Tracking Divergence of Objective and Subjective Health Across Age and Historical Time

Abstract

Influential work in the gerontological literature in the 1990s has demonstrated that the discrepancy between objective and subjective evaluations of health increases with age. It is an open question though whether the direction and size of the age gradient in the discrepancies has changed over historical time. In this study, we examine cohort differences in age-related divergences in health evaluations using two markers of objective health (illnesses/functioning) and two markers of subjective health (overall/compared to others) obtained from cross-sectional samples of older adults in the Berlin Aging Studies in 1990–1993 (n = 516, aged 70–103) and 2018–2020 (n = 625, aged 68–94). We use both person-oriented and variable-oriented methodological frameworks that specifically facilitate study of aging as a process that propels individuals through a multidimensional health space: permutational multivariate analysis of variance (PERMANOVA) and structural equation modeling (SEM). The person-oriented PERMANOVA results revealed that divergence across markers of health increased with age, but did not evidence any cohort differences. Complementary results from variable-oriented SEMs corroborated that the four-dimensional variance-covariance structure did not differ across cohorts and that the age pattern was consistent across cohorts. Results suggest that objective and subjective indicators of health are increasingly discrepant after age 70, but reveal no evidence that age gradient of the divergences has changed across historical time.

Keywords

age gradient health divergence cohort differences person-oriented methods variable-oriented methods

Get full access to this article

View all access options for this article.

References

Almeida

D. M.

Charles

S. T.

Mogle

Drewelies

Aldwin

C. M.

Spiro

III Gerstorf

(2020). Charting adult development through (historically changing) daily stress processes. American Psychologist, 75(4), 511–524. https://doi.org/10.1037/amp0000597

Anderson

M. J.

(2017). Permutational Multivariate Analysis of Variance (PERMANOVA). In Balakrishnan

Colton

Everitt

Piegorsch

Ruggeri

Teugels

J. L.

(Eds.), Wiley StatsRef: Statistics reference online (pp. 1–15). John Wiley. https://doi.org/10.1002/9781118445112.stat07841

Araújo

Teixeira

Ribeiro

Paúl

(2018). Objective vs. subjective health in very advanced ages: Looking for discordance in centenarians. Frontiers in Medicine, 5, Article 189. https://doi.org/10.3389/fmed.2018.00189

Badley

E. M.

Canizares

Perruccio

A. V.

Hogg-Johnson

Gignac

M. A.

(2015). Benefits gained, benefits lost: Comparing baby boomers to other generations in a longitudinal cohort study of self-rated health. The Milbank Quarterly, 93(1), 40–72. https://doi.org/10.1111/1468-0009.12105

Baltes

P. B.

Mayer

K. U.

(Eds.). (1999). The Berlin Aging Study: Aging from 70 to 100. Cambridge University Press.

Baltes

P. B.

Smith

(2003). New frontiers in the future of aging: From successful aging of the young old to the dilemmas of the fourth age. Gerontology, 49(2), 123–135. https://doi.org/10.1159/000067946

Benyamini

(2011). Why does self-rated health predict mortality? An update on current knowledge and a research agenda for psychologists. Psychology & Health, 26(11), 1407–1413. https://doi.org/10.1080/08870446.2011.621703

Bergman

L. R.

Magnusson

(1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(2), 291–319. https://doi.org/10.1017/s095457949700206x

Bertram

Böckenhoff

Demuth

Düzel

Eckardt

S. C.

Lindenberger

Pawelec

Siedler

Wagner

G. G.

Steinhagen-Thiessen

(2014). Cohort profile: The Berlin Aging Study II (BASE-II). International Journal of Epidemiology, 43(3), 703–712. https://doi.org/10.1093/ije/dyt018

10.

Bryant

F. B.

Satorra

(2012). Principles and practice of scaled difference chi-square testing. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 372–398. https://doi.org/10.1080/10705511.2012.687671

11.

Buchmann

Spira

König

Demuth

Steinhagen-Thiessen

(2019). Frailty and the metabolic syndrome–Results of the Berlin Aging Study II (BASE-II). The Journal of Frailty & Aging, 8(4), 169–175. https://doi.org/10.14283/jfa.2019.15

12.

Charlson

M. E.

Pompei

Ales

K. L.

MacKenzie

C. R.

(1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373–383. https://doi.org/10.1016/0021-9681(87)90171-8

13.

Demuth

Banszerus

Drewelies

Düzel

Seeland

Spira

Tse

Braun

Steinhagen-Thiessen

Bertram

Thiel

Lindenberger

Regitz-Zagrosek

Gerstorf

, & Additional BASE-II/GendAge investigators. (2021). Cohort profile: Follow-up of a Berlin Aging Study II (BASE-II) subsample as part of the GendAge study. BMJ Open, 11(6), Article e045576. https://doi.org/10.1136/bmjopen-2020-045576

14.

Drewelies

Agrigoroaei

Lachman

M. E.

Gerstorf

(2018). Age variations in cohort differences in the United States: Older adults report fewer constraints nowadays than those 18 years ago, but mastery beliefs are diminished among younger adults. Developmental Psychology, 54(8), 1408–1425. https://doi.org/10.1037/dev0000527

15.

