Quality of life and functional dependence in oncology patients: a comparative study

doi:10.21203/rs.3.rs-1516785/v1

Download PDF

Research Article

Quality of life and functional dependence in oncology patients: a comparative study

https://doi.org/10.21203/rs.3.rs-1516785/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Objectives: To compare quality of life and functioning of oncology patients under palliative care to patients in treatment and those with no evidence of disease.

Methods:A cross-sectional and comparative study was carried out in an outpatient oncology of the public hospital in the state of São Paulo, Brazil. Participants were divided into three groups: patients under palliative care, those in treatment for the disease and those in post treatment follow-up with no evidence of disease. Data collection involved the use of a questionnaire addressing sociodemographic and clinical characteristics as well as the Palliative Performance Scale and McGill Quality of Life Questionnaire.

Results: The sample was composed of 300 patients, with a predominance of the female sex (51.3%), individuals with a partner (68%) and catholics (62%). The Palliative Performance Scale revealed a predominance of stable patients (score: ≥ 70 points) in all three groups. Overall, participants under palliative care had lower quality of life scores (median: 6.5

points) compared to the other groups (p< 0.01). Age, palliative care, income, the male sex and pain control independently exerted influence on quality-of-life scores (p < 0.05). Age, time since diagnosis, being under palliative care, belonging to the Spiritism religion and income exerted an influence on palliative performance (p < 0.05).

Conclusion: Quality of life was poorer among the participants under palliative care. Functionally stable patients predominated in all three groups. An advanced age, being palliative care and low income exerted a negative influence on functioning and the perception of quality of life.

Palliative care

Quality of life

Medical Oncology

Adaptation

Psychological

Nursing

Humanization of Care

Cancer is a genetic disease characterized by uncontrollable cell growth, multiple mutations, and metastases (1). Its incidence and mortality rates are projected to increase globally. According to the World Health Organization, cancer continued to be the first or second cause of death (2) prior to the age of 70 years in 112 countries in 2019 (3). Estimates from the International Agency for Research on Cancer show that the world incidence of new cases of cancer was 18.1 million in 2018 (4). In Brazil, the Instituto Nacional do Câncer (INCA [National Cancer Institute]) estimates the occurrence of 625 thousand new cases in the period of 2020 to 2022, particularly cases of prostate and breast cancer (5). Despite the high rates of morbidity and mortality, a growing number of patients with a diagnosis of cancer survive due to early diagnoses and advances in treatment modalities (6).

Surgery and radiotherapy are effective local treatments for non-metastatic cancer but are ineffective when the disease has spread throughout the body. The use of medications in chemotherapy, hormone therapy and biological therapies is the current choice for the treatment of cancer that has spread, as such methods reach all organs of the body through the blood stream (7). Nowadays, few oncology patients are submitted to a single type of treatment due to the susceptibility of tumors to each of the therapeutic modalities and the determination of the optimum administration sequence (8). Although novel and traditional therapeutic modalities are employed in the treatment of cancer, adverse events and toxicity continue to exert a negative impact, particularly on quality of life (9).

Despite all available therapeutic options, incurability is a harsh reality faced by one group of patients due to the ineffectiveness of curative treatment and the inevitable progression of the disease (10). In such cases, care is offered in a palliative context, differing from curative care in that death is something that needs to be faced by the patient and family. The evaluation of quality of life (QoL) is fundamental in this phase, as it is the essential aspect to consider over the prolongation of life (11).

A diagnosis of cancer imposes significant changes to one’s mode of living as well as physical and emotional alterations. More than 50% of oncology patients have recurring symptoms, such as fatigue, weakness, pain, weight loss and anorexia, which can exert a negative impact on the performance of activities of daily living and affect one’s perception of QoL (12). The limitations imposed by both the disease and its treatment directly affect functioning, which is a broad concept that encompasses body functions and structures as well as the capacity to perform and participate in activities of daily living (13,14).

