Skip to main content
Download PDF

Predictors of dementia among the oldest old: longitudinal findings from the representative “survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)”

BMC Geriatrics volume 24, Article number: 680 (2024) Cite this article

Abstract

Background/Aims

Our current study aimed to investigate the determinants of dementia among the oldest old using longitudinal data from a representative sample covering both community-dwelling and institutionalized individuals.

Methods/Design

Longitudinal representative data were taken from the “Survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)” that surveyed community-dwelling and institutionalized individuals aged 80 years and above (n = 1,296 observations in the analytic sample), living in North Rhine-Westphalia (most populous state of Germany). The established DemTect was used to measure cognitive impairment (i.e., probable dementia). A logistic random effects model was used to examine the determinants of probable dementia.

Results

The mean age was 86.3 years (SD: 4.2 years). Multiple logistic regressions revealed that a higher likelihood of probable dementia was positively associated with lower education (e.g., low education compared to medium education: OR: 3.31 [95% CI: 1.10–9.98]), a smaller network size (OR: 0.87 [95% CI: 0.79–0.96]), lower health literacy (OR: 0.29 [95% CI: 0.14–0.60]), and higher functional impairment (OR: 13.45 [3.86–46.92]), whereas it was not significantly associated with sex, age, marital status, loneliness, and depressive symptoms in the total sample. Regressions stratified by sex were also reported.

Discussion

Our study identified factors associated with dementia among the oldest old. This study extends current knowledge by using data from the oldest old; and by presenting findings based on longitudinal, representative data (also including individuals residing in institutionalized settings).

Conclusions

Efforts to increase, among other things, formal education, network size, and health literacy may be fruitful in postponing dementia, particularly among older women. Developing health literacy programs, for example, may be beneficial to reduce the burden associated with dementia.

Peer Review reports

Introduction

In high-income countries such as Germany, the population continues to age. The number of the oldest people in particular (80 years and older) is steadily increasing. This stage of life (i.e., 80 years and over) is associated with various challenges. For example, marked decreases in health [1] (such as multimorbidity [2]) often occur, which can hinder social activities [3]. Moreover, other critical life events frequently take place such as the loss of friends and relatives (e.g., spousal loss [4]), nursing home admission [5] or serious falls [6]. Such factors can lead to loneliness [7]. Due to the large number of serious critical life events frequently taking place in this life stage, it is important to take a closer look at individuals aged 80 years and over.

Being 80 years and older is also associated with dementia [8]. Dementia is a progressive cognitive impairment syndrome caused most commonly by Alzheimer’s disease. For example, according to a recent systematic review/meta-analysis, the prevalence of all-type dementia is about 2.7% among individuals aged 65 to 74 years. In contrast, the prevalence of all-type dementia is 15.1% among individuals aged 80 to 89 years and 35.7% among individuals aged 90 to 99 years, and 65.9% among individuals aged 100 years and over [8].

Individuals suffering from dementia are at high risk of feeling lonely [9], having major depression [10] or reporting lower health-related quality of life [11]. Managing daily activities (e.g., complex activities such as handling finances and even more basic activities such as using the toilet) becomes increasingly difficult for affected patients as the disease progresses. As a result, individuals with dementia require extensive care and supervision [12]. Thus, dementia can lead to nursing home admission [5] and premature death [13]. It is also associated with a tremendous economic burden [14]. For example, according to Wimo et al., the annual global societal cost of dementia equaled about US$1313.4 billion for 55.2 million individuals suffering from dementia (US$23,796 per individual with dementia) [14]. Considering the various adverse consequences of dementia, investigating the determinants of dementia is of great importance to policy, clinical practice, and the well-being of individuals with dementia and their families.

As outlined by previous systematic reviews/meta-analyses [15, 16], while there is research that investigates the factors leading to dementia among middle-aged or older adults, there is very limited knowledge regarding the determinants of dementia exclusively among the oldest old (e.g., [17,18,19,20] and additionally based on longitudinal, representative data and including individuals residing in institutionalized settings).

