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Validation of the Spanish translation Sheffield Profile for Assessment and Referral for Care (SPARC-Sp) at the Hospital Universitario San Jose of Popayan, Colombia

Published online by Cambridge University Press:  27 March 2024

Cindy V. Mendieta
Affiliation:
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogota, Colombia Department of Nutrition and Biochemistry, Faculty of Sciences, Pontificia Universidad Javeriana, Bogota, Colombia
Jose A. Calvache*
Affiliation:
Department of Anesthesiology, Universidad del Cauca, Popayan, Colombia Department of Anesthesiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Martín A. Rondón
Affiliation:
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogota, Colombia
Carlos Javier Rincón-Rodríguez
Affiliation:
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogota, Colombia
Sam H. Ahmedzai
Affiliation:
School of Medicine, The University of Sheffield, Sheffield, UK
Esther de Vries
Affiliation:
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogota, Colombia
*
Corresponding author: Jose A. Calvache; Email: [email protected]
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Abstract

Objectives

We determined the validity and reliability of the Spanish translation Sheffield Profile for Assessment and Referral for Care (SPARC-Sp) questionnaire to identify the palliative care (PC) needs of patients with chronic noncommunicable diseases (NCDs) in Colombia.

Methods

We developed a cross-sectional observational study of scale assessment in adults with the aim of determining the validity and reliability of the SPARC-Sp questionnaire to identify the PC needs of patients with NCDs receiving outpatient or inpatient care at the Hospital Universitario San Jose of Popayan – ESE, Colombia, from 2021 to 2022.

Results

We applied a questionnaire consisting of demographic, clinical data, and SPARC-Sp to 507 participants. The constructed model explained 75% of the variance with an adequate fit according to the root mean square residual (0.03), the comparative fit index (0.98), and acceptable reliability (McDonald’s total omega 0.4–0.9). Opportunities for improvement are the reformulation and inclusion of particular words to improve the representativeness and clarity of the domains of communication and information, religious, and spiritual issues.

Significance of results

This research represents the first validation of SPARC in Spanish. SPARC-Sp is an instrument that allows initiating a conversation of the patient’s main needs through a systematic assessment of the patients’ main needs. Its psychometric validation demonstrated good fit and acceptable reliability.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is used to distribute the re-used or adapted article and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use.
Copyright
© The Author(s), 2024. Published by Cambridge University Press.

Introduction

According to the World Health Organization, in 2022, chronic noncommunicable diseases (NCDs) caused 41 million deaths worldwide, amounting to 74% of all deaths; 77% of these deaths occurred in low- and middle-income countries (Organización Mundial de la Salud 2022b). In Colombia, between 2012 and 2016, 69–83% (124,988 adults) of deaths were attributed to NCDs and potentially required palliative care (PC) (Calvache et al. Reference Calvache, Gil and de Vries2020). PC services in Colombia are primarily available in main cities (Calvache et al. Reference Calvache, Gil and de Vries2020), with a significant lack of offer in rural areas, compounded by other barriers such as limited supply of healthcare, lack of awareness among policymakers, health professionals, the general community, myths, cultural and social barriers framed by beliefs about death and dying well, and issues surrounding opioid drugs (Organización Mundial de la Salud 2022a). Health professionals, patients, and relatives associate PC with death and abandonment, leading to delays in accessing services (Cuadrado Reference Cuadrado2018; Gempeler et al. Reference Gempeler, Torregrosa Almonacid and Cuadrado2021; Kaasa et al. Reference Kaasa, Loge and Aapro2018). Poor communication of patients’ wishes for treatment and objectives and preferences for care were recently shown to be associated with increased suffering among Colombian cancer patients (Arango-Gutiérrez et al. Reference Arango-Gutiérrez, Moreno and Rondón2023).

The Sheffield Profile for Assessment and Referral for Care (SPARC) instrument, designed in 2008 to identify holistic care needs and facilitate early referral to PC, and is widely used in the United Kingdom (Ahmed et al. Reference Ahmed, Bestall and Payne2009). During its development process, patients and healthcare professionals concluded that SPARC can be applicable to patients with acute illnesses or chronic conditions and it is useful for the decision-making process (Hughes et al. Reference Hughes, Ahmed and Winslow2015). SPARC consists of 56 items across 8 domains including communication and information, physical symptoms, psychological issues, religious and spiritual symptoms, independence and activity, family and social life, treatment issues, and personal issues. It asks about the degree of concern or discomfort in 4 categories (not at all, a little, quite a lot, and a very much) (Ahmed et al. Reference Ahmed, Bestall and Payne2009).

