work by incorporating mental health measures into the analysis, aiming to shed light on the role of cognitive performance in the general health of older Europeans. By examining the combined effect of both physical and mental health-related metrics, a composite health index was constructed to more comprehensively reflect respondents’ overall health status. This index served as a health assessment tool, with higher scores indicating worse health. The analysis utilized data from the Survey of Health, Aging, and Retirement in Europe (SHARE), focusing on European residents aged 50 and older, and drew conclusions regarding gender differences in the health–cognition relationship.

By bridging these dimensions of health into a unified framework, this study provides valuable insights into the complex dynamics underlying cognitive function, potentially guiding more effective strategies for health management and intervention.

1.4. Research Hypotheses

The first hypothesis examines whether cognitive performance—assessed through cognitive characteristics such as self-rated reading and writing skills, orientation in time, numeracy, memory, verbal fluency, and word-list learning—predicts the constructed general health index. This hypothesis is based on the premise that cognitive functions are integral to overall health, influencing or reflecting an individual’s general health status. Cognitive abilities impact a person’s capacity to manage physical health, adhere to treatments, and engage in health-promoting behaviors. For example, impaired memory or verbal fluency might affect one’s ability to follow medical advice or remember health-related information, potentially impacting general health outcomes. Additionally, cognitive performance not only reflects a person’s ability to manage tasks but also acts as a valuable marker of broader mental health conditions, influencing how individuals process emotions, react to stress, and engage in social and personal activities. Cognitive impairments, such as difficulties with memory, attention, or decision-making, are often linked to mental health conditions like depression or anxiety, while cognitive decline is commonly associated with conditions like Alzheimer’s disease or other dementias, in which neuropsychiatric symptoms such as depression, apathy, aggression, and psychosis are recognized as core features. Cognitive performance can thus reflect broader health conditions, as declines in cognitive function may indicate worsening physical and/or mental health.

The second hypothesis examines whether the role of cognitive performance in general health differs between genders. It is based on the idea that cognitive decline and its health implications may vary by gender due to biological, social, and lifestyle factors, among others. For example, men and women aged 50 and older, spanning from middle age to later life, may experience different patterns of cognitive decline or face distinct health risks that influence how cognitive impairments affect overall health. Identifying these gender-specific differences is essential for developing targeted health interventions.

The third hypothesis examines whether there are differences in the magnitude of the relative weights of cognitive variables on health status within the total sample and across genders. Relative importance assesses the proportionate contribution of each cognitive variable to the total variance in health status, considering the predictor’s contribution both individually and in conjunction with other predictors. This hypothesis is based on the rationale that regression weights primarily assess incremental prediction. When predictors are correlated, variables displaying a statistically significant bivariate relationship might not necessarily exhibit a significant incremental relationship. In contrast, relative weights enable the identification of which predictors significantly contribute to the variance in the outcome of interest, even when other correlated predictors are present.

2. Materials and Methods

2.1. Transparency and Openness

We retrieved data from the sixth wave of SHARE [38], collected between February and November 2015. Access to the SHARE data is provided to registered users through the SHARE Research Data Center website https://share-eric.eu/data/data-access (accessed on 5 February 2024). Details on materials, questionnaires, the interview codebook, and the methodology of SHARE Wave 6 can be found in Malter and Börsch-Supan [39] and Börsch-Supan et al. [40]. Below, we present the sampling frame, imputation method, data preparation and analysis procedures, as well as the measures used in the present study.

2.2. Procedure and Participants

Data from SHARE, utilized for this study, included health, demographic, economic, and social aspects of life for European residents aged 50 and over [38,39,40]. The initial research sample consisted of 336,500 observations from the sixth wave of SHARE, conducted in 2015. This sample was then divided into five imputed datasets, each containing 67,300 individuals aged 50 and above, residing in 18 European countries, namely Austria, Germany, Sweden, Spain, Italy, France, Denmark, Greece, Switzerland, Belgium, Israel, the Czech Republic, Poland, Luxembourg, Portugal, Slovenia, Estonia, and Croatia. To address missing data—a common issue in large-scale surveys due to unit and/or item non-response—a hot-deck imputation technique was applied [41], following a similar approach to Serafetinidou and Parpoula [37]. The data were analyzed across all five iterations to avoid imprecise results [3].