Most of the existing representative studies among the oldest old focused almost exclusively on community-dwelling individuals (and thus not included individuals living in nursing or old age homes) and are often limited by their cross-sectional design. For example, one cross-sectional study [20] based on representative data from the “Mexican Health and Aging Study” and the “Hispanic Established Populations for the Epidemiologic Study of the Elderly” found that higher age and multiple cardiovascular conditions were associated with higher odds of probable dementia among the oldest old living in private households. Other research among the oldest old (e.g., in Calabria, Southern Italy [21]) was also restricted by, among other things, the cross-sectional design (and the almost complete exclusion of nursing home residents). However, longitudinal studies are required to get a better understanding of the determinants of dementia among this important population group. Moreover, it is important to examine the determinants of dementia exclusively in the age bracket of 80 years and above. This can be explained by the fact that some critical life events can occur among individuals aged 80 years and above – such as the death of a spouse or close friend, increasing feelings of loneliness or decline in both mental and physical health.

Overall, due to the limited knowledge in this area, our current study aimed to investigate the determinants of dementia among the oldest old using longitudinal data from a representative sample covering both community-dwelling and institutionalized individuals. Such knowledge is important to better understand the factors contributing to dementia over time. This knowledge, in turn, is important to reduce the economic burden associated with dementia and maintain autonomy and satisfaction with life among the oldest old. Ultimately, this may guide efforts to avoid or postpone the onset and progression of dementia in the latest life.

Guided by former research [22, 23] and theoretical considerations, we selected sociodemographic and health-related factors for our regression model (more details are presented in the materials and methods section). For example, education was included as independent variable based on the cognitive reserve hypothesis or brain reserve capacity [24]. This hypothesis refers to the ability to withstand changes in the brain caused by aging and disease without displaying any clinical symptoms or disease indicators [25]. The cognitive reserve can take different forms according to Stern [26]: The neural reserve, where brain networks are more efficient, or have greater capacity, may be less prone to disruption. Furthermore, neuronal compensation, in which alternative networks can balance for the disruption of already existing networks. Moreover, factors such as a smaller network size or loneliness may be associated with a lower likelihood of dementia. Such a link may be explained by social activities and social engagement [27]. According to vascular hypothesis, social engagement can reduce cardiovascular risk factors which attenuates the risk of neurodegenerative diseases [27]. Other authors also attribute the link of social activities and dementia risk to the cognitive reserve hypothesis [28, 29]. Factors such as health literacy may have a long-lasting impact on cognitive impairment in the later stages of life. This may be explained by a more favorable lifestyle [30]. Additionally, depression may also increase the risk of dementia. Such an association may be, among other things, attributed to vascular diseases, changes in alterations in glucocorticoid steroid levels and hippocampal atrophy as well as changes in inflammation [31].

Materials and methods

Sample

Longitudinal data (wave 1 and wave 2) from the “Survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)” were used in this study. The NRW80 + includes individuals over the age of 80 who live in North Rhine-Westphalia, Germany’s most populous state. The distribution of socio-demographic data in North Rhine-Westphalia is very similar compared to the German population as a whole. Various topics are included in the NRW80 + study such as quality of life, socioeconomic issues or health-related factors.

Based on nearly 100 communities in North Rhine Westphalia, a representative sample was drawn. Men and individuals aged 85 years and over were oversampled. Thus, sampling weights were used in regression analysis to account for the complex survey design and to compensate for attrition. The first wave took place between August 2017 and February 2018. In the second wave, data collection among the panel sample (i.e., individuals who already took part in wave 1) took place from June 2019 to February 2020. The average duration for face-to-face interviews was 80 to 90 min. In the first wave, the response rate was 23.4%. However, sociodemographic factors such as age group, living situation and sex were not linked to the likelihood of participation [32]. In the second wave, the response rate was 56.9% in the panel sample. In the provided dataset, individuals from wave 1 were only included when they also completed wave 2. The general dataset and further details can be found here [33].

