Health and the quality of life

ALEX C. MICHALOS, BRUNO D. ZUMBO and ANITA HUBLEY

HEALTH AND THE QUALITY OF LIFE ?

(Accepted 22 July, 1999)

ABSTRACT. The aim of this investigation was to explain the impact of people’sself-reported health on their levels of satisfaction with their health, and the impactof these things plus satisfaction with other specific domains of their lives on theperceived quality of their lives. The latter was operationalized as general happi-ness, satisfaction with life as a whole and overall satisfaction with the quality oflife. Seven hundred and twenty-three (723) usable questionnaires returned from amailout random sample of 2500 households of Prince George, British Columbiain November 1998 formed the working data-set for our analyses. Among otherthings, mean respondent scores on the SF-36 health profile were found to be lowerthan published norms from the UK, USA, Netherlands and Sweden, but higherthan scores from Aberdeen, Scotland. Mean scores on the CES-D depression scalealso indicated that our respondents tended to have more depressive symptoms thancomparison groups in Winnipeg and the USA. A review of trends in mean scoreson 17 quality of life items (e.g., satisfaction with family life, financial security,recreation, etc.) from 1994, 1997 and 1998 revealed that there were only 7 statis-tically significant changes across the four year period and they were all negative.Multivariate regression analysis showed that health status measured with a varietyof indicators could explain 56% of the variation in respondents’ reported satisfac-tion with their health. A combination of health status plus domain satisfactionindicators could explain 53% of the variation in respondents’ reported happiness,68% of reported life satisfaction and 63% of reported satisfaction with the over-all quality of life. Sixty percent of the explained variation in happiness scoreswas attributable to self-reported health scores, while only 18% of the explainedvariation in satisfaction with life and with the overall quality of life scores wasattributable self-reported health scores.

? The authors would like to express their appreciation to Shelley Rennick andShelley Webber for the help they gave us in the development, administration,analysis and reporting of the survey, and to George Deagle and J.C.J.M. de Haesfor comments on the first draft of the paper.

Social Indicators Research51: 245–286, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

246 ALEX C. MICHALOS ET AL.

INTRODUCTION

The aim of this investigation was to explain the impact of people’sself-reported health on their levels of satisfaction with their health,and the impact of satisfaction with health and satisfaction with otherspecific domains of their lives on the perceived quality of theirlives. The latter was operationalized as general happiness, satisfac-tion with life as a whole and overall satisfaction with the qualityof life. We begin with an extensive literature review that reveals awide variety of ways in which researchers have connected subjectivewell-being broadly conceived to health or aspects of health. Thereview is a bit longer than usual because we want to give readersof this journal a good view of the work already done in this area,most of which has been published elsewhere. This is followed bya brief review of the 36-item Medical Outcomes Study Short Form(SF-36) profile and its use in explaining the quality of life. We thenprovide an overview of our sampling technique, questionnaire andsample characteristics. After characterizing the demographics ofour sample, we summarize the information obtained about variousaspects of respondents’ health and, wherever possible, provide somecomparative statistics. We review results regarding smoking, drink-ing and socializing habits, and respondents’ feelings about their age.Then we reveal some assessments that some respondents made ofhealth care services in the area, and we show some trend data onsatisfaction levels with several domains of life and life as a wholesince 1994. Following this descriptive material, we review the res-ults of several regression analyses in which the dependent variablesare satisfaction with health, satisfaction with life as a whole, overallquality of life, and general happiness whereas the independent vari-ables are measures of various aspects of health and satisfaction withhealth as well as satisfaction with other domains of life.

HEALTH AND SUBJECTIVE WELL-BEING

Many studies have shown a positive association between subjectivewell-being (operationalized in a variety of ways) and objectively andsubjectively measured health, and satisfaction with one’s own health(e.g., Campbell et al., 1976; Andrews and Withey, 1976). Okun et

HEALTH AND THE QUALITY OF LIFE 247

al. (1984) did a meta-analysis of 104 American studies publishedbefore 1980 dealing mostly with elderly subjects, and reported thatobjectively and subjectively measured health explained between 8%and 14% of the variance in subjective well-being.

According to Aaronson et al. (1986), “a review of clinical trialsundertaken between 1956 and 1976” revealed that at most five per-cent of the studies involved some relatively obvious measure of theimpact of treatment on the quality of patients’ lives, with the lattermeasured in a variety of ways. Fayers and Jones (1983) identifiedover 200 papers with the term “quality of life” in their titles.

Bergner (1989) reviewed papers abstracted inIndex Medicusinorder to compare usage of the phrases “quality of life” and “healthstatus”. She found that most people using the former phrase concep-tualized it as a discrepancy between desired and attained goals, andmany people regarded it “as a risk factor or cause of illness as wellas an outcome of medical care” (p. S149). On the other hand, healthstatus is usually conceptualized as an extension of functional status.So, it usually includes performance measures for activities of dailyliving, measures of body functions, emotional status, symptoms andcognitive functions.

Following the World Health Organization’s (WHO) definition ofhealth as a “state of complete physical, mental and social well-being”, the promotion of good health might be indistinguishablefrom the promotion of a good life. The 1952 U.S. President’s Com-mission on Health Needs of the Nation seems to have taken thisview when it wrote that “All of those measures aimed at improv-ing the health aspects of the environment in which people live andat improving personal health practices constitute the promotion ofhealth. Better housing, better nutrition, better working conditions,better education will enhance the health of our people just as cer-tainly as will better physicians’ care” (Quoted by Breslow, 1989:pp. S211–S212). Breslow correctly remarked that “When health isseen as enabling people to lead a full (high-quality) life, health isclearly different from quality of life itself. Health professionals needto be concerned about the quality of life but their task is to enlargethe capacity for it, not to ‘take it over’ ” (p. S215).

Excellent reviews of quality of life measures used in the medicalliterature may be found in Hollandsworth (1988), Goodinson and


Singleton (1989), and in several articles in Spilker (1996). Hol-landsworth compared 69 studies published in the 1980–1984 periodto 23 studies published in the 1975–1979 period and found, amongother things, that (1) 60% of the recent studies included a subjec-tive measure of quality of life compared to only 10% in the earlierperiod, (2) the recent studies tended to use larger (but still mainlyconvenience) samples and more control groups, and (3) 51% of therecent studies reported either negative or mixed results for treatmentinterventions compared to the typically favourable results reportedin the earlier period.

Several studies have shown that, among older adults, there is apositive association between defining oneself as younger than one’schronological age and both physical and mental health (Busse etal., 1970; Milligan et al., 1985; Montepare and Lachman, 1989).Carp and Carp (1981) found that, in a sample of 352 older adults,both self-reported and observer-rated physical health were relatedto self-identifying as middle-aged rather than old, elderly, or aged,but were not related to chronological age. Logan et al. (1992) indic-ated that functional health affects the probability of reporting an oldidentity but not the probability of reporting a young identity. Andreset al. (1990) reported that better self-rated health and less illness wasrelated to a younger subjective age in a sample of 326 elderly men.In two different studies of older adults, Hubley (1991) found thatfeeling younger than one’s age was related to better self-reportedgeneral health and health relative to others in one’s age group, fewerchronic diseases, and fewer restrictions on one’s instrumental activ-ities due to health problems. Markides and Pappas (1982) reportedthat subjective age was a better predictor of mortality than age oreither objective or self-reported health among 460 predominantlyworking- or lower-class Mexican American and Anglo elderly.

There appears to be a gender difference in the relationshipbetween subjective age and health. Mutran and Burke (1979) re-ported that subjective age was directly related to health for women.For men, however, subjective age was only indirectly related tohealth (through retirement). In a later study, Steitz and McClary(1988) found that, in a small group of 93 male executives, poorself-rated health was related to thinking of oneself as being older.