2.3. Measures

2.3.1. Health-Related Indicators

The physical health status of participants in this study was assessed using the following indicators: the number of chronic diseases, the number of mobility limitations, the long-standing activity limitations as measured by the Global Activity Limitation Indicator (GALI) [42], and individuals’ self-perceived health (SPH), which reflects their subjective evaluation of their overall health. Mental health status was assessed with the EURO-D scale, an indicator of depression symptomatology in the aging population [43], consisting of 12 items assessing depression, pessimism, suicidality, guilt, sleep, interest, irritability, appetite, fatigue, concentration, enjoyment, and tearfulness.

The number of chronic diseases is determined by combining responses to a series of questions regarding whether the interviewee has been diagnosed with any of the chronic conditions listed in the questionnaire. Mobility limitations are assessed through a composite score that encompasses mobility, arm functioning and fine motor limitations reported. GALI is a self-reported measure of health-related activity limitations, assessed through a single question: “For at least the past six months, to what extent have you been limited because of a health problem in activities people usually do?”. Respondents choose from three options: severely limited, limited but not severely, and not limited at all. This indicator captures general activity restrictions without specifying the type of activity affected (work, household chores, leisure, personal care, etc.). Due to the low frequency of respondents reporting severe limitations, moderate and severe limitations are often combined into a single category (limited), resulting in a binary indicator that takes on a value of 0 for “not limited” and 1 for “limited”. The SPH scale captures the respondent’s subjective assessment of their health. It is assessed using a single-item question that asks respondents to rate their health status on a scale from excellent (1) to poor (5). Consequently, a higher score reflects a poorer perception of health. The EURO-D scale consists of 12 items assessing depression symptoms in older adults, with scores ranging from 0 to 12 based on the number of symptoms reported. A higher score indicates a greater level of depression, as each item measures the self-reported presence of a specific symptom.

The number of chronic diseases, mobility limitations, SPH, and ADL and IADL indicators were used by Serafetinidou and Parpoula [37] to construct a composite index for overall physical health assessment. In this paper, however, the GALI measure of disability was used in place of ADL and IADL measures. Although ADL, IADL, and GALI target similar activities and limitations [44], we followed Eurostat’s recommendation to include GALI and SPH as core social variables in the EU Labour Force Survey (LFS) and other European Social Surveys (ESS) to support the modernization of social statistics [45]. Additionally, the EURO-D factor was included to assess depressive symptoms in the aging population [43]. The focus on depressive symptomatology is due to its high prevalence and significant impact on older adults. Unlike other mental health disorders, such as anxiety and dementia, depression is sometimes underdiagnosed and undertreated in this age group, despite its strong link with common age-related challenges. Chronic illnesses, physical disabilities, and cognitive decline are more frequent in older adults, all of which correlate strongly with depressive symptoms. Additionally, older adults often face losses, such as the death of loved ones or a diminished social role post-retirement, leading to isolation and loneliness—key contributors to depression. Moreover, side effects of medications for managing chronic conditions can further intensify depressive symptoms.

2.3.2. General Health Status Metric

In this study, Principal Component Analysis (PCA) was applied to the previously described subjective (SPH) and objective (chronic diseases, mobility limitations, GALI, EURO-D) health-related indicators to derive a single component that represents an individual’s general health status, following a similar rationale to the study by Serafetinidou and Parpoula [37].