In our analytic sample, n equaled 1,296 observations (wave 1: 667 individuals; wave 2: 629 individuals). More details are given by Wagner et al. [32].

Informed consent was obtained from all participants or their legal representatives. The NRW80 + was approved by the ethics committee of the Medical Faculty of the University of Cologne (No. 17–169).

Outcome: dementia

DemTect [34, 35] was used to measure probable dementia. This tool consists of five subtests: Word list/delayed recall, number transcoding, verbal fluency, digit span reverse, and word list delayed recall. A detailed description of the subtests is given by Kalbe and Kessler [36]. The score ranges from 0 to 18, with higher values indicating less cognitive impairment. Dementia is indicated by a score less than 9, whereas values of 9 or higher indicate the absence of dementia. According to Kalbe et al. [34], the DemTect has been shown to be effective in screening for dementia (specificity: 97%; sensitivity: 85.1%).

Independent variables

The selection of independent variables was guided by former research in this area (for example: [22, 23]) and theoretical considerations. Thus, we included sociodemographic and health-related factors in regression analysis as follows:

With regard to sociodemographic factors, age (in years), sex (men; women), education (ISCED-97 classification [37]: low, medium, and high education), marital status (married, living together with spouse; Other: single, widowed, divorced, married, living separated from spouse), social network size and loneliness (from 1 = never/almost never to 4 = almost or almost always; higher values thus reflect higher loneliness levels) were included in regression analysis. The loneliness tool is highly correlated with the UCLA loneliness scale [38]. Additionally, we included health-related factors as follows: health literacy (score was built by averaging two items namely knowledge and compliance in the area of health literacy; scores ranges from 1 to 4, higher values reflect higher health literacy), functional impairment, depressive symptoms, and a count score for chronic conditions.

Functional impairment was quantified based on a tool to measure activities of daily living – an amended version of Katz et al. [39]. The tool to quantify functional impairment had seven items, each with three response categories: 0 = only possible with help, 1 = a little help, and 2 = no help needed. The following domains were covered: eating, dressing/undressing, personal hygiene, walking, getting up from bed and lying down, bathing/showering, reaching the toilet in time. The items were averaged and subsequently the coding was reversed. This means that the final score ranges from 0 to 2 whereby higher values reflect higher functional impairment. The “depression in old age scale” (DIA-S) [40, 41] was used to assess depressive symptoms. This tool has four items (each case: no or yes). A sum score was generated using these four items. The sum score ranges from 0 to 4 (higher values reflect more depressive symptoms). Favorable psychometric properties have been shown [40, 41]. To quantify chronic conditions, a count score was generated. To this end, these 19 self-reported chronic illnesses were included (in each case: 0 = absence and 1 = presence): myocardial infarction, heart failure, hypertension, stroke, mental illness, cancer, diabetes, respiratory or pulmonary disease, back pain, gastric or intestinal disease, kidney disease, liver disease, blood disease, joint or bone disease, bladder disease, sleep disorder, eye disease or visual disorder, ear disease or hearing impairment, and neurological disease.

Statistical analysis

Sample characteristics were first calculated (also stratified by probable dementia). Thereafter, a logistic random-effects (RE) model was estimated (outcome with two categories: individuals without probable dementia, and individuals with probable dementia). Of note, logistic RE models both use between- and within-variation over time. This is a common and widely used panel-econometric model [42]. Regressions were also computed stratified by sex. Statistical analyses were performed using Stata 16.1 (Stata Corp., College Station, Texas).

Results

Sample characteristics

In Table 1, sample characteristics (for the analytic sample; pooled over both waves) stratified by probable dementia are given. This means that the data are combined from wave 1 and wave 2. The analytic sample consisted of 1,296 observations (including 754 individuals). Overall, 35 individuals developed dementia from wave 1 to wave 2.

