0895-4356/92 $S.OO+O.OO Copyright0 1992Pergamon Press Ltd

J'CU~EpbkkblVol.45,No. 11,~~. 1303-1313, 1992 F’rintcd in GreatBritain.Allr@tsrcscrvd

QUALITY

OF LIFE AND FUNCTIONAL HEALTH PRIMARY CARE PATIENTS

OF

GEORGE R. PARKERSONJR,* W. EUGENE BROADHEAD and CHIU-KIT J. TSE Department of Community and Family Medicine, Box 2914, Duke University Medical Center, Durham, NC 27710, U.S.A. (Received in revised form 12 May 1992)

Ahatract+uality of life and functional health were measured cross-sectionally for 314 adult ambulatory primary care patients in a rural clinic and found to be much better for patients with low severity of illness who required no confinement to home because of health problems, than for patients with high severity of illness who required continement. Severity of illness was the strongest predictor for patient-reported physical health function and for patient quality of life when assessed by the health provider. Confinement was the strongest predictor for patient quality of life when assessed by the patient. There was very little agreement between patient-assessed and provider-assessed quality of life. Family stress was the strongest predictor of function in terms of mental health, social health, general health, self-esteem, anxiety, and depression. These data suggest that clinicians should direct increased attention to patient-assessed quality of life, patient-reported functional health status, and psychosocial factors such as family stress in an effort to improve medical outcomes. Quality of life Family health

Functional health status

Severity of illness

INTRODUCTION

Management of health problems in the primary care setting requires careful attention, not only to specific illnesses, but also to the various social and economic factors which contribute to each patient’s functional capacity and quality of life. Although definition and measurement of these variables are still in the process of development, definite progress has been made over the past 20 years. Quality of life has been defined in the medical literature as an attribute which includes multiple components, among which functional health status is very important. Patrick and Erickson have conceptualized health-related quality of life to include functional status, social opportunities, health perceptions, impairments, and *Author for correspondence [Tel: (919) 286-9896; Fax: (919) 286-10211.

Psychological stress

duration of life [l]. Bergner has outlined a similar multidimensional definition which in addition to functional status also includes cognition, sleep and rest, energy and vitality, and general life satisfaction [2]. Functional health status is usually conceptualized primarily on the basis of the three basic physical, mental, and social dimensions which constitute the World Health Organization definition of health [3]. The determinants of quality of life and functional health are multiple and may include illness, medical care, physical environment, social environment, economic factors, heredity, personal characteristics, personal beliefs, life events, chance, and supernatural forces [4]. Severity of illness and comorbidity have been shown to have important relationships to function [5-81. Family and financial stress have been studied independently [7-91 and as life events [lo] with an impact on health, and the

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GEORGE R. PARKER~NJR et al.

relationships of social support [l l] and the family [12] with health have been studied extensively. Past studies have been limited as to the number of factors examined concurrently in the same population. Nelson et al. in the Primary Care Cooperative Information (COOP) study examined primary care patient sociodemographic characteristics and their principal diagnoses in relation to their self-reported functional health status [13]. Stewart et al. used multiple regression analyses in the Medical Outcomes Study (MOS) to control for patient characteristics and comorbidity of nine chronic diseases [5]. Parkerson et al. included the additional factors of severity of illness, financial stress, and family and non-family support and stress as independent variables in functional health regression models [7-91. The present study attempts to compare quality of life and functional health concurrently by studying their relationships to an identical set of demographic, social, and illness-related factors in primary care patients. METHODS

