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Convergent and discriminative validity of the Frail-VIG index with the EQ-5D-3L in people cared for in primary health care

BMC Geriatrics volume 21, Article number: 243 (2021) Cite this article

A Correction to this article was published on 12 July 2021

This article has been updated

Abstract

Background

The Frail-VIG frailty index has been developed recently. It is an instrument with a multidimensional approach and a pragmatic purpose that allows rapid and efficient assessment of the degree of frailty in the context of clinical practice. Our aim was to investigate the convergent and discriminative validity of the Frail-VIG frailty index with regard to EQ-5D-3L value.

Methods

We carried out a cross-sectional study in two Primary Health Care (PHC) centres of the Catalan Institute of Health (Institut Català de la Salut), Barcelona (Spain) from February 2017 to January 2019. Participants in the study were all people included under a home care programme during the study period. No exclusion criteria were applied. We used the EQ-5D-3L to measure Health-Related Quality of Life (HRQoL) and the Frail-VIG index to measure frailty. Trained PHC nurses administered both instruments during face-to-face assessments in a participant’s home during usual care. The relationships between both instruments were examined using Pearson’s correlation coefficient and multiple linear regression analyses.

Results

Four hundred and twelve participants were included in this study. Frail-VIG score and EQ-5D-3L value were negatively correlated (r = − 0.510; P < 0.001). Non-frail people reported a substantially better HRQoL than people with moderate and severe frailty. EQ-5D-3L value declined significantly as the Frail-VIG index score increased.

Conclusions

Frail-VIG index demonstrated a convergent validity with the EQ-5D-3L value. Its discriminative validity was optimal, as their scores showed an excellent capacity to differentiate between people with better and worse HRQoL. These findings provide additional pieces of evidence for construct validity of the Frail-VIG index.

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Background

Validity is defined as “the degree to which an instrument truly measures the construct(s) it purports to measures” [1]. Validation is a continuous process and different forms of validation can be applied. Criterion validity and construct validity are two types of validity. Criterion validity is applicable when there is the gold standard for the construct that is measured, it refers to the degree to which the scores of a measurement instrument are an adequate reflection of a gold standard [1, 2]. By contrast, construct validity is applicable when there is no gold standard, it refers to the degree to which the scores of a measurement instrument are consistent with the available knowledge about the construct [1, 2].

On the other hand, frailty is defined as “a clinical state in which there is an increase in an individual’s vulnerability to develop negative health-related events (including disability, hospitalizations, institutionalizations, and death) when exposed to endogenous or exogenous stressors” [3]. It is a complex and multidimensional concept for which there are numerous and multiple operational definitions. This fact has contributed to the lack of an accepted gold standard [3, 4]. As a result, most frailty measurement instruments assess their validity by analysing the degree of consistency of their scores with different hypotheses about their relationship with other instruments or the differences between relevant groups. In other words, hypothesis testing for construct validity studies is carried out because of the difficulty in assessing criterion validity.

A new frailty index, the Frail-VIG index, has been developed recently [5, 6]. It is an instrument with a multidimensional approach and a pragmatic purpose that allows rapid and efficient assessment of the degree of frailty in the context of clinical practice. This measurement instrument has shown to have an optimal capacity to predict two-year mortality (Area Under Curve 0.85) [6]. The relationship of their scores with those of the Clinical Frailty Scale [7] has been evaluated in a cross-sectional study and a strong positive correlation (r = 0.706) has been established [8]. All of these studies have been conducted in an inpatient hospital setting and there have been no studies in primary health care (PHC) settings.

Previous research suggests an association between frailty and worse quality of life, but its findings are mixed and inconsistent. However, recent systematic reviews show a consistent negative association between frailty and quality of life among community-dwelling people [9, 10]. Besides, the validity of a measuring instrument does not reside in the instrument itself but in how it is used and hence depends on its appropriateness to the target population and the specific context of administration [2]. Therefore, a good approach to further developing evidence of the validity of the Frail-VIG index would mean to analyse the relationship of its scores with those of another instrument that measures the quality of life (both instruments administered in the context of PHC).

