quality of life vs quantity of life essay

Quantity Versus Quality of Life

When should we stop treating a terminal illness.

Posted February 11, 2011

Now that I've gotten the basics of my medical situation out there, I would like to discuss more interesting things: the general issues that my specific situation illustrates.

The big issue is how to balance the goal of maximizing quantity of life (the focus of traditional medical care) with the goal of maximizing quality of life (the focus of hospice/palliative care) in the treatment of terminal illness. This issue is only going to get bigger with the aging of America's population, advances in medical capabilities, and ongoing debates about health care reform and how to control health care costs. According to Atul Gawande: "The soaring cost of health care is the greatest threat to the country's long-term solvency, and the terminally ill account for a lot of it. Twenty-five per cent of all Medicare spending is for the five per cent of patients who are in their final year of life, and most of that money goes for care in their last couple of months which is of little apparent benefit." (See newyorker.com)

With regard to my particular situation, I feel that my doctors, family, and friends often weight the first goal (quantity of life), relative to the second one (quality of life), more heavily than I do. They would like me to exhaust every treatment option available. It's understandable - they don't want me to die; they want me around as long as possible, and I appreciate that. I don't mean to suggest that they don't care about my quality of life, but they don't know - they can't know - what my condition feels like to me. From the outside I look pretty much like my normal self, but on the inside I don't feel like my normal self at all. Most people see me when I'm feeling relatively well; only a couple of people (my partner Grace and my mom Kathy) see me when I'm feeling my worst.

I also don't mean to suggest that I don't care about my quantity of life. I'm not happy about maybe getting only about half of my expected lifespan. The prospect of not being able to be there for my sons as they grow up makes me really sad. I would like to see grandchildren, mentor junior colleagues as I have been mentored, take care of my parents in their old age, and grow old with Grace. But I also believe that there is a point where the quality of life that can be provided does not justify additional efforts to extend life. All terminally ill people (or, if they are incapable, their representatives) need to determine where that point is for them. If I am too sick and tired to interact with my sons, and there is no hope of recovering, then additional treatment seems pointless to me.

But given the uncertainty of my situation, there is always some hope, and that makes the decisions unclear and more difficult. The doctors can't say for sure "If you do X, then the outcome will be Y." A treatment that works well for me might be just over the horizon. I am reluctant to refuse treatment while there is still some hope, because I don't want my sons to ever think that I chose to leave them before I had to. I hope they will understand my decisions (that's part of the purpose of this blog). I feel a responsibility to my loved ones to fight as hard as I can. But I don't want to die angry. At some point we all need to accept the inevitable. If I make treatment decisions only to satisfy my loved ones, then I risk becoming resentful of the very people whose support I need the most.

Let me give you a specific example. One of the drugs I receive during my chemotherapy, Avastin (bevacizumab), is described by the doctors as having "basically no side effects." If pressed, they will allow that it causes bleeding, particularly in the nose. OK, nosebleeds. I've had nosebleeds before and they're annoying but not a big deal. But if you've had a slow but constant nosebleed for six months (as I have), then it becomes a big deal. If you wake up every night because your nose is clogged with dried blood and you are coughing up blood that has run down your throat and you have frequent headaches due to blood-clogged sinuses, then it becomes a quality of life issue. And yet I feel like a whiner for wanting to stop receiving Avastin. After all, this drug has "basically no side effects."

Ah, there is so much to talk about. But it will have to wait until next time.

Ethan Remmel, Ph.D. , was an associate professor at Western Washington University.

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Quality Versus Quantity of Life: Beyond the Dichotomy

Palliative Medicine Reports

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Dana Prescott

Advance directives (ADs) allow individuals to legally determine their preferences for end-of-life (EOL) medical treatment and designate a health-care proxy to act on their behalf prior to losing the cognitive ability to make informed decisions for themselves. An interprofessional group of researchers (law, nursing, medicine, and social work) conducted an exploratory study to identify the differences in quality-of-life (QOL) language found within the AD state statutes from 50 US states and the District of Columbia. Data were coded using constant comparative analysis. Identified concepts were grouped into 2 focus areas for EOL discussions: communication/awareness of surroundings and activities of daily living. Language regarding communication/awareness of surroundings was present in the half of the statutes. Activities of daily living were addressed in only 18% of the statutes. Only 3 states (Arkansas, Nevada, and Tennessee) specifically addressed QOL. Patients are best served when pr...

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The provision of care that enhances the quality of life at the end of life is a fundamental goal of palliative care services. This pilot study explored the application of the Quality of Life at the End of Life (QUAL-E) instrument in a sample of 52 patients who were hospitalized in two metropolitan Western Australian hospitals. Participants were given the option to complete the QUAL-E either as a self-report (n=9, 17.3 percent) or, aided by a research assistant, as a structured interview (n=43, 82.7 percent). The instrument demonstrated patient acceptability and face validity, particularly when it was administered with the assistance of a research assistant. Despite having difficulty with some questions, patients expressed their appreciation at being given the opportunity to contribute to the research and to reflect upon and give voice to their thoughts and feelings. Health practitioners indicated that the instrument could be a valuable tool for holistic assessment and service evalua...

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Despite the successful growth of the hospice movement during the past 30 years in the United States, almost 85% of Americans continue to die in hospitals or nursing homes. While the benefits of palliative care principles are well established, palliative care interventions remain underused in clinical practice in the settings in which most Americans die. Our premise is that physicians as a group perpetuate end-of-life suffering rather than ease the transition from life to death. We also believe that maintaining quality of life (QOL) at the end of life requires a multidimensional approach orchestrated by physicians drawing on the full range of available physical, psychological, social, and spiritual interventions. This article defines the meaning of QOL at the end of life and then examines the ramifications of failing to attend to QOL concerns in dying patients. It reviews strategies that physicians can use to advance palliative care approaches, thereby reducing terminally ill patients' suffering in the institutions in which most die.

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Our goal was to validate an instrument with which terminally ill patients could evaluate the quality of care they receive at the end of life (EOL). Questionnaire development followed a four-phase process: item generation and reduction, construction, pilot testing, and field-testing. Using relevance and priority criteria and pilot testing, we developed a 16-item questionnaire. Factor analyses of data from 235 patients resulted in the Quality Care Questionnaire-End of Life (QCQ-EOL) covering dignity-conserving care, care by health care professionals, individualised care, and family relationships. All subscales and total scores showed high internal consistency (Cronbach alpha range, 0.73-0.89). The ability of total score and selective subscale scores clearly differentiated patients on the basis of clinical situation, sense of dignity, and general rating of care quality. Correlations of scores between patients and caregivers were substantial. The QCQ-EOL can be adopted to assess the quality of care received by terminally ill patients.

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Sally Norton

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Marvella Ford

Objective:We examined the utility of a brief values inventory as a discussion aid to elicit patients' values and goals for end-of-life (EoL) care during audiotaped outpatient physician–patient encounters.Method:Participants were seriously ill male outpatients (n = 120) at a large urban Veterans Affairs medical center. We conducted a pilot randomized controlled trial, randomizing 60 patients to either the intervention (with the values inventory) or usual care. We used descriptive statistics and qualitative methods to analyze the data. We coded any EoL discussions and recorded the length of such discussions.Results:A total of 8 patients (13%) in the control group and 13 (23%) in the intervention group had EoL discussions with a physician (p = 0.77). All EoL discussions in the control group were initiated by the physician, compared with only five (38%) in the intervention group. Because most EoL discussions took place toward the end of the encounter, discussions were usually brief....

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Background and objectivesMeaning in life (MIL) represent a key topic in palliative care. The aims of this study were to explore (1) the differences in perceived MIL and in the meaning-relevant life areas between a representative sample of the Swiss population and palliative care patients, and (2) to what extent MIL can be considered as a significant predictor of quality of life (QOL).MethodsA cross-sectional study was conducted separately for the patients (face-to-face interviews) and the general population (telephone survey). MIL was measured with the Schedule for Meaning in Life Evaluation (SMILE) and QOL with a single-item visual analogue scale (0–10). Sociodemographic variables were controlled for in the analyses.Results206 patients and 1015 participants from the Swiss population completed the protocol. Results indicated high MIL scores in both populations even if the difference was significant (patients 81.9 vs general population 87, p<0.001). Patients were more likely to ci...

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Standard of Living vs. Quality of Life: What's the Difference?

Amy Fontinelle has more than 15 years of experience covering personal finance, corporate finance and investing.

Standard of Living vs. Quality of Life: An Overview

Standard of living refers to the level of wealth, comfort, material goods, and necessities available to a certain socioeconomic class or geographic area.  Quality of life , on the other hand, is a subjective term that can measure happiness.

The two terms are often confused because there may be some perceived overlap in how they are defined. But knowing the different nuances of each can affect how you evaluate a country where you might be looking to invest some money.

Key Takeaways

• Standard of living is a tangible, quantifiable term that refers to factors available to a certain socioeconomic class or geographic area.
• Quality of life is a subjective term that can measure happiness.
• Both can be flawed indicators because the factors can vary between people in the same geographic area or socioeconomic class.