PCA was chosen over Exploratory Factor Analysis (EFA)— both of which are often used interchangeably in multivariate data analysis—because no specific theory is assumed to underlie the relationships among the health-related indicators under consideration. In contrast, EFA would be more suited to cases where the goal is to develop a new theory by exploring latent factors that best account for the variations and interrelationships of the manifest variables. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.836, and Bartlett’s test of sphericity was statistically significant (p-value < 0.001). The highest correlation was observed between the number of mobility limitations and SPH (r_s(67298) = 0.548, p < 0.001), while the lowest correlation was between the number of chronic diseases and EURO-D (r_s(67298) = 0.325, p < 0.001). Data factorability was similarly evaluated for the other imputed datasets, yielding results consistent with those of the first. The first component (PC1) accounted for a significant portion—approximately 61%—of the total variance, with an eigenvalue of 9.32. Similar proportions and eigenvalues were observed across the remaining imputed datasets. The weights of the chosen component are shown in the equation below:

$$\begin{gathered} {\mathrm{P}\mathrm{C}1}_i=0.35\ast \mathrm{c}\mathrm{h}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{c} {\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{s}}_i+0.70\ast \mathrm{m}\mathrm{o}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y} {\mathrm{l}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}}_i+0.09\ast {\mathrm{G}\mathrm{A}\mathrm{L}\mathrm{I}}_i \\ +0.24\ast {\mathrm{S}\mathrm{P}\mathrm{H}}_i+0.57\ast {\mathrm{E}\mathrm{U}\mathrm{R}\mathrm{O}-\mathrm{D}}_i,i=1,\dots ,N. \end{gathered}$$

All measures under consideration have positive weights, with mobility limitations having the highest component weight, followed by EURO-D, number of chronic diseases, SPH, and GALI. Numerically, these weights correspond to the coefficients of the variables, indicating which variables contribute most to the component. The extracted component reflects a general measure of health, with a higher individual score corresponding to worse health status.

Regarding the descriptive characteristics of the health-related measures integrated into the component, participants reported an average of two chronic diseases (M = 1.82, SD = 1.63) and experienced mobility limitations (M = 1.72, SD = 2.43). They reported moderate levels of SPH (M = 3.20, SD = 1.07) and low levels of self-perceived depressive symptoms (M = 2.48, SD = 2.30), indicating a generally positive subjective evaluation of their physical and mental health. These results are based on the first imputed dataset; similar findings, with slight variations, were observed across the other imputed datasets. The GALI metric provided additional insight into the distribution of functional abilities within the study population, with over half (53.1%) of participants indicating no activity limitations, suggesting a relatively high level of activity independence. Meanwhile, 46.9% reported experiencing some level of limitation.

2.3.3. Measures of Cognition

Cognitive performance was assessed using seven indicators: self-rated reading and writing skills, orientation in time, numeracy, memory, verbal fluency, and word-list learning. Self-rated reading and writing skills, as well as the memory test score, range on a scale from 1 (indicating excellent abilities) to 5 (indicating poor abilities). Orientation in time is assessed on a reverse scale from 0 (indicating poor performance) to 4 (indicating good performance). Word-list learning skills and numerical performance are measured by calculating the mean values of the first and second trials for each respondent, while the verbal fluency score is based on the number of correct words produced in the animal task (where participants are asked to name as many animals as possible in 60 s). The range of scores is theoretically open-ended, depending on the number of words participants can produce, although a cut-off score of ≤6 words on the animal task is considered indicative of impaired performance, as suggested by Jenkins et al. [46]. Further details regarding the measurement of cognitive performance can be found in the SHARE Wave 6 release guide [38,39,40].

2.3.4. Controls

The control variables included respondents’ age at the time of the interview (measured in years), gender, and country of residence, with females and Austria as the reference categories, respectively. The selection of these controls follows the approach commonly used in extensive panel studies such as SHARE [37].

2.4. Statistical Data Analysis

For analysis purposes, PCA was first applied to create a single component representing individual’s general health status. The resulting PCA scores were then dichotomized to classify respondents as having either good or poor health status. To set the optimal binary threshold, Youden’s index [47] was maximized using coordinates from the Receiver Operating Characteristic (ROC) curve, which graphically evaluates binary classifiers. This approach allowed for grouping respondents into “better” or “worse” health levels based on PCA scores. The resulting binary health indicator was then used in multiple logistic regression (LR) models, applied first to the overall sample and subsequently by gender, to explore the relationship between health status and the predictor cognitive variables under consideration. Data analysis stages (PCA, ROC analysis, and LR modeling) and the assessment of statistical assumptions were conducted using IBM SPSS Statistics (v. 20) across all imputed datasets. For simplicity, detailed results are presented from the SPSS analysis on the first imputed dataset, with additional results from the other datasets available upon request. These analytical steps align with the rationale presented in Serafetinidou and Parpoula [37].