Table 1 Sample characteristics for the analytical sample stratified by probable dementia (pooled over both waves, n = 1,296 observations)
Full size table

In the total sample, average age was 86.3 years (SD: 4.2 years), with 47% being female. Additionally, 18.7% of the individuals had a low education (medium education: 52.8%; high education: 28.5%). Moreover, 6.6% (86 out of 1,296 observations) had probable dementia. Significant differences exist between individuals without probable dementia and individuals with probable dementia in terms of age, sex, marital status, educational level, size of the social network, health literacy, and functional impairment. More details are shown in Table 1.

Regression analysis

Results of multiple logistic regressions are shown in Table 2 (second column: among the total sample; third column: among men; fourth column; among women).

Table 2 Determinants of probable dementia. Results of logistic RE regressions
Full size table

In the total sample, a higher likelihood of probable dementia was positively associated with lower education (e.g., low education compared to medium education: OR: 3.31 [95% CI: 1.10–9.98]), a smaller network size (OR: 0.87 [95% CI: 0.79–0.96]), lower health literacy (OR: 0.29 [95% CI: 0.14–0.60]), higher functional impairment (OR: 13.45 [3.86–46.92]). However, a higher likelihood of probable dementia was not significantly associated with sex, age, marital status, loneliness, and depressive symptoms.

Among men, a higher likelihood of probable dementia was only significantly positively associated with functional impairment (OR: 8.72 [95% CI: 1.03–73.80]). Among women, a higher likelihood of probable dementia was positively associated with not being married (compared to being married, OR: 0.07 [95% CI: 0.01–0.54]), a smaller network size (OR: 0.84 [95% CI: 0.73–0.96]), lower health literacy (OR: 0.24 [95% CI: 0.10–0.59]), higher functional impairment (OR: 18.58 [3.85–89.69]), and fewer depressive symptoms (OR: 0.51 [95% CI: 0.30–0.87]).

In a sensitivity analysis (see Table 3), functional impairment was removed from the model because of the unclear directionality. The results of this model are mostly very similar (in terms of effect size and significance) when compared to the results presented in Table 2. However, the association between low education and likelihood of probable dementia is somewhat stronger (low education compared to medium education: OR: 5.53 [95% CI: 1.74–17.59]), particularly in women (OR: 6.20 [95% CI: 1.83 to 21.03]).

Table 3 Determinants of probable dementia. Results of logistic RE regressions (without functional impairment)
Full size table

Discussion

The aim of this study was to examine the determinants of dementia among the oldest old (also stratified by sex) using longitudinal data from a representative sample. Multiple logistic regressions revealed that a higher likelihood of probable dementia was positively associated with lower education, a smaller network size, lower health literacy, and higher functional impairment, whilst likelihood of probable dementia was not statistically significantly associated with sex, age, marital status, loneliness and depressive symptoms in the total sample. Sex stratified regressions were also reported. In sum, our current study extends our current knowledge because this study exclusively used data from the oldest old and is based on longitudinal, representative data (also including individuals residing in institutionalized settings).

Our study showed that a low educational level was associated with a higher likelihood of probable dementia – which is well in line with prior research [43]. Possible explanations for such a link mainly refer to the well-known hypothesis of cognitive reserve or brain reserve capacity [24]. Moreover, lifestyle factors such as dietary habits or an active lifestyle could explain the link between educational level and probable dementia [44] given that education may influence human behavior and lifestyle practices which may promote probable dementia during very old age [45].

Our study also identified an association between a smaller network size and a higher likelihood of probable dementia. This aligns with prior research [46], and possible explanations could include social activities and social engagement (e.g., vascular hypothesis or cognitive reserve hypothesis) – as outlined in the introduction section [27]. Other studies have also emphasized the link between a smaller social network size and higher stress or lower self-worth; factors that can contribute to dementia [47].