The study population consisted of a convenience sample of ambulatory adult patients in a rural primary care community health clinic in the small town of Yanceyville, North Carolina. During the study period medical care was provided by two family physicians, two general internists, and one physician assistant. In the year preceding the study 3000 patients of all ages were seen in the clinic. About half of the patients were women, half were black, and half were white. Patients presented with a full range of health problems except for obstetrical care, which was not offered at the time. Data were collected during the &month period ending in April 1991. A research assistant recruited patients when they presented to the clinic for their health care visits. Selection was designed to provide a minimum of 30 patients in each of 12 categories, which combined gender, black and white race, and three age groups (l&33, 3449 and 50-65 years). Patients who consented to participate, and who were literate enough to complete a 17-item demographic questionnaire, were included in the study and self-administered the full set of health questionnaires which contained a total of 129 items. Functional health was measured by the Duke Health Profile (DUKE), a 17-item measure de-

veloped by Parkerson et al. [14]. The DUKE contains 10 scales, 5 of which are independent (physical health, mental health, social health, perceived health, and disability), and 5 of which are subscales (general health, self-esteem, anxiety, depression, and pain) which include one or more items from the independent scales. The DUKE physical health scale includes 3 items for physical symptoms (sleeping, hurting, fatigue) and 2 for ambulation (walking, running). Mental health has 1 item for cognition (concentration), 2 for psychological symptoms (depressed feelings, nervousness), and 2 for personal self-esteem (like who I am, give up too easily). Social health has 2 items for social activities (socialize with friends or relatives, participate in group activities) and 3 for social self-esteem (not easy to get along with, comfortable around people, happy with family relationships). General health is the 15-item combination of physical, mental, and social health. Perceived health consists of one item which asks to what extent the participant is “basically a healthy person”. Self-esteem contains the 5 items for personal and social selfesteem. Scoring for these 6 measures is performed on a scale of O-100, with high scores indicating good health. The anxiety subscale contains the 6 items covering social self-esteem, nervousness, sleeping, and fatigue. Depression has 5 items on personal self-esteem, depressed feelings, sleeping, and fatigue. Pain has the 1 item on hurting, and disability has the 1 item on confinement. These 4 dysfunction measures are scored on a scale of O-100, with high scores indicating poor health. Quality of life was measured by the QLUniscale developed by Spitzer et al. [15]. To complete this questionnaire respondents place a mark on an analog scale line to indicate their assessment of quality of life from “lowest” to “highest” after reading a definition of these terms. A person with the lowest quality of life is defined as “someone dependent physically on others, seriously troubled mentally, unaware of surroundings, and in a hopeless position”. One with the highest quality of life is “someone physically and mentally independent, communicating well with others, able to do most of the things enjoyed, pulling own weight, with a hopeful yet realistic attitude”. The response is scored by measuring the number of millimeters from the zero point to the patient’s mark along the 100 mm line, thus permitting a score range

Quality of Life

of cl00 with high scores indicating good quality of life. Family and non-family support and stress were measured with the 24item Duke Social Support and Stress Scale (DUSOCS) developed by Parkerson et al. [7-91. Respondents state the extent to which certain types of family members or non-family members give support or cause stress. For the present study, scores were generated on a scale of O-100 with high scores on the support scales indicating high support, and high scores on the stress scales indicating high stress. Financial stress was measured with the 12item finance and business strains subscale of the Family Inventory of Life Events (FILE) developed by McCubbin et al. [16]. Respondents indicate the occurrence of specific stressful events that involve them or their family members. Items are weighed differentially by a scoring system with a possible range of scores from 0 to 416, with high scores indicating high financial stress. For the present study, final scores were transformed to a scale of O-100. Other measures which were included in the questionnaire packet, but the scores of which are not included in the present study, were the State Anxiety Inventory (SAI), a 20-item scale developed by Spielberger et al. [17], the 20-item Center for Epidemiologic Studies Depression Scale (CES-D) [18], the 8-item Duke-UNC Functional Social Support Questionnaire (DUFSS) developed by Broadhead et al. [19], and the lo-item Rosenberg Self-Esteem Scale [20]. Socioeconomic status of participants was quantified using Green’s 2-factor scale based on education and occupation with adjustment for race [21]. Possible Green scores range from 26 to 85. Severity of illness was measured using the Duke Severity of Illness Scale (DUSOI) developed by Parkerson et al. [7,22]. The DUSOI assesses patients’ burden of illness based upon symptom level, complications, prognosis without treatment, and expected response to treatment for each of their health problems. A diagnosis severity score is generated for each health problem, as well as an overall severity score which combines all diagnosis severity scores. In the present study health providers listed patient health problems on a DUSOI checklist immediately after the clinic visit and indicated their assessment along the parameters listed above. The checklists were scored on a