A younger subjective age has also been linked to life satisfaction(Logan et al., 1992) and quality of life. Mutran and Burke (1979)reported that subjective age was directly linked to life satisfactionfor women, but not for men. Steitz and McClary (1988) reportedthat neither job satisfaction nor life satisfaction was related to aperception of being younger or older, but financial satisfaction didshow a positive relationship. Staats et al. (1993) examined the rela-tionship between various aspects of subjective age and both presentand future quality of life. They found that the age group that one’sactivities are most like is a significant predictor of present perceivedquality of life. That is, identifying one’s activities as belonging to anage group younger than one’s own age group is related to a higherself-reported current quality of life. However, the age one’s interestsare most like is a significant predictor of future (i.e., 5 years fromnow) quality of life. Again, identifying one’s interests as belongingto an age group younger than one’s own age group is related to ahigher self-reported future quality of life.

Hayes and Ross (1986) cite several earlier studies indicating ahigh correlation between physical health and psychological well-being, with no clear causal order. In their probability sample of401 Illinois residents, they found interesting and complex interac-tions among physical and social factors influencing physical health.In particular, contrary to several authors cited below, they foundthat the impact of social forces was not primarily through socialcomparison mechanisms.

Social factors influence physical health. The determinants of physical character-istics are not purely biological or genetic. The well-educated exercise more, areless likely to be overweight, and are in better physical health than the poorlyeducated. Persons with high incomes exercise more and are in better physicalhealth than those with low incomes. Married persons exercise less and are morelikely to be overweight than the unmarried. And women are less likely to beoverweight than men. Thus, social forces affect weight, exercise, and physicalhealth. The latter two, in turn, affect psychological well-being, apparently throughinteractions with others . . . the link betweenbody and mind is socially mediated,not because our bodies affect our minds by way of social evaluations of others,but because social position affects the shape of our bodies (pp. 397–398).

Scheier et al. (1989) studied the effects of dispositional optimismon 51 middle-aged men before and after coronary artery bypasssurgery. They found optimism positively correlated with postsur-


gical quality of life at six months, with problem-focused coping,faster rate of physical recovery, faster rate of return to normal lifeactivities, satisfaction with each of health care, medical staff and theemotional support received from family and friends. As well, optim-ists were less hostile, less depressed and used denial as a copingmechanism less frequently than pessimists. While pessimists mightbenefit from extra care, the authors note that “providing the extracare that is needed may not be the easiest thing to do” because“pessimists were more hostile, more depressed, and expressedless satisfaction with the treatment they had been receiving bythe medical staff. They also asked less questions of the medicalteam and were generally less involved in the recovery process”(p. 1039).

Mai et al. (1990) studied 24 survivors of heart transplants 12months after surgery. Remarkably,

Pre-operatively, 14 had a psychiatric diagnosis and this figure had dropped tofive at 12 months follow-up. Those patients without a psychiatric diagnosis pre-operatively had not developed one at follow-up” (p. 223). The authors concludedthat “heart transplantation in selected subjects with terminal heart disease resultsin a substantial improvement in psychosocial adjustment and quality-of-life 12months following surgery (p. 223).

Angel and Thoits (1987) hypothesized that the perceived nor-mality or abnormality of one’s physical and mental condition is afunction of the similarity of one’s condition to that of most othersin one’s reference group. According to these authors, the featuresof one’s condition that are typically noticed or overlooked at thepre-symptom stage are subtle cultural effects on health and illnessidentification that are largely neglected in the medical sociologicalliterature. They also cite others who have shown that perceptions ofsymptom seriousness are influenced by one’s reference group.

Suchman and Phillips (1958) found that elderly people tendedto rate their health conditions more favourably than their physiciansrated them. Mechanic and Angel (1987) cited several similar studies,and claimed that some researchers just think that elderly people are“health optimists”, while others appeal to an adaptation/expectationsort of hypothesis (e.g., Thomas, 1981). Results of their own surveyof 2 431 Americans suggested that the “Subjective evaluations ofhealth [of older people] . . . are not absolute but, on the contrary, are


made in a context of self and other comparisons, and are alwaysrelative to some degree” (pp. 137–138).

Schulz and Fritz (1987) compared a sample of 120 adults aged65 years and older to a sample of 77 students from 17 to 32 yearsof age. Regarding eight problem areas, including poor health, bothgroups were more likely to attribute difficulties to people over theage of 65 than to themselves. When asked to “base their judgmentson older persons they know”, both groups judged the lives of elderlypersons more favourably than when basing judgments on genericolder adults.

Mechanic and Hansell (1987) studied the self-assessments ofhealth of 1 057 American adolescents in 19 public schools, andfound an interesting twist on the social comparison process. “Self-assessments of better health,” they wrote, “were influenced directlyby higher levels of competence, as measured by grades in school andparticipation in sports and exercise, and by greater psychologicalwell-being, as indicated by lower levels of depressed mood. In con-trast, the effect of physical symptoms was indirect, and was medi-ated through initial levels of self-assessed health” (pp. 370–371).Although their analyses were unidirectional, they explicitly acknow-ledged their belief in “reciprocal causal relationships involvingself-assessed health” and school achievement, participation in sportsand exercise.

Blalock et al. (1989) examined types of social comparisons madeby a group of 75 women with rheumatoid arthritis. Earlier studies ofbreast cancer patients by Taylor (1983) and Wood et al. (1985) andof other arthritis patients by Affleck et al. (1987) and DeVellis et al.(1990) had found that most patients compared themselves to otherpatients who were worse off than themselves. That is, most of thembehaved as predicted by downward comparison theory (Michalos,1985). Blalock, McEvoy, DeVellis and DeVellis found that mostpatients compared themselves to people without the disease whenthey were setting standards of desired performance for themselvesand they compared themselves to other patients when they wereexperiencing performance difficulties. In both cases satisfaction wasenhanced by social comparisons, but downward comparisons wereonly made in the latter case.


In a later study, Blalock et al. (1990) studied the directionality ofsocial comparisons by a group of 85 patients who had been sufferingfrom rheumatoid arthritis for an average of eleven years each. Theresearchers found that nearly two-thirds of the comparisons madeby their patients were either upward or lateral. “After controllingfor differences in physical health status,” they “found that patientswho emphasized their similarity to, as opposed to their differencesfrom, individuals not affected by RA exhibited better psycholo-gical adjustment . . . by comparing withnon-affected individuals,and focusing on one’s similarity to those individuals, patients maybe able to cast their own limitations into a more positive light”(p. 368). The results of this study were therefore similar to thoseof Blalock et al. (1989), and to those of Schultz and Decker (1985),who worked with victims of spinal cord injuries.

Buunk et al. (1990) constructed some interesting experimentsshowing that the directionality of a comparison is an ambiguousindicator of its impact on one’s feelings. The ambiguity arisesbriefly from the facts that, on the one hand, comparing oneselfto someone better off (upward comparison) reminds one that oneis worse off (producing negative affect) but that things may getbetter (producing positive affect), while on the other hand, compar-ing oneself to someone worse off (downward comparison) remindsone that one is better off (producing positive affect) but that thingsmay get worse (producing negative affect). Faced with an upwardor downward comparison to another person, the downside or theupside of the comparison may be more salient. Buunk et al.’s experi-ments with 55 cancer patients and 632 married people showed thatdownward positive affect comparisons were the most common (aspredicted by downward comparison theory), but upward positiveaffect comparisons were almost as common. Furthermore, “Bothstudies demonstrated that comparison direction (upward or down-ward) is not intrinsically linked to affect, as the previous literaturehas often assumed” (pp. 1245–1246).

Taking his lead from the World Health Organization, Brown(1989) distinguished disabled from handicapped people, with thelatter defined as disabled people with a poor fit between their func-tioning and their social roles and environment. From this point ofview, handicaps may be created or removed by altering the person-


environmental fit regardless of disabilities. Among other things,Brown concluded that “Relatives and professionals tend to under-estimate the ability of developmentally disabled persons to controltheir environment” (p. 557).

According to Bryant and Marquez (1986: pp. 143–144), somestudies have found that “women are more likely than men to reportmental health problems such as depression, nervous breakdown andalienation, even when the effects of other demographic variables,including education, are controlled. Moreover, women are morestrongly oriented toward help-seeking as a strategy for coping withmental health difficulties, whereas men are more strongly orientedtoward self-help”. These authors’ own analysis of a 1976 nationalAmerican sample of 960 males and 1304 females showed that men’sself-evaluations were a function of educational status but women’swere not.