Interestingly, lower health literacy was also associated with a higher likelihood of probable dementia in our study. This supports the findings of a previous systematic review and may be attributed to a more favorable lifestyle [30]. Lastly, the clear association between functional impairment and probable dementia supports the vast majority of studies (e.g., [48]). Initial problems with ADL may indicate neurodegenerative processes, often with subsequent clinically recognizable dementia [48].

With regard to the potential gender differences identified in our study, it may be worth noting that the results differ in terms of significance. This may be partly explained by differences in the number of cases with dementia between women and men (see Table 1). The signs and effect sizes are often comparable. Different biological (e.g., metabolic) processes may explain some differences, which could be directly related to cognitive function and the etiology of dementia in old age [49, 50]. In this case, different conditions may trigger dementia differently in older men than in their female counterparts [49, 50]. Moreover, particularly the significant link between more depressive symptoms and a lower likelihood of probable dementia exclusively among women is a bit puzzling – and not in accordance with prior literature [51]. It could be a random effect or it could be related to certain care measures imposed on individuals with depressive symptoms, either in clinical or social settings. For example, those women with more depressive symptoms may be guided through activities to manage the condition, which could indirectly reduce the likelihood of developing dementia. However, this is a speculative explanation and further research is required to examine this association in further detail.

Some advantages and shortcomings in this study should be mentioned: For this study, a large, longitudinal dataset from individuals aged 80 and up was used. Individuals living in both private households and institutionalized settings were included. The response rate was rather low. Thus, certain groups (such as individuals with very poor health or very low education) may have a lower likelihood of participation. However, overall it should be acknowledged that this study is representative of people aged 80 in North Rhine-Westphalia (Germany) [32]. Additionally, sampling weights were used in this study. Established tools were used to quantify the independent variables. Moreover, a screening tool was used to measure cognitive impairment – thus, future research with more sophisticated tools is required to confirm our current findings.

In summary, our study identified factors associated with dementia among the oldest old. Efforts to increase, among other things, formal education, network size, and health literacy may be fruitful to postpone dementia, particularly among women. For example, developing health literacy programs may be beneficial to reduce the burden associated with dementia.

Data availability

The NRW 80+ data are available via gesis. For interested researchers, please see: https://search.gesis.org/research_data/ZA7558.

References

  1. Hajek A, Brettschneider C, Ernst A, et al. Einflussfaktoren Auf die Pflegebedürftigkeit Im Längsschnitt. Gesundheitswesen. 2017;79(02):73–9.

    CAS  PubMed  Google Scholar 

  2. Hajek A, König H-H. Frequency and correlates of multimorbidity among the oldest old: study findings from the representative survey on quality of life and subjective well-being of the very old in north rhine-westphalia (NRW80+). Clin Interv Aging. 2023:41–8.

  3. Hajek A, Brettschneider C, Lühmann D, et al. Does visual impairment affect social ties in late life? Findings of a multicenter prospective cohort study in Germany. J Nutr Health Aging. 2017;21(6):692–8.

    Article  CAS  PubMed  Google Scholar 

  4. Förster F, Pabst A, Stein J, et al. Are older men more vulnerable to depression than women after losing their spouse? Evidence from three German old-age cohorts (AgeDifferent. De platform). J Affect Disord. 2019;256:650–7.

    Article  PubMed  Google Scholar 

  5. Hajek A, Luppa M, Brettschneider C, et al. Correlates of institutionalization among the oldest old—evidence from the multicenter AgeCoDe-AgeQualiDe study. Int J Geriatr Psychiatry. 2021;36(7):1095–102.

    Article  PubMed  Google Scholar 

  6. Petersen N, König H-H, Hajek A. The link between falls, social isolation and loneliness: a systematic review. Arch Gerontol Geriatr. 2020;88:104020.