Standard of Living

Standard of living is a comparison tool used when describing two different geographic areas. Metrics may include things like wealth levels, comfort, goods, and necessities that are available to people of different socioeconomic classes in those areas. The standard of living is measured by things that are easily quantified , such as income, employment opportunities, cost of goods and services, and poverty. Factors such as life expectancy , the inflation rate, or the number of paid vacation days people receive each year are also included.

Other factors commonly associated with the standard of living include:

• Class disparity
• Poverty rate
• Quality and affordability of housing
• Hours of work required to purchase necessities
• Gross domestic product (GDP)
• Affordable access to quality healthcare
• Quality and availability of education
• Incidence of disease
• Infrastructure
• National economic growth
• Economic and political stability
• Political and religious freedom
• Environmental quality

The standard of living in the United States may be compared to that of Canada. It may also draw comparisons to smaller geographic areas such as New York City versus Detroit. It can also be used to compare distinct points in time. For example, the standard of living in the U.S. is considered to have greatly improved compared to a century ago. Now, the same amount of work buys a larger quantity of goods and items that were once luxuries such as refrigerators and automobiles. Leisure time and life expectancy have also increased, while annual hours worked have decreased.

One measure of standard of living is the Human Development Index (HDI) , which has been used by the United Nations since 1990. It considers life expectancy at birth, expected years of schooling, mean years of schooling, and gross national income per capita to measure a country's level of development.

Quality of Life

Quality of life is a more subjective and intangible term than standard of living. As such, it can often be hard to quantify, but studies have been conducted for a number of years. This research has led to the creation of happiness economic indices such as the Gross National Happiness  (GNH) index.

The factors that affect the overall quality of life vary by people's lifestyles and their personal preferences. Regardless of these factors, this measure plays an important part in the financial decisions in everyone's lives. Some of the factors that can affect a person's quality of life can include conditions in the workplace, healthcare, education, and material living conditions.

The United Nations' Universal Declaration of Human Rights, adopted in 1948, provides an excellent list of factors that can be considered in evaluating quality of life. It includes many things that citizens of the United States and other developed countries take for granted, which are not available in a significant number of other countries around the world. Although this declaration is more than 70 years old, in many ways it still represents an ideal to be achieved, rather than a baseline state of affairs. Factors that may be used to measure the quality of life include the following:

• Freedom from slavery and torture
• Equal protection under the law
• Freedom from discrimination
• Freedom of movement
• Freedom of residence within one's home country
• Presumption of innocence unless proved guilty
• Right to marry
• Right to have a family
• Right to be treated equally without regard to gender, race, language, religion, political beliefs, nationality, socioeconomic status, and more
• Right to privacy
• Freedom of thought
• Freedom of religion
• Free choice of employment
• Right to fair pay
• Equal pay for equal work
• Right to vote
• Right to rest and leisure
• Right to education
• Right to human dignity

Standard of Living vs. Quality of Life: Flawed Indicators

Standard of living is somewhat of a flawed indicator. While the United States ranks high in many areas as a nation, the standard of living is very low for some segments of the population. For example, some of the country's poor, urban areas struggle with a lack of quality employment opportunities, short life expectancies, and higher rates of disease and illness.

Similarly, the quality of life can vary between people, making it a flawed indicator as well. There are various segments of the American population that may have a lower quality of life compared to others. They may experience discrimination in society and the workplace or don't have access to clean drinking water, proper healthcare, or education.

United Nations Development Programme-Human Development Reports. " Human Development Report 1990 ."

United Nations Development Programme. " Human Development Index ."

United Nations. " Universal Declaration of Human Rights ," Pages 1-72.

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Quality of Life, Two-Variable Theory

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• pp 5307–5309
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• Alex C. Michalos 3 , 4  

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Fool’s hell ; Fool’s paradise

While the quantity of our lives is notoriously limited to one per person, its quality is as varied as the perspectives or domains from which it is viewed. Viewed from one perspective, a person may be well-off, but from another not at all well-off. This fact of life is familiar to everyone. So, the whole research field of “quality of life” studies might be more accurately called “qualities of life” studies. In any case, the general sense of the phrase “quality of life” is here understood as a good life all things considered. However, one of the first questions ancient philosophers addressed as early as the fifth century BCE is “What is a good life?” As demonstrated in many essays in this encyclopedia, the question and its proposed answers continue to intrigue us. One general approach is discussed here. What is here called a “two-variable theory” is any theory that holds that the well-being or overall quality of life of a person or community...

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Diener, E., Lucas, R. E., Schimmack, U., & Helliwell, J. F. (2009). Well-being for public policy . Oxford: Oxford University Press.

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Michalos, A. C. (2003). Essays on the quality of life . Dordrecht: Kluwer Academic.

Michalos, A. C. (2004). Social indicators research and health-related quality of life research. Social Indicators Research, 65 (1), 27–72.

Michalos, A. C. (Ed.). (2005). Citation classics from social indicators research . Oxford, England: Springer.

Michalos, A. C. (2010). Stability and sensitivity in perceived quality of life measures: Some panel results. Social Indicators Research, 98 (3), 403–434.

Michalos, A. C., et al. (2011). The Canadian index of wellbeing: Technical report 1.0 . Canadian Index of Wellbeing and University of Waterloo.

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Michalos, A.C. (2014). Quality of Life, Two-Variable Theory. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_3930

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Quality of life and mortality in the general population: a systematic review and meta-analysis

• Aung Zaw Zaw Phyo 1 ,
• Rosanne Freak-Poli 1 , 2 ,
• Heather Craig 1 ,
• Danijela Gasevic 1 , 3 ,
• Nigel P. Stocks 4 ,
• David A. Gonzalez-Chica 4 , 5 &
• Joanne Ryan   ORCID: orcid.org/0000-0002-7039-6325 1 , 6  

BMC Public Health volume  20 , Article number:  1596 ( 2020 ) Cite this article

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Quality of life (QoL) is multi-dimensional concept of an individual’ general well-being status in relation to their value, environment, cultural and social context in which they live. This study aimed to quantitatively synthesise available evidence on the association between QoL and mortality in the general population.

An electronic search was conducted using three bibliographic databases, MEDLINE, EMBASE and PsycINFO. Inclusion criteria were studies that assessed QoL using standardized tools and examined mortality risk in a non-patient population. Qualitative data synthesis and meta-analyses using a random-effects model were performed.

Of 4184 articles identified, 47 were eligible for inclusion, involving approximately 1,200,000 participants. Studies were highly heterogeneous in terms of QoL measures, population characteristics and data analysis. In total, 43 studies (91.5%) reported that better QoL was associated with lower mortality risk. The results of four meta-analyses indicated that higher health-related QoL (HRQoL) is associated with lower mortality risk, which was consistent for overall HRQoL (HR 0.633, 95% CI: 0.514 to 0.780), physical function (HR 0.987, 95% CI: 0.982 to 0.992), physical component score (OR 0.950, 95% CI: 0.935 to 0.965), and mental component score (OR 0.980, 95% CI: 0.969 to 0.992).

These findings provide evidence that better QoL/HRQoL was associated with lower mortality risk. The utility of these measures in predicting mortality risk indicates that they should be considered further as potential screening tools in general clinical practice, beyond the traditional objective measures such as body mass index and the results of laboratory tests.

Peer Review reports

Quality of life (QoL) is a multi-dimensional concept of an individual’s general well-being status in relation to the value, environment, cultural and social context in which they live [ 1 ]. Since QoL measures outcomes beyond biological functioning and morbidity [ 2 ], it is recognised as an important measure of overall [ 1 ]. The origin of the term QoL dates back to the early 1970s, as a measure of wellness with linkage to health status like diseases or disability [ 3 , 4 ]. Since then, interest in QoL has increased considerably [ 5 ]. As life expectancy increases, more emphasis has been placed on the importance of better QoL, and the maintenance of good health for as long as possible [ 6 , 7 , 8 , 9 ]. Indeed, global leading health organizations have emphasized the importance of QoL and well-being as a goal across all life stages [ 10 , 11 , 12 ].

Moreover, QoL has increasingly been used in the wider context to monitor the efficacy of health services (e.g. patient reported outcome measures, PROMs), to assess intervention outcomes, and as an indicator of unmet needs [ 13 , 14 , 15 ]. Several studies have reported that QoL is negatively associated with rehospitalization and death in patients with diseases such as coronary disease [ 16 , 17 ], and pulmonary diseases [ 18 ]. Further, QoL is also predictive of overall survival in patients affected by cancer, chronic kidney disease or after coronary bypass graft surgery [ 19 , 20 , 21 , 22 ]. In recent years, an increasing number of studies have investigated whether QoL is also a predictor of mortality risk in the general population [ 23 , 24 , 25 , 26 , 27 ].