Moreover, while many researchers use multiple (linear or logistic) regression to determine the variable set that optimizes the prediction of an outcome, another significant application is in theory testing and explanation. In these cases, the focus is on how each variable contributes to the variance explained in the outcome of interest. To achieve this, a relative importance analysis enables researchers to make valid inferences about the relative contribution (i.e., relative importance) of multiple, often correlated, predictor variables in the regression analysis. This method also allows for the decomposition of the total variance explained by the regression model into weights that accurately reflect the proportionate contribution of each predictor variable. In this context, a relative weight analysis (RWA) [48] was conducted modifying the R code provided on the RWA-Web page by Tonidandel and LeBreton [49], allowing for an accurate partitioning of variance among the predictors under consideration. The focus was on exploring the portion of the explained variance in an individual’s general health status contributed by each cognitive variable.

3. Results

As mentioned earlier, the resulting PCA scores were dichotomized to classify SHARE participants into two groups (good or poor health status) based on their general health conditions. The cut-off point, determined through ROC curve analysis, was 3.68679.

Table 1. Relative and absolute frequency distribution (in parentheses) of health status for the first imputed dataset.

	Total Sample (N = 67,300)	Males (29,599)	Females (37,701)
Better general health status	56.8% (38,199)	65.4% (19,351)	50.0% (18,848)
Worse general health status	43.2% (29,101)	34.6 (10,248)	50.0% (18,853)

Notes. N denotes sample size.

shows the relative and absolute frequency distribution of SHARE respondents with better (PCA scores lower than 3.68679) and worse (PCA scores higher than 3.68679) general health status. The results are presented for the entire study sample, as well as separately by gender.

The results show that respondents with better health status constitute a larger proportion (around 60%) of the total sample compared to those classified as having worse health status. Gender comparisons reveal that females are evenly distributed between better and worse general health statuses (50% each), while males with better general health status significantly outnumber those with worse health status, comprising approximately 65% versus nearly 35%. Similar relative frequencies were observed across the remaining imputed datasets.

Table 2. Descriptive statistics (means and standard deviations in parentheses) of the study variables for the first imputed dataset.

Variable	Total Sample	Males	Females
Age	67.98 (10.05)	68.10 (9.68)	67.88 (10.32)
Reading	2.29 (1.11)	2.31 (1.10)	2.27 (1.13)
Writing	2.41 (1.15)	2.45 (1.15)	2.38 (1.16)
Orientation in time	3.83 (0.53)	3.84 (0.50)	3.82 (0.55)
Verbal fluency	19.89 (8.01)	20.02 (7.89)	19.79 (8.10)
Memory	2.99 (0.97)	2.95 (0.98)	3.02 (0.97)
Word-list learning	4.57 (1.86)	4.43 (1.77)	4.68 (1.92)
Numeracy	3.81 (0.96)	3.98 (0.88)	3.68 (1.01)

Notes. Age Age at the time of the interview; Reading Self-rated reading skills; Writing Self-rated writing skills; Orientation in time Score of orientation in time test; Verbal fluency Score of verbal fluency test; Memory Score of memory test; Word-list learning Mean score of word-list learning tests (trial 1 and trial 2); Numeracy Mean score of first and second numeracy tests.

presents descriptive statistics for the study variables, for both the overall sample and by gender. The study sample comprises 67,300 individuals aged 50 to 106 years, with a mean age of 67.98 years (SD = 10.05). Women represent a larger portion of the sample (56.0%) compared to men (44.0%), with both genders showing similar mean ages. For the total sample, respondents rate their reading (M = 2.29, SD = 1.11) and writing (M = 2.41, SD = 1.15) skills as very good, and their performance on the memory test can be characterized as good (M = 2.99, SD = 0.97). The mean scores for word-list learning trials and numeracy tests were 4.57 (SD = 1.86) and 3.81 (SD = 0.96), respectively, on scales of 0 to 10 and 0 to 5, indicating relatively stronger numerical performance.