Several other examples of the social construction of knowledgeand value were cited in Michalos 1991. All of these examples showthat both sides of perceived discrepancies, what is versus somethingelse, are thoroughly woven into and to some extent constructed bycontingent historical circumstances.

Burckhardt (1985) measured the quality of life of 94 adults withvarious forms of arthritis using an index consisting of life anddomain satisfaction scores and a single item quality of life scale.She found that 46% of the variance in the index could be explainedby positive self-esteem, perceived social support, internal controlover health and a low negative attitude toward illness. Severity ofimpairment had no direct effect on the index, although it did havedirect effects on self-esteem and internal control over health, whichgave it significant indirect negative effects.

Personal control over one’s health was also found to be an impor-tant predictor of psychosocial well-being in studies of patients withend-stage renal disease (Devins 1989), epilepsy (DeVellis, DeVellis,Wallston and Wallston 1980) and spinal cord injury (Schulz andDecker 1985). Devins and Seland (1987) speculated that it was amajor factor relating to patients’ coping with multiple sclerosis,primarily through a self/wants mechanism.

Gfellner (1989) studied 40 Canadians, 80 years of age andolder, and found that their “subjective health perceptions were a


better index of life satisfaction than number of health conditions,instrumental limitations, or perceived functional abilities” (p. 208).

Gray et al. (1985) found that the spouses of 50 patients with end-stage renal disease had lower estimates of the patients’ feelings ofsatisfaction with themselves than the patients own perceived self-satisfaction. Epstein et al. (1989) compared the general health statusreports of 60 elderly people seeking health care service with reportsabout the former from 60 proxies who were family members orfriends. Proxy reports were significantly positively correlated withsubject reports atr = 0.64 for social activity,r = 0.62 for emotionalstatus,r = 0.71 for overall health,r = 0.73 for functional statusand r = 0.43 for satisfaction with health care service. Neverthe-less, “the proxies described the subjects as less emotionally adjustedand as less satisfied [with health care] than the subjects describedthemselves” (p. S96).

Spitzer et al. (1981) found that cancer patients rated the qual-ity of their lives higher than their physicians rated it. Okun andGeorge (1984) found that people’s self/rated health scores weremore strongly related than physician ratings to people’s subjectivewell-being scores.

Hurny et al. (1987) cite several studies showing that cancerpatients have relatively higher scores than healthy controls on scalesof social desirability and lying, which would explain some of thediscrepancy between patients’ reported healthiness, satisfaction andso on and estimates by friends, family and physicians. Their ownresearch with 41 colorectal cancer and 40 bladder cancer patientsrevealed that both groups had relatively higher Crowne MarloweScale scores of social desirability than samples in the general popu-lation. Sabourin et al. (1989) found that “self-deception and impres-sion management accounted for only 9% of the variance in clientsatisfaction scores” (p. 128) for their sample of 81 former men-tal health outpatients in Quebec. In another study, Sabourin et al.(1989) found that both client “satisfaction reports and psychologicaldistress scores were contaminated by socially desirable responding”(p. 352).

Lomas et al. (1987) compared suggested quality of life measuresof two groups (N = 9, N = 5) of language-disabled (i.e., aphasic)adults with nine clinicians, and found that the patient measures


were more similar to each other than to the clinician measures.The patient measures had relatively more specific items with greaterfocus on social needs than the clinician measures.

Pearlman and Jonsen (1985) found that physicians’ assessmentsof the quality of life of patients “demonstrated marked variability”.In developing his quality adjusted life years scale, the health eco-nomist Alan Williams (1985) found that doctors“ “tended to givelower scores than did other people”, presumably because doctorshad “a much greater aversion to disability and distress than thepopulation at large” (quoted by Mulkay et al., 1987, p. 554). Thelatter authors make an excellent case for the claim that “quality oflife is not a measurable phenomenon out there in the social world,but an interpretative by-product of the social interaction betweenordinary people, doctors, NHS [National Health Service] man-agers and, most important of all in this context, health economists”(p. 559).

The relative impact of one’s objective health status on one’s sub-jective well-being is more modest than one might expect even inthe presence of the most catastrophic circumstances, e.g., followingspinal cord injury (Mayer and Andrews 1981; Chwalisz et al., 1988);for multihandicapped adults (Wacker et al., 1983; Stensman 1985;Ramund and Stensman 1988); for cancer patients (Haes and VanKnippenberg 1985); for the visually impaired (Gillman et al., 1986);and for chronic mental patients (Lehman et al., 1982 and Lehman,1983).

Again, Haes and Van Knippenberg’s (1985) review of researchon the quality of life (QL) of cancer patients found no differences

with respect to most QL indicators: satisfaction with family, friends, work,income, values, activities, community, local government, health and the overallquality of life . . . , psychological functioning . . . , anxiety, depression, positivewell-being, mental and general well-being . . . , daily activities . . . and work reha-bilitation . . . . Mastectomized patients do not differ from benign controls withrespect to quality of life. . . . The emotional well-being of melanoma patientsbeing treated is equal to the well-being of “normals” . . . . No difference was foundbetween chemotherapy patients and “normals” with respect to psychological com-plaints, satisfaction with life as a whole and care from partner and others . . . . Itis remarkable that comparisons between cancer patients and others do not seemto support the assumption that the QL of cancer patients in general is poorer thanthe QL of other groups (p. 811).


Chwalisz et al. (1988) compared the emotional experiences of23 nonhandicapped students to 18 spinal-cord-injured people and14 other people who were wheelchair-bound for other reasons. Onaverage, the student group was six years younger than the othertwo groups. The six primary emotions measured were joy, love,sadness, anger, sentimentality and fear. Although satisfaction withtheir own health is not measured by any of these emotions, onewould have thought that the great divergence in the health statusof the student group versus the others would make a difference totheir emotional status. However, “There were no differences in sub-jective well-being between the handicapped and nonhandicappedgroups in this study. . . . Thus, there appears to be no direct relationbetween severity of one’s handicap and subjective well-being” (p.826). Remarkably,

The spinal-cord-injured persons with the most limited bodily feelings oftenreported feeling several emotions more intensely than before their injury. Amajority of the spinal-cord-injured subjects in the low-feedback group reportedincreases in the intensity of feelings of love, joy, sentimentality, and sadnessin their lives. Furthermore, spinal-cord-injured persons significantly more oftenreported increases in the intensity of fear than those in the other groups (p. 825).

Johnson et al. (1982) compared four groups of patients with end-stage renal disease and found that those on hemodialysis or withsuccessful transplants reported levels of life quality similar to thoseof the general population, while those with unsuccessful transplantsreported relatively lower levels of life quality than all other groups.Binik and Devins (1986–87) studied 150 patients with end-stagerenal disease and could not replicate the Johnson, McCauley andCoply results. They concluded that their “results indicated no signi-ficant differences in levels of pain, depression, normal mood states,perceived intrusiveness, or perceived control among dialysis andtransplant patients associated with a history of transplant failure. Infact, these and other analyses we have performed. . . consistentlyhave failed to identify systematic differences in quality of lifedata amonganydialysis as compared to transplant patient groups”(p. 288).

Chuang et al. (1989) assessed the psychosocial distress of 24 gayor bisexual men with asymptomatic human immunodeficiency virus(HIV) infection, 22 with acquired immune deficiency syndrome


(AIDS)-related complex and 19 with AIDS. Among other things,they found that the men with AIDS were less distressed than thosein the other two groups, but there were no significant differences inlevels of reported happiness.

Callan and Hennessey (1989) compared the lives of 52 womenwith explained infertility, 25 women with unexplained infertility and54 mothers, and found no significant differences among the groupsin reported levels of self-esteem and happiness. In another study,Callan and Hennessey (1988) found that infertile women reportedlower levels of life satisfaction than mothers who had subsequentlybecome infertile.

Granting that the overall impact of objectively measurable healthproblems on one’s subjective well-being may be modest, it is never-theless negative and can be very debilitating for some people. Forexample, Gillman et al. (1986) reported that for their sample of 486public housing residents over 60 years of age, “Thirty percent of thevisually impaired said their lives were unpleasant, boring and worsethan their friends; they stated that they felt useless and trapped andgenerally were less satisfied with their lives than their sighted coun-terparts” ( p. 590). Kibele (1989) mentioned several studies showingthat “adults with significantly limiting disabilities including cerebralpalsy cited a low degree of life satisfaction, exemplified by difficultyobtaining regular medical care, coping with sexuality, and achievingindependence” (p. 371).