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scale of O-100, with high scores indicating high severity of illness. Also, at the time of the visit providers completed a QL-Uniscale to indicate their perception of the patient’s quality of life. Definitions of quality of life and the analogue scale were identical for provider and patient response sheets. Reliability was tested using Cronbach’s alphas [23] for multi-item scales and test-retest correlations for all scales. Data for the internal consistency analyses were provided by the 314 patients who completed all questionnaires. A subgroup of 54 study patients provided data for the test-retest analyses. This subgroup completed the entire questionnaire packet on their initial office visit and again when they were scheduled to return for medical follow-up. The mean interval between Time 1 and Time 2 for these patients was 75.5 f 52.4 SD days, with a minimum of 7 and a maximum of 205 days. Statistical methods included Spearman rankorder correlations to demonstrate associations among continuous variables and chi-square for associations among categorical variables. Student’s t-test was used to test for difference in group scores. Stepwise multiple regression analyses were used to identify those demographic, social, economic, and illness factors which significantly predicted functional health and quality of life, while controlling for the other variables. Intraclass correlation coefficients derived from 2-factor analysis of variance model described by Maxwell and Pilliner [24] were used to measure agreement between patient and provider assessments of quality of life. This statistic to test agreement for continuous variables is analogous to the kappa statistic to test agreement for categorical variables. It has been suggested that kappa values greater than 0.60 indicate substantial to almost perfect agreement; 0.21-0.60, fair to moderate agreement; and gO.20, poor to slight agreement [25].

RJEXJLTS

Study population Of the 561 patients who were asked to participate in the study, 534 consented (95.2% consent rate), and of those who consented 414 were included in the analyses. Of the 120 patients who were not included in the analyses, 78 were too ill or otherwise incapable of completing the questionnaires, 37 gave incomplete

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GEORGER. PARKERSON JR et al.

demographic data, and 5 lacked severity of illness data. All of the 414 patients who were included in the study completed at least the demographic questionnaire, and their providers completed the DUSOI. Severity of illness analyses from the entire group are being reported separately [22]. However, 100 (24.2%) failed to complete satisfactorily one or more of the other questionnaires in the packet, leaving 314 (75.8%) who successfully completed all of the questionnaires, thereby providing a complete dataset for the analyses in this report. For the 314 study patients the mean age was 39.1 f 12.9 SD years; 55.7% were women; 44.6% were black; 55.4% were married; 25.2% never married; 8.3% divorced; 7.3% separated; and 3.8% widowed. Eighty-four percent lived with their families, and there were 2.3 + 1.5 SD other people living in the households. Formal education beyond high school had been achieved by 36.0% of the group; 60.5% were working full-time, 32.4% were active although not employed full-time, and 7.1% were disabled. Their mean Green SES score was 56.4 & 8.0 SD. The sociodemographic characteristics of the 3 14 patients with complete data were compared with those of the 100 with incomplete data. Patients who did not complete all questionnaires were older (mean age = 44.8 f 12.8 SD years); more likely to be women (68.0%); had less formal education (17.0% educated beyond high school); were less likely to be employed full-time (45.9%) and more likely to be disabled (11.2%); and had lower socioeconomic status (Green score = 52.1 f 9.2 SD). There were no statistically significant differences between the two groups with regard to race, marital status, living arrangement, or the number of other people in the household. The prevalence of the 10 most common health problems for the 314 patients in the study group was as follows: hypertension 24.8%, tobacco abuse 10.2%, acute bronchitis 9.2%, diabetes mellitus 8.3%, obesity 6.7%, lipid disorder 6.1%, anxiety 5.4%, acute upper respiratory infection 4.8%, sinusitis 4.5%, and osteoarthritis 3.5%. In comparison with the 100 patients with incomplete questionnaires the 314 had a somewhat lower prevalence of hypertension, diabetes, lipid disorder, and stomach problems. In the complete group there were 1.9 problems per patient compared with 2.1 per patient for the incomplete group.