In oncology, functioning is directly related to the prognosis, as poorer estimated functioning is associated with a greater likelihood of toxicity from treatment and a lower survival rate (15). However, the association between QoL and functioning has been explored little in oncology patients in different stages of the disease. Most studies address QoL with regards to finitude (11) or during treatment for this disease (11,16).

The hypothesis is that oncology patients in palliative care have a poorer perception of QoL and functioning compared to those in treatment and/or those with no evident signs of the disease.

Therefore, the aim of the present study was to compare the quality of life and functioning of oncology patients under palliative care to patients in treatment and those in post-treatment follow-up with no evidence of disease.

A cross-sectional study was conducted at the Oncology, Hematology and Palliative Care Clinic of the public hospital affiliated to the Botucatu School of Medicine in the state of São Paulo, Brazil, between March 2018 and September 2019.

The inclusion criteria were: Male and female oncology patients in outpatient care, aged 18 years or older, who agreed to participate in the study The participants were divided into three groups: Group A – oncological patients under palliative care; Group B – oncological patients in post-treatment follow-up with no clinical evidence of the disease documented on the patient charts; and Group C – patients undergoing disease-modifying treatment. Individuals who reported not having the emotional capacity to participate in the study or continue the interview were excluded. The stratification of the sample into three groups of oncology patients was necessary for the evaluation of changes occurring throughout the different stages of the disease to which patients are subjected.

Data collection was performed with three instruments. A questionnaire was used to collect data on sociodemographic and clinical characteristics. The Portuguese version of the McGill Quality of Life Questionnaire (MQoLQ) was used for the assessment of QoL. The MQoLQ is one of the few specific questionnaires for assessing QoL in patients under palliative care and has the largest number of validations in other languages as well as the best measurement property scores. This questionnaire is composed of 16 items distributed among five subscales: physical wellbeing, psychological wellbeing, existential wellbeing, support and physical symptoms. There is an additional item (Part 1) that measures overall quality of life but is not considered in the calculation of the total MQoLQ score. The total score corresponds to the average of the five subscales, with scores closer to 0 denoting worse QoL and scores closer to 10 denoting better QoL (17). No generic QoL assessment tool was used, as none would be capable of assessing the impact of the disease on the group of patients under palliative care.

The third instrument was the Palliative Performance Scale (PPS), which is widely used in cases of palliative care, as it enables establishing the prognosis and functioning of the patient (18). The PPS has five domains: ambulation, activity level/evidence of disease, self-care capacity, spontaneous food intake and level of consciousness. The patient is classified in terms of functioning and prognosis as stable (100%-70%), transitional (40%-60%) and end of life (30%-10%). The score ranges from 0 and 100% in ten-point intervals (no intermediate values are considered). Scores closer to 100% denote better functioning and prognosis (19).

Prior to the data collection process, screening was performed of the charts of patients scheduled for appointments at the clinic, followed by the verification of inclusion and exclusion criteria and invitation to participate in the study. Consecutively treated patients at the clinic were included in the three groups: Group A – after first appointment; Group B – those that had concluded treatment six months to four years earlier; Group C – patients in disease-modifying treatment for more than one month.

The patients in the three groups were interviewed individually prior to their medical appointment without the presence of the person accompanying them and in a private setting. The average duration of the interviews was 40 minutes. One patient in Group A and two in Group B declined to participate due to pain complaints at the time of the interview or not having time available for the interview.

Given the intention of estimating the percentage of individuals with PPS and MQoLQ scores above the average, with a 10% margin of error, 95% confidence level and maximum amplitude, a minimum of 100 patients was needed for each group. This calculation was performed using the formula 1/M², in which M is the fixed margin of error.

Statistical analysis

All variables were first analyzed descriptively. In the comparison of proportions among the groups, the chi-square test was used for nominal variables, the Kruskal-Wallis test was used for ordinal variables and ANOVA and Kruskal-Wallis with Dunn’s post hoc test were used for continuous data. The comparison of functioning and QoL scores among the groups was performed using the Kruskal-Wallis test with Dunn’s post hoc test or the Kruskal-Wallis test. Generalized linear models were used to determine differences in functioning and QoL scores according to clinical and demographic variables. All analyses were performed using the IBM SPSS program, version 22, with the significance level set at 5% (p < 0.05).