Klonoff et al. (1986) worked with 71 patients with closed-headinjury, two to four years postinjury, and reported that “quality of lifeis adversely affected by increased severity of head injury and greaterresidual motor deficits” (p. 469). Similar findings were reported byLezak (1987) based on his study of 42 white male volunteers withtraumatic head injuries.

Magilvy’s (1985) research with 66 hearing-impaired olderwomen found that those “women experiencing a high degree ofhearing handicap had a lower perception of quality of life” (p. 143).Several studies have found that hearing impairment has a negativeimpact on interpersonal interaction, self-concept and morale, andis positively associated with depression, bitterness, hypochondrialcomplaints, resentment, hostility and feelings of inferiority (Cooper1976; Blackwell and Levy 1987).


Shadish et al. (1985) reported that their sample of 74 nursinghome patients were significantly less happy than a national randomsample of Americans (N = 2 225), a random sample from Chicago(N = 252), a sample of unemployed males from a small town in Iowa(N = 390) and a sample of schizophrenics (N = 178). However, themean reported happiness level of the nursing home patients was notsignificantly different from that of a sample of urban renewal blackpeople (N = 614).

Blodgett (1981–82) reviewed 19 studies of hemodialysis patientsand noted that the “median incidence of emotional maladjustmentwas slightly less than 50%” (as quoted by Nadel and Clark, 1986:p. 47). Nadel and Clark’s own research with 24 patients who hadundergone more than one kidney transplant indicated that about50% of them had “life satisfaction ratings of ‘good’ and ‘verygood’ ” (p. 41).

Query et al. (1990) interviewed 36 patients who had shuntsfor hydrocephalus between 14 and 36 years earlier, their parentsand siblings, and reported that while “most judged the quality oftheir family relationships and lives to be good”, “the families ofpatients with continuing or ultimately fatal problems reported themost negative effects” (p. 126).

One of the first models used to explain overall quality of lifeoperationalized as life satisfaction was a simple linear model. Inthis model the dependent variable is satisfaction with life as awhole and the independent variables are some measures of satis-faction in particular domains. For example, in Michalos and Zumbo(1999) we summarized results of seven studies in which life satisfac-tion and happiness scores were regressed on scores for satisfactionwith one’s own health, financial security, family relations, job,friendships, housing, area lived in, recreation activities, religion,self-esteem, transportation, government services, living partner andformal education.

In the context of these predictors, satisfaction with one’s ownhealth had average standardized regression coefficients (beta values)of 0.10, with a range of 0 to 0.18, when the dependent variable waslife satisfaction and when it was happiness.

This simple linear model was also used, among other things, inMichalos (1991, 1991a, 1993, 1993a). In this project samples of


undergraduates were drawn from 58 universities in 39 countries,including about 250 students in each sample. (Taiwan was a spe-cial case in which all 26 universities on the island were sampled.)Applying this model to a combined sample of about 5000 students, itwas found that satisfaction with one’s health had a beta value of 0.11when the dependent variable was life satisfaction and 0.08 when thedependent variable was happiness.

SF-36 AND PREVIOUS RESEARCH EXPLAINING QUALITYOF LIFE

The review in the previous section clearly reveals that a widevariety of alternative instruments have been used to measure healthas an independent variable to explain and predict the quality ofpeople’s lives operationalized primarily as overall life satisfactionor happiness. Thanks to the pioneering work of John Ware andhis colleagues, a multi-item scale of overall healthiness has beendeveloped and has been shown to have good reliability and validity(Ware and Sherbourne, 1992; McHorney et al., 1993; McHorney etal., 1994; Ware et al., 1993; Ware et al., 1994). Indeed, the 36-itemMedical Outcomes Study Short Form (SF-36) profile has achievedsuch a high status in the research community that it is sometimesreferred to as the ‘gold standard’ for health status measurementand its norms for several populations (e.g., American, Australian,British, German) provide useful benchmarks for other developedcountries. That is why it was included in our questionnaire. Exhibit Isummarizes its basic elements.

McHorney et al. (1994) reported results of a panel study of3 445 patients with chronic medical and psychiatric conditionswhich, among other things, allowed researchers to assess the rela-tive amount of difficulty different kinds of patients would havecompleting the questionnaire. They found that

Younger patients, those better educated, and MI [myocardial infarction] patientshad the highest rates of item completeness, while the worst groups were theelderly, the less educated, and the poor (p. 54).

With the exception of MI patients, the groups having more or lessdifficulty with the SF-36 questionnaire were precisely the groups

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Exhibit I

Content of the SF-36 Questionnaire

Concepts Number Meaning of low scores Meaning of high scores

of items

PhysicalFunctioning

10 Limited a lot in performing all physical activitiesincluding bathing or dressing due to health.

Performs all types of physical activities includ-ing the most vigorous without limitations due tohealth.

Role Physical 4 Problems with work or other daily activities as a resultof physical health.

No problems with work or other daily activitiesas a result of physical health.

Bodily Pain 2 Very severe and extremely limiting pain. No pain or limitations due to pain.

General Health 5 Evaluates personal health as poor and believes it islikely to get worse.

Evaluates personal health as excellent.

Vitality 4 Feels tired and worn out all of the time. Feels full of pep and energy all of the time.

Social Function-ing

2 Extreme and frequent interference with normal socialactivities due to physical or emotional problems.

Performs normal social activities without inter-ference due to physical or emotional problems.

Role Emotional 3 Problems with work or other daily activities as a resultof emotional problems.

No problems with work or other daily activitiesas a result of emotional problems.

Mental Health 5 Feelings of nervousness and depression all of the time. Feels peaceful, happy, and calm all of the time.

Reported HealthTransition

1 Believes general health is much better now than oneyear ago.

Believes general health is much worse now thanone year ago.

Source: Ware et al., 1993: p. 3:5.


that have more or less difficulty with most printed questionnaires.The internal-consistency reliability (Cronbach’s alpha) for each ofthe eight concepts measured in the SF-36 with this panel rangedfrom 0.78 to 0.93, with a mean of 0.85.

Using a random sample of 2 474 Americans, Ware et al. (1993:p. 9:24) correlated scores from the eight scales of SF-36 with scoreson the General Psychological Well-Being (GPWB) measure takenfrom Dupuy (1984) and they found statistically significant positivezero-order correlations running from 0.19 to 0.60. The highest cor-relation, 0.60, was with the Mental Health scale, which one mighthave expected given the content of the two measures.

The GPWB is a 22-item scale covering six dimensions: anxiety,depression, general health, positive well-being, self-control andvitality. Respondents are asked to rate their lives “during the lastmonth” on six-point scales ranging from “extremely happy, couldnot have been more satisfied or pleased” to “very dissatisfied orunhappy most of the time”. Naughton et al. (1996: pp. 122–123)indicated that the GPWB typically has Cronbach’s alpha values inthe 0.90 to 0.94 range. In our view, the GPWB scrambles so manydifferent things together that it is difficult to know exactly what itis measuring, although it does have reasonable levels of concur-rent validity with well-known measures like the Beck DepressionInventory.

For our purposes, the SF-36 scale works best as a measure of ahealth profile with eight dimensions, rather than as a single sum-mative measure. Ware et al. (1993: p. 12:4) explained that theyhad tried several combinations of the eight scales and that the bestcombination contained only the two summary indexes for Phys-ical Functioning and Mental Health. Furthermore, they reportedthat

Others have experimented with a summary measure based on a simple summationof the eight SF-36 scales (Katz et al., 1992), which we do not recommend. MOS[Medical Outcomes Study] studies to date indicate that this summary index is:(1) less sensitive to differences in health status (compared with SF-36 scalesand Summary Indexes), and (2) not specific (i.e., not interpretable) in relationto an underlying health concept.. . . [Katz et al.] . . . observed that their summaryscore for the SF-36 scales was no worse than other summary measures studied,including those for the SIP [Sickness Impact Profile, Bergner et al., 1981], sAIMS[shortened Arthritis Impact and Measurement Scales, Katz et al., 1992] and FSQ


[Functional Status Questionnaire, Katz et al., 1992]. The latter results may reflectproblems with summary indexes in general (p. 12:4).