To date, there has been only one pooled analysis of eight heterogeneous-Finnish cohorts. That study of 3153 older adults, focused exclusively on the prognostic value of the validated 15-dimentional (15D) health-related QoL (HRQoL) measures [ 28 ] for predicting all-cause mortality [ 29 ]. However, there has been no systematic review investigating the association between QoL measured by different instruments and all-cause mortality in population-based samples which could be used to monitor health changes in the general population. A broad and comprehensive systematic review of the prognostic value of QoL for all-cause mortality prediction is needed to determine the utility of this QoL measure as a potential screening tool in general clinical practice. Therefore, this systematic review and meta-analysis was conducted with the aim of determining whether QoL is predictive of mortality in the general population which includes individuals with or without a range of health conditions.

Search methods

This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 30 ]. The protocol for this review was registered with the International Prospective Register of Ongoing Systematic Reviews (PROSPERO) [ 31 ], under the registration number: CRD42019139994 [ 32 ]. The electronic bibliographic databases, MEDLINE, EMBASE and PsycINFO (through OVID) were searched from database inception until June 21, 2019. The search strategy was developed in consultation with a Senior Medical Librarian. The MeSH terms and key-words were developed for MEDLINE (through OVID) and were translated to EMBASE and PsycINFO using the OVID platform (See Supplementary Tables S1-S3, Additional File  1 ). When the full text of an article was not available, all attempts were made to obtain it by contacting the authors directly. To identify further potentially relevant studies, another search was also developed with those specific QoL / HRQoL measures which were found in this review (See Supplementary Table S4, Additional File 1 ). Additionally, the bibliography lists of the included articles were also hand searched.

Inclusion and exclusion criteria

Articles were included if they: (a) involved adults aged 18 years and older; (b) were general population-based samples with or without a range of health conditions; (c) assessed mortality from any cause or cause-specific mortality using a longitudinal design; and (d) included a QoL / HRQoL measure using a standard tool. QoL, the general well-being of individuals, consists of a range of contexts – health, education, employment, wealth, politics and the environment [ 33 ]. HRQoL, the self-perceived health status, includes physical, mental, emotional, and social domains [ 33 ]. We excluded papers not written in English, reviews, or studies including only specific groups of patients (e.g. patients on dialysis, those with fractures, after surgery, or individuals with a terminal illness).

Study selection

The screening of articles for eligibility according to title and abstract was undertaken independently by two reviewers (AZZP and HC). All relevant full-text articles were independently reviewed by two reviewers (AZZP and HC) for eligibility against inclusion criteria. The inter-coder reliability among two reviewers (AZZP and HC) was 98%. Discrepancies and disagreements between two reviewers (AZZP and HC) were resolved through discussion with a third reviewer (JR). The screening process was undertaken using Covidence online software [ 34 ] and EndNote X9 software.

Data extraction

A standard data extraction form was used which included the following fields – title, authors, year of publication, setting/country, name of the study and design, sample size, follow-up period, participant characteristics (age and sex), specific QoL measure, cause of death (if available), and results (risk estimates including 95% confidence intervals, CI) which were standardized in term of 1-unit increase or 1-SD increase for continuous risk estimate, or high vs. low for categorical risk estimates. The first reviewer (AZZP) completed the data extraction form and a second reviewer (HC) verified the extracted information. All efforts were made to contact authors when there was missing information.

Quality appraisal

The quality of included studies was appraised using ‘the Newcastle – Ottawa Quality Assessment Scale (NOS)’ [ 35 ]. The NOS includes eight items, categorized into three dimensions (a) Selection, (b) Comparability, and (c) Outcome. The NOS scale uses a star system to evaluate the quality of each study, and they can be accredited a maximum of one star for each item within the Selection and Outcome dimension and two stars for the Comparability item. When considering the comparability of each study, a star was provided for studies which controlled for relevant covariates – age, sex (where appropriate), socioeconomic status or proxy (including socioeconomic position, education level or income), and some measure of co-morbidity (for example a specific health condition). An additional star was given for studies which considered other factors associated with QoL and mortality, including clinical measures, BMI, or lifestyle factors (i.e. smoking, alcohol, physical activity). The range of NOS scoring was from 0 to 9 stars, with higher scores indicating less susceptibility to bias. The methodological quality of included studies was rated by one reviewer (AZZP) and verified by a second reviewer (HC). Disagreements were resolved through discussion with a third reviewer (JR).

Data synthesis

The clinical and methodical heterogeneity of the studies was examined, in particular considering the measure of QoL used, and the effect estimates reported (Hazard Ratio (HR), Relative Risk (RR) or Odds Ratio (OR)). Where studies were considered too methodically heterogeneous to enable pooling, the results were summarized quantitatively in tables according to related categories with risk estimates; and 95% CIs.

• Meta-analysis

A meta-analysis was performed when there was a sufficient number of studies (four or more) which used the same domain of QoL measure and equivalent effect estimate parameters. In the present study, four meta-analyses were conducted for a pooled risk estimate of studies using (a) physical component score (PCS) of 36-item Short Form (SF-36) and OR / RR; (b) physical function domain of SF-36 and HR; (c) mental component score (MCS) of SF-36 and OR / RR; and (d) the 15-dimensional measure (15D) and HR. A DerSimonian-Laird random-effects model was chosen given heterogeneity in the studies in terms of population characteristics and varying health status. When more than one risk estimate was reported in the study, the fully adjusted/final regression model was included. In addition, when the included studies from the same cohorts with the same follow-up were eligible for meta-analysis, only one study with larger sample size was chosen for meta-analysis. Effect estimates were standardized where possible, so all values corresponded to a 1-unit increase in SF-36 or a 1-SD increase in 15D (single index number). A pooled risk estimates of less than one indicates a decreased risk of mortality with higher QoL. Statistical heterogeneity was evaluated by using the I 2 statistic, and the results were interpreted based on the Cochrane guidelines (0–40% = no heterogeneity; 30–60% = moderate heterogeneity; 50–90% = substantial heterogeneity; and 75–100% = considerable heterogeneity) [ 36 ]. In addition, when the I 2 statistic showed considerable heterogeneity (≥ 75%), the influence of individual studies on the pooled risk estimate was assessed using the metaninf command of STATA. Funnel plots and Egger’s test were used to assess publication bias. Data analysis was undertaken using STATA statistical software, version 15.0 (StataCorpLP, College Station, TX, USA).

Search result

A total of 4175 articles were identified from the systematic database search, and six additional articles were found via searching the reference list of included articles (Fig.  1 ). After removing duplicates, 3140 records remained for review. After title and abstract screening, 3058 articles were excluded and the full-text of the remaining 82 articles were evaluated for eligibility. A total of forty-four (44) articles met all inclusion criteria. Excluded articles with reasons for exclusion are presented in Supplementary Table S5, Additional File 1 . Moreover, three articles from additional search were also added in this review. Therefore, a total of forty-seven (47) articles were included in this systematic review.

Flow Diagram of Review Process

Description of included studies

Table  1 presents the characteristics of the 47 included studies. The earliest study was published in 1993 while the remaining included articles were published between 2002 and 2019, with 28% published in the past 5 years. All studies except the retrospective cohort study of Ul-Haq et al., [ 75 ] were prospective cohort studies. The included studies were conducted in USA (34%), UK (9%), Australia (6%), Canada (6%), Spain (6%), Taiwan (6%), Belgium (4%), Finland (4%), Scotland (4%), Sweden (4%), Bangladesh (2%), China (2%), Germany (2%), South Korea (2%), Italy (2%), Norway (2%), and South Africa (2%). The sample sizes of the included studies ranged from 171 [ 41 ] to 559,985 [ 40 ]; 14 studies had a sample size of less than 1000, 17 studies between 1000 and 10,000, 13 studies between 10,000 and 100,000, and the remaining three studies [ 38 , 40 , 53 ] has a sample size of more than 100,000 participants. Five studies included only males [ 41 , 42 , 54 , 71 , 73 ] and three studies only females [ 56 , 59 , 74 ]. The remaining 39 studies recruited between 3 to 78% of women. The follow-up periods of the studies varied between 9 months [ 72 ] and 18 years [ 73 ].

This review included a variety of different QoL measures and half of the included studies (24 studies) measured QoL using the Short Form 36 (SF-36) (Tables  1 and 2 ). Of the 47 articles included in this review (Table 1 ), some studies involved the same cohorts and, in several cases, likely the same participants. Subsequent publications often reported effect estimates over different lengths of follow-up or using different QoL tools. Two published articles of De Buyser et al. reported the results of the same population-based cohort study [ 41 , 42 ], three published articles by De Salvo et al. and Fan et al. were from the same study and included participants enrolled in the Veterans Affairs Ambulatory Care Quality Improvement Project [ 24 , 43 , 47 ], two published studies of Mold et al. and Lawler et al. used the same community-dwelling cohort [ 57 , 61 ], two published studies of Higueras-Fresnillo et al. and Otero-Rodriguez et al. were from the same Spanish cohort [ 52 , 67 ], two published studies of Feeny et al. and Kaplan et al. were from the same Canadian cohort [ 48 , 55 ]; and Myint et al. published three articles [ 26 , 64 , 65 ] with different perspectives on the same population-based study. Additionally, Liira et al.’s study [ 29 ], included eight individual cohorts, however, only five of the cohorts met the inclusion criteria for this current systematic review, and thus are shown in Table 1 .