The face validity of the original English version of SPARC was assessed through cognitive interviews, emphasizing the emotional impact of psychological, religious, and spiritual issues (Ahmed et al. Reference Ahmed, Bestall and Payne2009). SPARC has been translated into Polish, Korean, and traditional Chinese. The linguistic, content, construct, and reliability validation of these translations were assessed in Poland (Leppert et al. Reference Leppert, Majkowicz and Ahmedzai2012), South Korea (Kwon et al. Reference Kwon, Baek and Kim2021), and Taiwan (Tsai et al. Reference Tsai, Chou and Loh2023). We recently translated and adapted SPARC to Colombian Spanish in the Colombian context (SPARC-Sp) (Reference Moreno, Mendieta and de Vries2024). In this work, we present the validity and reliability of the SPARC-Sp questionnaire to identify the PC needs of patients with NCDs at the Hospital Universitario San Jose de Popayan in Colombia.

Methods

The linguistic and cultural validation of SPARC tool to the Colombian Spanish are described in detail elsewhere (Reference Moreno, Mendieta and de Vries2024). The present study determines the validity and reliability of the SPARC-Sp instrument to assess holistic PC needs in patients with NCDs attended to at the Hospital San Jose of Popayan between 2021 and 2022 through a descriptive observational cross-sectional scale assessment study including factor analysis to evaluate internal consistency, relationship to other variables, consequences, and reliability of the test.

Population, patient recruitment, and data collection

Content-based evidence

To estimate the extent to which the items were related to the construct, multidisciplinary groups comprising patients, caregivers, family members, health professionals, administrative staff, and allied health and social care professionals, from different departments including rural and urban areas of Colombia (Reference Moreno, Mendieta and de Vries2024) were formed. Attendees were recruited through social networks (email, WhatsApp) and personal acquaintances as part of a research project.

Evidence based on internal structure, relationship with other variables, and consequences of the test

Adult patients with a diagnosis of NCD who received outpatient or inpatient care between 2021 and 2022 at the Hospital Universitario San Jose (HUSJ) of Popayan in Colombia were eligible to participate. We excluded patients with symptomatic brain metastasis, uncontrolled psychological conditions, cognitive impairment, and those unable to effectively communicate with the researcher. Following the guideline of including 5–10 people for each item in a factor analysis (Ruiz-Morales and Gómez-Restrepo Reference Ruiz-Morales and Gómez-Restrepo2015), and allowing for about 10% of participants with incomplete answers, our envisaged sample size was to include about 550 patients to reach 500 patients with complete answers (Ruiz-Morales and Gómez-Restrepo Reference Ruiz-Morales and Gómez-Restrepo2015).

Six trained research assistants (1 head nurse, 2 anesthesiology residents, 1 internist, 1 medical student, and 1 dietitian) recruited the patients and applied the study questionnaire including SPARC-Sp (supplementary file 1 for the questionnaire in English). Eligible patients could attend to the (i) medical or surgical inpatient wards, (ii) daytime outpatient services of the pain management service, or (iii) daytime oncology outpatient service – all of them at HUSJ. The questionnaire was administered on paper; in case the participant required assistance in filling out the forms, for example, in case of poor reading skills, the research assistants read the questions aloud to the participant. The answers of the paper formats were subsequently transcribed into REDCap. To improve the data quality, 2 researchers performed the transcription and quality analysis, checking the completeness of the record and reviewing the clinical history for the corresponding information.

To perform the descriptive analysis of results, we described demographic and clinical variables such as gender, age, area of origin, educational level, diagnosis, comorbidities, and treatment. Patients were stratified into 5 groups based on disease categories, frequency of the diagnosis, comorbidities, and their relationship to the requirement for PC, as endorsed by thematic experts (EdV, JAC). The groups consisted of (i) cardiovascular disease, which included diabetes, chronic obstructive pulmonary disease, (ii) cancer, (iii) musculoskeletal and neurological diseases, (iv) multimorbidity, and (v) other diseases.

Study measurements

We applied the SPARC-Sp instrument validated in Spanish (Reference Moreno, Mendieta and de Vries2024). We compared the scores of the domain of functionality against other instruments, including (Hernández-Quiles et al. Reference Hernández-Quiles, Bernabeu-Wittel and Pérez-Belmonte2017), SARC-F (Slowness, Assistance waking, Rising from chair, Climbing stairs, and Falls) (Cruz-Jentoft et al. Reference Cruz-Jentoft, Bahat and Bauer2019), the Karnofsky Performance Status Scale (KPS) and, for cancer patients only, the ECOG scale (Eastern Cooperative Oncology Group) (Schag et al. Reference Schag, Heinrich and Ganz1984).

Analysis

The analyses evaluated validity in terms of evidence based on the content, internal structure, relationship to other variables, and consequences of the test (American Educational Research Association et al. 2014). SPARC-Sp assigns a score from 0 to 3 (not at all to very much) reflecting the patient’s discomfort or discomfort with a given need. Although clinical behavior after implementation of SPARC-Sp indicates that scores of any individual item above 3 require referral for PC, for validation purposes the total score, obtained by summing the scores of each item, was used, as in previous validations (Kwon et al. Reference Kwon, Baek and Kim2021; Leppert et al. Reference Leppert, Majkowicz and Ahmedzai2012).