Ware et al. (1994: p. 2:1) claimed that further research showedthat the two indexes for Physical Functioning and Mental Healthaccounted for “from 80 to 85 percent of the reliable variance inthe eight SF-36 scales” and that “this result suggests that psycho-metrically-based summary measures have the potential to reducethe number of statistical comparisons required in analyzing SF-36data from eight to two without substantial loss of information”. Thisliterature suggests that if one wishes to use summary indexes thenone should use two (PF and MH). However, the eight dimensionswere consistently found to be better than the two summary indexes.

Fryback et al. (1997) used scores on the eight SF-36 scales topredict Quality of Well-Being (QWB) scores, with a sample of 1 430Americans in the Beaver Dam Health Outcomes Study. They foundthat “A six-variable regression equation drawing on five of the SF-36 components predicted 56.9% of the observed QWB variance”(p. 1). The QWB scale measures levels of mobility, physical andsocial activity, and requires a trained interviewer to administer it. Itwas developed by Balaban et al. (1986) following work on the Indexof Well-Being developed by Kaplan et al. (1976).

Shmueli (1998) reported results of a 1993–1994 survey of 2 030urban Jewish Israelis aged 45 to 75 years in which, among otherthings, scores from the eight scales and a summary score for SF-36were used to explain overall health-related quality of life (HRQoL).The HRQoL scale was based on responses to the question “Imaginethat health related quality of life could be graded on a numericscale. The number 100 represents the quality of life of a personin perfect health and the number 0 represents death. How wouldyou rate your health related quality of life during the last month?”Forty-five percent of the variance in HRQoL scores was explainablefrom the summary SF-36 scores, and 49% was explainable fromthe scores on the eight scales used as separate predictors. Shmueli(1998: p. 194) provided the following instructive overview of hisfindings.

The scores on the eight SF-36 scales are not equally important to the HRQoLscore. It is the more holistic scales – general health and vitality – that are the mainpredictors of the HRQoL scale. The scales measuring ‘roles performance’ have no


net effects on quality of life. Not considering the general health and vitality scales,the PH [physical health = the mean of the physical functioning, role-physical andbodily pain scales] and MH [mental health = the mean of the mental health, role-emotional and social functioning scales] scales (and the six individual scales onwhich these summary scales are defined) are equally important in the determina-tion of HRQoL. These results reiterate the importance of the distinction betweenmeasures of health which are validated by clinical considerations and those valid-ated by more subjective measures of health related quality of life. Items which arepowerful in classifying individuals into different clinical diagnosis groups mighthave no relation to different levels of self-reported quality of life (such as the role-performance items), while items that are highly correlated with subjective qualityof life might be weak in their clinical classification of persons (such as vitality orgeneral health reports).

SAMPLING TECHNIQUE AND QUESTIONNAIRE

Twenty-five hundred 10-page questionnaires were mailed to asimple random sample of Prince George’s 34 000 households inNovember 1998. The first three and a half pages contained the SF-36items. These were followed by a check list of 18 chronic conditions(heart disease, diabetes, etc.) and a single item about frequency ofworries about one’s own health. The twenty-item Center for Epi-demiologic Studies Depression (CES-D) scale (Radloff 1977) camenext. It was designed to measure frequency of depressive symptomsin broad-based survey research populations. Seven social supportitems, followed by 12 items measuring life-stress in general and 11items measuring stress at work came next. There were 5 items onsmoking and drinking and 10 dealing with assessments of healthcare services in Prince George. The latter included open-endedquestions asking about the best and worst things about health careservices here, and about changes that could be made to improvethese services. There was a page of questions about respondents’satisfaction with particular domains of their lives (e.g., their familyrelations, jobs and housing), their overall happiness and satisfactionwith the overall quality of their lives. The questionnaire ended withan item measuring subjective age (i.e., whether people felt older oryounger than they actually were) and two pages of demographicquestions.


SAMPLE CHARACTERISTICS

By the end of December 723 (29%) useable questionnaires werereturned, which formed the working data-set for the survey. Ofthe 719 respondents who identified their gender, 49% (354) werefemales. Sixty-five percent (465) of those who answered the maritalstatus question were now married. The average age was 46, andthe range ran from 17 to 92. Two hundred and twenty-two (30%)had some university education, with 154 (21%) holding a universitydegree. Four hundred and sixteen (58%) were employed full-timeand 87 (12%) part-time. Two hundred and one (29%) had totalfamily incomes of from $40 000 to $69 999, with the median in the$50 000 to $59 999 category.

According to the 1996 census, which is the last official full popu-lation count available from Statistics Canada, the average age ofPrince George residents over 18 years of age was 41, 49% of thecity’s adult residents were female, 50% were married and livingwith their spouse, 17% had some university education, 9% held auniversity degree and 66% were employed. The 1995 median familyincome for Prince George was $56 125. Broadly speaking, then, ourworking sample is a bit older, and has a few more married anduniversity educated people than the population from which it wasdrawn. Since health tends to be positively correlated with educationand negatively correlated with age, there may be some averagingeffect from the tendency of our health-related figures to be inflatedresulting from the former and deflated resulting from the latter.

ASPECTS OF HEALTH

In the next three exhibits we summarize the eight SF-36 profilescores for our respondents taken all together and for males andfemales, and compare their scores with normative scores from theUnited States, United Kingdom, Netherlands, Aberdeen (Scotland)and Sweden. SF-36 profile scores are designed such that 0 representsthe worst and 100 represents the best state of health. When PrinceGeorge scores are compared to those of the UK and the Netherlands,the sample is reduced to include only people in the same age rangeas those surveyed in those countries.


Exhibit II

SF-36 Comparisons of Prince George Scores with Country and City Normsa

Place P.G. USA UK Netherlands Aberdeen Sweden

PF 87.2 84.2 91.8 (89.1)b 88.9 (89.3) 79.2 87.9

RP 81.8 81 88.8 (83.5) 82.0 (83.9) 76.5 83.2

BP 60.9 75.2 83.7 (61.8) 81.5 (61.9) 76.9 74.8

GH 73.7 72 75.3 (74.4) 70.6 (74.2) 68.7 75.8

VT 58.4 60.9 61.7 (58.4) 65.6 (58.0) 61.2 68.8

SF 83.5 83.3 88.5 (83.8) 85.1 (83.8) 78.6 88.6

RE 80.7 81.3 82.1 (80.5) 81.6 (80.3) 75 85.7

MH 74.5 74.7 73.1 (73.8) 74.1 (73.7) 73.7 80.9

aPrince George N = 709, ages 18–92; USA N = 2474, ages 18 and older; UKN = 9332, ages 16–64; Netherlands N = 4041, ages 20–59; Sweden N = 8930,ages 15 and older; Aberdeen N = 542, ages 18 and older.bNumbers in parentheses are Prince George values using same age-range asother countries.Sources: Ware et al. (1993); Jenkinson et al. (1993); Han et al. (1998); Sullivanet al. (1995); Garratt et al. (1993).

Exhibit II lists the scores for our respondent group as a whole.The scores range from 87.2 for Physical Functioning to 58.4 forVitality, with a mean of 75.1. This mean score is lower than thoseof four of the other five groups. Only Aberdeen has a lower meanscore, at 73.7. If we take these means of the eight scores as broadlyindicating the health of the six groups listed in Exhibit II, then it isfair to say that our respondents are relatively less healthy than thosein four of the other five groups.

Comparing the eight component scores for Prince George and theUSA, we find that the American scores are superior on four compon-ents (Bodily Pain, Vitality, Role Emotional and Mental Health) andthe Prince George scores are superior on the other four (PhysicalFunctioning, Role Physical, General Health and Social Function-ing). All of the UK and Swedish component scores are superior tothe Prince George scores (except for MH for the UK), while fiveof the Netherlands scores and only two of the Aberdeen scores aresuperior to those of Prince George.