Risk of Bias assessment

The methodological quality of included studies based on NOS ranged between five and nine stars. Among the included studies, seven were of high methodological quality, with nine stars. Across the ten studies with less than seven stars, they were scored most poorly on the items assessing how representative the cohort was in relation to the overall population being sampled and whether they adjusted for potential confounding factors in their analysis (See Supplementary Table S6-S7, Additional File 1 ).

Qualitative synthesis

Of the total 47 included studies, 43 (91.5%) studies reported for at least one of the domains examined, that better QOL was associated with lower mortality risk (Table 1 ). Of 33 studies which assessed physical HRQoL (nine exclusively assessed physical HRQoL), 30 studies (91%) reported better HRQoL was associated with lower mortality risk. Among the 23 studies which examined mental HRQoL (one exclusively assessed MCS), 13 studies (57%) reported that higher mental HRQoL was associated with decreased mortality risk (Table 1 ). The five studies [ 49 , 52 , 57 , 59 , 76 ] that measured HRQoL using SF-36 or SF-20 reported not only the physical functioning and mental health domains, but also general health perception, bodily pain, vitality, and social functioning. The findings were generally consistent in general health perception and social functioning; and it was reported that better level of general health perception and social functioning was associated with decreased mortality risk (Table 1 ).

The mortality risk estimates of the studies which were not included in the meta-analyses are shown in Tables  3 , 4 and 5 . The 18 out of 20 studies which measured the PCS using the SF-36 or SF-12 or the physical functioning subscale using SF-36, RAND-36, or SF-20 reported these to be a predictor of mortality risk, with better physical health being associated with lower mortality risk (Table  3 ). Nine out of 16 studies which assessed the MCS or mental health subscale using SF-36 or SF-12, showed that better mental health was associated with lower mortality risk (Table  4 ). The 12 out of the 15 studies that measured the association between QoL and mortality risk, found that higher QoL scores were associated with lower mortality risk (Table  5 ).

Meta-analyses

Four studies including 53,642 participants [ 23 , 24 , 60 , 70 ] measured QoL using the SF-36 and examined the association between the PCS and all-cause mortality and provided estimates from logistic regression analysis (OR or RR). With an average 1.8-year follow-up, one unit increase in the SF-36 PCS was associated with a 5% decrease in all-cause mortality (pooled OR/RR = 0.950; 95% CI: 0.935 to 0.965; P -value < 0.001). There was substantial heterogeneity between studies (I 2  = 82.1%; P -value = 0.001) (Fig.  2 -a).

Forest plot of all-cause mortality risk per one unit increase in a SF-36 PCS, b SF-36 Physical-Functioning, c SF-36 MCS. CI = confidence interval; FU (yrs) = follow-up in years; N = sample size; OR = odds ratio; RR = relative risk; HR = hazard ratio

Six studies including 22,570 participants [ 42 , 46 , 57 , 59 , 68 , 76 ] measured QoL using the SF-36 and investigated the association between the physical functioning and all-cause mortality using time-to-event survival analysis. With an average 8.7-year follow-up, one unit increase in the SF-36 PF was associated with a 1.3% decrease in time to death (pooled HR = 0.987; 95%CI: 0.982 to 0.992; P -value < 0.001). There was substantial heterogeneity between studies (I 2  = 83.8%; P -value < 0.001) (Fig. 2 -b).

Four studies including 53,642 participants [ 23 , 24 , 60 , 70 ] measured QoL using the SF-36 and examined the association between the MCS and all-cause mortality reported estimates on logistic regression analysis (OR or RR). With an average 1.8-year follow-up, one unit increase in the SF-36 MCS was associated with a 2% decrease in all-cause mortality (pooled OR/RR = 0.980; 95% CI: 0.969 to 0.992; P -value = 0.001). There was substantial heterogeneity between studies (I 2  = 75.9%; P -value = 0.01) (Fig. 2 -c).

Given the heterogeneity identified in the three meta-analyses described above, the influence of individual studies on the pooled risk estimate was assessed. The removal of no single study affected the association (Supplementary Table S8 – S10, Additional File 1 ).

Five Finnish individual cohorts of the Liira et al. study including 2377 [ 29 ] measured QoL using the 15D index and explored its association with all-cause mortality using time-to-event survival analysis. With an average 2-year follow-up, one SD (0.14) increase in the 15D index was associated with a 36.7% decrease in all-cause mortality (pooled HR = 0.633; 95%CI: 0.514 to 0.780; P -value < 0.001). There was moderate heterogeneity between studies (I 2  = 49.4%; P -value = 0.10) (Fig.  3 ).

Forest plot of all-cause mortality risk per one-SD (0.14) increase in 15D index. CI = confidence interval; FU (yrs) = follow-up in years; HR = hazard ratio; N = sample size

Visual inspection of the funnel plots which were used to assess for publication bias were presented in the Supplementary Figures S1-S4, Additional File 1 . For three of the four meta-analyses, there was no strong evidence of publication bias, however for the meta-analysis of MCS, this test was statistically significant ( P  = 0.04).

This systematic review is the first to investigate the association between QoL and mortality in community-dwelling individuals with or without health conditions rather than patients in a hospital or people living in assisted living. It summarizes the findings from 47 studies including approximately 1,200,000 individuals aged predominantly 65 years and older (age range 18–101 years), with 46 studies (98%) conducted in high-income or upper-middle-income countries. Overall thirteen different instruments were used to assess the association between QoL or more specifically HRQoL and mortality risk after 9 months to 18 years of follow-up, with the SF-36 or its derivatives (RAND-36, SF-20, SF-6D) most commonly used. Overall, 43 (91.5%) studies of the 47 included studies reported for at least one of the domains examined, that better QoL was associated lower mortality risk, which was also supported by the results of four meta-analyses (11 studies, n  = 78,589) of PCS, physical function and MCS domains of the SF-36, and 15D HRQoL.

Our findings are in line with a previous study that used pooled analysis [ 29 ] of eight heterogenous Finnish cohorts using the 15D HRQoL measure and included a wide range of both community-dwelling participants with or without morbidity, such as cardiovascular disease, dementia, and hospitalized patients with delirium. They also found that the 15D HRQoL measure was associated with two-year survival, with a slightly higher hazard ratio than that found in our study (HR per 1-SD = 0.44, 95% CI 0.40 to 0.48) [ 29 ]. These differences may relate to their inclusion of patient groups in generally poorer health, while our systematic review focused on the community dwelling population. Moreover, our findings in the general non-patient population are also comparable with studies investigating people with specific diseases such as cancer and chronic kidney disease, which reported QoL to be a predictor of mortality risk [ 19 , 20 , 21 ].

The findings of the present study are also consistent with those of recent population-based systematic review which investigated on the association between QoL and multimorbidity [ 78 ]. In their recent study, Makovski et al. (2019) systematically reviewed the evidence on the relationship between QoL and multimorbidity. They observed a stronger relationship between the PCS of QoL and multimorbidity (overall decline in QoL per additional disease = − 4.37, 95%CI − 7.13% to − 1.61% for WHOQoL-BREF physical domain and − 1.57, 95%CI − 2.70% to − 0.44% for WHOQoL-BREF mental domain) [ 78 ]. These findings also align with the results of the present study, where the meta-analysis indicated a stronger effect size for PCS compared to MCS using the SF-36 tool (pooled OR/RR = 0.950; 95% CI: 0.935 to 0.965 for PCS; and pooled OR/RR = 0.980; 95%CI: 0.969 to 0.992 for MCS). Since physical health is generally recognised as a strong risk factor for comorbidity, hospitalisations and mortality [ 79 , 80 , 81 , 82 ], our findings add further support to the predictive capacity of physical HRQoL for mortality risk. Like other objective health measures such as body mass index, glycaemia, and blood pressure, these findings highlight the utility of assessing physical HRQoL in general clinical practice to help identify individuals at greatest risk of death [ 83 ].

Given the evidence regarding the longitudinal relationship between QoL and mortality risk, the utility of a QoL tool in general care may improve patient’ health which in turn would decrease mortality. Furthermore, mental health issues such as depression or anxiety could also be identified through QoL measures and this would enable initiation of early interventions for mental health which in turn could improve long term QoL of individuals. Hence, the finding of this review can help to increase the efficacy of disease prevention strategies in older people through identifying individuals at higher risk for adverse health outcomes in general practice / primary health settings. Thus, the mortality risk prediction by QoL might not be very relevant to younger healthy populations although QoL generic measures were designed to be used across a wide range of populations [ 84 ]. There is a need for further studies however, in particular to better understand the influence of gender on these associations, and whether differences could be observed for males and females. Understanding these specific relationships could help identify which particular groups are most at risk and enable specific targeting of interventions to these individuals.