This study received ethical approval from the institutional review board of the Botucatu School of Medicine (protocol number: 2.598.647).

Three hundred and three patients met the inclusion criteria, three of whom declined to participate in the study, resulting in a sample with 300 participants. Table 1 displays the sociodemographic characteristics of the participants. The sample was predominantly composed of female patients (n = 154; 51.3%), individuals with a partner (n = 204; 68%), Catholics (n = 186; 62%), individuals with a primary school education or illiterate individuals (n = 183; 61%) and individuals with family income equal to or less than three times of minimum monthly wage (n = 193; 64.7%). The participants in Group A were older (p < 0.01), had a lower level of schooling (p < 0.01). The most prevalent forms of cancer were gastrointestinal and breast in Groups A and B and hematological in Group C.

Table 2 displays the median and interquartile range (25^th to 75^th percentile) of the PPS scores as well as the QoL scores and respective domains in the three groups. Most patients were stable in the overall sample (n = 249; 83%), but a lower proportion was found in Group A (n = 53) (p < 0.001). The participants in Group A also had a lower overall QoL score (median: 6.5 points) in comparison to the other groups (p < 0.01) as well as lower scores on the psychological wellbeing (p < 0.01) and physical symptoms (p < 0.01) domains.

Table 3 displays the multivariate analyses performed using generalized linear models for quality of life and functioning. Age (p = 0.034), group (p = 0.015), male sex (p = 0.042) and income (p = 0.014; p = 0.005; p = 0.049) were significantly associated with the QoL, whereas age (p < 0 .001), time since diagnosis (p = 0.020), group (p < 0.001; p = 0.003), religion (p = 0.009), income (p = 0.001; p = 0.002; p = 0.006) and pain control (p = 0.001) were significantly associated with functioning.

Table 4 displays factors that affected the perception of QoL in the previous two days. Being with family and being happy exerted the most influence in the three groups. Hope for a cure stood out in Group B.

Measuring quality of life (QoL) is critical in oncology, as both cancer and its treatment can exert a negative impact of the perception of this construct. A diagnosis of cancer is accompanied by substantial changes in one’s way of living as well as physical and emotional changes. Cancer is also accompanied by stigmas and a feeling of finitude in the face of a disease that many still consider to be uncurable (20).

In the present investigation, overall QoL was considered relatively good among the participants, but those under palliative care had a poorer perception of the construct. In this group, substantial impacts were found on the physical wellbeing, psychological wellbeing and symptoms domains compared to the other groups.

The progression of the disease and symptoms contribute to a poorer QoL, as described in a randomized study involving 733 patients with advanced or metastatic lung cancer submitted to different medicinal treatments (21). More than half of all oncology patients have five common symptoms that can affect one’s perception of QoL, namely, fatigue, weakness, pain, weight loss and anorexia, demonstrating the close relationship between health and quality of life (12). Indeed, a study found that QoL was lower among patients with lung cancer compared to that of the general population, as this construct is affected by disease severity and the number of symptoms; moreover, fatigue and respiratory problems exerted a negative impact on the psychological dimension of QoL (22). Another study involving 158 patients with advanced cancer found that high levels of hopelessness, a compromised body image and emotional suffering were the main factors associated with psychological stress (23).

Total functioning scores were good in the present study, but the median was significantly lower in the group of patients under palliative care, demonstrating that this group had worse functioning and a poorer prognosis. This group also had a lower proportion of patients in the stable stage.

A study conducted in the northeast of Brazil with 100 patients under palliative care found that 62% of the patients had PPS scores between 80 and 90 (24), differing from the findings of the present investigation. A study conducted in Canada with 7882 oncology patients found that PPS scores dropped in the last six months of life, with an initial mean score of 68.4 diminishing to 54.7 and 41.3 in the month and week prior to death, respectively, indicating compromised functioning, autonomy and performance on activities of daily living in the final weeks of life (24). A multicentric study involving 1739 patients with advanced cancer undergoing palliative care at 30 healthcare centers in 12 countries found a significant reduction in quality of life and functioning prior to death in comparison to the preceding three to five months (25).