Drewelies

Homann

Vetter

V. M.

Düzel

Kühn

Deecke

Steinhagen-Thiessen

Jawinski

Markett

Lindenberger

Lill

C. M.

Bertram

Demuth

Gerstorf

(2025). There are multiple clocks that time us: Cross-sectional and longitudinal associations among 14 alternative indicators of age and aging. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 80(6), glae244. https://doi.org/10.1093/gerona/glae244

16.

Drewelies

Huxhold

Gerstorf

(2019). The role of historical change for adult development and aging: Towards a theoretical framework about the how and the why. Psychology and Aging, 34(8), 1021–1039. https://doi.org/10.1037/pag0000423

17.

Finkel

Reynolds

C. A.

McArdle

J. J.

Pedersen

N. L.

(2007). Cohort differences in trajectories of cognitive aging. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 62(5), 286–294. https://doi.org/10.1093/geronb/62.5.P286

18.

French

D. J.

Sargent-Cox

Luszcz

M. A.

(2012). Correlates of subjective health across the aging lifespan: Understanding self-rated health in the oldest old. Journal of Aging and Health, 24(8), 1449–1469. https://doi.org/10.1177/0898264312461151

19.

Gerstorf

Hülür

Drewelies

Eibich

Duezel

Demuth

Ghisletta

Steinhagen-Thiessen

Wagner

G. G.

Lindenberger

(2015). Secular changes in late-life cognition and well-being: Towards a long bright future with a short brisk ending? Psychology and Aging, 30(2), 301–310. https://doi.org/10.1037/pag0000016

20.

Gerstorf

Hülür

Drewelies

Willis

S. L.

Schaie

K. W.

Ram

(2020). Adult development and aging in historical context. American Psychologist, 75(4), 525–539. https://doi.org/10.1037/amp0000596

21.

Gerstorf

Ram

Drewelies

Duezel

Eibich

Steinhagen-Thiessen

Liebig

Goebel

Demuth

Villringer

Wagner

G. G.

Lindenberger

Ghisletta

(2023). Today’s older adults are cognitively fitter than older adults were 20 years ago, but when and how they decline is no different than in the past. Psychological Science, 34(1), 22–34. https://doi.org/10.1177/09567976221118541

22.

Gerstorf

Ram

Hoppmann

Willis

S. L.

Schaie

K. W.

(2011). Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study. Developmental Psychology, 47(4), 1026–1041. https://doi.org/10.1037/a0023426

23.

Grimm

K. J.

Ram

Estabrook

(2016). Growth modeling: Structural equation and multilevel modeling approaches. Guilford Publications.

24.

Grund

Lüdtke

Robitzsch

(2016). Pooling ANOVA results from multiply imputed datasets: A simulation study. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 12(3), 75–88. https://doi.org/10.1027/1614-2241/a000111

25.

Heller

D. A.

Ahern

F. M.

Pringle

K. E.

Brown

T. V.

(2009). Among older adults, the responsiveness of self-rated health to changes in Charlson comorbidity was moderated by age and baseline comorbidity. Journal of Clinical Epidemiology, 62(2), 177–187. https://doi.org/10.1016/j.jclinepi.2008.05.009

26.

Hess

T. M.

O’Brien

E. L.

Voss

Kornadt

A. E.

Rothermund

Fung

H. H.

Popham

L. E.

(2017). Context influences on the relationship between views of aging and subjective age: The moderating role of culture and domain of functioning. Psychology and Aging, 32(5), 419–431. https://doi.org/10.1037/pag0000181

27.

Jurek

. (2021). Macro-level predictors of old-age threshold perception: A comparative study using ESS, Ipsos, and Eurobarometer data. European Research Studies Journal, 24(4B), 723–739.

28.

Kleinspehn-Ammerlahn

Kotter-Grühn

Smith

(2008). Self-perceptions of aging: Do subjective age and satisfaction with aging change during old age? The Journals of Gerontology: Series B, 63(6), 377–385. https://doi.org/10.1093/geronb/63.6.P377

29.

Kline

R. B.

(2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.

30.

Klumb

P. L.

Maier

(2007). Daily activities and survival at older ages. Journal of Aging and Health, 19(4), 594–611. https://doi.org/10.1177/0898264307301167

31.

König

Drewelies

Norman

Spira

Buchmann

Hülür

Eibich

Wagner

G. G.

Lindenberger

Steinhagen-Thiessen

Gerstorf

Demuth

(2018). Historical trends in modifiable indicators of cardiovascular health and self-rated health among older adults: Cohort differences over 20 years between the Berlin Aging Study (BASE) and the Berlin Aging Study II (BASE-II). PLOS ONE, 13(1), Article e0191699. https://doi.org/10.1371/journal.pone.0191699

32.

Lenis

Nguyen

T. Q.

Dong

Stuart

E. A.

(2019). It’s all about balance: Propensity score matching in the context of complex survey data. Biostatistics, 20(1), 147–163. https://doi.org/10.1093/biostatistics/kxx063

33.