    Article  CAS  PubMed  Google Scholar 

  7. Hajek A, Gyasi RM, Kretzler B, Riedel-Heller SG, König H-H. Determinants of psychosocial factors amongst the oldest old: longitudinal evidence based on the representative survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia. Int J Geriatr Psychiatry. 2023;38(12):e6031.

    Article  PubMed  Google Scholar 

  8. Cao Q, Tan C-C, Xu W, et al. The prevalence of dementia: a systematic review and meta-analysis. J Alzheimers Dis. 2020;73(3):1157–66.

    Article  PubMed  Google Scholar 

  9. Moyle W, Kellett U, Ballantyne A, Gracia N. Dementia and loneliness: an Australian perspective. J Clin Nurs. 2011;20(9–10):1445–53.

    Article  PubMed  Google Scholar 

  10. Kitching D. Depression in dementia. Australian Prescriber. 2015;38(6):209.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Shearer J, Green C, Ritchie CW, Zajicek JP. Health state values for use in the economic evaluation of treatments for Alzheimer’s disease. Drugs Aging. 2012;29:31–43.

    Article  PubMed  Google Scholar 

  12. Hajek A, Brettschneider C, Ernst A, et al. Longitudinal predictors of informal and formal caregiving time in community-dwelling dementia patients. Soc Psychiatry Psychiatr Epidemiol. 2016;51(4):607–16.

    Article  PubMed  Google Scholar 

  13. Collaborators GBD. Global mortality from dementia: application of a new method and results from the global burden of Disease Study 2019. Alzheimer’s Dementia: Translational Res Clin Interventions. 2021;7(1):e12200.

    Google Scholar 

  14. Wimo A, Seeher K, Cataldi R, et al. The worldwide costs of dementia in 2019. Alzheimer’s Dement. 2023;19(7):2865–73.

    Article  Google Scholar 

  15. Claudia Cooper PD, Sommerlad MRCPA, Lyketsos MRCPCG,, MDMHS. Gill Livingston MD, F.R.C.Psych. Modifiable predictors of dementia in mild cognitive impairment: a systematic review and Meta-analysis. Am J Psychiatry. 2015;172(4):323–34.

  16. Peters R, Booth A, Rockwood K, Peters J, D’Este C, Anstey KJ. Combining modifiable risk factors and risk of dementia: a systematic review and meta-analysis. BMJ open. 2019;9(1):e022846.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Nicoli C, Galbussera AA, Bosetti C, et al. The role of diet on the risk of dementia in the oldest old: the Monzino 80-plus population-based study. Clin Nutr. 2021;40(7):4783–91.

    Article  CAS  PubMed  Google Scholar 

  18. Escourrou E, Durrieu F, Chicoulaa B, et al. Cognitive, functional, physical, and nutritional status of the oldest old encountered in primary care: a systematic review. BMC Fam Pract. 2020;21(1):1–10.

    Article  Google Scholar 

  19. Lucca U, Tettamanti M, Tiraboschi P, et al. Incidence of dementia in the oldest-old and its relationship with age: the Monzino 80-plus population-based study. Alzheimer’s & Dementia; 2019.

  20. Mejia-Arango S, Aguila E, López-Ortega M, et al. Health and social correlates of dementia in oldest-old mexican-origin populations. Alzheimer’s Dementia: Translational Res Clin Interventions. 2020;6(1):e12105.

    Google Scholar 

  21. De Rango F, Montesanto A, Berardelli M, et al. To Grow Old in Southern Italy: a Comprehensive description of the Old and Oldest Old in Calabria. Gerontology. 2011;57(4):327–34.