In the present study, multiple linear regression analysis of the QoL scores, PPS scores and explanatory variables revealed statistically significant positive associations between QoL and age, being under palliative care, male sex, income and pain control. These results are in agreement with data reported in a previous study conducted in Brazil involving 96 patients under palliative care, in which male participants had a better perception of QoL. In a study developed in Sweden based on the analysis of records from the last week of life of 26,976 adults who died of cancer in 2011–2012, an advanced age was a risk indicator for poor end-of-life care quality (26).

Another finding of the present investigation was the fact that patients without pain control had a better quality of life. This finding is likely due to the fact that these participants did not have the symptom.

In the present study, income was associated with QoL. A previous study conducted with 327 survivors of cervical and breast cancer also found that income was a determinant factor of QoL.

Diminished functioning was associated with age, the palliative care group, income and pain control. These factors also exerted an influence on QoL, demonstrating that the degree of functioning alters one’s perception of quality of life.

However, this investigation also showed that QoL is unique to each individual, as it is linked to personal values and often changes with the progression of the disease. Moreover, the perceptions of individuals regarding their QoL can evolve and change over time (27).

Limitations

The present study has limitations that should be considered. The interviews were held at a single moment in time, which may not be sufficient to detect the magnitude of changes experienced by the individuals in the different groups. Moreover, the study was conducted at a single center with local particularities and a heterogeneous group of participants in terms of the type of cancer and clinical condition.

The results of the present study indicate that the patients under palliative care had a poorer quality of life compared to the other groups and that the psychological wellbeing domain was the most compromised. In the multivariate analysis of sociodemographic and clinical variables, an advanced age, being in palliative care and a low income exerted a negative influence on the perception of quality of life.

In terms of functioning, most participants were in a stable stage, but a lower proportion of stable patients was found in the group under palliative care, demonstrated worse functioning and a poorer prognosis in these patients. Participants with an advanced age, those in palliative care, those pertaining to the Spiritism religion and those with a low income had worse functioning.

Ethics approval and consent to participate: The project was approved by the ethics committee of the São Paulo State University (UNESP), Sao Paulo (protocol number 2.598.647). All of the patients were informed of the benefits and risks related to the study and provided their written informed consent for the study and for the publication of results.

Consent for Publication: Not applicable.

Availability of data and material: The data that support the findings of this study are not publicly available because they contain information that compromise the privacy of research participants. However, an anonymous dataset can be made available from the corresponding author under reasonable request.

Competing interests: The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
Funding: This work was supported by Economic and Social Research Council (ESRC) (Grant number ES/T008822/11)

Acknowledgements: The authors thank all of the subjects who participated in the study.

Author Contributions: Study design and the data collection tool: S.M. and H.T.F.C.: Data analysis: H.M.H.: Drafting and revising of the manuscript: S.M.; Approval of the final version: C.O.