Strengths of the review

Strengths of this systematic review are that it was performed in a rigorous manner, adhering to strict systematic review guidelines. The protocol was registered with the International prospective register of systematic reviews (PROSPERO), and the review was undertaken in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. A reproducible and rigorous search strategy using three electronic databases was used, which helped ensure that all relevant articles were included. The literature screening was independently performed by two reviewers, who were also involved in the process of data extraction and methodological quality assessment of the included studies in accordance with NOS. Based on the NOS, all studies received greater than or equal to five out of nine stars, which indicates that there was generally a low risk of bias. Similarly, most studies provided risk estimates that controlled for important factors including current health and socio-economic status. Since our review criteria were not limited to articles with the commonly used QoL (or HRQoL) tools such as the SF-36, this has increased the generalisability of the findings. Therefore, this review has a broad and comprehensive perspective, with results that are rigorous and can be reproduced.

Limitations of the review

Among included articles, large heterogeneity was observed in terms of country-of-origin, participant characteristics, and evaluation of QoL. The majority of the included articles were conducted in English speaking counties, and restriction to English language articles as part of our inclusion criteria, may impact the generalisability of these findings. Since the different QoL standard tools examine different aspects [ 33 , 85 ] and are not directly comparable, this made comparison of included studies in data synthesis difficult. There were also some differences in the way the data analysis was performed and the results were presented, reporting OR versus HR for example. In addition, some articles reported the risk estimates by comparing categorical QoL groups while others provided the risk estimates per 1 or more units change in the continuous scale. Hence, the different nature of each QoL scale and inconsistency in risk comparison precluded us from including some articles in the meta-analyses. As such, only 11 studies were included across the four meta-analyses of this systematic review, and the meta-analyses still showed substantial heterogeneity. Therefore, caution should be taken with the interpretation of the overall effect estimates. Moreover, since the numbers of studies included in each meta-analysis were fewer than 10 studies, the results of funnel plots or Egger’s test should also be interpreted with caution. Of particular interest here, it has commonly been reported that gender differences exist in QoL and women of all age groups have lower QoL than their male counterparts [ 86 , 87 , 88 , 89 , 90 ]. However, in this review, it was not possible to perform statistical pooling by gender and age groups due to the different reporting strategies of the reviewed studies. Finally, it is important to consider that although studies of mortality are not directly affected by reverse causation, individuals with severely declining health prior to death, would likely report a decreased HRQoL. An ideal study design would involve excluding individuals who died in the first year of the study, or at least, to run sensitivity analysis to ensure these early deaths were not driving the results. Most of the studies included in this review, did not undertake such analyses. Furthermore, around 10% of the included studies have very short follow-up periods of less than 2 years.

This is the first systematic review and meta-analysis that has determined whether QoL is associated with mortality in the general non-patient population. In summary, the findings provide evidence that better QoL or HRQoL measured by different tools were associated with lower mortality risk in the general population. Therefore, our findings could be applied more generally to QoL or HRQoL assessed using different instruments. Our unique and first review indicates that QoL measures can be considered as potential screening tools beyond the existing traditional clinical assessment of mortality risk. Additionally, our result also encourages clinicians to incorporate QoL measure into routine data collection of health system which in turn could enable initiation of early primary health care for people at high risk of premature death. Furthermore, this study also adds further support to the predictive capacity of physical HRQoL for mortality risk. Additional research is needed to determine whether these associations differ across gender, and other populations in low- and lower-middle-income countries, who have suffered of a double burden of infectious and chronic diseases, with having difficulties for accessing quality health services. Ultimately these findings suggest the utility of QoL measures to help identify populations at greatest risk of mortality and who might benefit most from routine screening in general practice and possible interventions.

Availability of data and materials

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Abbreviations

15-dimentional

Confidence intervals

Euroqol-5 dimension

Hazard ratio

• Health-related quality of life

Health utilities index 3

Mental component score

NEWCASTLE-Ottawa quality assessment scale

Physical component score

Preferred reporting items for systematic reviews and meta-analyses

Patient reported outcome measures

International prospective register of systematic reviews

• Quality of life

Relative risk

Standard deviation

12-items short form

20-item short form

36-item short form

Six-dimension utility index

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Acknowledgements

We would like to thank Lorena Romero, the Senior Medical Librarian, Alfred Health, and Cassandra Freeman, the Subject Librarian, Faculty of Medicine, Nursing and Health Sciences, Monash University Library for technical support involved in developing the search strategy.

This work was supported by Monash International Tuition Scholarship and Monash Graduate Scholarship. AZZP is supported by Monash International Tuition Scholarship (Medicine, Nursing, and Health Sciences) and Monash Graduate Scholarship (30072360). JR is supported by a National Health and Medical Research Council Dementia Research Leader Fellowship (APP1135727). None of the funders were involved in the design of the study, in the collection, analysis, and interpretation of data and in the writing of the manuscript.

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Aung Zaw Zaw Phyo, Rosanne Freak-Poli, Heather Craig, Danijela Gasevic & Joanne Ryan

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RFP conceived the study. JR and AZZP designed the study. AZZP undertook the literature searches, screened the articles, extracted the data, performed quality assessment and data analysis. HC was the independent assessor, also completing all data screening, extraction and quality assessment. AZZP and JR interpreted the data, with input from DAGC, DG, and NPS. AZZP wrote the initial manuscript draft. All authors provided critical comments and approved the final version.

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Additional file 1: figure s1..

Funnel plot of all-cause mortality risk per one unit increase in SF-36 PCS. Figure S2. Funnel plot of all-cause mortality risk per one unit increase in SF-36 Physical-Functioning. Figure S3 . Funnel plot of all-cause mortality risk per one unit increase in SF-36 MCS. Figure S4. Funnel plot of all-cause mortality risk per one-SD (0.14) increase in 15D index. Table S1. Search Strategy using Ovid MEDLINE 1946 to June 212,019. Table S2. Search Strategy using Embase Classic 1947 to June 212,019. Table S3. Search Strategy using PsycINFO 1806 to June Week 32,019. Table S4. Additional Search Strategy up to June Week 32,019. Table S5. The list of excluded articles and reasons for exclusion ( n  = 38). Table S6. Appraisal Standard of Newcastle/Ottawa Scale. Table S7. Quality appraisal of included studies based on the Newcastle–Ottawa Quality Assessment Scale. Table S8. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 PCS. Table S9. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 Physical-Functioning. Table S10. One study removed analysis for all-cause mortality risk per one unit increase in SF-36 MCS.

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Phyo, A.Z.Z., Freak-Poli, R., Craig, H. et al. Quality of life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health 20 , 1596 (2020). https://doi.org/10.1186/s12889-020-09639-9

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Quality versus Quantity of Life: Listening to Cancer Patient Perspectives

G1 therapeutics september 7, 2021.

The past eighteen months have turned our worlds upside down, dramatically altering how we live, work, and socially connect. Those of us who have remained healthy throughout the pandemic consoled ourselves with the knowledge that our lives would return to some semblance of normalcy once infection rates waned. Cancer patients have no such assurances. They face the daunting possibility that their treatments may not yield success; that their hope or faith alone must sustain them as they traverse the unknowns that loom ahead.

It is not just length of life that is critical to cancer patients. Their quality of life is equally at stake. While targeted therapies have significantly reduced side effects for many patients, aggressive cancers require more toxic treatments that patients sometimes decide to forego.

In fact, a literature review of 30 articles comparing quality versus length of life revealed that many cancer patients choose quality over length. In one such study , 55 percent of patients with advanced cancers placed equal value on quality and length of life when weighing both options.  When required to commit to a preference, 80 percent chose quality of life.

It is incumbent upon the pharmaceutical industry to help alleviate this painstaking choice. We can do so by developing new therapies that aim to support and protect patients throughout cancer treatment, not just aim drugs at the cancer target and accept the collateral damage as unavoidable.

Toward that end, in 2021 the FDA approved a medication designed to help protect patients with small-cell lung cancer against one of the harmful side effects of chemotherapy: bone marrow damage, or myelosuppression. While this is but one drug in a growing arsenal of supportive care therapies, it is an important step toward incorporating the voices of patients into our cancer treatment paradigm.

G1 Therapeutics, Inc.

To learn more, click here .

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Quality of life versus length of life considerations in cancer patients: A systematic literature review

Affiliations.

• 1 Department of Oncology and Metabolism, University of Sheffield, Sheffield.
• 2 Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield.
• 3 School of Health and Related Research, University of Sheffield, Sheffield.
• PMID: 30838697
• PMCID: PMC6619389
• DOI: 10.1002/pon.5054

Objective: Patients with cancer face difficult decisions regarding treatment and the possibility of trading quality of life (QoL) for length of life (LoL). Little information is available regarding patients' preferences and attitudes toward their cancer treatment and the personal costs they are prepared to exchange to extend their life. The aim of this review is to determine the complex trade-offs and underpinning factors that make patients with cancer choose quality over quantity of life.

