Next Article in Journal
Changes in Community-Dwelling Elderly’s Activity and Participation Affecting Depression during COVID-19 Pandemic: A Cross-Sectional Study
Previous Article in Journal
Chinese Translation and Cross-Cultural Adaptation of the Return-to-Work Self-Efficacy Scale among Chinese Female Breast Cancer Survivors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 20
Issue 5
10.3390/ijerph20054227
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Use of Intrinsic Capacity Domains as a Screening Tool in Public Health

by
Livia Maria do Nascimento
1,
Thainá Gabriele Camargo da Cruz
1,
Juliana Fernanda de Lima e Silva
2,
Letícia Prado Silva
2,
Beatriz Bigatão Inácio
2,
Carolina Masumi Oki Sadamitsu
2 and
Marcos Eduardo Scheicher
1,2,*
1
Graduate Program in Human Development and Technologies, Institute of Biosciences, Paulista State University—UNESP, Rio Claro 13506-900, SP, Brazil
2
Department of Physical Therapy and Occupational Therapy, Paulista State University—UNESP, Marília1 7525-900, SP, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(5), 4227; https://doi.org/10.3390/ijerph20054227
Submission received: 20 December 2022 / Revised: 16 February 2023 / Accepted: 20 February 2023 / Published: 27 February 2023
(This article belongs to the Section Aging)
Browse Figures
Review Reports Versions Notes

Abstract

:
The World Health Organization (WHO) created the concept of Integrated Care for Older People and one of its constructs is intrinsic capacity (IC). The study aimed to carry out a screening with the tools designated by the WHO to assess the IC domains and whether they can be used as indicators for decision-making on integrated care for older people based on risk categorization. The interaction between the risk category and the domain scores was verified. One hundred and sixty three (163) community-dwelling older people of both genders were evaluated. Domains assessed: cognitive, psychological, vitality, locomotion, and sensory. Scores indicating a low, moderate and high risk were assigned to each domain. For all domains, there were individuals in all risk groups. Effect of risk on the domains: cognitive [χ2(2) = 134.042; p < 0.001], psychological [χ2(2) = 92.865; p < 0.001], vitality [χ2(2) = 129.564; p < 0.001], locomotion [χ2(2) = 144.101; p < 0.001], and sensory [χ2(2) = 129.037; p < 0.001]. Scores of the CI domains were affected by the risk category. There were individuals in all risk groups, demonstrating the importance of screening as a public health strategy, making it possible to know which risk category each elderly person belongs to and thus develop strategies in the short-, medium- and long-term.

1. Introduction

Population aging is a worldwide phenomenon. The number of people over the age of 60 years is estimated to reach 2 billion by 2050 [1]. In addition to excessive cellular and molecular modifications, aging is caused by an accumulation of damage, which leads to a gradual loss of physiological reserve that results in greater vulnerability, a higher risk of developing diseases, and greater functional dependence [1]. In view of the growth of the elderly population, the World Health Organization (WHO) created the concept of Integrated Care for Older People (ICOPE) that arose from the need to overcome traditional health models focusing only on the disease, deficits, and limitations. One of the constructs used by this strategy is intrinsic capacity (IC), which is defined as the composite of all the mental and physical capacities that a person can draw on over a lifetime [2]. Five domains were considered essential but not unique for structuring IC: locomotion (balance, gait, and muscle strength), vitality (balance between energy production and consumption), cognition (memory, intelligence, and problem solving), psychological (mood and sociability), and sensory (hearing and vision) [3]. The assessment of IC domains permits contextualized monitoring considering the specificities of the individual and the population analyzed, as well as to evaluate the effectiveness of already established interventions or to indicate necessary short-, medium- and long-term interventions. Based on this assessment, public health authorities can, for example, identify care needs, monitor critical signs, and avoid care dependency [4,5]. In addition, they can propose care models for the maintenance of domains that were not affected by decline. Step 1 of ICOPE proposes screening people based on the five IC domains, and individuals with IC losses are referred to step 2 on person-centered assessment in primary care [6]. Only four studies evaluated step 1 proposed by the WHO, showing a gap of knowledge [7]. Thus, the objective of this study was to carry out a screening with the tools designated by the WHO to assess the IC domains and whether they can be used as indicators for decision-making on comprehensive health care for the elderly based on risk categorization. In addition, it was verified whether there is an interaction between the risk category and the domain scores.

2. Materials and Methods

Individuals aged 60 years or over who lived in the catchment area of the two Family Health Units (USFs in the Portuguese acronym) of the city of Marília, São Paulo, Brazil, participated in the study. A total of 1113 older adults are registered with the two USFs. The Ethics Committee of the Faculty of Philosophy and Sciences, Unesp, Marília, approved the study (Approval number 4.168.934).
Inclusion criteria: aged 60 years or over, both sexes, resident of the area covered by the USFs. Exclusion criteria: not signing the free and informed consent form, after agreeing to participate in the research.
A search of individuals aged 60 years or over who were registered with the USFs was performed through the e-SUS Primary Care digital platform. Next, the assessment was scheduled in two ways: (1) the older adults were contacted by telephone and invited to come to the USF to participate in the study; (2) community agents of the USFs visited the homes of the older adults and performed the assessments on those who agreed to participate.