To assess the content-based evidence of the test, we designed a questionnaire asking participants to score the relevance of the inclusion of the SPARC-Sp items and domains on the construct of holistic care needs in the abovementioned multidisciplinary group of professionals, patients, and caregivers (English model of the questionnaire in supplementary file 2). This questionnaire included 51 Likert-type questions about the items relevance and was applied through SurveyMonkey (2022). We used the Aiken’s V coefficients as a measure to determine the proportion of judges who had a positive assessment of the SPARC-Sp items assessed, which represents the relevance of revising or eliminating the items (Martin-Romera and Molina Ruiz Reference Martin-Romera and Molina Ruiz2017).

To evaluate the internal structure of SPARC-Sp, we performed an exploratory factor analysis (EFA) applying a factor analysis of axes or main factors (20). We used the eigenvalue to retain the factors (University of California 2022) and an oblique promax rotation (Finch Reference Finch2006; Hair and Gómez Suárez Reference Hair and Gómez Suárez2010). We estimated the root mean square residual (RMSR) to assess the amount of fit error between the observed data and the values estimated by the model; values below 0.05 reflect a good fit (Shi et al. Reference Shi, Maydeu-Olivares and DiStefano2018). The comparative fit index (CFI) indicates the quality of the model fit compared to the null model (no factors or correlations between variables), values of 0.95 and above reflect a good model fit (van Laar and Braeken Reference van Laar and Braeken2021). In addition, the communalities (h2*) were assessed to determine the amount of variance of an observed variable that is explained by a set of underlying common factors (Hogarty et al. Reference Hogarty, Hines and Kromrey2005; Taherdoost et al. Reference Taherdoost, Sahibuddin and Jalaliyoon2020).

Due to the characteristics of the construct of “holistic palliative care needs,” there is no criterion for validation. However, we could assess the correlation between the SPARC-Sp domain of independence and activity and its total score with other instruments close to these domains as SARC-F, Karnofsky, and ECOG using Spearman’s correlation coefficient (American Educational Research Association et al. 2014).

We evaluated the evidence based on the consequences of the test, indicating that the application of SPARC-Sp may generate other consequences beyond the identification of holistic needs in PC (American Educational Research Association et al. 2014). For this evaluation, we determined whether patients with comorbidities have higher SPARC-Sp scores than patients without comorbidities. This assessment was made by the Mann–Whitney U test.

For the internal consistency assessment, we estimated McDonald’s total omega, which indicates the proportion of the total variance of the scores to which differences between participants can be attributed and not to measurement error (Ventura-León Reference Ventura-León2017). All analyses were performed using R; we used the packages cli, tidyr, dplyr, psych, polycor, ggcorrplot, GPArotation, spearman CI, and gglpot2 (Bernaards and Jennrich Reference Bernaards and Jennrich2005; Csárdi Reference Csárdi2023; de Carvalho Reference de Carvalho2018; Fox Reference Fox2022; Kassambara Reference Kassambara2022; Revelle Reference Revelle2023; Rizopoulos Reference Rizopoulos2006; Wickham Reference Wickham2016; Wickham et al. Reference Wickham, François and Henry2023a, Reference Wickham, Vaughan and Girlich2023b).

The protocol of this project was evaluated and approved by the Ethics Committee at the Hospital Universitario San Jose, Popayan, Colombia (8.2.9–92/031). All included patients and participants confirmed their voluntary and informed participation by signing informed consent forms.

Results

Content-based evidence

A total of 37 participants participated in the evaluation of the content-based evidence: 48% of them (n = 13) were nurses, 22% (n = 6) physicians, 19% (n = 5) other health professionals (n = 5), 15% (n = 4) patients, 7% (n = 2) caregivers, 4% (n = 1) user representatives, and 4% (n = 1) decision-makers. All items presented an Aiken’s V coefficients >0.5, reflecting that the items adequately represent the content domain (Table 1).

Table 1. Aiken’s V coefficients for each item

Evidence based on internal structure

A total of 522 individual patients answered the questionnaire for the scale assessment study, but 15 of them left at least one SPARC-Sp question unanswered. Consequently, data from participants were available for analysis. The clinical and demographic characteristics of this population are described in Table 2.

Table 2. Clinical and demographic characteristics of the population

COPD = chronic obstructive pulmonary disease, SPARC = Spanish translation Sheffield Profile for Assessment and Referral for Care, SARC-F = Slowness, Assistance waking, Rising from chair, Climbing stairs, and Falls, ECOG = Eastern Cooperative Oncology Group.

Patients were stratified into 5 groups based on logical clinical categories (rather than specific mechanisms). The results of this ranking with their frequency and different intersections that make up the group of comorbidities are presented in Figure 1.

CVD: cardiovascular disease. The blue bars represent the frequency of each of the disease groups (CVD, other, oncological, musculoskeletal). The black bars represent the frequency of the intersection of a comorbidity and the dots specify the diseases with which this intersection occurs. For example, the graph shows that 390 patients had CVD as a main disease or comorbidity (blue bar), 209 of them had CVD as the only or main disease and 76 participants had CVD and “other pathologies,” 50 had oncological disease and 40 had musculoskeletal disease and CVD.