Methods: A systematic review of the literature was conducted using MeSH terms: cancer, longevity or LoL, QoL, decision making, trade-off, and health utility. Articles retrieved were published between 1942 and October 2018.

Results: Out of 4393 articles, 30 were included in this review. Older age, which may be linked to declining physical status, was associated with a preference for QoL over LoL. Younger patients were more likely to undergo aggressive treatment to increase survival years. Preference for QoL and LoL was not influenced by gender, education, religion, having children, marital status, or type of cancer. Patients with better health valued LoL and inversely those with poorer physical status preferred QoL.

Conclusion: Baseline QoL and future expectations of life seem to be key determinants of preference for QoL versus LoL in cancer patients. In-depth studies are required to understand these trade-offs and the compromises patients are willing to make regarding QoL or LoL, especially in older patients with naturally limited life expectancy.

Keywords: cancer; decision making; longevity; quality of life; trade-off.

© 2019 The Authors. Psycho-Oncology Published by John Wiley & Sons Ltd.

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Quality of Life, Essay Example

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Quality of life is a multidimensional notion that is associated with person’s happiness and satisfaction which are in their turn connected with a person’s physical and mental well-being and environment. Viewed broadly, the quality of life comprises such aspects as culture, rights, values, aspirations, social status etc. However, in this paper we will view health-related quality of life which heavily depends on the quality and accessibility of healthcare, ecology and people’s lifestyles. Health-related quality of life is a multilateral issue which may appear elusive in estimation. Some methods of estimating health-related quality of life include global assessments (people are asked to rate their health on a particular scale), healthy days survey (the number of days out of the past 30 when people felt mentally or physically unwell), years of healthy life survey (it calculates the years of life by an individual in optimal health to compare the number with average life expectancy), and life expectancy estimation.

Quality of life is closely connected with health improvement policies, campaigns and services at all levels – global, national, community and individual. It is directly determined by the state’s healthcare ability to fight infectious and chronic diseases, control tobacco use and substance abuse, provide immunization and access to healthcare, raise the population’s health literacy, and create a generally healthy environment. Health improvement is immediately connected with life expectancy rate and the quality of life in older age that is usually associated with the appearance of chronic and other diseases.

For ease of estimation and managing, certain determinants of health have been outlined and the connection between them defined. Individual’s health depends first of all on his/her biology, i.e. inheritance, a complex of physical and mental health problems acquired during life and lifestyle: a person’s diet, habits, physical activity, alcohol or drug abuse and other elements that change the initial picture of a person’s biology. The person’s lifestyle is such an outweighing factor that it is usually described separately as behavior. Behavior and biology are interconnected. While individual health depends on what a person does, some choices are predetermined by health level. This biology-behavior complex is in its turn influenced by physical environment – a person’s conscious or unwilling exposure to toxic substances, irritants, infectious agents, and physical hazards during his/her life. In the aspect of health improvement, physical environment is first of all connected with ecology and providing personal safety in homes, schools, and workplaces. Another important element is a person’s social environment. In some cases it is predetermined by a person’s biology (an individual born disabled may be likely to live in a specific community) and surely depends on the person’s behavior, i.e. choices. However, social environment influences the behavior and biology of a person, especially in terms of opportunities and expectations, the variety of social institutes and psychological comfort. Both physical and social environments are influenced by state policies and interventions at different levels: ecology, promoting healthy lifestyle, immunization campaigns, disease prevention policies etc. It goes without saying that policies and interventions are a result of the healthy-nation-building work of the social environment. Finally, individual health of every citizen depends on his/her lifetime access to quality health care, which is a result of social environment organization just like policies and interventions.

Both the statistics and my personal impressions support me in the idea that the population of our country and my community, which does not seem to differ much in the main parameters, are relatively healthy. 100% of the population both in cities and in the country are reported to be using adequate sanitation and 99% have access to improved water sources. Unfortunately, the access to healthcare is not as high (between 80 and 90%) and depends on having health insurance and income level. Strong connection between quality healthcare access and personal finances breeds health disparities and reduces the general health level. I feel that state financing should provide for a greater portion of healthcare services. Other health disparities include gender and racial ones and are connected with both biology of individuals and healthcare quality and access in each case.

I think we should be encouraged to go in for sports more actively since only 15% of all adults were reported to have enough physical activity in 1997. This figure is really alarming and logically results in high obesity rates (up to a quarter of the population is overweight) and, consequently, cardiac and other associated disease rates. We have quite big percentage of non-smoking sexually responsible population who are not prone to substance abuse and good perspectives to make the figure higher. To my mind, the areas of most urgent concern are financial policies in healthcare and promoting healthier lifestyles.

I consider myself a healthy, fit and health literate person. I have long been involved in regular exercising and cannot imagine myself leading an unhealthy life now. I drink little alcohol, have never tried drugs and do not smoke. I also have never experienced exposure to radiation or toxic substances. There is no history of serious hereditary diseases in my family. I live an active social life and have a rewarding profession, which means I feel comfortable in my social environment.

Healthy People 2010. A Systematic Approach to Health Improvement. Retrieved March 20, 2009, from http://www.healthypeople.gov/Document/html/uih/uih_2.htm

Healthy People 2010. Leading Health Indicators. Retrieved March 20, 2009, from http://www.healthypeople.gov/Document/html/uih/uih_2.htm

United States: Demographic Highlights. Retrieved March 20, 2009, from http://prb.org/Datafinder/Geography/Summary.aspx?region=72&region_type=2

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Quality of life versus length of life considerations in cancer patients: A systematic literature review

Anne shrestha.

1 Department of Oncology and Metabolism, University of Sheffield, Sheffield

Charlene Martin

Maria burton.

2 Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield

Stephen Walters

3 School of Health and Related Research, University of Sheffield, Sheffield

Karen Collins

Associated data.

Patients with cancer face difficult decisions regarding treatment and the possibility of trading quality of life (QoL) for length of life (LoL). Little information is available regarding patients' preferences and attitudes toward their cancer treatment and the personal costs they are prepared to exchange to extend their life. The aim of this review is to determine the complex trade‐offs and underpinning factors that make patients with cancer choose quality over quantity of life.

A systematic review of the literature was conducted using MeSH terms: cancer, longevity or LoL, QoL, decision making, trade‐off, and health utility. Articles retrieved were published between 1942 and October 2018.

Out of 4393 articles, 30 were included in this review. Older age, which may be linked to declining physical status, was associated with a preference for QoL over LoL. Younger patients were more likely to undergo aggressive treatment to increase survival years. Preference for QoL and LoL was not influenced by gender, education, religion, having children, marital status, or type of cancer. Patients with better health valued LoL and inversely those with poorer physical status preferred QoL.

Baseline QoL and future expectations of life seem to be key determinants of preference for QoL versus LoL in cancer patients. In‐depth studies are required to understand these trade‐offs and the compromises patients are willing to make regarding QoL or LoL, especially in older patients with naturally limited life expectancy.

1. BACKGROUND

A diagnosis of cancer can be devastating, and deciding on the appropriate treatment can be complicated and daunting. Patients are asked to consider factors that include mortality from the disease and the potential for acute and chronic morbidity from the treatment. Appropriate decision making requires satisfactory patient understanding of these treatment choices, which includes the potential benefits and harms. 1 The primary focus of cancer treatment has always been to increase overall and disease free survival; however, quality of life (QoL) has been increasingly recognized as an important end point. 2

Although there is an instinctive understanding of the term “quality of life,” there are multiple definitions, which gives testimony to the fact that it is a complex concept with many diverse facets and components. The standard dimensions used in QoL questionnaires measure the presence or absence of specific symptoms or overall general health. They do not measure patients' beliefs or attitudes toward treatment and intervention outcomes. 3 Decision making in a cancer setting can be a difficult process due to its multifaceted nature. The patients' outlook and beliefs are paramount, but this is heavily influenced by their own experiences and those of friends and family. 4 In addition, current QoL and physical status can affect subsequent decisions.

Most cancer trials primarily focus on the standard oncology end points relating to survival, but it is possible to derive composite measures, which assess the impact of QoL on the final outcome of different therapies. These are called quality adjusted survival metrics or health utility metrics, and a wide range of them have been developed over the past 30 years. Utility measures allow patients a chance to value a different perspective on treatment and outcomes. Two methods of utility measurement that may be used to calculate quality adjusted life years (QALY) or quality adjusted survival are standard gamble and time trade‐off (TTO). 5 In standard gamble, patients are asked to choose between staying in a state of ill health for a specified time period or choosing a treatment that may either cause their death or restore perfect health. In the case of TTO, the individual expresses a preference between two choices, usually between LoL or a better health status. 4 These methods have been increasingly adapted in cost‐utility analyses of pharmaceuticals and various health‐care interventions. In reality, scenarios are often more complex with disease and treatment effects impacting variably on QoL over a prolonged time course. There may be a significant drop in QoL after an intervention but an overall better long‐term QoL and increased life expectancy. QoL measurement should not just focus on a single time point when assessing an intervention.