Consequently, we carried out this study in a PHC setting to investigate the convergent and discriminative validity of the Frail-VIG index regard to health-related quality of life (HRQoL) measured by the EQ-5D three-level version (EQ-5D-3L). Concerning convergent validity, we hypothesised that the relationship of scores between instruments was moderate to strong and negative. With regard to discriminative validity, we hypothesised that non-frail people would have higher scores on HRQoL than frail people.

Methods

Study design

We conducted a cross-sectional study on measurement properties.

Setting and participants

The study was carried out in two PHC centres of the Catalan Institute of Health (Institut Català de la Salut), Barcelona (Spain) from February 2017 to January 2019. In these centres, people who cannot visit the centre for PHC services are included under a home care programme and are cared at-home by PHC centre’s professionals. Participants in the study were all people included under a home care program during the study period. No exclusion criteria were applied.

Variables and data measurements

We used the EQ-5D-3L to measure HRQoL which is one of the most widely used measurement instruments [11,12,13]. It is a generic measurement instrument because it measures HRQoL in a way that can be used across different types of patients, health conditions, and treatments. This instrument comprises two parts. The first part consists of five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has three levels: no problems, some problems, and extreme problems. Unique health status is defined by combining a level of each of the five dimensions resulting in a five-digit number, with 11,111 reflecting the best possible health status and 33,333 the worst. The second part of the instrument is the EQ VAS which comprises a visual analogue scale from 0 (the worst health imaginable) to 100 (the best health imaginable). EQ-5D-3L is designed for self-completion by respondents, but several other modes of administration (interview administered, face-to-face interview, or telephone interview) are also possible. Existing research has established that self-completion and assisted completion produce equivalent scores overall and therefore both methods can be used [14, 15]. For the present study we used the Spanish face-to-face interview version as a large majority of the participants were unable to read and write [16].

Frailty was measured using the original Spanish version of Frail-VIG index. It is composed of 22 items that evaluate 25 deficits based on the comprehensive geriatric assessment [5, 6]. It is constructed using only variables recorded during the usual clinical evaluation process. The value of the index is obtained from the sum of the identified deficits divided by 25, the total number of potential deficits, so the higher the presence of deficits the higher the score in the index. Likewise, different index cut-off points have been established that distinguish between four degrees of frailty: non-frailty, < 0.20; mild, 0.20 to 0.35; moderate, 0.36 to 0.50 and severe > 0.50 [6].

Researchers developed an instruction manual for the administration of instruments. Trained PHC nurses administered both instruments during face-to-face assessments in a participant’s home during usual care. These interviews had an average duration of 30 min. A pilot test with 20 participants was also carried out to detect possible problems and to introduce improvement strategies. After this pilot test, no changes in the procedure were necessary.

Statistical methods

We applied a scoring algorithm based on the Spanish population to convert EQ-5D-3L states into a single summary value [13, 17]. This value is “attached to an EQ-5D profile according to a set of weights that reflect, on average, people’s preferences about how good or bad the state is” [18] and ranges from 1 (full health) to 0 (a state as bad as being death), although there are negative values for the value, corresponding to those states of health that are rated as worse than death. This value is often used in economic evaluations, but it can also be used to describe the health of a population or the severity of disease among patients [19].

We calculated central tendency and dispersion measures for the quantitative variables. For categorical variables, we estimate absolute and relative frequencies. The relationships between the Frail-VIG index and the EQ-5D-3L value were examined using Pearson’s correlation coefficient and multiple linear regression analyses. Correlation coefficients of ≤0.29 were considered weak, 0.30–0.49 as low, 0.50–0.69 as moderate, and ≥ 0.70 was considered strong correlation [20]. To examine whether non-frail people had higher scores on HRQoL than frail people, a one-way ANOVA was conducted, with frailty status as the independent variable and the EQ-5D-3L value as the dependent variable. We used the statistical software IBM SPSS Statistics version 24 for all analyses.