The results by gender closely mirror those of the total sample. Women reported slightly better reading (M = 2.27, SD = 1.13) and writing (M = 2.38, SD = 1.16) skills than men, while men performed better on memory tests (M = 2.95, SD = 0.98). Verbal fluency scores are similar for males (M = 20.02, SD = 7.89) and females (M = 19.79, SD = 8.10), as are orientation in time scores for males (M = 3.84, SD = 0.50) and females (M = 3.82, SD = 0.55). Finally, females (M = 4.68, SD = 1.92) perform slightly better on word-list learning trials compared to males (M = 4.43, SD = 1.77), while males (M = 3.98, SD = 0.88) slightly outperform females (M = 3.68, SD = 1.01) in numeracy tests.

The results from LR modeling are shown in

Table 3. Odds ratios (95% CIs in parentheses) for the constructed general health index—results for the total study sample and by gender for the first imputed dataset ^a.

Variable	Total Sample	Males	Females (Ref. Cat.)
Controls
Gender		0.489 ** (0.472, 0.507)	1
Age	1.040 ** (1.038, 1.042)	1.037 ** (1.034, 1.040)	1.043 ** (1.040, 1.045)
Cognitive Function Characteristics
Reading	1.050 ** (1.020, 1.080)	1.045 ** (1.002, 1.090)	1.052 ** (1.012, 1.094)
Writing	1.167 ** (1.135, 1.200)	1.191 ** (1.144, 1.241)	1.147 ** (1.104, 1.191)
Orientation in time	0.761 ** (0.731, 0.792)	0.756 ** (0.713, 0.801)	0.763 ** (0.721, 0.808)
Verbal fluency	0.978 ** (0.976, 0.981)	0.981 ** (0.976, 0.985)	0.977 ** (0.973, 0.980)
Memory	1.414 ** (1.386, 1.443)	1.408 ** (1.365, 1.452)	1.420 ** (1.382, 1.459)
Word-list learning	0.932 ** (0.921, 0.944)	0.929 ** (0.911, 0.947)	0.933 ** (0.918, 0.948)
Numeracy	0.836 ** (0.817, 0.854)	0.843 ** (0.814, 0.873)	0.831 ** (0.808, 0.856)

Notes. ^a All models were controlled for country of residence; ** p-value < 0.01.

. Specifically, the odds ratios and 95% confidence intervals (CIs) for the constructed general health metric are provided for the total sample and separately by gender. In the total population and for both sexes, older age was associated with a 4.0% increase in the odds of reporting worse health status. However, males had a 51.1% lower relative risk of reporting poorer general health compared to females.

Regarding cognitive function characteristics, the analysis of relative risks revealed that low levels of self-rated reading skills increase the odds of reporting poor health by 5% in the total sample and for both sexes. Furthermore, poor self-rated writing skills and memory performance increase the likelihood by approximately 17% and 41%, respectively. In particular, poor self-rated writing skills are associated with a 19.1% increase in the odds of worse health for males, compared to a 14.7% increase for females, while lower memory performance scores are associated with a 42% increase in the odds of worse health for females, compared to a 40.8% increase for males. Higher scores in verbal fluency and average word-list learning trials reduce the likelihood of worse general health by 2.2% and 6.8%, respectively, with similar odds observed for both sexes. Furthermore, higher average scores in numeric performance decrease the odds of worse general health by 16.4%, while higher orientation in time scores reduce the likelihood the most, by 23.9%, with similar odds observed for both sexes. These findings are consistent with those from other imputed datasets, despite slight numerical variations among the cognitive variables.

A RWA was then conducted, with the results presented in

Table 4. Summary of RWA.