In cancer treatment, patients are often required to make trade‐offs between QoL and length of life (LoL). 6 Tumor‐specific therapy can potentially prolong life; however, this may reduce QoL significantly. Some patients are willing to endure toxicities associated with treatment in order to increase their LoL, while others value QoL more and are reluctant to spend their remaining years in a compromised state. 7 This involves weighing the risks and benefits of treatment and managing the patients' concerns and expectations. There may be personal reasons associated with their health, the effect on their family and friends, and the consequences of the treatment itself. A trade‐off for potential gain in life expectancy may involve short‐term debility from treatment (postsurgical pain, chemotherapy‐induced nausea and alopecia, and etc) or permanent side effects (stoma, disfigurement, physical dependency, and etc). Moreover, the compromise is not always related to health but instead may be about financial burdens and increased dependency on friends and family.

To understand cancer treatment choices concerning trade‐off, various questionnaires and methodologies have been devised to understand patient preferences and priorities toward cancer treatment. Quality‐adjusted time without symptoms or toxicity (Q‐Twist) allows the combination of both quality and quantity of survival time. 8 , 9 The principle hypothesis of this method is that patients without disease symptoms or treatment toxicity have a better health‐related quality of life (HrQoL) than those who have disease‐specific symptoms and toxicity. Q‐TWiST was initially used to assess adjuvant therapy for breast cancer and has now been adapted in other cancers. 10 , 11 , 12 The Quality/Quantity Questionnaire designed by Stiggelbout and colleagues was created to assess patients' preferences toward either QoL or LoL when deciding about cancer treatments. 7 Other methods include discrete choice experiments and various bespoke questionnaires tailored to a specific study. 13 , 14 , 15

The aim of this review was to determine the factors influencing patient preferences for either QoL or LoL and how these impacts on cancer treatment choices.

2.1. Search strategy and selection criteria

A systematic literature search was performed according to PRISMA guidelines (see supporting information ) using five databases between 1942 and October 2018. The databases included MEDLINE, SCOPUS, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, and Web of Science. A pilot search on MEDLINE, was performed to identify the relevant keywords contained in the title, abstract, and subject descriptors. Five broad categories of concepts were searched: “quality of life,” “cancer,” “length of life,” “health utilities,” and “decision making.” The search terms included (cancer* OR neoplasm* OR oncolog* or tumo?r*) AND (quality of life OR QoL) AND (Longevity OR Length of Life) AND (decision making OR patient participation OR patient preference OR patient participation OR treatment choice) AND (health state utilit* OR standard gambl* OR trade‐off). See Appendix S1 for the search strategy as used in Ovid Medline. The literature search was carried out by two authors (A.S. and C.M.).

A study was only included if there was reference made to preference for QoL or LoL with or without determinants that may influence treatment choice. These factors could be either demographic influences, health status, or personal factors. Study designs could be qualitative, quantitative, or of mixed methods. Studies included were limited to adults with cancer and published in English. A PRISMA format was used to filter through articles. Editorials, reviews, and expert opinions were excluded. Hypothetical studies with healthy volunteers were also excluded as it was felt that these studies were unrealistic in their assessment of whether LoL or QoL would be favored in a cancer setting. Health status utilities were included in the search to include any trade‐off papers suitable for review. Time trade‐off studies may indicate treatment preferences, however not necessarily in the context of a preference for QoL versus LoL. Only those focusing on QoL versus LoL preferences were included.

Study selection was by a two‐step process by two independent reviewers (A.S. and C.M.), at titles and abstract stage with arbitration for articles with uncertainty. In the second stage, full‐text articles were independently reviewed (Figure  1 ). Reference lists of all selected articles were reviewed to identify any additional relevant articles, identifying five further articles. When an article referred to additional publications for more details concerning study methods and design, those publications were also acquired.

PRISMA flow chart of study selection

2.2. Data abstraction

Data extraction was performed by two independent reviewers (A.S. and C.M.). The information collected included study design, aim of study, location of study, sample size and response rate, age of the sample, type of cancer, any research tools used in the form of questionnaires and the findings of the study relating to QoL versus LoL preferences.

2.3. Quality assessment

The Mixed Methods Appraisal Tool (MMAT) was used to quality assess the articles that were included in the study. The 2011 MMAT tool encompasses five types of mixed methods study components or primary studies: qualitative, quantitative randomized controlled trials, quantitative nonrandomized, quantitative descriptive, and mixed methods, each with its own set of methodological quality criteria. For each item the response categories were “yes,” “no,” or “can't tell” followed by comments. 16 Higher quality is denoted by the number of stars (*) in the tables. Quality assessment was independently scored by two reviewers (A.S. and C.M.). No study was excluded based on quality assessment, as all were of acceptable quality.

The literature search revealed 4388 articles. A total of 843 abstracts were excluded because of duplication, and 3494 articles were declined as they were either reviews, expert opinions/editorials, or not suitable for the topic under review. A total of 56 articles were reviewed fully, and only 30 deemed suitable for inclusion. The 26 rejected papers were not suitable as they were either reviews or not relevant (Figure  1 ). Included studies are summarized in Tables  1 (quantitative), 2 (mixed methods), and 3 (purely qualitative) (Tables  2 and ​ and3 3 ).

Details of quantitative studies included in this review, associated with the trade‐offs related to length of life (LoL) and quality of life (QoL) (NR—not reported)

Details of mixed method studies included in this review, associated with the trade‐offs related to length of life (LoL) and quality of life (QoL) (NR—not reported)

Details of purely qualitative studies included in this review, associated with the trade‐offs related to LoL and QoL (NR – not reported)

The majority of studies identified in this review were quantitative. Generic questionnaires (EORTC‐QLQ‐C30 and FACT‐G) and disease specific questionnaires (EORTC‐QLQ‐H&N) were used to assess QoL. The studies were mainly conducted to understand the decision‐making process in the advanced cancer setting. The studies had wide focus that included understanding the role of the doctor and the attitude the patient has toward their treatment, among other themes. Understanding QoL and LoL trade‐offs as part of the decision‐making process, usually formed a limited part of many of these studies.

3.1. QoL versus LoL

Meropol and colleagues (2008) suggested that QoL and LoL are both equally important; however, the majority of patients with advanced cancer in this study prioritized QoL over LoL. 41 This was also reflected by the study of Jenkins and associates. 36 Silvestri and associates noted although there were some patients who would endure treatment and associated toxicities just to live a single day longer, there were also patients who would decline all treatments. These latter patients would rather maintain their QoL and having to withstand the adverse effects of treatment would not be a worthwhile trade‐off. 20 The authors postulated that patients may opt for enhanced QoL only if the chance of survival was less than 50% relative to baseline survival (without treatment). 42

Many patients in the study by Brom and colleagues felt that they ought to have some sort of intervention for their cancer and found it difficult to accept the concept of LoL and QoL. Although some patients opted for treatment initially, they expressed the view that if it was affecting their QoL, they would cease treatment. 39 Marta and colleagues noted that the majority of patients in their study wanted to undergo a treatment that would prolong life but not compromise their QoL. 43 In a qualitative study by Gerber and colleagues, patients stated that they were keen to maintain their activities and not be a burden on family, and therefore not undergo chemotherapy if those factors were compromised, indicating the importance of QoL. 38

3.2. Survival and baseline QoL

Survival seemed to be a key feature in the decision‐making process and patients were found to opt for treatment if they felt that their prognosis was likely to improve. 15 , 19 , 28 , 40 Their current health status also affected their choice. Perez and associates found that those who wanted to trade time, scored lower in many of the domains of the baseline HRQoL questionnaires. 3 Patients in better health were found to rate LoL more highly, whereas those who were in poorer health strived to maintain their QoL. 7 , 22 , 32 , 44 Kiebert and associates noted that issues patients felt were important were baseline QoL and the probability of survival. 17

3.3. Demographic factors

Kiebert and associates assessed factors affecting decision making for cancer treatment and noted that important factors were age, marital status, children, inability to work due to side effects, disease related life expectancy, and baseline QoL. No significant associations were found between the various determinants; however, patients did rate having children and marital status as somewhat important in decision making. 17

Other studies have shown different results, with gender, children, education, religion, and cancer type not influencing treatment choices. 3 , 6 , 23 , 35 Those with strong family links preferred survival. Unemployed patients prioritized QoL. 6 Wong and colleagues concluded that those who were able to pay for their treatment chose to have treatment to prolong their life. 45 These latter findings are only relevant in self paying health care systems.