Martincorena I, Campbell PJ. Somatic mutation in cancer and normal cells. Science. 25 de setembro de 2015;349(6255):1483–9.
Pérez-Herrero E, Fernández-Medarde A. Advanced targeted therapies in cancer: Drug nanocarriers, the future of chemotherapy. Eur J Pharm Biopharm. June 2015;93:52–79.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–49.
Ferlay J, Colombet M, Soerjomataram I, et al. Global and Regional Estimates of the Incidence and Mortality for 38 Cancers: GLOBOCAN 2018. Lyon: International Agency for Research on Cancer/World Health Organization; 2018; 2018.
Instituto Nacional de Câncer José Alencar Gomes da Silva. Estimativa 2020: Incidência de Câncer no Brasil. Rio de Janeiro; 2019.
Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival: analysis of individual records for 37,513,025 patients diagnosed with one of 18 cancers during 2000–2014 from 322 population-based registries in 71 countries (CONCORD-3). Lancet. 17 de março de 2018;391(10125):1023–75.
Chabner BA, Roberts TG. Chemotherapy and the war on cancer. Nat Rev Cancer. janeiro de 2005;5(1):65–72.
Marco DJT, White VM. The impact of cancer type, treatment, and distress on health-related quality of life: cross-sectional findings from a study of Australian cancer patients. Support Care Cancer. 1^o de setembro de 2019;27(9):3421–9.
Mun EJ, Babiker HM, Weinberg U, Kirson ED, Von Hoff DD. Tumor-Treating Fields: A Fourth Modality in Cancer Treatment. Clin Cancer Res. 15 de janeiro de 2018;24(2):266–75.
Floriani CA, Schramm FR. Cuidados paliativos: interfaces, conflitos e necessidades. Ciênc saúde coletiva. dezembro de 2008;13(suppl 2):2123–32.
Meneguin S, Matos TD de S, Ferreira M de L da SM. Perception of cancer patients in palliative care about quality of life. Rev Bras Enferm. agosto de 2018;71(4):1998–2004.
Teunissen SCCM, Wesker W, Kruitwagen C, de Haes HCJM, Voest EE, de Graeff A. Symptom prevalence in patients with incurable cancer: a systematic review. J Pain Symptom Manage. julho de 2007;34(1):94–104.
Babcock M, Gould Kuntz J, Kowalsky D, Calitri N, Kenny AM. The Palliative Performance Scale Predicts Three- and Six-Month Survival in Older Adult Patients Admitted to the Hospital through the Emergency Department. Journal of Palliative Medicine. 1^o de outubro de 2016;19(10):1087–91.
Harris PS, Stalam T, Ache KA, Harrold JE, Craig T, Teno J, et al. Can hospices predict which patients will die within six months? J Palliat Med. agosto de 2014;17(8):894–8.
Sanvezzo VM de S, Montandon DS, Esteves LSF. Instrumentos de avaliação de funcionalidade de idosos em cuidados paliativos: uma revisão integrativa. Rev bras geriatr gerontol. outubro de 2018;21:604–15.
Freuding M, Keinki C, Kutschan S, Micke O, Buentzel J, Huebner J. Mistletoe in oncological treatment: a systematic review: Part 2: quality of life and toxicity of cancer treatment. J Cancer Res Clin Oncol. abril de 2019;145(4):927–39.
de Oliveira Faria S. Adaptação transcultural e validação da versão em português de questionário de qualidade de vida para pacientes com câncer em cuidados paliativos no contexto cultural brasileiro. 2013.
Baik D, Russell D, Jordan L, Dooley F, Bowles KH, Masterson Creber RM. Using the Palliative Performance Scale to Estimate Survival for Patients at the End of Life: A Systematic Review of the Literature. J Palliat Med. novembro de 2018;21(11):1651–61.
Clara MGS, Silva VR, Alves R, Coelho MC de R. The Palliative Care Screening Tool as an instrument for recommending palliative care for older adults. Rev bras geriatr gerontol (Online). 2019;e190143–e190143.
Holmenlund K, Sjøgren P, Nordly M. Specialized palliative care in advanced cancer: What is the efficacy? A systematic review. Palliat Support Care. dezembro de 2017;15(6):724–40.
Twelves C, Cortés J, O’Shaughnessy J, Awada A, Perez EA, Im S-A, et al. Health-related quality of life in patients with locally recurrent or metastatic breast cancer treated with etirinotecan pegol versus treatment of physician’s choice: Results from the randomised phase III BEACON trial. Eur J Cancer. maio de 2017;76:205–15.
Polanski J, Jankowska-Polanska B, Rosinczuk J, Chabowski M, Szymanska-Chabowska A. Quality of life of patients with lung cancer. Onco Targets Ther. 2016;9:1023–8.
Diaz-Frutos D, Baca-Garcia E, García-Foncillas J, López-Castroman J. Predictors of psychological distress in advanced cancer patients under palliative treatments. Eur J Cancer Care (Engl). julho de 2016;25(4):608–15.
Seow H, Barbera L, Sutradhar R, Howell D, Dudgeon D, Atzema C, et al. Trajectory of performance status and symptom scores for patients with cancer during the last six months of life. J Clin Oncol. 20 de março de 2011;29(9):1151–8.
Verkissen MN, Hjermstad MJ, Van Belle S, Kaasa S, Deliens L, Pardon K. Quality of life and symptom intensity over time in people with cancer receiving palliative care: Results from the international European Palliative Care Cancer Symptom study. PLoS One. 2019;14(10):e0222988.
Lindskog M, Tavelin B, Lundström S. Old age as risk indicator for poor end-of-life care quality - a population-based study of cancer deaths from the Swedish Register of Palliative Care. Eur J Cancer. julho de 2015;51(10):1331–9.
Haraldstad K, Wahl A, Andenæs R, Andersen JR, Andersen MH, Beisland E, et al. A systematic review of quality of life research in medicine and health sciences. Qual Life Res. 1^o de outubro de 2019;28(10):2641–50.