The subgroup of 54 patients who returned for follow-up medical visits, and whose data were used for test-retest reliability analyses, had a mean age of 43.8 + 14.2 years, and were 64.8% women and 3 1.5% blacks. They included 5 1.8% who were married, 81.5% who lived with their families, 38.9% educated beyond high school, and 35.9% who were working full-time. Their mean Green SES score was 57.4 f 8.4 SD. The prevalence was higher in the test-retest group than in the entire study group of 314 for the following most common health problems: hypertension (33.3%), lipid disorder (14.8%), diabetes mellitus (ll.l%), and anxiety (11.1%). Scores and reliability

The scores of the 314 study patients are shown in Table 1. The DUKE functional health mean scores in this group were comparable to those reported for primary care patients in a different primary care population of 683 patients [14]. For example, the self-esteem mean score was 74.3 in the present study, compared with 76.9 in the previous study, and the disability score was 19.3 compared with 16.5 [14]. Scores for severity of illness and family and non-family support and stress were comparable to those of a 249 patient subset of the 683 group which had been reported separately [I. For example, in that group the mean DUSOI severity score was 35.0 compared with 42.1 in the present group, and DUSOCS family stress was 19.0 compared with 21.6 [7]. Reliability estimates are also shown in Table 1. Cronbach’s alphas ranged from 0.49 for self-esteem to 0.77 for general health, and statistically significant test-retest correlations ranged from 0.41 for pain to 0.73 for family support. The only non-significant correlation was that of 0.27 for non-family stress. Comparison values from the previous studies were alphas of 0.64 for self-esteem and 0.78 for general health [14], test-retest correlations of 0.41 for pain [14] and 0.76 for family support [7], and a test-retest of 0.68 for non-family stress [7]. Score comparisons by severity of illness and disability

Because of the high frequency of comorbidity, where over half the patients had more than one diagnosis, the number of patients with “pure” diagnoses was not sufficient to allow meaningful comparisons of quality of life and functional health scores by diagnosis. Nor was it possible to compare health scores by categories of health

Quality of Life

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Table 1. Scores and reliability estimates for quality of life, functional health, stressors and supports Reliability

Scores

Measurest

Minimum (n=314)

Es

Quality of life

Patient-assessedj Provider-assessed$

1 1

Functional health

Physical healtht Mental healths Social health# General health1 Perceived health1 Self-esteemj Anxiety4 Depression5 Pain4 Disability5

5 5

0

10 0

Maximum (n=314)

Mean f SD (n = 314)

100.0 100.0

74.4 f 25.0 68.0 f 20.5

100.0 100.0 100.0 96.7 100.0 100.0 91.7 100.0 100.0 100.0

60.3 f 73.1 f 65.3 + 66.3 f 72.1 f 74.5 f 34.0 f 32.2 f 48.4 f 19.3 f

Alphafl (n = 314) -

T, - Tzll (n = 54) 0.56*** 0.52***

23.6 20.4 20.0 16.4 31.7 19.2 19.9 21.3 33.2 30.5

0.70 0.64 0.52 0.77 0.49 0.58 0.63 -

0.59*** 0.66*** 0.67*** 0.72*** 0.54*** 0.66*** 0.64*** 0.70*** 0.41’ 0.45**

5 15 1 5 6 5 1 1

0 10.0 23.3 0 20.0 0 0 0 0

12 7 5

0 0 0 0

93.8 89.7 85.7 80.0

42.1 f 18.6 15.2 rf: 15.9 21.6 f 19.5 13.9 f 17.0

0.66 0.69 0.53

0.65.‘. 0.66*** 0.58*** 0.27

7 5

0 0

100.0 100.0

54.4 f 24.2 49.7 f 23.5

0.71 0.70

0.73*** 0.50**

Stressors

Severity of ilInes@ Financial stress!$ Family stre@ Non-family stres4 supports

Family support$ Non-family support#

*p c 0.01; ?? *p < 0.001; ***p d 0.0001. $Scale = O-100.0 for all measures. $High scores = high quality, function, or support. $High scores = high dysfunction, severity of illness, or stress. TCronbach’s alpha for multi-item measures. l/Time 1 to Time 2 temporal stability using Speannan rank-order correlation coefficients.