Exhibit III lists the scores for males in the four groups for whichwe had data. The scores for Prince George male respondents range


Exhibit III

SF-36 Comparisons of Prince George Male Scores with Country Normsa

Place P.G. USA UK Netherlands

PF 87.1 87.2 91.8 (88.9)b 90.3 (89.0)

RP 83.6 86.6 88.8 (85.7) 84.7 (85.7)

BP 61.9 76.9 83.7 (62.9) 83.5 (63.1)

GH 74 73.5 75.3 (74.8) 70.8 (74.6)

VT 60.2 63.6 61.7 (60.1) 67.9 (59.7)

SF 85.3 85.2 88.5 (86.0) 86.9 (86.0)

RE 81.5 83.3 82.1 (81.8) 85.0 (81.6)

MH 75.5 76.4 73.1 (74.9) 76.2 (74.8)

aPrince George N = 361, ages 18–92; USA N = 1055, ages 18 and older;UK N = 9332, ages 16–64; Netherlands N = 4041, ages 20–59.bNumbers in parentheses are Prince George values using same age-rangeas other countries.Sources: Ware et al. (1993); Jenkinson et al. (1993); Han et al. (1998).

from 87.1 for Physical Functioning to 60.2 for Vitality, with a meanof 76.1. Again, this mean is lower than that of the three countrymeans for males, although the range is wider than that of the USAand the Netherlands and narrower than that of the UK.

Comparing the eight component scores for Prince George malerespondents and the American male scores, we find that the formerscores are superior on only General Health. All of the UK malescores are superior to the Prince George male scores (except MH)and only two of the latter are superior to the Dutch male scores (RolePhysical and General Health).

Exhibit IV lists the scores for females in the four groups. Thescores for Prince George female respondents range from 87.3 forPhysical Functioning to 56.5 for Vitality, with a mean of 74. So, onaverage our female respondents are less healthy than our males. Themean female score is also lower than that of the other three groups,while the range is wider than all the others.

Comparing the eight component scores for Prince George femalerespondents and the American female scores, we find that only twoof the latter scores are superior to those of the former (Bodily Painand Vitality). In fact, the very low level of the Prince George scores


Exhibit IV

SF-36 Comparisons of Prince George Female Scores with Country Normsa

Place P.G. USA UK Netherlands

PF 87.3 81.5 91.8 (89.3)b 87.6 (89.6)

RP 79.8 77.8 88.8 (81.3) 79.7 (82.1)

BP 59.7 73.6 83.7 (60.7) 79.7 (60.6)

GH 73.4 70.6 75.3 (74.0) 70.4 (73.9)

VT 56.5 58.4 61.7 (56.7) 63.5 (56.3)

SF 81.7 81.5 88.5 (81.7) 83.5 (81.7)

RE 79.8 79.5 82.1 (79.2) 80.4 (79.0)

MH 73.5 73.3 73.1 (72.6) 72.2 (72.5)

a Prince George N = 348, ages 18–92; USA N = 1412, ages 18 and older;UK N = 9332, ages 16–64; Netherlands N = 4041, ages 20–59.b Numbers in parentheses are Prince George values using same age-rangeas other countries.Sources: Ware et al. (1993); Jenkinson et al. (1993); Han et al. (1998).

on these two components is largely responsible for the low overallmean score. All of the UK female scores and four of the Dutchfemale scores (Bodily Pain, Vitality, Social Functioning and RoleEmotional) are superior to Prince George female scores.

Scores on the twenty-item CES-D scale provide a narrower viewof the health of our respondents, a view focused on depressive symp-toms. Items measure “depressed mood, feelings of guilt and worth-lessness, feelings of helplessness and hopelessness, psychomotorretardation, loss of appetite, and sleep disturbance” (Radloff, 1977).Scores range from 0 representing no symptoms to 60 representingsignificant depressive symptoms. Barnes et al. (1988) reviewed four-teen studies in which the CES-D scale was used in broad surveys andthey found that the most frequently used cut-point for an indicationof depression was a score greater than or equal to 16.

For our respondent group as a whole, scores ranged from 0 to54, with a mean of 10.3. For males, scores ranged from 0 to 50,with a mean of 9.5, while for females, scores ranged from 0 to54, with a mean of 11.1. About 84% of males had scores lowerthan 16, compared to 73% of females. Apparently, then, our femalerespondents experienced more depressive symptoms than our male


respondents. This gender difference is consistent with results fromthe National Population Health Survey 1994–1995, for the North-ern Interior Health Region, British Columbia and Canada (HealthyCommunities Committee 1997: p. 36).

Barnes et al. (1988: p. 390) reported mean scores of 8.6 for the231 males and 9.8 for the 290 females in their Winnipeg Area Studyof 1983. Lowinsohn et al. (1997: p. 281) reported mean scores of 7.9for the 419 men and 8.7 for the 586 women in their American sampleof people fifty years of age and older in the 1982–1984 period.Compared to these two surveys, then, our results indicate that ourrespondents have higher mean scores or report more depressivesymptoms.

Another narrow view of health is provided by our eleven-itemStress Index. Respondents simply indicate ‘yes’ or ‘no’ to a bat-tery of stress-related items (e.g., ‘Too much is expected of you byothers.’). Scores ranged from 0 to 10 for the whole group, with amean of 2.1. The Cronbach reliability coefficient alpha for the indexwas 0.74.

The Stress Index items were followed by a single item askingpeople how stressful their lives were, all things considered, on anine-point scale from ‘very unstressful’ to ‘very stressful’. Thegroup mean score on this item was 4.8, which indicated their liveswere ‘a little stressful’.

Body Mass Index scores provided an additional aspect of health.These were obtained by dividing individuals’ weight in kilogramsby height in meters squared. The calculations include people aged20–64 and exclude pregnant women. Exhibit V summarizes resultsfrom our survey and provides comparisons with the National Popu-lation Health Survey 1994–1995 for Canada and a special sampleof Prince George taken at the same time. The latter two columnsindicate practically no change in the Prince George scores over theperiod from the 1994–1995 survey to ours, and both sets of figuresshow that we have relatively more excessively heavy people herethan in the country as a whole.

Exhibit VI gives the numbers and percentages of respond-ents reporting chronic health conditions. Slightly more than 67%reported some chronic health condition with over 37% reportingmore than one chronic condition. The relatively high percentage


Exhibit V

Body Mass Index Scores

Weight type Canada 1994–95 P. G. 1994–95 P.G. 1998

Insufficient 7.6 4.7 4.9

Acceptable 43.3 37.5 37.8

Some Excess 23.9 29.9 29.9

Overweight 25.2 27.9 27.7

Total N = 12318 706 617

Exhibit VI

Numbers and Percentages of Respondents with Chronic HealthConditions

Number of conditions Respondents Percentages

No chronic problems 238 32.9

One problem 215 29.7

Two problems 123 17

Three problems 76 10.5

Four problems 38 5.2

Five problems 24 3.3

Six problems 7 1

Seven problems 3 0.4

724 100

of people reporting chronic health conditions is to some extenta function of the number of conditions in our checklist. Theseincluded heart disease, hypertension, diabetes, epilepsy, back prob-lems, bronchitis/emphysema, stroke, arthritis, asthma, headache,thyroid problems, cirrhosis of the liver, cancer, HIV/AIDS, sinusitis,dementia/Alzheimer’s, bowel problems, hearing loss/tinnitus, other.

We obtained another indirect measure of health from our ques-tion about how frequently respondents worried about their health,on a five point scale from ‘almost never’ to ‘almost always’. Sixty-six percent reported that they ‘almost never’ or only ‘occasionally’


worried about their health, and another 24% said they worried aboutit ‘sometimes’. The mean score on this item was 2.1.

SMOKING, DRINKING, SOCIALIZING AND FEELING YOUR AGE

Seventy-seven percent of our respondents indicated that they ‘never’smoke, compared to 9% who smoke ‘hourly’ and another 8% whosmoke ‘daily’. The percentage of non-smokers in our survey of June1998 was 73%. So, given our typical error margins, there was prob-ably no change in the few months from the summer to the fall survey.About 69% of our respondents said they were ‘never’ exposed tosecond-hand smoke in their homes, compared to only 9% who saidthey were never exposed to second-hand smoke outside their homes.Seventy-four percent said they were at least ‘occasionally’ exposedto second-hand smoke outside their homes.