2.1. Assessment of Intrinsic Capacity Domains

2.1.1. Cognitive Domain

The Montreal Cognitive Assessment (MoCA) was used to assess the cognitive domain of the participants. The total possible score of the test is 30 points and scores are classified as follows: ≥25 points: low risk/normal; 21 to 24 points: moderate risk/mild cognitive impairment (MCI); <21 points: high risk/Alzheimer’s disease, with sensitivity of 82.2% and specificity of 92.3% [8].

2.1.2. Psychological Domain

The 15-item Geriatric Depression Scale (GDS-15) was used to analyze the psychological domain. Its advantages include easy-to-understand questions, small variation in the possible answers (yes/no), and self-administration or administration by an interviewer. The score ranges from 0 (absence of depressive symptoms) to 15 points (maximum score of depressive symptoms). Almeida and Almeida [9] suggested a cut-off score ≥5 to indicate the presence of depressive symptoms in older adults. Paradela et al. [10] proposed the following cut-off scores: 0 to 5 points: low risk of depression; 6 to 10 points: moderate risk of depression; 11 to 15 points: high risk of depression, with sensitivity of 81% and specificity of 71%.

2.1.3. Sensory Domain

Hearing and visual impairments were assessed by self-report using previously validated questions, which were found to be accurate when compared to objective measures [11,12]. Hearing status was evaluated by asking the participants to rate their hearing as excellent, very good, good, fair, or poor (with the use of a hearing aid, if usually worn), with sensitivity of 66.9% and specificity of 85.1% [12]. For vision assessment, the participants were asked “How good is your vision for seeing things at a distance, for example, recognizing a friend across the street?” and “How good is your vision for seeing close up things, for example, reading a newspaper?”. The response options were categorized as excellent, very good, good, fair, or poor. This assessment was performed with the participant wearing glasses or corrective lenses if they usually did. Self-reported vision obtained a sensitivity of 79% and specificity of 75% [11]. The responses regarding hearing and vision were scored on a Likert-type scale: 1—poor, 2—fair, 3—good, 4—very good, and 5—excellent. The responses were classified as follows: 1 and 2—high risk of problems in the sensory domain; 3—moderate risk, and 4 and 5—low risk.

2.1.4. Vitality Domain

The term vitality is understood as the bodily functions devoted to metabolizing dietary intake in order to produce the amount of energy necessary for maintaining optimal homeostasis [13]. With aging, changes occur in energy expenditure and metabolism that can interfere with food intake [14]. To maintain proper function, the body needs to balance energy intake and expenditure. Since there is no consensus on the best approach to assessing the vitality domain, the Mini-Nutritional Assessment (MNA), a tool recommended by the WHO, was used [15]. According to the WHO, acceptance of the MNA by older adults is good, showing 80% sensitivity and 90% specificity [16]. The instrument was translated and validated for Brazilian Portuguese by Machado et al. [17]. The following scores were considered for screening: 12 to 14 points: low risk; 8 to 11 points: moderate risk; ≤7 points: high risk [18].

2.1.5. Locomotion Domain

The Short Physical Performance Battery (SPPB) was used to assess the locomotion domain. This instrument evaluates lower limb function through a battery of tests of static balance, gait speed, and five-times repeated chair sit-to stand (lower limb strength). The tests can be evaluated individually or combined, generating a total score that ranges from 0 (worst performance) to 12 (best performance) [19]. The following risk criterion was adopted: 0 to 6 points: high risk; 7 to 9 points: moderate risk; 10 to 12 points: low risk [20].

2.2. Statistical Analysis

The distribution of the variables (parametric or nonparametric) was verified by the Kolmogorov-Smirnov test. Data were expressed as the mean ± standard deviation and the effect of risk on the IC domains was evaluated by one-way ANOVA, with pairwise comparisons by the Bonferroni post-test. The cognitive domain scores were corrected for educational level. A classification (high, moderate, and low risk of developing a given problem) was assigned according to the cut-off scores of each test described above. Based on this classification, the absolute frequency in each risk group was determined. A p value < 0.05 was considered to be significant. The analyses were performed using the SPSS 20.0 software.