Results

Four hundred and twelve participants were included in this study. Table 1 shows their general characteristics and frailty status according to the Frail-VIG index scores.

Table 1 General characteristics of participants overall and by frailty states. Values are absolute frequencies (percentages) unless stated otherwise
Full size table

Table 2 describes the 10 most frequent EQ-ED-3 L profiles according to frailty status. The worst of these profiles was most prevalent among people with severe frailty, while the best was more frequent among non-frailty people.

Table 2 Prevalence of the 10 most frequently observed EQ-ED-3 L profiles and frequency of reporting of the worst possible profile according to frailty state (these were the 10 profiles of 63.1% (n = 260) of the study population (the most frequent profiles in each of the frailty states are highlighted in bold).
Full size table

Pearson’s correlation coefficient between Frail-VIG index and EQ-5D-3L value was negative and moderate (r = −0.510; P < 0.001). After adjusting for age and sex variables, the multiple linear regression model revealed that Frail-VIG index independently correlated with EQ-5D-3L value (B = -0.945; 95% Confidence Interval, −1.098 to − 0.791; R2 = 0.287). As you can see from Table 1, non-frail people reported a substantially better HRQoL than people with moderate and severe frailty people. Likewise, closer inspection of Table 3 shows that the EQ-5D-3L value declined significantly as the Frail-VIG index score increased.

Table 3 EQ-5D-3L value (from “0”, “a state as bad as being dead” to “1”,“full health”) for the total study population and the seven groups based on their Frail-VIG score (from “0”, “absence of frailty” to “1”, “severe frailty”)
Full size table

Discussion

In this study, involving people cared at-home by PHC professionals, we found that the Frail-VIG index demonstrated convergent validity with the EQ-5D-3L value. Furthermore, its discriminative validity was optimal, as their scores showed an excellent capacity to differentiate between people with better and worse HRQoL.

Consistent with the literature, this research found that HRQoL and frailty were negatively associated among community-dwelling older people [9, 10]. Studies on other cumulative deficit models of frailty have also found a negative relationship between the frailty state and the scores of instruments measuring HRQoL, well-being and life satisfaction [9, 10, 21,22,23]. However, the results of this study show that not only is the frailty state associated with a worse HRQoL but also that there is a linear association between both indexes (Frail-VIG index and EQ-5D-3L value). Hypothesis testing is an ongoing process, so the more hypotheses are tested the more evidence is generated for construct validity [24]. Therefore, this research supports evidence from previous studies carried out in the hospital setting on the construct validity of the Frail-VIG index [6, 8].

This study has several strengths. Most validation studies of measurement instruments of frailty in community-dwelling people focus on demonstrating their predictive potential for adverse outcomes or resource use, such as disability, institutionalisation or hospital admissions [25,26,27]. In contrast, studies that analyse their relationship to more positive outcomes such as HRQoL are less common [10, 28]. Likewise, the use of the Frail-VIG index in primary care has been poorly studied, and this is one of the first studies to analyse its construct validity in this care setting. However, some limitations exist. The representativeness of non-frailty people is very low (12 people), probably because the study population were people in a home-care programme. A greater representation of this population might have influenced the Frail-VIG index’s discriminative validity observed. In addition, we used the EQ-5D values for this assessment of the measurement properties of the Frail-VIG index instead of other values such as the EQ VAS. The EQ VAS can be considered a measure closer to the patient’s perspective than the EQ-5D values [29]. Nevertheless, as Devlin and Parkin point out [19], being able to summarise and represent a health profile with a single value has important advantages, including simplifying statistical analyses. On the other hand, both the EQ-5D values and the EQ VAS are able to discriminate between the quality of life of most groups of individuals with different socio-demographic factors, and those with or without clinical conditions [30]. Furthermore, some studies [31] and experts in the field of psychometrics [32] report that older people experience difficulties in understanding and completing direct estimation methods, such as the visual analogue scale. For all these reasons, we chose the EQ-5D values to carry out this psychometric study.