Table 1 – Sociodemographic and clinical characteristics of participants. Botucatu, SP, Brazil, 2020.

Variable	Groups
Variable	A B C p
Age (years)
Median	62.0	56.0	56.0
(p25-p75)	(52.2-69.0)	(41.2-67.0)	(45.0-64.0)	<0.011
Time since diagnosis
Median (months)	23.0	16.5	4
(p25-p75)	11-42.3	9-28.3	3-10	p<0.013
Sex
Female	50 (50.0)	54 (54.0)	50 (50.0)
Male	50 (50.0)	46 (46.0)	50 (50.0)	0.812
Marital status
Without partner	36 (36.0)	31 (31.0)	29 (29.0)
With partner	64 (64.0)	69 (69.0)	71 (71.0)	0.582
Religion
Catholic	66 (66.0)	59 (59.0)	61 (61.0)
Non-Catholic	33 (33.0)	38 (38.0)	35 (35.0)	0.582
None	1 (1.0)	3 (3.0)	4 (4.0)
Schooling
Illiterate	4 (4.0)	0 (0.0)	1 (1.0)
Primary school	74 (74)	50 (50.0)	54 (54.0)	<0.013
High school	18 (18)	34 (34.0)	28 (28.0)
University	4 (4)	16 (16.0)	17 (17.0)
Family income*
Less than monthly minimum wage	2 (2.0)	3 (3.0)	4 (4.0)
1 to 3 x monthly minimum wage	74 (74.0)	56 (56.0)	55 (55.0)	0.054
4 to 10 x monthly minimum wage	20 (20.0)	34 (34.0)	40 (40.0)
> 10 x monthly minimum wage	4 (4.0)	7 (7.0)	1 (1.0)
Primary diagnosis
Hematological	10 (10.0)	23 (23.0)	27 (27.0)
Gastrointestinal	19 (19.0)	26 (26.0)	21 (21.0)
Breast	16 (16.0)	24 (24.0)	33 (33.0)	<0.0012
Prostate	12 (12.0)	2 (2.0)	3 (3.0)
Genitourinary	13 (13.0)	7 (7.0)	14 (14.0)
Respiratory	15 (15.0)	5 (5.0)	1 (1.0)
Other	15 (15.0)	13 (13.0)	1 (1.0)

¹ANOVA post-hoc Tukey ²chi-squre ³Kruskal-Wallis + post-hoc Dunn ⁴Kruskal-Wallis

* Monthly minimum wage = R$1,100.00 (U$ 196.00)

Table 2 – Distribution of median (25^th to 75^th percentile) functioning scores (Palliative Performance Scale) and quality of life scores (McGill Quality of Life Questionnaire and domains) (n = 300). Botucatu, SP, Brazil, 2021.