problems, such as was done in the original DUKE validation study, where DUKE scores for patients with painful physical problems were compared with those of patients with mental health problems and those with only health maintenance [14]. In the present study group of 3 14, only 23 had painful physical problems; 12 had mental health problems; and 9 had health maintenance alone. However, it was possible in the present study to compare quality of life and function scores of patients by different levels of illness severity and disability. Patients were divided into severity groups by approximate tertiles of DUSOI scores: 103 patients had scores in the lowest tertile (< 37.5), 100 patients had scores in the middle tertile (>, 37.5 to < 50.0), and 111 patients had scores in the highest tertile (2 50.0). Disability groups were determined by patient responses to the DUKE disability item, which asks about disability in terms of confinement to home, nursing home, or hospital because of a health problem during the preceding week. No confinement was reported by 214 patients, l-4 days confinement was reported by 79 patients, and 5-7 days by 21 patients. Review of medical records indicated

that only one of these patients was confined to the hospital and all the others were confined to their homes. For disability analysis, patients were divided into a non-confined group (the 214 patients who reported no confinement) and a confined group (the 100 with at least one day of confinement). Comparison of health problems between the non-confined and the confined groups revealed a higher prevalence of hypertension in the nonconfined group (29.9 vs 14.0%), more diabetes (9.8 vs 5.0%), and more obesity (8.9 vs 2.0%). The confined group had a higher prevalence of acute bronchitis (19.0 vs 4.7%), tobacco abuse (14.0 vs 8.4%), and anxiety (8.0 vs 4.2%). There was no statistically significant difference in the number of health problems between the two groups (1.9 & 1.2 SD for the non-confined and 1.8 + 1.0 SD for the confined, p = 0.55). Analyses in which quality of life and functional health scores of all 314 patients were compared by level of severity alone showed statistically significant differences between the highest and lowest tertile severity groups for provider-assessed quality of life, physical health, mental health, general health, perceived health,

1308

GEORGE R. PARKERSON JR et

anxiety, pain, and depression, but not for patient-assessed quality of life, social health, self-esteem, or disability. For example, the provider-assessed quality of life score for the highest tertile severity group was 58.3 + 21.2 SD, compared with 77.8 f 17.9 SD for the lowest tertile severity group (p = O.OOOl),while the patient-assessed quality of life scores were 72.2 + 26.0 SD and 76.4 f 23.0 SD, respectively (p = 0.21). The physical health score for patients in the highest tertile severity group was 51.4 + 22.5 SD, compared with 69.3 f 22.5 SD for those in the lowest tertile (p < O.OOOl), while the social health scores did not differ significantly (62.3 f 21.5 SD vs 65.6 + 17.3 SD, p = 0.22). In the analyses by disability alone, the patient-assessed quality of life score for the non-confined group (80.1 + 20.3 SD) was higher than that for the confined group (62.1 f 29.4 SD) (p = 0.0001). On the other hand, the provider-assessed quality of life scores did not differ significantly between the non-confined and the confined group (69.2 + 20.4 SD vs 65.4 + 20.5 SD, p = 0.13). Comparison of functional health scores between the two groups showed statistically significantly higher scores for the non-confined than for the confined (i.e. physical health scores: 64.3 + 21.8 SD vs 51.7 + 25.0 SD, p = O.OOOl), with the one exception of perceived health (74.1 & 30.9 SD vs 68.0 + 33.0 SD, p > 0.10). Since most of the functional health scores were worse in both the high severity of illness group and the high disability group, these two groups were combined in an effort to identify the sickest patients in the study population. Those 76 patients who had severity scores in the lowest tertile and who were not confined (low severity/disability) were compared with the 38 patients who had the highest tertile severity scores and who were confined (high severity/disability). Statistically significant differences between these two groups were shown for age (34.9 & 12.0 SD years for the low severity/disability group, vs 40.7 + 11.8 SD years for the high severity/disability group, p < 0.02), marital status (59.2 vs 39.5% married, p < 0.05), living arrangement (7.9 vs 29.0% living alone, p < 0.02), number of other people in the household (2.6 + 1.6 SD vs 1.9 f 1.7 SD, p c 0.04), and occupation (63.2 vs 44.7% employed full-time, p < 0.01). No significant differences were shown for gender, race, socioeconomic status, or educational level.

al.