3. Results

A total of 163 community-dwelling older adults from the city of Marília, São Paulo, Brazil, were evaluated (16.64% of all older adults registered with the USFs). The characteristics of the participants can be seen in Table 1.
A risk interaction was observed in the CI domains: cognitive [χ2(2) = 134.042; p < 0.001, η2: 0.64], psychological [χ2(2) = 92.865; p < 0.001, η2: 0.74], vitality [χ2(2) = 129.564; p < 0.001, η2: 0.86], locomotion [χ2(2) = 144.101; p < 0.001, η2: 0.83], and sensory [χ2(2) = 129.037; p < 0.001, η2: 0.58], indicating the large effect of risk category on domain scores.
Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5 show the comparisons (A) and the absolute distributions (B) between the risk levels for each evaluated domain. It is possible to observe that in all Figures and, therefore, in all domains evaluated and categorized by risk, there was a significant difference in the mean scores in the comparison between low and high risk, making clear the importance of screening to know which risk category the elderly is classified and thus make decisions.
Figure 1, for example, shows a large number of people categorized as high and moderate risk of developing dementia or mild cognitive impairment (B) and how their scores differed significantly from those categorized as low risk (A). In Figure 2, similarly, it is possible to observe a large number of people categorized as high and moderate risk of having self-reported sensory impairments (B) and how the scores differed significantly from people categorized as low risk (A).
In Figure 3, although a small number of elderly people were categorized as high and moderate risk of having psychological disorders (B), there was also a significant difference in relation to the scores between the risk categories (A). Few people categorized as high/moderate risk does not mean they do not require short-term attention.
In Figure 4, there is a large number of elderly people in the high and moderate risk categories of developing locomotor impairments (B) and a significant difference in relation to the scores between the risk categories (A). The presence of people in two categories with a high probability of developing locomotor disorders means that the public authorities need to pay attention to this population.
In Figure 5, a small number of elderly people categorized as high risk for disturbances in the vitality domain can be observed, compared to those at low risk (B). Despite this, there was a significant difference in scores between risk categories (A). Few people classified as high risk does not mean that they do not need care and that it should be short-term, mainly because this domain evaluated the issue of malnutrition.

4. Discussion

Population aging is an unprecedented phenomenon. In Brazil, IBGE data project that the elderly population will increase from 9.2% in 2018 to 25.5% of the country’s total population in 2060 [21]. Together with the demographic growth, challenges of this population arise that include comorbidities, geriatric syndromes, and the decrease in physiological reserve [22,23]. In view of this situation, the WHO developed the concept of comprehensive care for the elderly with emphasis on IC in an attempt to change paradigms of elderly health, focusing on prevention and the maintenance of capacities [2]. Particularly within the context of public health, the availability of tools that assist in short-, medium- and long-term decision-making is important.
In 2015, the WHO established healthy aging as that during which the older adult maintains autonomy and independence and defined it as “the process of developing and maintaining the functional ability that enables well-being in older age” [24]. To provide healthy aging, public health authorities must know the profile of the population and establish goals to achieve the objective.
The results of the present study showed an effect of the risk category on the domains and that there were individuals in all risk categories, indicating that this stratification is important. The WHO itself established that the first step in the implementation of ICOPE is the screening of the population [7]. The effect of the degree of risk on the domains means that elderly people categorized as high/moderate risk are much more likely to already have or develop the disorder than those stratified as low risk.
Screening evaluations are aimed at indicating developmental conditions of a given clinical situation. In this respect, a comprehensive assessment of the elderly can identify those who are undergoing the process of physiological aging and need care over time to maintain their skills, as well as those who have an accelerated physiological decline and need immediate care.
In the cognitive domain assessed by the MoCa, 55 older adults (34%) had scores that classified them as having cognitive impairment without dementia or MCI. The MoCa was developed as a screening test to detect MCI [25] and its properties are superior to the Mini-Mental State Examination [26]. In a proportion of older adults, cognition cannot be classified as normal or dementia [27]; cognitive alterations are present while functional abilities are preserved [28]. Studies have shown that individuals with MCI are at increased risk of developing dementia [29,30,31]. In a two-year follow-up study, the incidence of dementia was 14.9% among older adults with MCI [32].
The psychological domain evaluated by the GDS-15 classified 32 older adults (19.6%) as being at moderate risk of developing subclinical or minor depression [33,34]. This clinical condition can cause functional impairment [35] and is also a risk factor for major depressive disorder and suicidal behavior [36,37], in addition to its association with functional impairment, poor quality of life, and increased health costs [38,39]. Since patients with depression do not initially present with specific symptoms [40], it is important to screen for possible disease in order to prevent the onset/deterioration of the clinical condition.
Regarding the sensory domain, 88 older adults (53.9%) reported poor or fair vision and/or hearing. Changes in vision and hearing are one of the most common clinical conditions of older adults [41] and can have disastrous consequences such as restrictions in functional ability [42,43], cognitive alterations [44,45], decreased well-being and quality of life [46,47], and changes in interpersonal relationships [48]. Screening for alterations in the sensory domain can avoid worsening of other domains, such as the cognitive and psychological domains.
The SPPB, which is used to assess the locomotion domain, is divided into three parts: static balance, dynamic balance, and lower limb strength. In this domain, 103 older adults (63.1%) were classified as moderate/high risk of having reduced locomotion. A safe gait and activities of daily living require the systems that control postural balance be able to adapt to the demands of the environment [49]. Postural instability and falls are recognized as a geriatric syndrome and their consequences include high morbidity and mortality, loss of physical function, fear of falling [50], hospitalization, institutionalization, and high costs for health services [51]. A study found that, over 20 years, Brazil has spent about 2.3 billion Reais with authorizations for hospitalization in the public health system due to falls [52]. The early identification of individuals at increased risk in the locomotion domain can be crucial to face the challenge of falls and their consequences for public health [53].
Vitality can be understood as the bodily functions devoted to metabolizing dietary intake in order to produce the amount of energy necessary for maintaining optimal homeostasis [13]. With aging, changes occur in energy expenditure and metabolism that can interfere with food intake [14]. To maintain proper function of the body, there must be a balance between energy intake and expenditure. The vitality domain evaluated by the MNA showed that 49 older adults (30%) were at moderate risk of malnutrition. There are several reasons for malnutrition/pre-malnutrition in older adults. However, initial assessment of vitality is important since this domain has been recognized to be of great importance for successful aging [2].
Screening evaluations of older adults to identify conditions/diseases is a key component of healthcare. The aim is to detect an individual’s risk of developing or having the disease and to make short-, medium- and long-term decisions in order to reduce morbidity and mortality and associated costs [54]. A study aimed at exploring the association between healthcare costs and deficits in IC domains showed that visual, locomotion and psychological changes were associated with increased healthcare costs [55]. This type of evaluation should be introduced into practice in order to guide health teams which, in turn, can refer individuals at increased risk to faster and more specialized care and those at moderate or low risk to continuous care in an attempt to prevent deficits in domains and to minimize losses (Figure 6).
Proposed in 2015 by the WHO, the concept of intrinsic capacity focuses on the maintained capabilities of the elderly and not just on losses, changing the paradigm of the concept of aging. Although there seems to be consensus around the concept of IC, there is no consensus on the tools used for its evaluation, requiring more robust approaches [56]. Researches have used retrospective data to evaluate IC domains and, for this reason, they have not always used the tools designated by the WHO a priori. There is a discussion, for example, regarding which tools would best assess the vitality domain, due to the lack of consensus on its definition [56]. Another important issue is the need to develop a standard IC score, and for that the tools must be standardized.
Furthermore, other markers could be incorporated into the IC assessment. Known as biomarkers of aging, some are easy to apply, such as body mass index, handgrip strength, waist and hip circumference. Others would be more difficult to incorporate into the public health assessment, such as DNA-based markers, protein-based markers and their modifications, immunological markers and oxidative stress markers [57].
There is no doubt that disease/risk screening is of the utmost importance in public health [54]. Assessment alone is not sufficient for effective risk screening. Once the diagnosis of the condition is made, a service network is necessary that supports the needs of individuals who require short-term care and also of those who require medium- and long-term care (Figure 6). The idea is not only to cure (short-term care) but also to prevent installation of the problem/worsening in order to promote healthy aging.
In this sense, the practical implication of the study was to show the importance of screening, and that the tools proposed by the WHO to assess the domains of intrinsic capacity can be used, being easy to apply, with low cost and with good sensitivity/specificity.