    Article  PubMed  Google Scholar 

  22. Galvin JE, Chrisphonte S, Chang L-C. Medical and social determinants of brain health and dementia in a multicultural community cohort of older adults. J Alzheimers Dis. 2021;84(4):1563–76.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Deckers K, Cadar D, van Boxtel MP, Verhey FR, Steptoe A, Köhler S. Modifiable risk factors explain socioeconomic inequalities in dementia risk: evidence from a population-based prospective cohort study. J Alzheimers Dis. 2019;71(2):549–57.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Meng X, D’arcy C. Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative analyses. PLoS ONE. 2012;7(6):e38268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fratiglioni L, Wang H-X. Brain reserve hypothesis in dementia. J Alzheimers Dis. 2007;12(1):11–22.

    Article  PubMed  Google Scholar 

  26. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Disease Assoc Disorders. 2006;20:S69–74.

    Article  Google Scholar 

  27. Penninkilampi R, Casey A-N, Singh MF, Brodaty H. The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis. J Alzheimers Dis. 2018;66(4):1619–33.

    Article  PubMed  Google Scholar 

  28. Robertson IH. A noradrenergic theory of cognitive reserve: implications for Alzheimer’s disease. Neurobiol Aging. 2013;34(1):298–308.

    Article  CAS  PubMed  Google Scholar 

  29. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Disease Assoc Disorders. 2006;20(2):112–7.

    Article  Google Scholar 

  30. Oliveira D, Bosco A, Di Lorito C. Is poor health literacy a risk factor for dementia in older adults? Systematic literature review of prospective cohort studies. Maturitas. 2019;124:8–14.

    Article  PubMed  Google Scholar 

  31. Byers AL, Yaffe K. Depression and risk of developing dementia. Nat Reviews Neurol. 2011;7(6):323–31.

    Article  CAS  Google Scholar 

  32. Wagner M, Rietz C, Kaspar R, et al. Quality of life of the very old. Zeitschrift für Gerontologie Und Geriatrie. 2018;51(2):193–9.

    Article  PubMed  Google Scholar 

  33. Albrecht A, Fey J, Kaspar R, Wagner M, Zank S. Quality of Life and Well-being of very old people in NRW (Representative Survey NRW80+) - panel. GESIS, Cologne. ZA7893 Data file Version 1.0.0, https://doi.org/10.4232/1.13985; 2022.

  34. Kalbe E, Brand M, Kessler J, Calabrese P. Der DemTect in Der Klinischen Anwendung: Sensitivität Und Spezifität eines Kognitiven Screeninginstruments. Z für Gerontopsychologie &-psychiatrie. 2005;18(3):121–30.

    Article  Google Scholar 

  35. Kessler J, Fengler S, Kaesberg S, et al. DemTect 40–und DemTect 80+: Neue Auswertungsroutinen für diese Altersgruppen. Fortschr Der Neurologie· Psychiatrie. 2014;82(11):640–5.

    Article  CAS  Google Scholar 

  36. Kalbe E, Kessler J. DemTect. In: Larner AJ, editor. Cognitive Screening instruments: a practical Approach. Cham: Springer International Publishing; 2017. pp. 197–208.

    Chapter  Google Scholar 

  37. UNESCO. International Standard Classification of Education. ISCED 1997. Re-edition ed. Paris: UNESCO; 2006.

    Google Scholar 

  38. Nersesian PV, Han H-R, Yenokyan G, et al. Loneliness in middle age and biomarkers of systemic inflammation: findings from midlife in the United States. Soc Sci Med. 2018;209:174–81.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. JAMA. 1963;185:914–9.

    Article  CAS  PubMed  Google Scholar 

  40. Heidenblut S, Zank S. Entwicklung eines neuen depressionsscreenings für den Einsatz in Der Geriatrie. Zeitschrift für Gerontologie Und Geriatrie. 2010;43(3):170–6.

    Article  CAS  PubMed  Google Scholar 

  41. Heidenblut S, Zank S. Screening for Depression with the Depression in Old Age Scale (DIA-S) and the geriatric Depression Scale (GDS15): diagnostic accuracy in a geriatric inpatient setting. Geropsych: J Gerontopsychology Geriatric Psychiatry. 2014;27(1):41–9.