Many of the studies carried out have not been age specific; therefore, it has been difficult to make inferences about the influence of age on LoL/QoL preferences. The studies in this review show a mixed picture. Older patients have a preference for QoL, which is not surprising considering natural limitations to life expectancy and the often reduced QoL associated with advanced age. 34 Younger cancer patients were more likely to tolerate aggressive treatments to increase survival years. 30 , 35 , 46 A study by Pisu and colleagues involving 170 ovarian cancer patients, showed that maintaining QoL and living as long as possible were both important. In women less than 65 years old, 96.9% felt longevity was important, and 95.9% felt that preserving QoL was important, compared with 87.5% and 90.3%, respectively, in the greater than 65‐year‐old age group. 33 Stiggelbout and associates noted that when age was adjusted for in their statistical calculations, those in relationships and with children preferred longevity. 7 Derks and colleagues found that older patients were less likely to receive standard treatment, an effect that was more evident in those above the age of 80 years old. Reasons behind this included lack of social support and being widowed. Patients who did not receive standard treatment also prioritized QoL more strongly. 27

3.4. Symptom trade‐off

When looking at symptom tradeoffs against longevity, patients were prepared to tolerate certain treatment side effects to live longer. Patients were willing to prioritize survival over intact sexual function in prostate cancer for instance. 18 , 44 When patients with advanced cancer reached the end of their lives and had to endure pain and discomfort, 47% of patients chose to have palliative surgery to maintain or enhance their current health status and independence. 37

3.5. Cancer‐specific trade‐off

Patients suffering from cancers with a good prognosis such as breast and testicular cancers, compared with recurrent colorectal or lung cancer had similar thoughts regarding QoL and LoL. 7 Despite the type of cancer, patients felt that QoL and LoL were equally important when considering treatment. 41 In the study by Pisu and colleagues involving ovarian cancer, more than 90% stated that QoL and LoL were equally important. 33 Another study by Jenkins and associates, involving participants with ovarian cancer showed that 57% felt LoL and QoL were equally important, 9% prioritized LoL, and 33% favored QoL. 36 However, Donovan and colleagues demonstrated that women who had recurrent ovarian cancer, would opt for LoL, and choose to receive aggressive treatment, QoL was a secondary issue. 23 Patients with a shorter history of cancer preferred LoL; however, those with poorer prognosis and closer to their predicted time of death valued QoL more. 35 In contrast, Meropol and colleagues found that there was no association between time since diagnosis and QoL/LoL preference. 41

4. DISCUSSION

This study presents the first comprehensive review of studies looking at trade‐offs between QoL and LoL in a cancer setting. The aim of this review was to highlight whether patients prioritize QoL or Lol and the determining factors that influence the decision‐making process for cancer treatment. In fact, the findings indicate that many of the studies do not directly test determinants. The QQ questionnaire has been designed specifically to quantify the patient's choice of QoL or LoL and also to what extent patients would be inclined toward either. The questionnaire does not capture the psychological reasoning behind the preference however. It is also perhaps more suited for patients with advanced cancers where the cancer will inevitably cause death regardless of whether it was treated or not. 7 For some patients, where curative treatments may be available, albeit with a high cost (for example, mutilating operations leading to disfigurement, ie, head and neck resections, mastectomy, and amputations) or where death due to old age or other, noncancer comorbidities is imminent; this trade‐off may also be relevant and the QQ tool is not designed to explore these scenarios.

This review highlights the importance of carrying out baseline QoL assessments prior to treatment and evaluating the impact of life expectancy. The importance of performing age specific studies is also noted as priorities between younger and older patients are different. The preferences for QoL or LoL by younger patients, may be influenced by their desire to spend time with their partner or children. Older patients are more likely to suffer from multiple comorbidities and be frailer, and discussions may need to include whether a treatment will be tolerated less well because of these limitations, or result in an increased risk of harm. Considerations should include patient intolerance to certain chemotherapy agents or surgery, as well as an understanding that they may never reach their preoperative baseline physical fitness again after treatment. This “step down” in function tends to be more prominent in the older age group, 47 , 48 an effect that is widely recognized across many medical interventions in older patients. They may feel that time spent receiving treatment may not be worth the extension of life for a relatively short period. Older individuals have a good overall understanding that they have lived their lives and are more accepting of the inevitability of death and of their physical limitations. Studies suggest that a good QoL in older people is often based around the following: independence, a strong social circle, and an ability to retain their “inner selves.” 49 These values may be compromised by having treatment. Other studies have shown that the most consistent factor influencing treatment decision making in older patients is a recommendation from doctors. 50 In breast cancer, undertreatment is well‐documented in older patients. 51 This has led to avoidable disease‐specific deaths. 52 Exploring the patients' views regarding treatment at an early stage would help reduce the impact of age‐related clinician bias, which is well recognized. 53

5. CONCLUSIONS

Decision making in cancer treatment is difficult as there are multiple components to consider aside from the purely medical aspects. Likewise, the compromises the patient is willing to make can vary greatly depending on many factors including patient age, personal family dynamics, social structures, and, patients' likely survival and baseline QoL. This may subsequently impact on whether the patient is more inclined towards longevity or QoL. Although there are studies trying to understand the factors influencing the final decision, there is limited information on preferences between QoL and LoL and the trade‐off the patient is willing to make. Clinicians have influence over the final decision, and therefore it is vital for the patient to have a full understanding of their treatment and the impact it may have on their life.

5.1. Study limitations

This study is the first to use a rigorous and systematic approach to review studies based on patient preferences regarding QoL or LoL in a cancer treatment setting. Despite a comprehensive database search strategy, it is possible that some relevant articles may have been missed and despite the various methodologies, all papers included were of an acceptable design and standard for inclusion. However, the main findings of the review are likely to be robust to missing studies. On the basis of our interpretation and weighting of the evidence, we are confident in the conclusions that have been drawn from findings across several studies rather than be based on isolated studies. None of the studies in this review has looked at the impact of preexisting, noncancer‐related limitations to life expectancy as part of this trade‐off, such as is seen in the oldest age groups and the impact of acceptance of impending age‐related mortality. With the aging of Western populations, this is an important gap in the literature.

The studies included in this review are exploratory cohort studies carried out in a retrospective manner, whereby patients have already made their decision regarding treatment. There may be a source of bias influencing their responses, as many issues may not have been considered prior to treatment or the decision‐making process.

Many of these studies have mainly focused on advanced cancers of all types. For patients who are facing mortality imminently, the decision to prioritize QoL and LoL is pertinent. In the case of slow growing cancers such as prostate and breast cancers, where conservative management is widely accepted, the choice between QoL and LoL can be more complicated. Patients often die from other causes rather than the cancer itself. 54 As the majority of the articles identified in this search did not involve early stage cancer, it is difficult to know what patients envisage from their treatment and what trade‐offs they were willing to make as well as how these factors may change with the course of the natural disease process. This is where patients' age and comorbidities may play a larger role in whether the patient opts for QoL or LoL.

5.2. Clinical implications

This review has several important clinical and research implications. With treatment and care now becoming more patient centered, it has become more pertinent to understand the impact of the cancer diagnosis on the patient and the motivations behind their treatment choices. The impact of treatment of certain cancers may be extreme and may involve a great deal of compromise and acceptance of change in circumstances. Factoring the likely impact of treatments on QoL relative to that at baseline should be discussed with every patient. This would ensure that patients have a full understanding of what their treatment entails and that they are aware of the consequences of treatment and nontreatment. Further in‐depth studies are required to understand the emotional and physical considerations and personal priorities the patients may have during the decision‐making process. This may go a long way in elucidating what aspects of their life they are willing to trade to maintain their QoL or increase LoL. Older age specific issues and cancer specific decision‐making processes also need exploring.

CONFLICTS OF INTEREST

The authors have declared no conflicts of interest. The views expressed are those of the authors and not those of the NHS, the NIHR, or the Department of Health.

Supporting information

Data S1. PRISMA checklist.

Appendix S1. Search Strategy used in Ovid (Medline).

ACKNOWLEDGEMENTS

This paper presents independent research funded by the National Institute for Health Research under its Programme Grants for Applied Research Programme (Grant Reference Number RP‐PG‐1209‐10071).

Shrestha A, Martin C, Burton M, Walters S, Collins K, Wyld L. Quality of life versus length of life considerations in cancer patients: A systematic literature review . Psycho‐Oncology . 2019; 28 :1367–1380. 10.1002/pon.5054 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Readers React: Sanctity of life vs. quality of life

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To the editor: Previously, I wrote in a letter published in The Times concerning the plight of the family of brain-dead teen Jahi McMath that there are “no answers that are going to please everybody” and that concerns over dignity and respect are paramount. Hotly debated SB 128, California’s assisted suicide bill, presents similar bioethical issues. (“Assisted-death bill approved by California Senate,” June 4)

Permitting terminally ill patients to end their lives with prescription drugs presents a plethora of moral and ethical considerations, including distinguishing between “sanctity of life” and “quality of life.” The notion of sanctity of life can be viewed as a basic duty to preserve life and derives from theological perspectives. It considers preservation of life as the highest value.

In contrast, the notion of quality of life applies to circumstances in which the obligation to preserve life may no longer exist. In an effort to reconcile these positions, it is necessary to consider the patient’s purposeful “meaning” and pursuit of his or her life goals.

An overriding concern, a la Jahi and Brittany Maynard, is the conditions under which people live rather than whether they live.

Richard Boudreau, MD, Marina del Rey

The writer is a bioethicist at Loyola Marymount University.

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