Strengths and Limitations

Strengths: first, we had a good sample size in our research, producing statistically significant results and representing a good portion of the population of the USFs included; second, a relatively short application time of the assessment instruments (about 45 min), considering the information that can be collected; third, the possibility of application in basic health care, where the elderly are advised to seek the first assistance and, finally, considering the low cost of application and the results collected, the study demonstrates potential, being able to be applied to a biggest population.
Limitations: first, it is worth mentioning that only one method was used to measure the vitality domain (MNA, as designated by the WHO). Vitality needs further investigation with other tools (equally easy and cheap), mainly due to the difficulty of definition. It should be considered that a cross-sectional study, although not considered a limitation, raises important issues such as not being able to determine cause and effect. For this, studies with a longitudinal design must be carried out.

5. Conclusions

The results permit us to conclude that the scores of the assessed CI domains were affected by the risk category. In addition, there were individuals in all risk groups in the five domains evaluated, demonstrating the importance of this type of screening as a public health strategy, making it possible to know which risk category each elderly person belongs to and thus develop strategies in the short-, medium- and long-term.

Author Contributions

L.M.d.N., T.G.C.d.C., J.F.d.L.e.S., L.P.S., B.B.I. and C.M.O.S., collected the data; M.E.S., conceptualization and methodology; M.E.S., formal analysis; L.M.d.N. and M.E.S., writing—original draft preparation.; M.E.S., writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Faculty of Philosophy and Sciences, Unesp, Marília, (Approval number 4.168.934) for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