The most prevalent health problems in the low severity/disability group were hypertension (18.4%), acute upper respiratory infection (7.9%), vaginitis (7.9%), medical examination with no disease found (7.9%), tobacco abuse (6.6%), diabetes mellitus (3.9%), and stomach problems (3.9%). For the high severity/disability group the most prevalent problems were hypertension (23.7%), acute bronchitis (21.1%), tobacco abuse (18.4%), diabetes mellitus (10.5%), anxiety (10.5%), low back pain (7.9%), and convulsions (7.9%). There were fewer health problems per patient for the low severity/disability group than for the sicker group (1.4 f 0.7 SD, vs 2.3 f 1.3 SD, p = 0.0002). Comparison of quality of life, functional health, stressor, and support scores by levels of combined severity and disability are shown in Table 2. All quality of life and functional health scores for the low severity/disability group were better than those of the high severity/disability group, at high levels of statistical significance. Although the mean score values shown in Tables 1 and 2 were similar in magnitude for patient-assessed and provider-assessed quality of life, the scores showed very low levels of agreement. The coefficient of agreement was 0.13 (p < 0.02) for the quality of live scores of the entire study group in Table 1, and only 0.03 (p > 0.40) for those of the low severity/disability group and 0.20 (p > 0.10) for those of the high severity/disability group in Table 2. Likewise, agreement was very low in other analyses by mental health score tertiles, in which the coefficient of agreement between patient and provider assessments for patients in the lowest tertile was 0.14 (p > 0.07) and for those in the highest tertile was 0.15 (p > 0.05). Even those 229 providers who reported that they were “absolutely” or “very confident” in their assessments of patient quality of life had ratings that showed very low agreement with patient-assessed ratings (coefficient = 0.12, p < 0.05). Comparison of the stressor and support scores in Table 2 shows that severity of illness and family stress scores were statistically significantly higher for the high severity/disability group and family support scores were lower, than for the low severity/disability group. There were no statistically significant differences for the other stressor and support variables.

Quality of Life

1309

Table 2. Comparison of quality of life, functional health, stressor and support scores by levels of combined severity of illness and disability Combined severity of illness and disability Measures Quality of Life Patient-assessedl: Provider-assessed#

High? (n = 38)

(*EZ)

p-Value

80.5 f 19.111 17.8 f 18.4

56.8 f 30.4 54.9 f 21.1

0.0001 o.OOOo

70.9 + 76.3 + 66.8 f 71.4 + 82.2 f 75.1 + 30.2 f 27.0 f 37.5 f

42.4 f 59.7 f 55.0 + 52.4 f 56.6 + 65.5 f 48.7 + 48.7 + 68.4 f

0.0000 0.0000 0.0035 O.oooO o.tnlO2 0.0079 0.0000 0.0000 0.0000

Functional Health

Physical health1 Mental healthi Social healths General health1 Perceived healths Self-esteem1 Anxiety4 Depression$ Paine

21.1 18.0 15.7 13.6 26.7 17.2 17.3 19.1 33.8

24.3 20.5 21.3 14.8 35.2 19.3 20.3 20.6 31.7

Stressors

Financial stressy Family stresslj Non-family stress7

14.4 f 16.5 18.5 + 18.6 14.6 + 18.4

18.3 + 17.0 32.0 f 24.2 12.9 f 17.4

0.2366 0.0038 0.6351

56.6 jI 22.5 46.6 f 21.2

46.6 f 26.2 47.4 + 25.6

0.0373 0.8625

supports Family supportt Non-family supportS

*Patients with DUSOI severity scores in the lowest tertile and who were not confined during the preceding week because of health problems. tPatients with DUSOI severity scores in the highest tertile and who were confined at least one day during the preceding week because of health problems. tHigh scores = high quality of life, functional health, or support. §High scores = poor functional health. fHigh scores = high severity of illness or stress. /[Mean + standard deviation. Scale = O-100.0.