    Article  Google Scholar 

  42. Cameron AC, Trivedi PK. Microeconometrics: methods and applications. New York: Cambridge University Press; 2005.

    Book  Google Scholar 

  43. Caamaño-Isorna F, Corral M, Montes-Martínez A, Takkouche B. Education and dementia: a meta-analytic study. Neuroepidemiology. 2006;26(4):226–32.

    Article  PubMed  Google Scholar 

  44. Paillard-Borg S, Fratiglioni L, Xu W, Winblad B, Wang H-X. An active lifestyle postpones dementia onset by more than one year in very old adults. J Alzheimers Dis. 2012;31(4):835–42.

    Article  PubMed  Google Scholar 

  45. Chafjiri RT, Shirinkam F, Karimi H. Investigating the effect of education on health-promoting lifestyle among the elderly of Ramsar in 2017. J Family Med Prim care. 2018;7(3):612.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kuiper JS, Zuidersma M, Voshaar RCO, et al. Social relationships and risk of dementia: a systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev. 2015;22:39–57.

    Article  PubMed  Google Scholar 

  47. Wilson RS, Evans DA, Bienias JL, De Leon CM, Schneider JA, Bennett DA. Proneness to psychological distress is associated with risk of Alzheimer’s disease. Neurology. 2003;61(11):1479–85.

    Article  CAS  PubMed  Google Scholar 

  48. Luck T, Pabst A, Roehr S et al. Determinants of incident dementia in different old age groups: results of the prospective AgeCoDe/AgeQualiDe study. Int Psychogeriatr. 2019:1–15.

  49. Mielke MM, Vemuri P, Rocca WA. Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences. Clin Epidemiol. 2014:37–48.

  50. Podcasy JL, Epperson CN. Considering sex and gender in Alzheimer disease and other dementias. Dialogues in clinical neuroscience. 2022.

  51. Wiels W, Baeken C, Engelborghs S. Depressive symptoms in the elderly—An early symptom of dementia? A systematic review. Front Pharmacol. 2020;11:34.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

We acknowledge financial support from the Open Access Publication Fund of UKE - Universitätsklinikum Hamburg-Eppendorf.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations

  1. Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany

    André Hajek, Benedikt Kretzler & Hans-Helmut König

  2. Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany

    Steffi G. Riedel-Heller

  3. African Population and Health Research Center, Nairobi, Kenya

    Razak M. Gyasi

  4. National Centre for Naturopathic Medicine, Faculty of Health, Southern Cross University, Lismore, NSW, Australia

    Razak M. Gyasi

Authors
  1. André Hajek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benedikt Kretzler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steffi G. Riedel-Heller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Razak M. Gyasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hans-Helmut König
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AH: Conceptualization; Data curation; Methodology; Project administration, Visualization; Roles/Writing - original draft, Writing - review & editing, Formal analysis. BK: Conceptualization; Writing - review & editing, Visualization. SRH: Conceptualization; Writing - review & editing, Visualization. RG: Conceptualization; Writing - review & editing, Visualization. HHK: Conceptualization; Resources; Writing - review & editing; Supervision; Visualization. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to André Hajek.

Ethics declarations

Ethics approval and consent to participate

The NRW80 + was approved by the ethics committee of the Medical Faculty of the University of Cologne (No. 17–169). Informed consent was obtained from all participants or their legal representatives.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hajek, A., Kretzler, B., Riedel-Heller, S.G. et al. Predictors of dementia among the oldest old: longitudinal findings from the representative “survey on quality of life and subjective well-being of the very old in North Rhine-Westphalia (NRW80+)”. BMC Geriatr 24, 680 (2024). https://doi.org/10.1186/s12877-024-05255-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-024-05255-z

Keywords

BMC Geriatrics

ISSN: 1471-2318

Contact us