To the professionals of the Family Health Units and to the elderly for participating in the research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization Ageing. Available online: https://www.who.int/health-topics/ageing#tab=tab_2 (accessed on 18 May 2021).
  2. Belloni, G.; Cesari, M. Frailty and Intrinsic Capacity: Two Distinct but Related Constructs. Front. Med. 2019, 6, 133. [Google Scholar] [CrossRef] [Green Version]
  3. Cesari, M.; De Carvalho, I.A.; Thiyagarajan, J.A.; Cooper, C.; Martin, F.C.; Reginster, J.Y.; Vellas, B.; Beard, J.R. Evidence for the Domains Supporting the Construct of Intrinsic Capacity. J. Gerontol.-Ser. A Biol. Sci. Med. Sci. 2018, 73, 1653–1660. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Althoff, T.; Hicks, J.L.; King, A.C.; Delp, S.L.; Biohub, Z.; Francisco, S. Large-Scale Physical Activity Data Reveal Worldwide Activity Inequality. Nature 2018, 547, 336–339. [Google Scholar] [CrossRef] [PubMed]
  5. WHO. Integrated Care for Older People Guidelines on Community-Level Interventions to Manage Declines in Intrinsic Capacity; World Health Organization: Geneva, Switzerland, 2017; pp. 7–9.
  6. World Health Organization. Integrated Care for Older People (ICOPE): Guidance for Person-Centred Assessment and Pathways in Primary Care; World Health Organization: Geneva, Switzerland, 2019.
  7. World Health Organization. Integrated Care for Older People (ICOPE) Implementation Pilot Programme: Findings from the ‘Ready’ Phase; World Health Organization: Geneva, Switzerland, 2022.
  8. Cecato, J.F.; Montiel, J.M.; Bartholomeu, D.; Martinelli, J.E. Poder Preditivo Do MoCa Na Avaliação Neuropsicológica de Pacientes Com Diagnóstico de Demência. Rev. Bras. Geriatr. Gerontol. 2014, 17, 707–719. [Google Scholar] [CrossRef] [Green Version]
  9. Almeida, O.P.; Almeida, S.A. Confiabilidade Da Versão Brasileira Da Escala de Depressão Em Geriatria (GDS) Versão Reduzida. Arq. Neuropsiquiatr. 1999, 57, 421–426. [Google Scholar] [CrossRef] [PubMed]
  10. Paradela, E.M.P.; Lourenço, R.A.; Veras, R.P. Validação Da Escala de Depressão Geriátrica Em Um Ambulatório Geral. Rev. Saude Publica 2005, 39, 918–923. [Google Scholar] [CrossRef] [PubMed]
  11. Zimdars, A.; Nazroo, J.; Gjonça, E. The Circumstances of Older People in England with Self-Reported Visual Impairment: A Secondary Analysis of the English Longitudinal Study of Ageing (ELSA). Br. J. Vis. Impair. 2012, 30, 22–30. [Google Scholar] [CrossRef]
  12. Ferrite, S.; Santana, V.S.; Marshall, S.W. Validity of Self-Reported Hearing Loss in Adults: Performance of Three Single Questions. Rev. Saude Publica 2011, 45, 824–830. [Google Scholar] [CrossRef] [Green Version]
  13. Manini, T.M. Energy Expenditure and Aging. Ageing Res. Rev. 2010, 9, 1–11. [Google Scholar] [CrossRef] [Green Version]
  14. Riera, C.E.; Dillin, A. Tipping the Metabolic Scales towards Increased Longevity in Mammals. Nat. Cell Biol. 2015, 17, 196–203. [Google Scholar] [CrossRef]
  15. Araujo De Carvalho, I.; Martin, C.; Cesari, M.; Sumi, Y.; Thiyagarajan, J.A.; Beard, J.R. Operationalising the Concept of Intrinsic Capacity in Clinical Settings. WHO Clin. Consort. Health Ageing 2017, 31, 1–29. [Google Scholar]
  16. Harris, D.G.; Davies, C.; Ward, H.; Haboubi, N.Y. An Observational Study of Screening for Malnutrition in Elderly People Living in Sheltered Accommodation. J. Hum. Nutr. Diet. 2007, 21, 3–9. [Google Scholar] [CrossRef] [PubMed]
  17. Machado, R.S.P.; Coelho, M.A.S.C.; Veras, R.P. Validity of the Portuguese Version of the Mini Nutritional Assessment in Brazilian Elderly. BMC Geriatr. 2015, 15, 132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Rubenstein, L.Z.; Harker, J.O.; Salva, A.; Guigoz, Y.; Vellas, B. Screening for Undernutrition in Geriatric Practice: Developing the Short-Form Mini-Nutritional Assessment (MNA-SF). J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2001, 56, M366–M372. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Nakano, M.M.; Diogo, M.J.D.; Filho, W.J. Versão Brasileira da Short Physical Performance Battery—SPPB: Adaptação Cultural e Estudo Da Confiabilidade; Universidade Estadual de Campinas: Campinas, Brazil, 2007. [Google Scholar]
  20. Guralnik, J.M.; Ferrucci, L.; Simonsick, E.M.; Salive, M.E.; Wallace, R.B. Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability. N. Engl. J. Med. 1995, 332, 556–562. [Google Scholar] [CrossRef] [Green Version]