Variable	Groups
Variable	A	B	C	p
	Median (p25-p75)	Median (p25-p75)	Median (p25-p75)
Functioning				<0.01¹
Total PPS score	70 (50-70)	80 (80-100)	90 (70-90)	<0.01¹
Quality of life
Physical wellbeing	7.0 (4.0-8.0)	8.0 (7.0-9.0)	8.0 (7.0-9.0)	<0.01²
Psychological wellbeing	3.8 (1.3-7.6)	7.0 (5.3-8.5)	7 .0 (3.9-8.8)	<0.01²
Existential wellbeing	8.3 (7.0-9.0)	8.8 (8.2-9.2)	8.8 (8.3-9.3)	<0.01²
Support	9.0 (7.0-10.0)	8.5 (7.0-10.0)	9.0 (8.0-10.0)	0.37²
Physical symptoms	5.0 (3.6-6.7)	8.5 (6.7-10.0)	7.7 (6.7-9.5)	<0.01²
Total	6.5 (4.9-7.8)	7.9 (7.2-8.6)	8.0 (7.3-8.7)	<0.01²

¹Kruskal-Wallis + post hoc Dunn ²Kruskal-Wallis

Table 3 - β coefficients of generalized linear models for quality of life (McGill Questionnaire) and functioning (Palliative Performance Scale) (n = 300). Botucatu, SP, Brazil, 2021.

	Quality of life		Functioning
	β coefficient	p	β coefficient	p
Age	-0.0148	0.034	-0.2490	<0 .001
Time since diagnosis (months)	0.6994	0.922	0.0516	0.020
Residents in home	-0.0515	0.424	-0.3110	0.541
Groups
A vs. B	-0.8226	0.015	-15.7741	< .001
C vs. B	0.1242	0.572	-5.2569	0.003
Sex
(ref.: male)	0.3643	0.042	-0.5357	0.704
Marital status
(ref.: without partner)	0.1984	0.300	0.7356	0.627
Religion
Spiritism vs. Catholic	-0.0369	0.935	-9.4354	0.009
Schooling
High school vs. Primary school	-0.4536	0.505	-2.1302	0.692
Family income (ref.: > 10 x monthly minimum wage)
< monthly minimum wage	-1.6488	0.014	-17.3986	0.001
1 to 3 x monthly minimum wage	-1.3129	0.005	-11.2318	0.002
4 to 10 x monthly minimum wage	-0.9025	0.049	-9.9077	0.006
Pain control
None vs. pharmacological	0.6618	0.039	8.2904	0.001

Table 4 - Answers on Part D of McGill Quality of Life Questionnaire: factors that contributed to improved perception of quality of life in previous two days. Botucatu, SP, Brazil, 2021.

Factors	A n (%)	B n (%)	C n (%)
Being with family and grandchildren	52 (52.0)	44 (44.0)	40 (40.0)
Being happy and being well	10 (10.0)	14 (14.0)	11 (11.0)
Receiving visitors/going out with friends	7 (7.0)	2 (2.0)	10 (10.0)
Having treatment proposal/medications	7 (7.0)	4 (4.0)	9 (9.0)
Not having pain	6 (6.0)	1 (1.0)	2 (2.0)
Practicing hobbies (physical activities, fishing, sewing, watching TV)	6 (6.0)	5 (5.0)	6 (6.0)
Religiousness	2 (21.0)	2 (2.0)	2 (2.0)
Having autonomy	3 (3.0)	9 (9.0)	8 (8.0)
Not depending on permanent or temporary devices and surgical interventions (stoma, catheters)	0 (0.0)	5 (5.0)	0 (0.0)
Having hope for a cure	3 (3.0)	17(17.0)	0 (0.0)
Being with pets	2 (2.0)	0 (0.00)	0 (0.0)
Being able to feed yourself	2 (2.0)	3 (3.0)	0 (0.0)
Other	10 (10.0)	15 (15.0)	12 (12.0)

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Quality of life and functional dependence in oncology patients: a comparative study

Status:

Version 1

Abstract

Introduction

Methods

Results

Discussion

Limitations

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1