Predictor	$\mathit{b}$	$\mathit{\beta }$	RW	CL-L	CL-U	RS-RW (%)
$\mathrm{Criterion}=\mathrm{General}\mathrm{Health}\mathrm{Status}(R^2=0.2221;F\left[7,67292\right]=2745.01;p<0.001)$
Intercept	6.763
Reading a,b	0.044 *	0.016	0.0191 *	0.0178	0.0206	8.63
Writing a,b	0.225 *	0.085	0.0238 *	0.0221	0.0254	10.69
Orientation in time a,b	−0.601 *	−0.104	0.0241 *	0.0216	0.0266	10.83
Verbal fluency a,b	−0.037 *	−0.098	0.0292 *	0.0273	0.0312	13.16
Memory b	0.631 *	0.201	0.0571 *	0.0541	0.06025	25.68
Word-list learning a,b	−0.160 *	−0.097	0.0332 *	0.0312	0.0354	14.97
Numeracy a,b	−0.386 *	−0.122	0.0356 *	0.0333	0.0381	16.03

Notes. $b$ stands for unstandardized regression weight; $\beta$ stands for standardized regression weight; RW stands for raw relative weight (sums to $R^2$); CI-L stands for lower confidence interval bound; CI-U stands for upper confidence interval bound; RS-RW stands for rescaled relative weight (sums to 100%); * p-value < 0.05; ^a The RW for this variable differs significantly from the RW obtained for memory. ^b There was no statistical evidence of gender differences for this RW.

. CIs for the individual relative weights [50] and the associated significance tests were estimated using a bootstrap method with 20,000 replications, as suggested by Tonidandel et al. [51]. We constructed bias-corrected and accelerated 95% CIs, which were shown to provide superior coverage accuracy [51]. Briefly, the results show that a weighted linear combination of our seven cognitive variables accounted for 22% of the variance in general health status ($R^2$ = 0.22). Since none of the 95% CIs for the significance tests included zero, each of the seven cognitive variables was found to account for a significant amount of variance in health status, with the most important variables being memory (RW = 0.06), numeracy (RW = 0.04), word-list learning (RW = 0.03), and verbal fluency (RW = 0.03).

The relative weight results were consistent with those from the standard multiple regression analysis. In particular, the conventional regression analysis revealed that higher levels of self-rated reading and writing skills, and memory performance (indicating poorer skills) provided a statistically significant incremental effect in predicting worse general health status while holding all other variables constant. In contrast, orientation in time, word-list learning, numeracy, and verbal fluency (with higher scores indicating better performance) provided a statistically significant decreasing effect on the prediction of worse general health status, holding constant all of the remaining variables. It is worth noting that the concordance in the significance of the regression coefficients and the relative weights, as evidenced in our study, is not necessarily expected [51] since these two statistics address different research questions, and the results derived from traditional multiple regression and relative weigh analysis work in a supplementary fashion [52].

Since memory was identified as the most significant variable in predicting poorer general health status, we assessed whether the relative contribution of memory to the overall $R^2$ was significantly different from that of the other cognitive variables. The results indicated that the relative weight of memory (RW = 0.06) was significantly higher than that of the other cognitive variables, as the CIs for these comparisons did not include zero. Additionally, we explored potential gender-related differences in the magnitude of the relative weights. The results showed no statistically significant differences based on gender, as the confidence intervals for the male–female comparisons included zero. In summary, it appears that the majority of the explained variance in worse general health status is attributed to memory (26% of the model $R^2$), numeracy (16% of the model $R^2$), word-list learning (14% of the model $R^2$), and verbal fluency (13% of the model $R^2)$. The next most important contributors were self-rated writing skills and orientation in time (11% of the model $R^2$ each), followed by self-rated reading skills (9% of the model $R^2).$

4. Discussion

A substantial body of literature has examined cognitive changes associated with healthy aging, as well as the structural and functional correlates of these changes and the prevalence and cognitive impacts of age-related diseases [53]. These diseases can exacerbate neuronal dysfunction and loss, as well as cognitive deterioration, leading to significant impairments that affect the daily lives of many individuals. Emerging evidence suggests that adopting a healthy lifestyle may contribute to a slower rate of age-related cognitive decline and potentially postpone the onset of cognitive symptoms linked to age-related diseases. Additionally, experts emphasize that associations between cognitive functioning and both physical and mental health are substantial and can vary due to genetic factors. Studies by Balbaid et al. [54] and Hagenaars et al. [55] found that cognitive impairment was associated with coronary artery disease, stroke, Alzheimer’s disease, and major depressive disorder, while a history of cardiac disease, stroke, physical inactivity, and poor physical function was also strongly linked to cognitive decline [56,57].