  21. IBGE. Projeção da População do Brasil e das Unidades da Federação. Available online: https://www.ibge.gov.br/apps/populacao/projecao/index.html (accessed on 23 September 2022).
  22. McRae, P.J.; Walker, P.J.; Peel, N.M.; Hobson, D.; Parsonson, F.; Donovan, P.; Reade, M.C.; Marquart, L.; Mudge, A.M. Frailty and Geriatric Syndromes in Vascular Surgical Ward Patients. Ann. Vasc. Surg. 2016, 35, 9–18. [Google Scholar] [CrossRef]
  23. Mcrae, P.J.; Phty, B.; Peel, N.M.; Walker, P.J.; De Looze, J.W.M.; Mudge, A.M. Geriatric Syndromes in Individuals Admitted to Vascular and Urology Surgical Units. J. Am. Geriatr. Soc. 2014, 62, 1105–1109. [Google Scholar] [CrossRef]
  24. WHO. 1st World Report on Ageing and Health. Available online: http://www.who.int/ageing/events/world-report-2015-launch/en/ (accessed on 18 October 2022).
  25. Nasreddine, Z.S.; Phillips, N.A.; Bédirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool for Mild Cognitive Impairment. J. Am. Geriatr. Soc. 2005, 53, 695–699. [Google Scholar] [CrossRef]
  26. Freitas, S.; Simões, M.R.; Alves, L.; Santana, I. Montreal Cognitive Assessment: Validation Study for Mild Cognitive Impairment and Alzheimer Disease. Alzheimer Dis. Assoc. Disord. 2013, 27, 37–43. [Google Scholar] [CrossRef]
  27. Graham, J.E.; Rockwood, K.; Beattie, B.L.; Eastwood, R.; Gauthier, S.; Tuokko, H.; McDowell, I. Prevalence and Severity of Cognitive Impairment with and without Dementia in an Elderly Population. Lancet 1997, 349, 1793–1796. [Google Scholar] [CrossRef]
  28. Petersen, R.C. Mild Cognitive Impairment. N. Engl. J. Med. 2011, 364, 2227–2234. [Google Scholar] [CrossRef] [Green Version]
  29. Lopez, O.L. Mild Cognitive Impairment. Contin. Lifelong Learn. Neurol. 2013, 19, 411–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Prestia, A.; Caroli, A.; van der Flier, W.M.; Ossenkoppele, R.; Van Berckel, B.; Barkhof, F.; Teunissen, C.E.; Wall, A.E.; Carter, S.F.; Scholl, M.; et al. Prediction of Dementia in MCI Patients Based on Core Diagnostic Markers for Alzheimer Disease. Neurology 2013, 80, 1048–1056. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Busse, A.; Hensel, A.; Guhne, U.; Angermeyer, M.C.; Riedel-Heller, S.G. Mild Cognitive Impairment: Long-Term Course of Four Clinical Subtypes. Neurology 2006, 67, 2176–2185. [Google Scholar] [CrossRef] [PubMed]
  32. Petersen, R.C.; Lopez, O.; Armstrong, M.J.; Getchius, T.S.D.; Ganguli, M.; Gloss, D.; Gronseth, G.S.; Marson, D.; Pringsheim, T.; Day, G.S.; et al. Practice Guideline Update Summary: Mild Cognitive Impairment. Neurology 2018, 90, 126–135. [Google Scholar] [CrossRef] [PubMed]
  33. Pincus, H.A.; Davis, W.W.; McQueen, L.E. ‘Subthreshold’ Mental Disorders. A Review and Synthesis of Studies on Minor Depression and Other “Brand Names”. Br. J. Psychiatry 1999, 174, 288–296. [Google Scholar] [CrossRef]
  34. Sadek, N.; Bona, J. Subsyndromal Symptomatic Depression: A New Concept. Depress. Anxiety 2000, 12, 30–39. [Google Scholar] [CrossRef]
  35. Fils, J.M.; Penick, E.C.; Nickel, E.J.; Othmer, E.; Desouza, C.; Gabrielli, W.F.; Hunter, E.E. Minor versus Major Depression: A Comparative Clinical Study. Prim. Care Companion CNS Disord. 2010, 12, 27496. [Google Scholar] [CrossRef] [Green Version]
  36. Cuijpers, P.; Smit, F. Subthreshold Depression as a Risk Indicator for Major Depressive Disorder: A Systematic Review of Prospective Studies. Acta Psychiatr. Scand. 2004, 109, 325–331. [Google Scholar] [CrossRef]
  37. Fergusson, D.M.; Horwood, L.J.; Ridder, E.M.; Beautrais, A.L. Subthreshold Depression in Adolescence and Mental Health Outcomes in Adulthood. Arch. Gen. Psychiatry 2005, 62, 66. [Google Scholar] [CrossRef] [Green Version]
  38. Chachamovich, E.; Fleck, M.; Laidlaw, K.; Power, M. Impact of Major Depression and Subsyndromal Symptoms on Quality of Life and Attitudes Toward Aging in an International Sample of Older Adults. Gerontologist 2008, 48, 593–602. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Lyness, J.M. Naturalistic Outcomes of Minor and Subsyndromal Depression in Older Primary Care Patients. Int. J. Geriatr. Psychiatry 2008, 23, 773–781. [Google Scholar] [CrossRef] [PubMed]