Figure 1. Frequency of diseases (CVD, oncology, musculoskeletal and others) and intersections of comorbidities.

Factorial analysis (Table 3) established 12 factors and 45 of the 56 initial items represented the best solution, explaining 75% of the variance (Table 4). Four items had high communalities (E2, E3, B8, and G2), 41 items had moderate to low communalities and 7 items had low communalities (A1, A5, A7, B5, B7, B15, and B18).

Table 3. SPARC factor loadings after exploratory factor analysis

SPARC = Spanish translation Sheffield Profile for Assessment and Referral for Care.

h2*: communalities.

Seven underlying domains were identified: (i) a “sadness” domain linked psychological issues and religious and spiritual issues about thoughts about death or dying; (ii) a “functional limitations” domain linked physical symptoms, uncontrolled symptoms, and personal issues related to needing help with personal issues; (iii) a “communication needs” domain which includes physical symptoms such as dry and sore mouth, and personal issues about requiring other support and financial issues; (iv) a “gastrointestinal symptoms” domain; (v) a domain on communication with health professionals; (vi) a domain on “respiratory symptoms and anxiety,” and (vii) “cognitive symptoms.”

This factor analysis excluded items with low factor loadings (<0.3). Within these items were identified: communication with religious or spiritual advisor or counsellor, communication with family, pain, headache, bowel disturbances (constipation, diarrhea, and incontinence), bladder disturbances, sleeping at night, weight loss or gain, feeling that everything is an effort, and unmet spiritual needs. Both the RMSR (0.03) and the CFI (0.98) reflect a good model fit (van Laar and Braeken Reference van Laar and Braeken2021). The model explained 75% of the observed variance (Table 4).

Table 4. Determination of variance explained by SPARC-Sp

* F2: personal issues, F1: psychological issues, F3: independence and activity, F7: depression, F8: functional limitations, F12: communication needs, F4: gastrointestinal symptoms, F9: family and social life, F10: treatment, F5: communication with health personnel, F6: respiratory symptoms and anxiety, F11: cognitive symptoms. SPARC-Sp = Spanish translation Sheffield Profile for Assessment and Referral for Care.

Evidence based on the relationship with other variables

SARC-F had a low correlation with the independence and activity domain (0.31; IC 95%: 0.22; 0.30) and with the SPARC-Sp total score (0.35; IC 95%: 0.27; 0.43). The ECOG scale showed low correlations with SPARC-Sp total score (0.20; IC 95%: – 0.09; 0.49), where the higher the level of functionality in cancer patients, the higher the score in the domain of independence and activity (0.31; IC 95%: 0.03; 0.61). Karnofsky had a low negative correlation with the independence and activity domain (−0.25; IC 95%: −0.34; −0.16) and the SPARC-Sp total score (−0.36; IC 95%: −0.44; −0.28). As the functionality determined by Karnofsky decreases (100 points: normal, 0: deceased), the needs for this domain and the holistic needs in PC increased.

Evidence based on the consequences of the test

Based on the comorbidity group (Figure 1), we evaluated whether patients with more than one disease had higher SPARC-Sp scores. The median SPARC-Sp score in patients with comorbidities (40 points) was significantly higher than those without comorbidities (36 points) (Difference 4 points, p = 0.002).

Assessment of the internal consistency of the instrument

The reliability for each domain of the original SPARC-Sp structure is described in Table 5. The domains with the lowest reliability were communication and information and religious issues; these domains also presented low factor loadings and were regrouped or excluded in the EFA (Table 5). The remaining domains showed adequate reliability (total omega: 0.7–0.9).

Table 5. Reliability per McDonald’s total omega for each SPARC-Sp domain

*A: communication and information, B: physical symptoms, C: psychological issues, D: religious and spiritual issues, E: independence and activity, F: family and social life, G: treatment-related issues, H: personal issues, H: personal issues. SPARC-Sp = Spanish translation Sheffield Profile for Assessment and Referral for Care.

The reliability per McDonald’s total omega of the new SPARC-Sp structure was higher than the original structure except for communication with health personnel (total omega: 0.46), cognitive symptoms (total omega: 0.56) and respiratory symptoms and anxiety (total omega: 0.57) (Table 6).

Table 6. Reliability per McDonald’s total omega for each domain of the new SPARC-Sp structure

*F1: personal issues, F2: independence and activity, F3: psychological issues, F4: depression, F5: functional limitations, F6: communication needs, F7: gastrointestinal symptoms, F8: family and social life, F9: treatment, P10: communication with health staff, F11: respiratory symptoms and anxiety, F12: cognitive symptoms. SPARC-Sp = Spanish translation Sheffield Profile for Assessment and Referral for Care.

