Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy).
Aging
Health
Health profiles
Italy
Latent class analysis
Nonagenarians
Oldest-old
Socioeconomic status
Journal
BMC geriatrics
ISSN: 1471-2318
Titre abrégé: BMC Geriatr
Pays: England
ID NLM: 100968548
Informations de publication
Date de publication:
15 08 2020
15 08 2020
Historique:
received:
20
12
2019
accepted:
03
08
2020
entrez:
18
8
2020
pubmed:
18
8
2020
medline:
22
12
2020
Statut:
epublish
Résumé
Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles. Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis. This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: "healthy", "physically healthy with cognitive impairment", "unhealthy", and "severely unhealthy". Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers. Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.
Sections du résumé
BACKGROUND
Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles.
METHODS
Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis.
RESULTS
This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: "healthy", "physically healthy with cognitive impairment", "unhealthy", and "severely unhealthy". Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers.
CONCLUSIONS
Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.
Identifiants
pubmed: 32799807
doi: 10.1186/s12877-020-01689-3
pii: 10.1186/s12877-020-01689-3
pmc: PMC7429096
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
289Investigateurs
Guglielmo Bonaccorsi
(G)
Roberta Boni
(R)
Chiara Castagnoli
(C)
Francesca Cecchi
(F)
Francesca Cesari
(F)
Francesco Epifani
(F)
Roberta Frandi
(R)
Betti Giusti
(B)
Maria Luisa Eliana Luisi
(MLE)
Rossella Marcucci
(R)
Raffaello Molino-Lova
(R)
Anita Paperini
(A)
Lorenzo Razzolini
(L)
Francesco Sofi
(F)
Nona Turcan
(N)
Debora Valecchi
(D)
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