People living with frailty risk experiencing a decline in their quality of life [9, 10, 33]. The findings of this study suggest that the interventions aimed at decreasing frailty could have the added benefit of improving the HRQoL. PHC professionals are naturally positioned to identify frailty early and to implement interventions that prevent related adverse effects on the most vulnerable people [4]. Moreover, the assessment of frailty in PHC settings requires tools that are not time consuming as well as valid and reliable which is way the Frail-VIG index could provide a useful and appropriate tool for this care setting [34].

Conclusions

This study has identified a negative moderate correlation between the Frail-VIG index and the EQ-5D-3L values. It has also shown that the Frail-VIG index was able to discriminate significantly home-dwelling older people according to their HRQoL. These findings provide additional pieces of evidence for construct validity of the Frail-VIG index. Further research is needed on this new measurement instrument to determine its suitability for screening and preventing adverse effects of frailty in PHC settings.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author, [EZO], upon reasonable request.

Change history

Abbreviations

ADLs:

Activities of Daily Living

ALS:

Amyotrophic Lateral Sclerosis

COPD:

Chronic Obstructive Pulmonary Disease

GDS:

Global Deterioration Scale

GFR:

Glomerular Filtration Rate

HRQoL:

Health Related Quality of Life

IAVDs:

Instrumental Activities of Daily Living

PHC:

Primary Health Care

SD:

Standard Deviation

References

  1. Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63(7):737–45.

    Article  Google Scholar 

  2. DeVellis RF. Validity. In: Scale development theory and applications. 4th ed. Los Angeles: Sage Publications Inc.; 2017. p. 84–5.

    Google Scholar 

  3. Morley JE, Vellas B, Abellan van Kan G, Anker SD, Bauer JM, Bernabei R, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14(6):392–7.

    Article  Google Scholar 

  4. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–75.

    Article  Google Scholar 

  5. Amblàs-Novellas J, Espaulella-Panicot J, Inzitari M, Rexach L, Fontecha B, Romero-Ortuno R. The challenge of clinical complexity in the 21st century: could frailty indexes be the answer? Rev Esp Geriatr Gerontol. 2017;52(3):159–66 Spanish.

    Article  Google Scholar 

  6. Amblàs-Novellas J, Martori JC, Espaulella J, Oller R, Molist-Brunet N, Inzitari M, et al. Frail-VIG index: a concise frailty evaluation tool for rapid geriatric assessment. BMC Geriatr. 2018;18(1):29.

    Article  Google Scholar 

  7. Rockwood K. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005;173(5):489–95.

    Article  Google Scholar 

  8. Moreno-Ariño M, Torrente Jiménez I, Cartanyà Gutiérrez A, Oliva Morera JC, Comet R. Assessing the strengths and weaknesses of the Clinical Frailty Scale through correlation with a frailty index. Aging Clin Exp Res. 2020;32(11):2225.

    Article  Google Scholar 

  9. Crocker TF, Brown L, Clegg A, Farley K, Franklin M, Simpkins S, et al. Quality of life is substantially worse for community-dwelling older people living with frailty: systematic review and meta-analysis. Qual Life Res. 2019;28(8):2041–56.

    Article  Google Scholar 

  10. Kojima G, Iliffe S, Jivraj S, Walters K. Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):716–21.

    Article  Google Scholar 

  11. Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72.

    Article  CAS  Google Scholar 

  12. EuroQol Group. EuroQol--a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208.

    Article  Google Scholar 

  13. EuroQol Research Foundation. EQ-5D-3L User Guide, 2018. Available from: https://euroqol.org/publications/user-guides/.

    Google Scholar 

  14. Rutherford C, Costa D, Mercieca-Bebber R, Rice H, Gabb L, King M. Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis. Qual Life Res. 2016;25(3):559–74.

    Article  Google Scholar 

  15. Wu AW, Jacobson DL, Berzon RA, Revicki DA, Van Der Horst C, Fichtenbaum CJ, et al. The effect of mode of administration on medical outcomes study health ratings and EuroQol scores in AIDS. Qual Life Res. 1997;6(1):3–10.