Forty-two percent drank alcoholic beverages only once a monthor less, with 12% saying they ‘never’ drank such beverages. Of the88% who drank such beverages at all, 71% said that on average theyhad one or two drinks per sitting.

Just about half of our respondents (49%) said they belonged tovoluntary organizations or associations, such as school groups orchurch social groups. Of those who belonged to such organizations,46% said they participated in activities sponsored by the groups‘at least once a week’ and another 32% said they participated ‘atleast once a month’. Apart from special occasions such as weddingsand funerals, 65% of all our respondents said they ‘never’ attendedreligious services or meetings in the past 12 months.

We combined responses to our four questions indicating thatrespondents had someone who they ‘could confide in’, ‘count on ina crisis’, ‘count on for advice’ and who ‘made them feel loved andcared for’ into an Index of Social Support. Scores ranged from 0 to4, with a mean of 3.6. The Cronbach alpha for the Index was 0.75.Evidently, most of our respondents had good social support. In fact,nearly 80% of them answered ‘yes’ to each of the four questions inthe Index.

Regarding respondents’ age identity, while 11% said that ‘physi-cally’ they felt ‘much younger’ than their age, only 2% said they felt‘much older’ than their age. Nineteen percent felt ‘mentally’ ‘much


younger’ than their age, compared to 3% who felt ‘much older’.Eight percent believed they ‘look’ ‘much younger’ than their age,but only 0.1% believed they look ‘much older’. Thirteen percent‘would choose to be much younger’ than they are, while only 0.4%‘would choose to be much older’.

HEALTH CARE ASSESSMENTS

As usual, most respondents did not offer any suggestions aboutthe worst things, best things and things to change to improve thehealth care in Prince George. Regarding the worst things, 128 peoplementioned the lack of available specialists and 70 mentioned theexcessively long waiting times for surgery. Ninety-two people men-tioned the general availability or accessibility of health care as thebest thing about it and 40 mentioned walk-in clinics. By far somefeature of the Prince George Regional Hospital was most oftenmentioned as needing improvement in order to improve health careservices here. For examples, 131 people recommended things likebuilding a new hospital or renovating the current one, updatingand increasing equipment and services, allowing more operatingand emergency room time, and increasing the number of availablebeds. Eighty-one people suggested bringing in more specialists andanother 56 just wanted more doctors.

QUALITY OF LIFE ASSESSMENTS

Exhibit VII summarizes the results of three Prince George surveysin which the same battery of questions was asked about the qualityof life in general and in specific domains of life. Generally speak-ing, for random samples of our sizes (about 700 respondents), scoredifferences of 0.3 percentage points or greater are statistically sig-nificant at a 95% level of confidence. From June 1994 to November1998 there were 7 significant changes in the 17 indicators, and all 7were negative. Satisfaction with jobs and financial security deteri-orated, perhaps as one might have expected given the economicdownturn in this period. However, satisfaction with health, religion,recreation activities, self-esteem and the provincial government also


Exhibit VII

Respondent Mean Quality of Life Scores: 1994, 1997, 1998

1998 1997 1994

Domains of life Nov. Nov. June

Your house, apartment, mobile home 5.8 5.5 5.8

Your neighbourhood as a place to live 5.9 5.3 5.7

Your family relations, generally 5.7 6 5.9

Your living partner 5.9 6.3 6.1

Your job 5.1 5.4 5.4

Your life as a whole 5.5 5.8 5.6

Your friendships 5.6 5.8 5.8

Your health 5.3 5.4 5.6

Your religion or spiritual fulfilment 5 5.3 5.4

Your financial security 4.3 4.6 4.7

Your recreation activities 4.7 4.9 5

Your self-esteem 5.2 5.5 5.5

Federal government officials 2.8 2.8 3

Provincial government officials 2.2 2.5 3

Local government officials 3.4 3.6 3.5

Your overall quality of life 5.5 5.6 5.7

Your overall happiness 5.6 5.5 5.6

Mean Score 4.9 5.1 5.1

deteriorated. From November 1997 to November 1998, there were10 significant changes in the 17 indicators, 8 of which indicatedsome deterioration. The only two positive changes were for levels ofsatisfaction with people’s homes and neighbourhoods. While therewere no significant changes in the mean scores for the global indi-cators of happiness and satisfaction with the overall quality of lifein any of the surveys, there was a significant negative change inreported satisfaction with life as whole from the 1997 to the 1998survey. The greatest deterioration occurred for satisfaction with theprovincial government, which dropped from 3.0 in the summer of1994 to 2.2 in November 1998.


Exhibit VIII

Regression of Health Satisfaction Scores onHealth Scores

Explanatory Variables Beta Values

General Health 0.51

Vitality 0.17

Body Mass Index –0.06

Health Worries –0.14

Stress Index –0.09

Explained Variance 56%

EXPLAINING RESPONDENTS’ SATISFACTION WITH THEIRHEALTH

As the previous exhibit reveals, mean scores for health satisfactionranged from 5.6 in the summer of 1994 to 5.3 in the current sur-vey. This is the first time we have measured health itself in order toexplain its impact on satisfaction with health. We applied a stepwiseregression procedure using as explanatory variables scores from theeight components of SF-36, CES-D, Stress Index, life stress item,Body Mass Index, numbers of chronic conditions and worries abouthealth. Results of the regression are summarized in Exhibit VIII.

Our health scores were able to explain 56% of the variation inhealth satisfaction scores. The General Health items from the SF-36clearly dominated the set of significant explanatory variables, witha standardized regression coefficient (Beta value) ofβ = 0.51. TheVitality items had a relatively small impact,β = 0.17, and scoresfor Body Mass, Health Worries and Stress were appropriately nega-tive at β = –0.06,β = –0.14 andβ = –0.09, respectively. Usingthe Pratt measure (Thomas et al., 1998), we found that the GeneralHealth items accounted for 65% of all the variance explained by ourvariables.


EXPLAINING THE IMPACT OF HEALTH AND DOMAINSATISFACTION ON HAPPINESS

In the next three sections we make similar analyses explaining inturn our global indicators of happiness with life as a whole, satisfac-tion with life as a whole and satisfaction with the overall quality oflife. In this section, first we regress happiness scores on our batteryof health scores. Second, we regress happiness scores on our batteryof domain satisfaction scores. Third, we apply a hierarchical regres-sion procedure entering the block of health scores first and then theblock of domain satisfaction scores. Fourth, we reverse the entry ofthe two blocks, and finally, we enter all the explanatory variables atonce. While the beta values of distinct variables do not change as aresult of being entered in a block regardless of the order in which theblock is entered, the percent of variance explained by the block maychange considerably. Accordingly, we will review the impact on theexplanatory power of the two blocks relative to their order of entry,but only the results of the first two and fifth regression analyses aresummarized in Exhibit IX.

The first column of Exhibit IX shows that our battery of healthscores accounted for 45% of the variation in happiness scores, withCES-D scores having the greatest influence(Pratt = 45%), followedby the SF-36 Mental Health items (Pratt = 33%). Only two otherscores found their way into the equation, the Stress Index score(Pratt = 16%) and the SF-36 General Health items (Pratt = 6%).

The second column shows that our battery of domain satisfac-tion scores accounted for 40% of the variation in happiness scores,with self-esteem satisfaction dominating the set of predictors (Pratt= 45%), followed at some distance by satisfaction with familyrelations (Pratt = 18%), financial security (Pratt = 18%), and liv-ing partner (Pratt = 16%). Given the number of apparently morepersonal domains in our battery that failed to have any influenceon happiness, we found it remarkable that satisfaction with theprovincial government should have had a positive impact. The satis-faction with self-esteem and with the provincial government hadPratt indices of about 4% each.