Discussion

This is the first validation of SPARC in the Spanish language in the Colombian clinical context based on the answers of 507 patients with NCDs. According to the participants in the multidisciplinary group, all the domains included in SPARC-Sp are relevant to the concept of holistic care needs assessment (Aiken’s V coefficients >0.5) (Merino-Soto Reference Merino-Soto2018). The EFA yielded a large number of factors (12 factors). The model explained 75% of the variance, and the RMSR (0.03) and the CFI (0.98) indicate a good fit of the model to the observations. This high proportion of explained variance may be partially due to the large number of factors extracted (Peterson Reference Peterson2000), and the very good model fit may be partly due to the large sample size, the high number of variables, and the dependence relationships between the variables (van Laar and Braeken Reference van Laar and Braeken2021). Eleven items had low factor loadings (<0.3) due to the frequency in the distribution of responses, which did not allow for any discrimination (40). We identified mainly moderate to low communalities (0.4–0.7), indicating highly sensitive solutions (Hogarty et al. Reference Hogarty, Hines and Kromrey2005).

The EFA retained some of the original SPARC-Sp domains (personal issues, family and social issues, and treatment). Other domains had a different conformation, which could improve their representativeness and potential applicability. New domains related to sadness included items from the psychological and religious and spiritual domains. This domain was coined “depression” as it represents symptoms concerning this pathology (sadness, difficulty concentrating, loneliness, feeling that life is not worth living, thoughts about ending everything and worries about death, dying, or passing away) (Tolentino and Schmidt Reference Tolentino and Schmidt2018). The domain “functional limitations” refers to the reduced ability to perform daily activities and maintain independence as a result of alterations in anatomical, psychological, physiological, emotional, or mental structure or function (Ballesteros Reference Ballesteros2017).

A new domain emerged, which we named “communication needs,” and was related to the necessity to talk to social workers and other professionals, as well as the need of information about financial matters, other types of support and physical symptoms such as xerostomia and mouth pain. Although it may come as a surprise to observe such physical symptoms in the communication domain, the salivary hypofunction has a direct link to difficulties in talking: it has been associated with clinical changes in vocal effort, phonation, and communication (Roh et al. Reference Roh, Kim and Kim2006). Another domain linked to communication with health professionals was identified, which includes communication with physicians or nurses. The EFA results fragmented the physical symptoms into new domains such as gastrointestinal symptoms (nausea and vomiting), respiratory symptoms, and anxiety (shortness of breath, coughing, thoughts about death, dying, or passing away) and cognitive symptoms (memory loss and difficulty concentrating). The greatest opportunities for improvement were the lack of representativeness of the items in relation to the construct assessed in the domains of religion and spirituality. Similar to the Korean SPARC validation, multiple taboos, and obstacles about death, dying, and passing away may have limited responses to these items (Kwon et al. Reference Kwon, Baek and Kim2021).

The newly identified domains reveal constructs that may be interesting for research purposes, particularly if they can later be confirmed by other studies. However, it was decided not to change the structure of SPARC-Sp according to these domains for several reasons: (i) it may be very burdensome for patients to answer who domains related to depressive feelings and communication needs; (ii) some items are related to more than one of these domains; (iii) changing SPARC-Sp according to these domains will hamper international comparisons with the same instrument in other countries. Moreover, the question order in the original SPARC has a logic for the patients (communication and information, physical, psychological, religious, and spiritual symptoms; independence and activity; family and social life; treatment and personal issues) and keeping the items in the original SPARC order “spreads” the questions throughout the questionnaire, but the underlying domains can still be analyzed.

We expected weak correlations between the SPARC-Sp total score or the independence and functional domain with ECOG and Karnofsky. The very heterogeneous population of patients in our study may have diluted potential stronger correlations within subgroups. Moreover, the domain of independence and activity of SPARC-Sp measures similar but not equal constructs. Furthermore, ECOG and Karnofsky scales are assessed by clinicians. Previous research into measurement of care needs using other instruments suggested that patient-provided assessment results in lower (poorer) scores compared to those provided by healthcare staff (Kelly and Shahrokni Reference Kelly and Shahrokni2016).

For the purpose of this validation, the Korean (Kwon et al. Reference Kwon, Baek and Kim2021) and Taiwanese (Tsai et al. Reference Tsai, Chou and Loh2023) validation of SPARC used the FACT-G (the Functional Assessment of Cancer Therapy scale) instrument (Cella et al. Reference Cella, Tulsky and Gray1993). FACT-G includes physical, emotional, social/family, and functional domains for cancer patients, which correlated well with the domain of independence and functionality (Kwon et al. Reference Kwon, Baek and Kim2021; Tsai et al. Reference Tsai, Chou and Loh2023). As our sample included many NCD patients without a cancer diagnosis, FACT-G could not be used. We observed a weak correlation with SARC-F assessment and the total score of SPARC-Sp (rho: 0.35, 95% IC: 0.27; 0.43, p< 0.001) and independence and activity domain (rho: 0.31, 95% IC: 0.22; 0.30, p< 0.001) and for our study 51% of participants presented sarcopenia (SARC-F ≥ 4).