    Article  CAS  Google Scholar 

  16. EQ-5D. EQ-5D-3L Face-to-face interview version. In: EQ-5D. 2021. https://euroqol.org/eq-5d-instruments/eq-5d-3l-available-modes-of-administration/face-to-face-interview-version/. Accessed 11 Feb 2021.

  17. Szende A, Oppe M, Devlin N. EQ-5D value sets: inventory, comparative review and user guide. The Netherlands: Springer; 2007. p. 1–102.

    Book  Google Scholar 

  18. Devlin N, Parkin D, Janssen B. Methods for Analysing and reporting EQ-5D data. Methods for Analysing and reporting EQ-5D data. Cham: Springer International Publishing; 2020.

    Book  Google Scholar 

  19. Devlin N, Parkin D. Guidance to users of EQ-5D value sets. In: Szende A, Oppe M, Devlin N, editors. EQ-5D value sets: inventory, comparative review and user guide. Dordrecht: Springer Netherlands; 2007. p. 39–52.

    Chapter  Google Scholar 

  20. Hinkle DE, Wiersma W, Jurs SG. Applied statistics for the behavioral sciences. 4th ed. Boston: Houghton Mifflin; 1998.

    Google Scholar 

  21. Hubbard RE, Goodwin VA, Llewellyn DJ, Warmoth K, Lang IA. Frailty, financial resources and subjective well-being in later life. Arch Gerontol Geriatr. 2014;58(3):364–9.

    Article  Google Scholar 

  22. Bilotta C, Bowling A, Casè A, Nicolini P, Mauri S, Castelli M, et al. Dimensions and correlates of quality of life according to frailty status: a cross-sectional study on community-dwelling older adults referred to an outpatient geriatric service in Italy. Health Qual Life Outcomes. 2010;8(1):56.

    Article  Google Scholar 

  23. Yang F, Gu D, Mitnitski A. Frailty and life satisfaction in Shanghai older adults: the roles of age and social vulnerability. Arch Gerontol Geriatr. 2016;67:68–73.

    Article  Google Scholar 

  24. Strauss ME, Smith GT. Construct validity: advances in theory and methodology. Annu Rev Clin Psychol. 2009;5(1):1–25.

    Article  Google Scholar 

  25. Kojima G. Frailty as a predictor of hospitalisation among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):722–9.

    Article  Google Scholar 

  26. Kojima G. Frailty as a predictor of disabilities among community-dwelling older people: a systematic review and meta-analysis. Disabil Rehabil. 2017;39(19):1897–908.

    Article  Google Scholar 

  27. Kojima G. Frailty as a predictor of nursing home placement among community-dwelling older adults. J Geriatr Phys Ther. 2018;41(1):42–8.

    Article  Google Scholar 

  28. Duppen D, Rossi G, Dierckx E, Hoeyberghs L, De Donder L. Focusing on positive outcomes in frailty research: development of a short well-being instrument for older adults (SWIO). Int Psychogeriatr. 2019;31(06):767–77.

    Article  Google Scholar 

  29. Feng Y, Parkin D, Devlin NJ. Assessing the performance of the EQ-VAS in the NHS PROMs programme. Qual Life Res. 2014;23(3):977–89.

    Article  Google Scholar 

  30. Barton GR, Sach TH, Doherty M, Avery AJ, Jenkinson C, Muir KR. An assessment of the discriminative ability of the EQ-5Dindex, SF-6D, and EQ VAS, using sociodemographic factors and clinical conditions. Eur J Health Econ. 2008;9(3):237–49.

    Article  Google Scholar 

  31. Hulme C, Long AF, Kneafsey R, Reid G. Using the EQ-5D to assess health-related quality of life in older people. Age Ageing. 2004;33(5):504–7.

    Article  Google Scholar 

  32. Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. 5th ed. Oxford: Oxford University Press; 2015.

    Book  Google Scholar 

  33. Renne I, Gobbens RJJ. Effects of frailty and chronic diseases on quality of life in Dutch community-dwelling older adults: a cross-sectional study. Clin Interv Aging. 2018;13:325–34.