  40. Kessler, R.C.; Bromet, E.J. The Epidemiology of Depression Across Cultures. Annu. Rev. Public Health 2013, 34, 119–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Bainbridge, K.E.; Wallhagen, M.I. Hearing Loss in an Aging American Population: Extent, Impact, and Management. Annu. Rev. Public Health 2014, 35, 139–152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Chen, D.S.; Betz, J.; Yaffe, K.; Ayonayon, H.N.; Kritchevsky, S.; Martin, K.R.; Harris, T.B.; Purchase-Helzner, E.; Satterfield, S.; Xue, Q.-L.; et al. Association of Hearing Impairment with Declines in Physical Functioning and the Risk of Disability in Older Adults. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2015, 70, 654–661. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Liljas, A.E.M.; Wannamethee, S.G.; Whincup, P.H.; Papacosta, O.; Walters, K.; Iliffe, S.; Lennon, L.T.; Carvalho, L.A.; Ramsay, S.E. Hearing Impairment and Incident Disability and All-Cause Mortality in Older British Community-Dwelling Men. Age Ageing 2016, 45, 661–666. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Maharani, A.; Dawes, P.; Nazroo, J.; Tampubolon, G.; Pendleton, N.; Bertelsen, G.; Cosh, S.; Cougnard-Grégoire, A.; Dawes, P.; Delcourt, C.; et al. Associations Between Self-Reported Sensory Impairment and Risk of Cognitive Decline and Impairment in the Health and Retirement Study Cohort. J. Gerontol. Ser. B 2020, 75, 1230–1242. [Google Scholar] [CrossRef]
  45. Pronk, M.; Lissenberg-Witte, B.I.; van der Aa, H.P.A.; Comijs, H.C.; Smits, C.; Lemke, U.; Zekveld, A.A.; Kramer, S.E. Longitudinal Relationships between Decline in Speech-in-Noise Recognition Ability and Cognitive Functioning: The Longitudinal Aging Study Amsterdam. J. Speech Lang. Hear. Res. 2019, 62, 1167–1187. [Google Scholar] [CrossRef] [Green Version]
  46. Hajek, A.; Wolfram, C.; Spitzer, M.; König, H.-H. Association of Vision Problems with Psychosocial Factors among Middle-Aged and Older Individuals: Findings from a Nationally Representative Study. Aging Ment. Health 2020, 25, 946–953. [Google Scholar] [CrossRef]
  47. Brunes, A.; B. Hansen, M.; Heir, T. Loneliness among Adults with Visual Impairment: Prevalence, Associated Factors, and Relationship to Life Satisfaction. Health Qual. Life Outcomes 2019, 17, 24. [Google Scholar] [CrossRef] [Green Version]
  48. Davis, A.; McMahon, C.M.; Pichora-Fuller, K.M.; Russ, S.; Lin, F.; Olusanya, B.O.; Chadha, S.; Tremblay, K.L. Aging and Hearing Health: The Life-Course Approach. Gerontologist 2016, 56, S256–S267. [Google Scholar] [CrossRef] [PubMed]
  49. Sakurai, R.; Kodama, K.; Ozawa, Y. Adaptive Locomotion during Subtle Environmental Changes in Younger and Older Adults. Sci. Rep. 2022, 12, 12438. [Google Scholar] [CrossRef] [PubMed]
  50. Cesari, M.; Marzetti, E.; Canevelli, M.; Guaraldi, G. Geriatric Syndromes: How to Treat. Virulence 2016, 8, 577–585. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Inouye, S.K.; Studenski, S.; Tinetti, M.E.; Kuchel, G.A. Geriatric Syndromes: Clinical, Research and Policy Implications of a Core Geriatric Concept. J. Am. Geriatr. Soc. 2007, 55, 780–791. [Google Scholar] [CrossRef] [PubMed]
  52. Lima, J.D.S.; de Quadros, D.V.; da Silva, S.L.C.; Tavares, J.P.; Pai, D.D. Custos Das Autorizações de Internação Hospitalar Por Quedas de Idosos No Sistema Único de Saúde, Brasil, 2000–2020: Um Estudo Descritivo. Epidemiol. Serviços Saúde 2022, 31. [Google Scholar] [CrossRef]
  53. Kyrdalen, I.L.; Thingstad, P.; Sandvik, L.; Ormstad, H. Associations between Gait Speed and Well-Known Fall Risk Factors among Community-Dwelling Older Adults. Physiother. Res. Int. 2018, 24, e1743. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Saquib, N.; Saquib, J.; Ioannidis, J.P. Does Screening for Disease Save Lives in Asymptomatic Adults? Systematic Review of Meta-Analyses and Randomized Trials. Int. J. Epidemiol. 2015, 44, 264–277. [Google Scholar] [CrossRef] [Green Version]
  55. Pagès, A.; Costa, N.; González-Bautista, E.; Mounié, M.; Juillard-Condat, B.; Molinier, L.; Cestac, P.; Rolland, Y.; Vellas, B.; De Souto Barreto, P. Screening for Deficits on Intrinsic Capacity Domains and Associated Healthcare Costs. Arch. Gerontol. Geriatr. 2022, 100, 104654. [Google Scholar] [CrossRef]
  56. Gonzalez-Bautista, E.; Andrieu, S.; Gutiérrez-Robledo, L.M.; García-Chanes, R.E.; de Souto Barreto, P. In the Quest of a Standard Index of Intrinsic Capacity. A Critical Literature Review. J. Nutr. Health Aging 2020, 24, 959–965. [Google Scholar] [CrossRef]