Persons with multiple comorbidities (41% of our population) had higher SPARC-Sp scores, indicating more holistic PC needs. Previous research suggests that comorbidities may occur within the natural history of disease, where the symptomatology of comorbidities may concomitantly lead to more holistic needs in PC (Luo et al. Reference Luo, Du and Chong2019).

Previous validations of SPARC assessed reliability using Cronbach’s alpha, determining good reliability for the domains of independence and activity, treatment, physical and psychological symptoms (Kwon et al. Reference Kwon, Baek and Kim2021). Our research using McDonald’s omega also showed acceptable reliability (0.7–0.9) for these domains. We identified low reliability for the domain of religious and spiritual themes, similar that in previous validations (Kwon et al. Reference Kwon, Baek and Kim2021; Leppert et al. Reference Leppert, Majkowicz and Ahmedzai2012) as well as for the communication and information domain – a domain not previously assessed in other SPARC-Sp validations. For the religious and spiritural themes, participants of the content-based evidence study suggested the inclusion of access to spiritual support, spiritual companionship, and peace of mind (Reference Moreno, Mendieta and de Vries2024).

This research had multiple limitations; it was conducted in a single hospital in Colombia, so the generalizability of the information does not represent the Colombian context, especially in areas with a higher level of urbanization and education. Although we had a large sample size that included diverse NCDs, the heterogeneity of the PC needs of each of these and the small number of patients with specific pathology groups such as cancer, could limit the correlations of the SPARC-Sp domain of independence and functionality with other functionality scales, as well as the low correlations between SPARC-Sp and tumor stage.

The construct of holistic needs in PC does not have a gold standard for its evaluation, therefore, the evaluation of evidence based on the relationship with other variables was an approximation to one of the domains, nor was it possible to implement other scales to assess evidence based on convergence and divergence. Neither the reliability related to the time of application, also called test–retest, nor the reliability related to the subject was evaluated due to the impossibility of performing a second application of the instrument because of the variability linked to the patient’s clinical condition – the SPARC-Sp score could change rapidly due to deterioration (or improvement) in the patient’s particular situation.

This validation of SPARC in the NCD population and in Spanish showed a high reliability of the instrument and to propose its internal structure, which we hope will be an input for future research with comparable populations. We hope that SPARC-Sp will help guide the holistic identification of needs in PC, but future research is needed to adapt and validate this modification of SPARC-Sp to other forms of administration considering the low educational level of the population and the integration of relevant contextual domains.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S1478951524000476.

Acknowledgments

We would like to thank Javier Orozco, Camilo Cortes, Karen Rivera, Laura Isabela Bolaños, and Ángela Muñoz for their support during data collection. We would also like to thank Socorro Moreno for sharing her extensive experience and knowledge with us and proofreading the manuscript prior to submission.

Authors contributions

Conceptualization and methodology CVM, EdV, CJRR, MAR, JAC, SHA; formal analysis CVM, EdV, MAR; writing – original draft preparation, CVM. EdV; writing – review and editing, CVM, EdV, CJRR, MAR, JAC, SHA; project administration, EdV, JAC. All authors have read and agreed to the published version of the manuscript.

Funding

This work was not externally funded and was carried out with the researchers’ own resources. University employees’ time were paid for by the university’s internal sources as their normal salary.

Competing interests

The authors declare no conflict of interest.

Ethical approval

This research was endorsed by the Ethics and Research Committee of the Hospital San Jose de Popayan – ESE (8.2.9-92/031).