The third column shows that the two batteries together accountedfor 53% of the variation in happiness scores, with CES-D scoreshaving the greatest influence (Pratt = 31%), followed by self-esteem


Exhibit IX

Regressions of Happiness Scores on Health and Domain SatisfactionScores

Explanatory variables Beta values Beta values Beta values

General Health 0.1 a a

Mental Health 0.24 a 0.14

CES-D –0.32 a –0.26

Stress Index –0.16 a –0.10

Role Physical a a 0.06

Vitality a a –0.04

Role Emotional a a 0.07

Chronic Cond. a a 0.04

Family Rel. Sat. a 0.17 0.10

Living Partner Sat. a 0.16 a

Financial Sec. Sat. a 0.15 a

Self-esteem Sat. a 0.33 0.17

Prov. Gov. Sat. a 0.13 a

Fed. Gov. Sat a a 0.07

Present Age Sat. a a 0.14

Variance Explained 45% 40% 53%

a = variable not in equation

satisfaction (Pratt = 18%). The seven health scores accounted for60% of the total variance explained and the four domain satisfactionscores accounted for the remaining 40%.

Results of the two hierarchical regressions indicated that if theblock of health scores was entered first, it accounted for 46% of thevariance in happiness scores, leaving only 7 percentage points to beadded by the block of domain satisfaction scores. However, if theblock of domain satisfaction scores was entered first, it accountedfor 40% of the variance in happiness scores, leaving 13 percent-age points to be added by the block of health scores. In fact, bothprocedures provided a misleading impression of the relative import-ance of health and domain satisfaction to happiness by exaggeratingthe importance of the first block entered into the equation. That


was revealed admirably by their Pratt measures in the previousparagraph.

EXPLAINING THE IMPACT OF HEALTH AND DOMAINSATISFACTION ON SATISFACTION WITH LIFE AS A WHOLE

The first column of Exhibit X shows that our battery of health scoresaccounted for 47% of the variation in life satisfaction scores, withCES-D scores having the greatest influence(Pratt = 46%), followedat some distance by the Stress Index scores and the SF-36 MentalHealth items (Pratt = 22% each). Only three other scores found theirway into the equation, SF-36 General Health (Pratt = 6%), the lifestress item (Pratt = 4%) and Physical Functioning (Pratt less than1%).

The second column shows that our battery of domain satisfactionscores accounted for 65% of the variation in life satisfaction scores,with self-esteem satisfaction dominating the set of predictors (Pratt= 24%), followed fairly closely by satisfaction with friendships(Pratt = 16%) and living partner (Pratt = 12%). Six other domainsalso had some influence, namely, job satisfaction, satisfaction withpresent age and health, financial security and religion.

The third column shows that the two batteries together accoun-ted for 68% of the variation in life satisfaction scores, with CES-Dscores having the greatest influence (Pratt = 21%), followed by satis-faction with self-esteem (Pratt = 18%), health satisfaction (Pratt =15%) and friendship satisfaction (Pratt = 12%).

Results of the two hierarchical regressions indicated that if theblock of health scores was entered first, it accounted for 38% of thevariance in life satisfaction scores, leaving 30 percentage points tobe added by the block of domain satisfaction scores. However, ifthe block of domain satisfaction scores was entered first, it accoun-ted for 64% of the variance in life satisfaction scores, leaving only4 percentage points to be added by the block of health scores.Again, both procedures provided a misleading impression of therelative importance of health and domain satisfaction to life satisfac-tion. The Pratt measures showed that the eight domain satisfactionscores actually explained about 82% of the explained variance in lifesatisfaction.


Exhibit X

Regressions of Life Satisfaction Scores on Health and Domain SatisfactionScores

Explanatory variables Beta values Beta values Beta values

General Health 0.09 a –0.07

Mental Health 0.17 a a

CES-D –0.34 a –0.25

Stress Index –0.21 a a

Life Stress Item –0.06 a a

Physical Functioning –0.09 a –0.07

Family Relations Sat. a 0.08 0.07

Living Partner Sat. a 0.15 0.13

Financial Security Sat. a 0.08 a


Job Sat. a 0.12 0.10

Friendship Sat. a 0.17 0.14

Present Age Sat. a 0.12 0.15

Health Sat. a 0.12 0.19

Religion Sat. a 0.09 0.06



EXPLAINING THE IMPACT OF HEALTH AND DOMAINSATISFACTION ON SATISFACTION WITH THE OVERALL

QUALITY OF LIFE

The first column of Exhibit XI shows that our battery of healthscores accounted for 42% of the variation in quality of life satisfac-tion scores, with CES-D scores having the greatest influence(Pratt= 52%), followed at some distance by the the SF-36 General Healthitems (Pratt = 21%), and Stress Index scores (Pratt = 19%). Onlytwo other scores found their way into the equation, the life stressitem (Pratt = 8%) and the Body Mass Index (Pratt is less than 1%).

The second column shows that our battery of domain satisfac-tion scores accounted for 63% of the variation in quality of lifesatisfaction scores, with present age satisfaction dominating the set


Exhibit XI

Regressions of Satisfaction with the Overall Quality of Life Scores on Healthand Domain Satisfaction Scores

Explanatory Variables Beta values Beta values Beta values

General Health 0.21 a a

CES-D –0.37 a –0.16

Stress Index –0.18 a a

Life Stress Item –0.11 a –0.08

Body Mass Index 0.07 a 0.06

Family Rel. Sat. a 0.08 0.12

Living Partner Sat. a 0.10 a

Financial Sec. Sat. a 0.17 0.10


Neighbourhood Sat. a 0.11 0.13

Friendship Sat. a 0.13 a

Present Age Sat. a 0.19 0.21

Health Sat. a 0.16 0.18

Recreation Sat. a 0.06 0.08

Local Gov. Sat. a 0.12 0.07



of predictors (Pratt = 16%), followed by financial security (Pratt =15%), satisfaction with self-esteem and health (Pratt = 13% each),living partner (Pratt = 8%), and five other domains with Pratt valuesof 7% or less each.

The third column shows that the two batteries together accountedfor 63% of the variation in quality of life satisfaction scores, withpresent age satisfaction scores having the greatest influence (Pratt= 18%), followed by health satisfaction (Pratt = 15%), and CES-Dscores and self-esteem satisfaction with Pratt values of 13% each.

Results of the two hierarchical regressions indicated that if theblock of health scores was entered first, it accounted for 33% of thevariance in quality of life satisfaction scores, leaving 30 percentagepoints to be added by the block of domain satisfaction scores. How-


ever, if the block of domain satisfaction scores was entered first,it accounted for 60% of the variance in quality of life satisfactionscores, leaving only 3 percentage points to be added by the block ofhealth scores. Again, both procedures provided a misleading impres-sion of the relative importance of health and domain satisfaction tolife satisfaction. The Pratt measures showed that the eight domainsatisfaction scores actually explained about 82% of the explainedvariance in quality of life satisfaction.

CONCLUSION

The aim of this investigation was to explain the impact of people’shealth on their levels of satisfaction with their health, and the impactof these things plus satisfaction with other specific domains oftheir lives on the perceived quality of their lives operationalizedas general happiness, satisfaction with life as a whole and overallsatisfaction with the quality of life. Seven hundred and twenty-threeusable questionnaires returned from a mailout random sample of2500 households of Prince George, British Columbia in Novem-ber 1998 formed the working data-set for our analyses. Amongother things, mean respondent scores on the SF-36 health profilewere lower than published norms from the UK, USA, Nether-lands and Sweden, but higher than scores from Aberdeen, Scotland.Mean scores on the CES-D depression scale also indicated thatour respondents tended to have more depressive symptoms thancomparison groups in Winnipeg and the USA. A review of trendsin mean scores on 17 quality of life items (e.g., satisfaction withfamily life, financial security, recreation, etc.) from 1994, 1997 and1998 revealed that there were only 7 statistically significant changesacross the four year period and they were all negative. Multivari-ate regression analysis showed that health status measured with avariety of indicators could explain 56% of the variation in respond-ents’ reported satisfaction with their health. A combination of healthstatus plus domain satisfaction indicators could explain 53% of thevariation in respondents’ reported happiness, 68% of reported lifesatisfaction and 63% of reported satisfaction with the overall qualityof life. Sixty percent of the explained variation in happiness scoreswas attributable to self-reported health scores, while only 18% of


the explained variation in satisfaction with life and with the overallquality of life scores was attributable self-reported health scores.

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Institute for Social Research and EvaluationUniversity of Northern British ColumbiaPrince George, B.C.E-mail: [email protected]

Health and the quality of life

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