    Article  Google Scholar 

  34. Abbasi M, Rolfson D, Khera AS, Dabravolskaj J, Dent E, Xia L. Identification and management of frailty in the primary care setting. Can Med Assoc J. 2018;190(38):E1134–40.

    Article  Google Scholar 

  35. Mokkink LB, Prinsen CAC, Patrick DL, Alonso J, Bouter LM, de Vet HCW, et al. COSMIN Study Design checklist for Patient-reported outcome measurement instruments. Amsterdam: COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN); 2019. [cited 2020 Oct 14]. Available from: https://www.cosmin.nl/wp-content/uploads/COSMIN-study-designing-checklist_final.pdf

    Google Scholar 

  36. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of Observational studies in epidemiology (STROBE). Epidemiology. 2007;18(6):805–35.

    Article  Google Scholar 

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Acknowledgements

The authors appreciate the review of the English text by Patryk Bialoskorski, MA.

Funding

This research was supported by MUTUAMCONVIURE Foundation’s Research Award in Social and Health Care (18th Edition), the Health Department grant number (SLT008/18/00011) from the Generalitat of Catalunya (Spain) and by a grant from the Territorial Management of Barcelona of the Institut Català de la Salut (Catalan Institute of Health) to the first author (JJZS) in the 2018 edition. The funders had no role in review design, decision to publish, or preparation of this manuscript.

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Authors and Affiliations

  1. Gerència Territorial de Barcelona, Institut Català de la Salut, Gran Via Corts Catalanes 587 àtic, 08007, Barcelona, Spain

    Juan-José Zamora-Sánchez & Edurne Zabaleta-del-Olmo

  2. School of Nursing, Universitat de Barcelona, Barcelona, Spain

    Juan-José Zamora-Sánchez

  3. Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    Edurne Zabaleta-del-Olmo

  4. Universitat Autònoma de Barcelona, Bellaterra, Cerdanyola del Vallès, Spain

    Edurne Zabaleta-del-Olmo

  5. Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, Spain

    Edurne Zabaleta-del-Olmo

  6. Nursing school “La Fe”, Valencia, Spain

    Vicente Gea-Caballero

  7. GREIACC Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain

    Vicente Gea-Caballero

  8. Nursing Department, Universitat de Valencia, Valencia, Spain

    Iván Julián-Rochina

  9. Fragilidad y Deterioro Cognitivo (FROG) Research Group, Universitat de Valencia, Valencia, Spain

    Iván Julián-Rochina

  10. Primary care centre “Fondo”, Gerència Territorial Metropolitana Nord, Institut Català de la Salut, Santa Coloma de Gramenet, Spain

    Gemma Pérez-Tortajada

  11. Central Catalonia Chronicity Research Group (C3RG), Centre for Health and Social Care Research (CESS), Universitat de Vic – University of Vic-Central University of Catalonia (UVIC-UCC), 08500, Vic, Spain

    Jordi Amblàs-Novellas

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Contributions

JJZS: Designed the study and participated in data collection. JJZS and EZO: Designed the analysis; and drafted the manuscript. EZO: Performed the statistical analyses presented. VGC, IJR, GPT, and JAN: Participated in analysis, interpretation of data, and the critical review of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Edurne Zabaleta-del-Olmo.

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Ethics approval and consent to participate

We followed the guidelines established by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) [35] and the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) for cross-sectional studies [36]. This research and collection of data used were approved by the IDIAPJGol Clinical Research Ethics Committee (registration number P17/150). All data were collected with informed consent from participants before beginning the interviews.

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The authors declare that they have no competing interests.

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Zamora-Sánchez, JJ., Zabaleta-del-Olmo, E., Gea-Caballero, V. et al. Convergent and discriminative validity of the Frail-VIG index with the EQ-5D-3L in people cared for in primary health care. BMC Geriatr 21, 243 (2021). https://doi.org/10.1186/s12877-021-02186-x

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BMC Geriatrics

ISSN: 1471-2318

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