Multivariate relationships Stepwise multiple regression models were developed using patient-assessed and providerassessed quality of life and each of the seven multi-item DUKE functional health scales as the dependent variables, and sociodemographic factors, stressors, supports, and disability levels (confined vs non-confined) as the independent variables. R-square values generated by these analyses were used to estimate the percentage of variation in the scores of the dependent variables which may be explained by the scores of one or more of the independent variables in the model. As shown in Table 3, the total R-square for all the variables in each of the two quality of life regression models was 0.17, which indicates that the independent variables in each of these models explained 17% of the variance in the scores of both patient-assessed and providerassessed quality of life. However, while disability in terms of confinement because of health problems explained most of the variance for patient-assessed quality of life (9%), it explained

none for provider-assessed quality of life. While severity of illness explained most of the variance for provider-assessed quality of life (12%), it explained none for patient-assessed quality of life. Only higher socioeconomic status was a predictor in both models. For functional health the total variance explained by the independent variables ranged from 16% for social health to 31% for general health (Table 3). Severity of illness explained most of the variance for physical health (15%), while family stress explained most for the other health measures (ranging from 7% for social health to 13% for general health). Disability was a predictor for all except mental health, with partial percentages of explained variance ranging from 1% for self-esteem to 4% for physical health, general health, and anxiety. The same independent variables were used in stepwise logistic regressions for the dependent variables, perceived health and pain, because they are single-item measures. Perceived health was dichotomized as 0 = excellent perceived health and 1 = poor perceived health, and pain

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Quality of Life

was dichotomized as 0 = no pain and 1 = some or a lot of pain. For poor perceived health, statistically significant positive associations were shown with severity of illness (scaled O-100; odds ratio [OR] = 1.03, p = O.OOOl),female gender (scaled 0,l; OR = 1.70, p = O&l), and family stress (scaled O-100; OR = 1.Ol, p = 0.04), after controlling for the other independent variables. An example of how to interpret the odds ratios given here is that n units of increase in severity of illness would increase the risk of poor perceived health by 1.03” times, so that a patient with a severity score of 40 units is 1.34 times more likely to have poor perceived health than a patient with a severity score of 30 units (n = 40 units - 30 units = 10 units, 1.03”= 1.03” = 1.34), and a female patient is 1.70 times more likely to have poor perceived health than a male patient (n = 1, 1.70”= 1.70’= 1.70). For some or a lot of pain, positive associations were shown with severity of illness (scaled O-100; OR = 1.03, p = 0.0002), disability (scaled 0, 1; OR = 2.36, p = 0.02), and socioeconomic status (scaled 26-85; OR = 1.04, p = 0.03).

DISCUSSION

The definition of quality of life used in the QL-Uniscale is directly related to physical, mental, and social function. It follows that quality of life in the present study could be considered a measure of health-related quality of life, or perhaps a global measure of both functional health and quality of life. The empirical findings of the present study emphasize the similarities between quality of life and functional health in that often their scores are similar in magnitude for similar categories of patients, and they have similar relationships to the same set of independent variables. Differences which were shown between quality of life and functional health in this study may be related partly to the fact that the QL-Uniscale is a single-item global measure, while each of the DUKE functional health scales measures a single dimension of health, and most DUKE scales have multiple items. Another major difference is that the QLUniscale asks for an assessment of quality of life by the patient or the provider, whereas all of the DUKE scales except perceived health ask the respondent only to report symptoms, feelings, activities, or physical capacities, rather than to assess what these items may mean in terms of functional health.