References

Ahmed, N, Bestall, JC, Payne, SA, et al. (2009) The use of cognitive interviewing methodology in the design and testing of a screening tool for supportive and palliative care needs. Supportive Care in Cancer 17(6), 665673. doi:10.1007/s00520-008-0521-2CrossRefGoogle ScholarPubMed
American Educational Research Association, American Psychological Association, and National Council on Measurement in Education (2014) Estándares para Pruebas Educativas y Psicológicas. Washington DC: EE. UU.Google Scholar
Arango-Gutiérrez, A, Moreno, S, Rondón, M, et al. (2023) Factors associated with suffering from dying in patients with cancer: A cross-sectional analytical study among bereaved caregivers. BMC Palliative Care 22(1), . doi:10.1186/s12904-023-01148-xCrossRefGoogle Scholar
Ballesteros, SM (2017) Factores individuales y colectivos asociados con la prevalencia de limitaciones funcionales del adulto mayor en Colombia. Análisis multinivel (Tesis de maestría), Universidad del Rosario, Bogotá.Google Scholar
Bernaards, CA and Jennrich, RI (2005) Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement 65(5), 676696. doi:10.1177/0013164404272507CrossRefGoogle Scholar
Calvache, JA, Gil, F and de Vries, E (2020) How many people need palliative care for cancer and non-cancer diseases in a middle-income country? Analysis of mortality data. Colombian Journal of Anesthesiology 48(4), 17. doi:10.1097/CJ9.0000000000000159CrossRefGoogle Scholar
Cella, DF, Tulsky, DS, Gray, G, et al. (1993) The Functional Assessment of Cancer Therapy scale: Development and validation of the general measure. Journal of Clinical Oncology 11(3), 570579. doi:10.1200/JCO.1993.11.3.570CrossRefGoogle ScholarPubMed
Cruz-Jentoft, AJ, Bahat, G, Bauer, J, et al. (2019) Sarcopenia: Revised European consensus on definition and diagnosis. Age and Ageing 48(1), 1631. doi:10.1093/ageing/afy169CrossRefGoogle ScholarPubMed
Csárdi, G (2023) Cli: Helpers for developing command line interfaces.Google Scholar
Cuadrado, D (2018) Tratamientos no proporcionados al final de la vida en pacientes fallecidos en un hospital universitario de 4 nivel (Tesis de maestría), Pontificia Universidad Javeriana, Bogotá.Google Scholar
de Carvalho, M (2018) spearmanCI: Jackknife Euclidean/empirical likelihood inference for Spearman rho.CrossRefGoogle Scholar
Finch, H (2006) Comparison of the performance of varimax and promax rotations: Factor structure recovery for dichotomous items. Journal of Educational Measurement 43(1), 3952. doi:10.1111/j.1745-3984.2006.00003.xCrossRefGoogle Scholar
Fox, J (2022) Polycor: Polychoric and polyserial correlations.Google Scholar
Gempeler, FE, Torregrosa Almonacid, L, Cuadrado, DM, et al. (2021) Intervenciones médicas no proporcionales al final de la vida en un hospital de alta complejidad en Colombia. Universitas Médica 62(1), 18. doi:10.11144/Javeriana.umed62-1.imnpCrossRefGoogle Scholar
Hair, JF and Gómez Suárez, M (2010) Análisis Multivariante, 5th edn. Madrid: Prentice-Hall.Google Scholar
Hernández-Quiles, C, Bernabeu-Wittel, M, Pérez-Belmonte, LM, et al. (2017) Concordance of Barthel Index, ECOG-PS, and Palliative Performance Scale in the assessment of functional status in patients with advanced medical diseases. BMJ Supportive & Palliative Care 7(3), . doi:10.1136/bmjspcare-2015-001073CrossRefGoogle ScholarPubMed
Hogarty, KY, Hines, CV, Kromrey, JD, et al. (2005) The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination. Educational and Psychological Measurement 65(2), 202226. doi:10.1177/0013164404267287CrossRefGoogle Scholar
Hughes, P, Ahmed, N, Winslow, M, et al. (2015) Consumer views on a new holistic screening tool for supportive and palliative-care needs: Sheffield Profile for Assessment and Referral for Care (SPARC): A survey of self-help support groups in health care. Health Expectations 18(4), 562577. doi:10.1111/hex.12058CrossRefGoogle ScholarPubMed
Kaasa, S, Loge, JH, Aapro, M, et al. (2018) Integration of oncology and palliative care: A Lancet Oncology Commission. The Lancet Oncology 19(11), e588e653. doi:10.1016/S1470-2045(18)30415-7CrossRefGoogle ScholarPubMed
Kassambara, A (2022) Ggcorrplot: Visualization of a correlation matrix using “ggplot2.”Google Scholar
Kelly, CM and Shahrokni, A (2016) Moving beyond Karnofsky and ECOG performance status assessments with new technologies. Journal of Oncology 2016, . doi:10.1155/2016/6186543CrossRefGoogle ScholarPubMed
Kwon, JH, Baek, SK, Kim, DY, et al. (2021) Pilot study for the psychometric validation of the Sheffield profile for assessment and referral to care (SPARC) in Korean cancer patients. Cancer Research and Treatment 53(1), 2531. doi:10.4143/crt.2020.235CrossRefGoogle ScholarPubMed
Leppert, W, Majkowicz, M and Ahmedzai, SH (2012) The adaptation of the Sheffield Profile for Assessment and Referral for Care (SPARC) to the Polish clinical setting for needs assessment of advanced cancer patients. Journal of Pain and Symptom Management 44(6), 916922. doi:10.1016/j.jpainsymman.2011.12.286CrossRefGoogle Scholar