Furthermore, several studies [58,59,60,61,62,63] indicate that cognitive decline has broad health implications and that a clear relationship exists between better health-related quality of life and cognitive function. However, many questions remain about how these findings—largely derived from studies focusing on either subjective or objective health-related measures, and often specific to either physical or mental health—could contribute to personalized preventive healthcare and medicine. Globally, community-level data in this direction are limited. Most relevant studies are cross-sectional, while longitudinal studies generally have limited follow-up periods and often lack representative sampling designs. Thus, health researchers and practitioners could greatly benefit from leveraging published epidemiological and psychosocioeconomic data based on representative sampling (such as the “Survey of Health, Ageing and Retirement in Europe—SHARE”) to better understand the impact of aging on cognition in relation to the general health of older adults aged 50 or higher.

To our knowledge, no previous studies have examined the “combined effect” of established subjective and objective health-related measures or assessed the protective role of various cognitive variables on a single health index designed to provide an overall assessment of older Europeans’ health status. This study addresses this gap by constructing a composite index representing general health status, based on a linear combination of concurrent health-related factors. This approach reduces the computational complexity involved in examining all main effects and interactions while controlling for multiple testing errors [64,65]. Additionally, this study provides inferences concerning the relative contribution (i.e., relative importance) of the multiple (typically correlated) cognitive variables under consideration through RWA. This approach allows for estimating weights that accurately reflect the proportionate contribution of the various cognitive variables to worse general health status, with importance weights interpreted as estimates of relative effect size, as they are scaled in the metric of variance explained.

Our findings indicate that older individuals with better health status account for a larger share of the total sample (approximately 60%) compared to those with poorer health status. Gender comparisons show that females are evenly split between better and poorer health statuses (50% each), while males with better health status considerably outnumber those with poorer health, at approximately 65% versus 35%. Among SHARE respondents, good memory skills are observed, with participants generally rating their reading and writing skills as very good. They also score high in orientation in time and moderate in verbal fluency, with mean scores on word-list learning trials and numeracy tasks indicating relatively stronger numeric performance. Gender-based results are consistent with the overall sample. Women reported slightly higher self-rated reading and writing skills than men, a finding that aligns with Quinn’s study [66] on reading skills and Roivainen’s findings [67] on writing skills. Men demonstrated better memory skills than women, a finding that contrasts with McDougall et al. [68], who reported equal memory capabilities across genders. Verbal fluency and orientation in time scores are similar between men and women. Additionally, women slightly outperform men in word-list learning trials, while men show a slight advantage in numeracy skills. The recent literature supports these findings: Hirnstein et al. [69] observed a small verbal fluency advantage for women, Balart and Oosterveen [70] noted a narrowing gender gap in math abilities, leading to balanced numeric performance between the sexes, and Cremona et al. [71] confirmed a female advantage in word-list learning.

Additionally, older age was found to increase the odds of reporting poorer health status by 4.0% across the total population and within each gender. However, males had a 51.1% lower relative risk of reporting poorer general health compared to females. These findings confirm that the predictive strength of cognitive performance for physical and mental health may increase with age, particularly from midlife to older adulthood, as suggested by Jokela et al. [72]. To our knowledge, the recent literature shows a notable gap in directly linking cognitive predictors with an individual’s overall health status, especially through measures that assess both physical and mental health. This gap underscores the need for a comprehensive approach to understanding how cognitive function influences health in older populations. In this context, our study offers valuable insights by examining the impact of various cognitive characteristics on a general health index for the European population aged 50 and over, including gender-specific analyses. Specifically, the analysis of relative risks showed that low self-rated reading skills increased the odds of reporting poorer health by 5% for both sexes and the total sample. Poor self-rated writing and memory skills were associated with a greater likelihood of poorer health, with increases of approximately 17% (19.1% for males and 14.7% for females) and 41% (40.8% for males and 42% for females), respectively. Higher verbal fluency scores and better average scores on word-list learning trials were associated with a 2.2% and 6.8% reduction, respectively, in the likelihood of reporting poorer general health, with similar odds across genders. Additionally, higher average scores in numeric performance were linked to a 16.4% decrease in the odds of reporting poorer health, while better orientation scores reduced the likelihood the most, by 23.9%, again with similar odds across genders.