  57. Bürkle, A.; Moreno-Villanueva, M.; Bernhard, J.; Blasco, M.; Zondag, G.; Hoeijmakers, J.H.J.; Toussaint, O.; Grubeck-Loebenstein, B.; Mocchegiani, E.; Collino, S.; et al. MARK-AGE Biomarkers of Ageing. Mech. Ageing Dev. 2015, 151, 2–12. [Google Scholar] [CrossRef]
Ijerph 20 04227 g001 550
Figure 1. (A): Mean ± standard deviation for each risk group of the cognitive domain. * p < 0.001 compared to high risk; #: p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Figure 1. (A): Mean ± standard deviation for each risk group of the cognitive domain. * p < 0.001 compared to high risk; #: p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Ijerph 20 04227 g001
Ijerph 20 04227 g002 550
Figure 2. (A): Mean ± standard deviation for each risk group of the sensory domain. * p < 0.001 compared to high risk. (B): Absolute frequency of older adults in each risk group.
Figure 2. (A): Mean ± standard deviation for each risk group of the sensory domain. * p < 0.001 compared to high risk. (B): Absolute frequency of older adults in each risk group.
Ijerph 20 04227 g002
Ijerph 20 04227 g003 550
Figure 3. (A): Mean ± standard deviation for each risk group of the psychological domain. * p < 0.001 compared to high risk. (B): Absolute frequency of older adults in each risk group.
Figure 3. (A): Mean ± standard deviation for each risk group of the psychological domain. * p < 0.001 compared to high risk. (B): Absolute frequency of older adults in each risk group.
Ijerph 20 04227 g003
Ijerph 20 04227 g004 550
Figure 4. (A): Mean ± standard deviation for each risk group of the locomotion domain. * p < 0.001 compared to high risk; #: p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Figure 4. (A): Mean ± standard deviation for each risk group of the locomotion domain. * p < 0.001 compared to high risk; #: p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Ijerph 20 04227 g004
Ijerph 20 04227 g005 550
Figure 5. (A): Mean ± standard deviation for each risk group of the vitality domain. * p < 0.001 compared to high risk; # p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Figure 5. (A): Mean ± standard deviation for each risk group of the vitality domain. * p < 0.001 compared to high risk; # p = 0.002 compared to moderate risk. (B): Absolute frequency of older adults in each risk group.
Ijerph 20 04227 g005
Ijerph 20 04227 g006 550
Figure 6. Algorithm for implementing and monitoring the domains of intrinsic capacity in public health.
Figure 6. Algorithm for implementing and monitoring the domains of intrinsic capacity in public health.
Ijerph 20 04227 g006
Table
Table 1. Sample characteristics.
Table 1. Sample characteristics.
TotalFemaleMale
Participants1639469
Age (years)72.22 ± 7.6072.18 ± 7.9972.28 ± 7.11
Level of education (n, %)
Illiterate13 (7.97)11 (11.70)2 (2.89)
Literate14 (8.58)7 (7.44)7 (10.44)
Elementary School
Incomplete89 (54.60)51 (54.25)37 (53.62)
Complete16 (9.81)11 (11.70)6 (8.69)
High school
Incomplete7 (4.29)2 (2.12)5 (7.24)
Complete12 (7.36)7 (7.44)5 (7.24)
Higher education
Incomplete1 (0.61)01 (1.44)
Complete10 (6.13)4 (4.25)6 (8.69)
Postgraduate1 (0.61)1 (1.06)0
Living arrangements (n, %)
Alone26 (15.95)20 (21.27)6 (8.69)
Accompanied137 (84.04)74 (78.72)63 (91.30)
Medications3.59 ± 2.673.87 ± 2.593.22 ± 2.75
Habits (n, %)
Alcohol40 (24.53)16 (17.02)24 (34.78)
Smoke75 (46.01)30 (31.91)45 (65.21)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Nascimento, L.M.d.; Cruz, T.G.C.d.; Silva, J.F.d.L.e.; Silva, L.P.; Inácio, B.B.; Sadamitsu, C.M.O.; Scheicher, M.E. Use of Intrinsic Capacity Domains as a Screening Tool in Public Health. Int. J. Environ. Res. Public Health 2023, 20, 4227. https://doi.org/10.3390/ijerph20054227

AMA Style

Nascimento LMd, Cruz TGCd, Silva JFdLe, Silva LP, Inácio BB, Sadamitsu CMO, Scheicher ME. Use of Intrinsic Capacity Domains as a Screening Tool in Public Health. International Journal of Environmental Research and Public Health. 2023; 20(5):4227. https://doi.org/10.3390/ijerph20054227

Chicago/Turabian Style

Nascimento, Livia Maria do, Thainá Gabriele Camargo da Cruz, Juliana Fernanda de Lima e Silva, Letícia Prado Silva, Beatriz Bigatão Inácio, Carolina Masumi Oki Sadamitsu, and Marcos Eduardo Scheicher. 2023. "Use of Intrinsic Capacity Domains as a Screening Tool in Public Health" International Journal of Environmental Research and Public Health 20, no. 5: 4227. https://doi.org/10.3390/ijerph20054227

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop