Measuring Quality of Life

This paper considers quality of life (QOL) to be a global, yet unidimensional, subjective assessment of one's satisfaction with life. This conceptualization is consistent with viewing QOL assessments as resulting from the interaction of multiple causal dimensions, but it is inconsistent with proposals to limit QOL to health-related quality of life (HRQOL). We test the unidimensional yet global conceptualization of QOL using data from coronary artery bypass graft (CABG) patients. The Self-Anchoring Striving Scale (SASS) and four other indicators derived from the literature, all seemed to function as indicators of a single concept (QOL) that was repeatedly drawn upon as the patients determined their responses to these indicators. However, only about half the variance in each indicator was attributable to that common QOL source. Several structural equation models are used to assess whether the superior performance of the Life 3 indicator is an artifact of the repetition of an item within this indicator. The data convincingly indicate that the superior performance is not a memory artifact, and that even the repetition of an identically worded item prodded the patients into drawing yet again upon the same QOL factor that grounded all the other measures.     

Using quality of life information to set priorities in health policy

Health care has as primary objectives extending life expectancy and improving quality of life in years prior to death. This paper offers a General Health Policy Model as a method for quantifying these outcomes. The model adjusts life expectancy for diminished quality of life, which is measured using a standardized instrument known as the Quality of Well-being (QWB) scale. The Well-year or Quality Adjusted Life Year (QALY) results from these analyses and serves as a single quantitative expression of health benefit. QALY units integrate side effects and benefits of treatment by combining into a single number, mortality, morbidity, and duration of each health state. Examples show the application of the model relevant to a variety of medical and public health problems, including diabetes, arthritis, AIDS, neonatal circumcision, and tobacco tax. It is suggested that the General Health Policy Model has advantages for guiding both individual and public health decisions.     

Global QUALITY OF LIFE (QOL), health and ability are primarily determined by our consciousness. Research findings from Denmark 1991–2004

Objective: To explain the global quality of life (QOL) from 2000 indicators representing all aspects of life. Design and setting: Two cross sectional population studies, one prospective cohort study and one retrospective cohort study. Participants: (1) Representative sample of 2500 Danes (18–88 years), (2) 7222 members of the Copenhagen Perinatal Birth Cohort 1959–1961 (31–33 years), (3) 9.006 mothers and their 8820 children born in Copenhagen 1959–1961, (4) 746 Danes (55–66 years). Main outcome measures: Global QOL measured by SEQOL (self evaluation of QOL) containing eight global QOL measures: Well-being, life-satisfaction, happiness, fulfilment of needs, experience of temporal and spatial domains, expression of lifes potentials and objective factors. Results: 2000 associations; strongest between QOL and health, ability, the personal philosophy of life, the relationships to oneself, the partner and friends; weakest between QOL and 1000 early life factors, 1000 life events and 100 objective factors like income. Conclusions: Quality of life is associated with personal health and attitude towards life, rather than objective factors, life style, or life events. We conclude that QOL can be developed independently and thus be used as medicine.     

Yea-saying and mood-of-the-day effects in self-reported quality of life

This paper focuses on some of the complications that may arise from errors of measurement in quality of life (QOL) scales based on self-report. It is argued that systematic errors as well as random errors (specifically in the shape of mood-of-the-day effects) will tend to suppress, mask or “wash out” statistical associations between “objective”, sociologically relevant, indicators of well-being and self-reported quality of life. Results from a Norwegian sample of middle-aged and old participants in a health screening operation (N=610) are reported. The findings indicate that response acquiescence (“yea-saying”) may be a source of systematic error even in balanced QOL-scales, and that this bias may lead one to underestimate QOL among the well-educated and overestimate it among older respondents. Utilizing over-time data we are able to show that self-reported QOL appears particularly vulnerable to mood-of-the-day effects among younger females. Implications for sociological research on subjective well-being are pointed out.     

Optimally Scaled Quality-of-Life Indicators

When studying quality of life, researchers have to rely on the subjective evaluations, which are typically categorized, collected in surveys. When statistical analysis are applied to these data, they used to apply the simplistic approach including (i) direct quantification, which assigns discrete numerical values to ordinal response scale, and (ii) complete-case analysis, which discards all observations selecting any of the off-scales choices like Dont Know No Answer from the analysis. The present paper examines the disadvantages of this approach and introduces the optimal scaling method as a remedy. The new scheme attempts to restore the continuity property of the measurements as well as provide estimates for most of the missing responses. Application of the new scheme to the Hong Kong QOL data illustrates how the scheme works, demonstrates its advantages and shows how the QOL indicators, the global QOL indicator as well as its inherited indicators, can be constructed from series of principal component analyses. Factor analysis of the 20 life domain indicators verifies Wan's opinion (1992) that the global sense of well-being can best be captured by two dimensions, namely the personal well-being and the societal well-being. Although previous QOL studies had seldom included perceptions towards various societal conditions to identify life satisfaction in general, our data analysis shows that satisfaction on these conditions do constitute an important component of the global QOL.     

Quality of life indicators and health: Current status and emerging conceptions

Quality of life is an increasingly common theme in the health status and health promotion literatures. Six approaches that consider quality of life and health are reviewed. These are (a) health-related quality of life; (b) quality of life as social diagnosis in health promotion; (c) quality of life among persons with developmental disabilities; (d) quality of life as social indicators; (e) the Centre for Health Promotion (University of Toronto) model, and (f) Lindstrom's quality of life model. Each approach is considered as to its emphasis on objective or subjective indicators, individual or system-level measurement, value-laden or value-neutral assumptions, and potential relationship to social policy and social change goals. The links among the social indicators, quality of life, and health promotions areas are examined.     

Construct and Criterion Validity of a Norwegian Instrument for Health Related Quality of Life Among Elderly Women Living at Home

Purpose of the study: The aim of the current study is to validate an instrument consisting of five items and first used in the Nord-Trondelag Health Survey (HUNT-5), as a measure of health related quality of life (QOL) in a population of elderly women living at home. Design and methods: A random sample of 307 women aged 75 years and over (mean 80.8 years, response rate 74.5%) and living at home were interviewed using the HUNT-5 instrument, the 20-question version of the General Health Questionnaire (GHQ-20) and the respondents’ records of their health status and functional ability. Results: No significant relationships were found between age and the sumscores of GHQ-20 and HUNT-5. Factor analysis indicated that HUNT-5 is primarily unidimensional. The Cronbach α for HUNT-5 was 0.79 and that for GHQ-20 was 0.87. The correlation between the HUNT-5 and the GHQ-20 sumscores was 0.75 (p < 0.001). There were significant correlations between some of the health and function items and the two QOL instruments (ranging from 0.14 to 0.68). Implications: This evaluation supports the psychometric validity of HUNT-5 for elderly women living at home. It is important to take into consideration the women’s own experience of their health and functional ability in planning therapy and in obtaining the best possible QOL for them.     

Health and quality of life of older people,a replication after six years

Replicating a survey of 875 people 55 years old or more undertaken in September 1999 throughout the former Northern Interior Health Region (NIHR) of British Columbia, in September 2005 a sample of 656 people completed a 22-page questionnaire. The average age of the respondents was 68, with a range running from 55 to 96 years, and 64% were women. Responses to the SF-36 questionnaire indicated that for male respondents aged 55–64, the mean score for the 8 dimensions was 76.1. This mean was a bit higher than the 74.4 mean of 1999. For male respondents aged 65 and older the mean was 69.0, which was also higher than the 68.3 mean of 1999. For females aged 55–64, the mean score for 8 dimensions was 73.1, versus 73.0 in 1999. For female respondents aged 65 and older, the mean score was 67.0, versus 65.4 in 1999. Based on these mean scores for the 8 dimensions, then, it is fair to say that the overall health status of males and females aged 55 years and older in the region in 2005 was at least as good as (i.e., the same as or better than) that in 1999. Comparing 28 average figures for the 2005 respondents on satisfaction with specific domains of life (e.g., financial security, health, sense of meaning) and three global indicators (satisfaction with life as a whole and with the overall quality of life, and happiness) with those of the 1999 respondents, we found that the scores for the 2005 sample were at least as high as those of the other sample. Thus, it seems fair to say that the perceived quality of life of older people in the former NIHR so far as it is revealed in domain and global satisfaction and happiness scores, is at least as good as the perceived quality of life of a similar sample in 1999. Although a large majority perceived increases in crime in the 2 year periods prior to both surveys, smaller percentages of the 2005 sample than of the 1999 sample thought that crime had increased over the past two years, avoided going out at night, feared for their safety, had crime-related worries, engaged in crime-related defensive behaviours and were actually the victims of any crimes. Therefore, it seems fair to say that, so far as crime-related issues are concerned for the two samples of seniors responding to our surveys, there is more evidence of improvement than of deterioration. Applying stepwise multiple regression, each of the eight dependent variables was explained on the basis of four clusters of predictors separately and then a final regression was run using only the statistically significant predictors from the four clusters. Broadly speaking, 7 SF-36 health status scales explained from 28% to 45% of the variance in the 8 dependent variables, running from satisfaction with the overall quality of life (28%) to the single item measure of general health (45%). The seven predictors in the Social Relations cluster explained from 7% of the variance in the SF-36 General Health scale scores to 57% of the variance in the Life Satisfaction scores. The four predictors in the Problems cluster explained from 10% of the variance in the SF-36 General Health scale scores to 24% of the variance in the SWLS scores. The 11 predictors in the Domain Satisfaction cluster explained from 14% of the variance in the SF-36 General Health scale scores to 64% of the variance in the SWB scores. Putting all the significant predictors together for each dependent variable, in the weakest case, 4 of 11 potential predictors explained 33% of the variance in the SF-36 General Health scale scores and in the strongest case, 9 of 15 potential predictors explained 70% of the variance in Life Satisfaction scores. Among other things, these results clearly show that respondents’ ideas about a generally healthy life are different from, but not independent of, their ideas about a happy, satisfying or contented life, or about the perceived quality of their lives or their subjective wellbeing. Finally, the 7 core discrepancy predictors of MDT plus incomes were used to explain the eight dependent variables. From 13% of the variance in the SF-36 General Health scale scores to 57% of the variance in SWLS scores was explained using those predictors. Based on an examination of the Total Effects scores for the predictors of the 8 dependent variables, the most influential predictors were Self-Wants, followed by Self-Others and then Self-Best. In other words, the most influential discrepancy predictors of respondents’ overall life assessments were those between what respondents have versus what they want, followed by what they have versus what others of the same age and sex have, and then by what they have versus the best they ever had in the past. We would like to thank the Social Sciences and Humanities Research Council of Canada for support of this research with funds granted to Alex C. Michalos through the Gold Medal Award in 2004.     

Ideology and social indicators of the quality of life

This paper explores the possibility that social-psychological, evaluative measures of social well-being/quality of life (QOL) perceptions may embody unintended ideological elements. We argue that individual QOL satisfactions are likely caused, in part, by ‘satisfactions’, or conservative orientations, vis-à-vis societal institutions. Four dependent measures of QOL attitudes-overall life satisfaction, service satisfaction, community satisfaction, and powerlessness-are derived from factor analyses and established measurement procedures. Each of these QOL indicators is found to exhibit significant bivariate associations with measures of political-economic ideology. These relationships are somewhat reduced, but persist at statistically significant levels, when socio-demographic background variables are held constant. We then discuss the implications of our results for theory and method in the social indicators field.     

Determinants of Health and the Quality of Life in the Bella Coola Valley

The aim of this investigation is to obtain some baseline self-reported data on the health status and overall quality of life of all residents of the Bella Coola Valley of British Columbia aged 17 years or older, and to measure the impact of a set of designated health determinants on their health and quality of life. In the period from August to November 2001, a variety of procedures were used to ensure that all eligible residents of the Valley received a copy of our questionnaire, and 687 useable questionnaires were obtained for our working dataset. Health status was measured by SF-36 and the U.S. Centers for Disease Control healthy days items. Thirty-one items were used to measure the Provincial Health Officer’s designated determinants of health in four clusters, namely, biological, social and economic, health behaviours and health services determinants. Quality of life was measured by satisfaction levels in 13 specific domains of life (e.g., family, financial security), four global items (e.g., happiness, life satisfaction) and one global Subjective Well-Being Index. Besides obtaining baseline figures on all our measures for the Valley, we made some comparisons among our figures and those from other areas, e.g., Prince George, BC. Most of the measures indicated that the health status and quality of life of Bella Coola Valley residents were lower than those of Prince George residents. For the sample as a whole, SF-36 scores on the eight dimensions ran from 82.3 (physical functioning) to 50.0 (social functioning), with a mean of 62.7. Residents in the Valley averaged 6.5 days in the past 30 in which their health was physically not good, 5.5 days when it was mentally not good and 4.1 days when their health limited their usual daily activities. Eleven percent of respondents described their general health as “excellent” and another 27% said it was “very good”. On a 7-point scale from 1=very dissatisfied to 7=very satisfied, respondents had average life satisfaction and satisfaction with the overall quality of life scores of 5.5. For specific domains of life, the lowest mean level of satisfaction was reported for federal and provincial government officials (3.3) and the highest was reported for living partners and personal safety around home (5.8). Regarding bivariate relations, each of the eight dimensions of SF-36 was significantly correlated with a single item measure of general health, and five of the eight were significantly correlated with the number of good health days. Happiness and the Subjective Well-Being Index were positively but moderately correlated with six of the eight dimensions, and life satisfaction was positively correlated with five. Age was negatively related to general health, but positively related to life satisfaction. Not being of aboriginal descent was positively related to all of the four global health indicators and to the Subjective Well-Being Index. Education was positively related to the four global health measures but not to the three global quality of life measures. The Social Support and Good Family Indexes were positively related to all seven global measures. There was a positive correlation between six of the seven global measures and the frequency with which respondents participated in activities sponsored by voluntary organizations. Frequency of smoking was negatively associated with every global dependent variable except the Physical Health Index. Frequency of skipping meals was negatively associated and average hours of sleep per night was positively associated with all seven global measures. Turning to multivariate relationships, the four clusters of health determinants explained from 12% (SF-36 Mental Health Index) to 24% (general health) of the variance in the dependent global health variables, and from 20% (happiness) to 26% (Subjective Well-Being Index) of the variance in the dependent global quality of life variables. Adding domain satisfaction scores to the total set of predictors allowed us to explain from 20% (SF-36 Mental health Index) to 29% (general health) of the variance in the dependent global health variables, and from 39% (happiness) to 62% (life satisfaction) in the dependent global quality of life variables. By including measures of social support and good family relationships in our set of health determinants, we practically guaranteed that the latter would be relatively strongly predictive of global quality of life.     

Global QUALITY OF LIFE (QOL), health and ability are primarily determined by our consciousness. Research findings from Denmark 1991–2004

Social Indicators Research - Objective: To explain the global quality of life (QOL) from 2000 indicators representing all aspects of life. Design and setting: Two cross sectional population...     

The weighting of an international health status index

The major purpose of this paper is to suggest possible weights for a global index of health status. The indicators for the global index are taken from the World Health Organization's definition of health as physical, mental and social well-being. These indicators are combined with mortality indicators to arrive at a finalized index. Survey methodology is used to make initial estimates of the weighting of appropriate indicators, based upon a sample of international health scholars. Preliminary results indicate that mortality is weighted 40%, physical health (morbidity and disability) 25%, mental health 15%, and social health 20%. These results are intended as a starting point for future research.     

Healthy Days,Health Satisfaction and Satisfaction with the Overall Quality of Life

In this note we report results of 6 surveysusing the United States Centers for DiseaseControl and Prevention indicators of healthstatus, taken from the Behavioral Risk FactorSurveillance System. Generally speaking, wefound that the CDC healthy days variables couldplay a useful role in survey research aimed atassessing the impact of measured health statuson people's satisfaction with their own healthand with the overall quality of their lives.More precisely, using stepwise regressions wefound: (1) The three healthy days variablesexplained from 16 to 27 percent of the variancein General Health scores. The limited activitydays variable remained as a significantpredictor in only one of the six regressionsand the not good physical health days variablewas most influential in every sample. (2) Thethree healthy days variables explained from 19to 32 percent of the variance in healthsatisfaction scores, and the not good physicalhealth days variable was again most influentialin every sample. (3) The three healthy daysvariables explained from 12 to 39 percent ofthe variance in quality-of-life satisfactionscores, with the not good mental healthvariable most influential in every sample. (4)The four CDC variables together explained from40 to 55 percent of the variance in healthsatisfaction scores, with the General Healthvariable always dominating the set ofpredictors by a fairly wide margin. (5) Thefour CDC variables together explained from 17to 28 percent of the variance inquality-of-life satisfaction scores, with theGeneral Health variable most influential inthree samples and the not good mental healthvariable most influential in the other three.(6) When the four CDC variables plus the healthsatisfaction variable were used as potentialpredictors, we were able to explain from 29 to40 percent of the variance in quality-of-lifesatisfaction scores. In every sample, theGeneral Health and limited activity daysvariables had no significant impact. (7) Usingstructural equation modeling, we found GeneralHealth does not have a direct effect onsatisfaction with the overall quality of lifebut rather only an indirect effect throughhealth satisfaction. The General Health, notgood physical health, and not good mentalhealth days variables account for 51% of thevariation in health satisfaction, and healthsatisfaction, not good physical health and notgood mental health days variables account for30% of the variation in satisfaction with theoverall quality of life.     

Basic Minimum Needs,Quality of Life and Selected Correlates: Explorations in Villages in Northern India

For those involved in international development, one of the major goals is an improvement in the quality of life of the poor. Bhargava and Chakrabati (1992: 133) see the “primary objective of development at any given time is to improve the quality of life”. Indeed, the mission statement for an international development organization explicitly commits itself to the improvement of the quality of life for the “poorest of the poor” (DID, 1994). Social indicators, as “transeconomic” measures of quality of life, have “become an integral part of 'development indicators”' (Kao and Liu, 1984: 400; see, also Kahn, 1991). The connection between quality of life and development extends beyond the Third World. For example, in the U.S. Myers (1987) found quality of life influenced inmigration to Austin, Texas, thus affecting its economic development. Undoubtedly the majority of the connections between social indicators and development has been examined at the macro, or national levels using economic, health, education and other objective, comparative indices. Recognizing that such measures as GNP are oftentimes inadequate, assorted indices have been derived to gauge the changes in social development over time, e.g., the well-known Physical Quality of Life Index (PQLI) constructed by the Overseas Development Council (Morris, 1976). Many of these tend to focus on objective, material indicators (commodity possessions) as opposed to the more subjective ones (Anand and Ravallion, 1993). It is not the purpose of this paper to examine the various critics' arguments about the strengths and weaknesses of extant macro versus micro indices, but rather to lend support for the general need to assess development and social change through social indicators, whether macro or micro, objective or subjective. Ultimately, the purpose of the assessment should guide which social indicators are selected. The purpose of this paper is to examine several issues arising from the linkages between development efforts and quality of life (QOL). Using empirical data which were gathered to evaluate a community development project in the Garhwal region of Northern India, several issues, germane to both social indicators and development, will be investigated. These include: (1) the relationship between “Basic Minimum Needs” (BMNs) and QOL, (2) some methodological innovations for measuring both BMNs and QOL, and (3) selected correlates of BMNs and social indicators of QOL for Garhwali villagers. Before describing the project and its findings, we will first place it in the overall development context.     

Theoretical Perspectives Guiding QOL Indicator Projects

Most of the theoretically based QOL indicators projects can be classified in terms of six major theoretical concepts: (a) socio-economic development (b) personal utility, (c) just society, (d) human development, (e) sustainability, and (f) functioning. I explain the core aspects of these six theoretical paradigms and show how they help guide QOL researchers to select and develop QOL indicators that are significantly and qualitatively distinct. A taxonomy of QOL indicators guided by a given theoretical concept is likely to be very different from others taxonomies guided by different theoretical concepts. Thus, the objective of this paper to explain these theoretical paradigms and show how they guide QOL researchers to select and develop QOL indicators that are significantly and qualitatively distinct.     

Quantitative assessment of response shift in QOL research

Two assumptions which characterize the measurements of quality life (QOL) studies, are questioned by the present report: First, the assumption that QOL is the same thing for all subjects, is usually related to health problems and can easily be measured across subjects. Second, when this assumption is given up by introducing subjective base-rates in an intra-subject (before-after) design, researchers still tend to assume that no response shifts occur on the QOL scales, e.g., that these scales maintain the same meaning and values for subjects over time and interventions. In our study we found that QOL had different subjective interpretations [1--4]. While for some certain issues in their family-life determined their quality of life, for others these were issues at their workplace or of their health conditions. Second, when QOL was tested against a subjective base-line (for each individual according to their choices of domains), about eleven percent of the hypertensives and normotensives showed a clear response shift of scale-calibration over the period of one year. When the scores of these subjects were excluded, the significance of certain previously reported results changed. For example, the significant difference between normotensives and hypertensives concerning the change in their subjective evaluation of QOL over the year and their initial depression became more significant, while similar changes in their evaluation of sexual impairment and control at their work-place became insignificant. These results suggest that response shifts have to be traced and quantified, before one can claim any results (or lack of results) in 'before-after' designs, concerning subjective meaningful issues like quality of life.     

Subjective Health-Related Quality of Life in Homeless and Vulnerably Housed Individuals and Its Relationship with Self-Reported Physical and Mental Health Status

Although the association between homelessness and objective indicators of poor health is well-established, little research has focused on the subjective health-related quality of life of homeless and vulnerably housed (HVH) individuals. This study examined the subjective health-related quality of life of HVH individuals, using the Multiple Discrepancies Theory (MDT) Scale for Health of the Quality of Life for Homeless and Hard-to-House Individuals (QoLHHI) Inventory, and its association with self-reported functional health status, as measured by the SF-12, and self-reported physical and mental health conditions in a sample of 100 HVH individuals recruited in Vancouver, Toronto and Ottawa. Our findings indicate that physical and mental health conditions are highly prevalent among HVH individuals and that the SF-12 Physical and Mental Component Summary scores are substantially lower compared to US population norms. The MDT Health items were not statistically significantly associated with physical or mental health conditions and only showed correlations of small to moderate magnitude with the SF-12 Component Summary Scales. These findings suggest that the QoLHHI MDT Scale for Health can provide information about HVH individuals’ subjective health experiences that is different from, and can serve as a valuable complement to, health status information for use in research and evaluation studies, as well as for policy purposes to make informed decisions based on subjective health-related quality of life data from HVH individuals.     

MEASURING HUMAN WELLNESS

A conceptual framework is presented as a means to organize the many measures of human wellness that are now available. Health and human wellness are now often used interchangeably and there has been considerable augmentation of the indicators of progress – in addition to traditional indicators such as life expectancy and mortality rates, there is now an ability to use more positive indicators such as self-related health and self-esteem.     

Objective and Subjective Quality of Life: an Interactive Model

This paper reviews the relationship between objective and subjective quality of life indicators. It proposes that the interaction of these variables occurs within a system that homeostatically maintains subjective quality of life within a narrow range. Due to the capacity of this system to adapt to varying environmental circumstances, the objective and subjective indicators are normally poorly correlated. However, it is also proposed that very poor objective conditions can defeat homeostasis and, once this occurs, the objective and subjective indicators display stronger covariation. Empirical data are provided to support this model and the implications for understanding the QOL construct are discussed.