Luo, L, Du, W, Chong, S, et al. (2019) Patterns of comorbidities in hospitalised cancer survivors for palliative care and associated in-hospital mortality risk: A latent class analysis of a statewide all-inclusive inpatient data. Palliative Medicine 33(10), 12721281. doi:10.1177/0269216319860705CrossRefGoogle Scholar
Martin-Romera, A and Molina Ruiz, E (2017) Valor del conocimiento pedagógico para la docencia en Educación Secundaria: Diseño y validación de un cuestionario. Estudios Pedagógicos XLIII(2), 195220. doi:10.4067/S0718-07052017000200011CrossRefGoogle Scholar
Merino-Soto, CA (2018) Intervalos de confianza para la diferencia entre coeficientes de validez de contenido (V Aiken): Una sintaxis SPSS. Anales de Psicología/Annals of Psychol 34(3), 587590. doi:10.6018/analesps.34.3.283481CrossRefGoogle Scholar
Moreno, S, Mendieta, CV, de Vries, E, et al. (2024) Translation and linguistic validation of the Sheffield Profile for Assessment and Referral for Care (SPARC) to Colombian Spanish. Palliative & Supportive Care, 110. doi:10.1017/S1478951524000038CrossRefGoogle ScholarPubMed
Organización Mundial de la Salud (2022a) Palliative care – Key facts.Google Scholar
Organización Mundial de la Salud (2022b) Noncommunicable diseases.Google Scholar
Peterson, RA (2000) A meta-analysis of variance accounted for and factor loadings in exploratory factor analysis. Marketing Letters 11(3), 261275. doi:10.1023/A:1008191211004CrossRefGoogle Scholar
Revelle, W (2023) Psych: Procedures for psychological, psychometric, and personality research.Google Scholar
Rizopoulos, D (2006) Package for latent variable modeling and item response theory analyses. Journal of Statistical Software 17(5), 125. doi:10.18637/jss.v017.i05CrossRefGoogle Scholar
Roh, JL, Kim, HS and Kim, AY (2006) The effect of acute xerostomia on vocal function. Archives of Otolaryngology–Head & Neck Surgery 132(5), . doi:10.1001/archotol.132.5.542CrossRefGoogle ScholarPubMed
Ruiz-Morales, A and Gómez-Restrepo, C (2015) Epidemiología Clínica - Investigación Clínica Aplicada, II. Bogotá: Editorial Médica Internacional.Google Scholar
Schag, CC, Heinrich, RL and Ganz, PA (1984) Karnofsky performance status revisited: Reliability, validity, and guidelines. Journal of Clinical Oncology 2(3), 187193. doi:10.1200/JCO.1984.2.3.187CrossRefGoogle ScholarPubMed
Shi, D, Maydeu-Olivares, A and DiStefano, C (2018) The relationship between the standardized root mean square residual and model misspecification in factor analysis models. Multivariate Behavioral Research 53(5), 676694. doi:10.1080/00273171.2018.1476221CrossRefGoogle ScholarPubMed
SurveyMonkey (2022) SurveyMonkey Audience.Google Scholar
Taherdoost, H, Sahibuddin, S and Jalaliyoon, N (2020) Exploratory factor analysis; concepts and theory. Hal Open Science 27.Google Scholar
Tolentino, JC and Schmidt, SL (2018) DSM-5 criteria and depression severity: Implications for clinical practice. Frontiers in Psychiatry 9, 19. doi:10.3389/fpsyt.2018.00450CrossRefGoogle ScholarPubMed
Tsai, MC, Chou, YY, Loh, EW, et al. (2023) Validation of traditional Chinese version of Sheffield Profile and Assessment and Referral for Care Questionnaire in Taiwanese patients. Journal of the Chinese Medical Association 87(1), 5863. doi:10.1097/JCMA.0000000000000993CrossRefGoogle ScholarPubMed
University of California (2022, May 20 ) A practical introduction to factor analysis: Exploratory factor analysis.Google Scholar
van Laar, S and Braeken, J (2021) Understanding the Comparative Fit Index: It’s all about the base! Practical Assessment, Research, and Evaluation 26(26), 123. doi:10.7275/23663996Google Scholar
Ventura-León, JL (2017) Intervalos de confianza para coeficiente Omega: Propuesta para el cálculo. Adicciones 30(1), 7778. doi:10.20882/adicciones.962CrossRefGoogle Scholar
Wickham, H (2016) ggplot2: Elegant graphics for data analysis.CrossRefGoogle Scholar
Wickham, H, François, R, Henry, L, et al. (2023a) Dplyr: A grammar of data manipulation.Google Scholar
Wickham, H, Vaughan, D and Girlich, M (2023b) Tidyr: Tidy messy data.Google Scholar
Figure 0

Table 1. Aiken’s V coefficients for each item

Figure 1

Table 2. Clinical and demographic characteristics of the population

Figure 2

Figure 1. Frequency of diseases (CVD, oncology, musculoskeletal and others) and intersections of comorbidities.

CVD: cardiovascular disease. The blue bars represent the frequency of each of the disease groups (CVD, other, oncological, musculoskeletal). The black bars represent the frequency of the intersection of a comorbidity and the dots specify the diseases with which this intersection occurs. For example, the graph shows that 390 patients had CVD as a main disease or comorbidity (blue bar), 209 of them had CVD as the only or main disease and 76 participants had CVD and “other pathologies,” 50 had oncological disease and 40 had musculoskeletal disease and CVD.
Figure 3

Table 3. SPARC factor loadings after exploratory factor analysis

Figure 4

Table 4. Determination of variance explained by SPARC-Sp

Figure 5

Table 5. Reliability per McDonald’s total omega for each SPARC-Sp domain

Figure 6

Table 6. Reliability per McDonald’s total omega for each domain of the new SPARC-Sp structure

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