Vogtmann

Graubard

B. I.

Gail

M. H.

Abnet

C. C.

Shi

(2022). fast.adonis: A computationally efficient non-parametric multivariate analysis of microbiome data for large-scale studies. Bioinformatics Advances, 2(1), vbac044. https://doi.org/10.1093/bioadv/vbac044

34.

Lindenberger

Baltes

P. B.

(1997). Intellectual functioning in old and very old age: Cross-sectional results from the Berlin Aging Study. Psychology and Aging, 12(3), 410–432. https://doi.org/10.1037/0882-7974.12.3.410

35.

Martin

L. G.

Freedman

V. A.

Schoeni

R. F.

Andreski

P. M.

(2009). Health and functioning among baby boomers approaching 60. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 64(3), 369–377. https://doi.org/10.1093/geronb/gbn040

36.

McArdle

J. J.

Nesselroade

J. R.

(1994). Using multivariate data to structure developmental change. In Cohen

S. H.

Reese

H. W.

(Eds.), Life-span developmental psychology: Methodological contributions (pp. 223–267). Lawrence Erlbaum Associates, Inc.

37.

McArtor

D. B.

Lin

B. D.

Hottenga

J. J.

Boomsma

D. I.

Willemsen

Lubke

G. H.

(2017). Using a multivariate model to assess the interactive effects of demographics and lifestyle on the hematological profile. Biomarkers in Medicine, 11(6), 427–438. https://doi.org/10.2217/bmm-2016-0285

38.

McArtor

D. B.

Lubke

G. H.

Bergeman

C. S.

(2017). Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic. Psychometrika, 82(4), 1052–1077. https://doi.org/10.1007/s11336-016-9527-8

39.

Meyer

Salewsky

Spira

Steinhagen-Thiessen

Norman

Demuth

(2016). Leukocyte telomere length is related to appendicular lean mass: Cross-sectional data from the Berlin Aging Study II (BASE-II). The American Journal of Clinical Nutrition, 103(1), 178–183. https://doi.org/10.3945/ajcn.115.116806

40.

Muthén

L. K.

(2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24(6), 882–891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x

41.

Oksanen

(2009). Ordination and analysis of dissimilarities: Tutorial with R and vegan (pp. 1–25). http://www.pelagicos.net/MARS6910_spring2015/manuals/R_vegan_ordination.pdf

42.

Pinquart

(2001). Correlates of subjective health in older adults: A meta-analysis. Psychology and Aging, 16(3), 414. https://doi.org/10.1037/0882-7974.16.3.414

43.

Ram

Grimm

K. J.

(2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576. https://doi.org/10.1177/0165025409343765

44.

Rice

N. E.

Lang

I. A.

Henley

Melzer

(2010). Baby boomers nearing retirement: The healthiest generation? Rejuvenation Research, 13(1), 105–114. https://doi.org/10.1089/rej.2009.0896

45.

Rosseel

(2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

46.

Saßenroth

Kroh

Wagner

G. G.

(2014). Selectivity processes in and weights for the Berlin Aging Study II (BASE-II) (SOEP Survey Papers 229). DIW Berlin/SOEP. https://www.diw.de/documents/publikationen/73/diw_01.c.432989.de/diw_sp0608.pdf

47.

Satorra

Bentler

P. M.

(2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://doi.org/10.1007/bf02296192

48.

Spuling

S. M.

Wurm

Tesch-Romer

Huxhold

(2015). Changing predictors of self-rated health: Disentangling age and cohort effects. Psychology and Aging, 30(2), 462–474. https://doi.org/10.1037/a0039111

49.

Steinhagen-Thiessen

Borchelt

(1999). Morbidity, medication, and functional limitations in very old age. In Baltes

P. B.

Mayer

K. U.

(Eds.), The Berlin aging study: Aging from 70 to 100 (pp. 131–166). Cambridge University Press.

50.

van Buuren

Groothuis-Oudshoorn

. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03

51.

van Ginkel

J. R.

Kroonenberg

P. M

. (2014). Analysis of variance of multiply imputed data. Multivariate Behavioral Research, 49(1), 78–91. https://doi.org/10.1080/00273171.2013.855890

52.

Vetter

V. M.

Kalies

C. H.

Sommerer

Spira

Drewelies

Regitz-Zagrosek

Bertram

Gerstorf

Demuth

(2022). Relationship between 5 epigenetic clocks, telomere length, and functional capacity assessed in older adults: Cross-sectional and longitudinal analyses. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 77(9), 1724–1733. https://doi.org/10.1093/gerona/glab381

53.

Weiss

Freund

A. M.

(2012). Still young at heart: Negative age-related information motivates distancing from same-aged people. Psychology and Aging, 27(1), 173–180. https://doi.org/10.1037/a0024819

54.

Wettstein

Park

Kornadt

A. E.

Wurm

Ram

Gerstorf

(2024). Postponing old age: Evidence for historical change toward a later perceived onset of old age. Psychology and Aging, 39(5), 526–541. https://doi.org/10.1037/pag0000812

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.86 MB

0.00 MB