Furthermore, lower self-rated reading and writing skills, as well as poorer memory performance, were significantly associated with worse general health status after controlling for all other variables. In contrast, higher scores in orientation in time, word-list learning, numeracy, and verbal fluency were significantly linked to better general health, suggesting a protective effect when other variables were held constant. Memory emerged as the most significant predictor of health status, with its relative contribution notably greater than that of other cognitive function variables. No statistically significant gender-based differences were found in the magnitude of these relative weights. Finally, the results from the relative importance analysis were consistent with those of the traditional multiple regression analysis. Significant bidirectional relationships between memory decline and both physical and emotional health were also identified in a related study by Carmel and Tur-Sinai [30] on SHARE European retirees. The authors analyzed changes in these variables over time, considering the effects of early retirement as well as nation-specific and personal characteristics. The results showed that greater declines in physical health and functioning, along with increased depressive symptoms over the four-year study period, were significantly correlated with greater memory decline. Despite the studies’ differences in methodological approaches, these findings are consistent with previous research that has also demonstrated the protective influence of educational attainment and occupational achievements on cognitive decline. This body of evidence supports the cognitive reserve theory, which suggests that individuals with higher education levels and those employed in intellectually demanding careers develop a cognitive reserve that enhances their resistance to degenerative brain changes. However, this reserve can sometimes conceal underlying disease pathology, leading to sharper, more rapid cognitive decline and delays in dementia diagnoses [30]. Furthermore, aside from gender, the authors’ regional division of countries based on a socio-economic perspective provided statistical evidence of a significant unique effect on memory change, even after accounting for factors such as age, physical health, mental health, and other well-established correlates of memory and cognitive decline.

Limitations

This study has some limitations that should be acknowledged to prevent drawing misleading conclusions. First, some of the data are based on self-reported responses, which may be prone to recall bias. Second, the index created for overall health assessment does not include hereditary predictors and retrospective self-reports from childhood, due to the insufficient availability of such information in the SHARE Wave 6 database. Third, the cross-sectional and correlational study design does not allow for causal inferences. Finally, the findings are based solely on the first imputed dataset, which may introduce some bias in the interpretation of the results. However, comparisons with other imputed datasets revealed only minor differences.

5. Conclusions

Despite the limitations mentioned above, this study provides valuable insights into the role of cognitive performance in older Europeans’ general health. It suggests an overall health assessment that incorporates the combined effect of well-established physical and mental health-related measures, both subjective (SPH) and objective (chronic diseases, mobility limitations, GALI, EURO-D). As such, the findings have important clinical implications. Memory emerged as the most significant factor in predicting health status, with its relative contribution notably larger than that of the other cognitive function variables. No statistically significant gender-based differences were observed in the magnitude of the relative weights. The majority of the explained variance in general health status was attributed to memory, numeracy, word-list learning, and verbal fluency, together accounting for nearly 70% of the explained variance. The next most important contributors were self-rated writing skills and orientation in time, followed by self-rated reading skills.

While this study estimates the odds of reporting poorer health in relation to gender and various cognitive characteristics, adopting a lifespan approach could provide valuable insights into the longitudinal associations between cognitive functioning and health outcomes. Additionally, it would be of significant interest to assess the impact of cognitive characteristics on health across European populations, considering variations in educational attainment, country of residence, healthcare systems, and other socioeconomic and demographic factors. Moreover, future studies could explore the effectiveness of supervised methods (as an alternative to PCA) in improving modeling accuracy and compare the performance of other two-class discrimination methods with that of the LR classifier.