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The present study supports the validity and reliability of the QL-Uniscale in a primary care population. Most previous studies have been conducted with sicker populations. In Spitzer’s original validation study most of the patients had cancer or chronic disease [15]. Although most were ambulatory clinic or office patients, their types of cancer and chronic illness and their level of disease severity were not specified in the report. Most of the subsequent studies which have used the QL-Uniscale support its validity in patients with serious illness, such as cancer [26], chronic renal disease [27,28], AIDS [29] and medical and surgical diagnoses requiring intensive hospital inpatient care [30]. In the present study, where all the patients are ambulatory, where there is a mix of chronic and acute illness, and where overall severity of illness measures 42.1 on a scale of O-100, QL-Uniscale validity is supported by the statistically signiflcant differences between quality of life scores for patients in the high and the low severity/disability groups (Table 2). Test-retest reliability is supported by statistically significant correlations greater than 0.50 (Table 1). Factors which demonstrated statistically significant associations with quality of life and functional health in this cross-sectional study are important as potential determinants of medical outcomes. Of particular interest to health providers traditionally has been severity of illness. As shown in this study, severity of illness was the strongest predictor of patient quality of life, but only when the health provider made the assessment. The implication is that the provider equates minimal illness with maximum quality of life, and that reduction of illness will result in improvement of quality of life. In contrast, when the patient assessed his or her own quality of life, severity of illness was not an independent statistically significant predictor of quality of life. From the patient’s perspective, of all the variables measured in this study, the most important predictor of quality of life was disability in terms of home confinement because of illness. Patient-assessed quality of life scores demonstrated a significant difference by level of disability but not by level of severity of illness. However when disability and severity groups were combined, differences in patient-assessed quality of life scores became statistically significant. The implication is that as long as patients are able to get out of the home and go about their usual activities, regardless of their illness severity, their quality of life remains reasonably

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GEORGER. PARKERWNJR et al.

high. However, once they are confined because of health problems, their quality of life drops significantly. Discordance between patient and provider assessments of functional health was reported in the COOP study, where near-perfect agreements on physical and emotional ratings was reached less than half the time [13]. Other studies have shown that physicians’ knowledge of the physical disorders of their patients was greater than their knowledge of psychosocial problems [31-331. These findings have important implications for the future of medical practice if improvement of quality of life and multi-dimensional functional health are to be considered as clinical outcomes. Not only is there the danger that poor function or quality of life may be overlooked, but also that medical interventions may be perceived as successful by the provider, when they may be very unsuccessful in the eyes of the patients who are being treated. Based upon the present study and previous studies by the same investigators [7-91, one example of a very important factor which deserves closer evaluation by clinicians is family stress, the screening and assessment of which has not been a major component of traditional medical workups in most settings. Family stress was a statistically significant predictor of patient-assessed, but not of provider-assessed quality of life of patients in this study. Family stress was the strongest predictor of mental health, social health, general health, self-esteem, anxiety, and depression. In contrast, severity of illness was the strongest predictor only of physical health. These data support the importance of increased attention to psychosocial factors by health care providers. The relatively minor impact of severity of illness upon mental health was demonstrated in previous studies by Parkerson et al. [7-91 and in the MOS study, where mental health was the functional scale least affected by the nine chronic diseases which were studied [5]. On the other hand, physical function was strongly predicted by chronic disease in the MOS study [5], just as in the present study where 15% of the variance was explained by overall severity of illness, and in the COOP study, where patients with low physical functioning had more chronic diseases than patients with high physical function [13]. Also, in both the present study and the MOS study, perceived health was found to be significantly related to severity of illness, leading one to infer that patients tend to base their

perceptions of health status more heavily on their physical than on their mental function. Although the present study was performed in a single primary care site with a relatively small patient population, support for generalizability and validity of its findings may be found in certain similarities of patient characteristics, disease prevalence, and analytic results with those of the much larger MOS study which was performed in multiple sites. For example, comparison of the present study with the MOS study shows a mean patient age of 39.1 years compared with 46.0 years, respectively, and 55.7 vs 61 .O% women, 65.4 vs 78.0% white, and 55.4 vs 55.0% married, and with a 24.8 vs 29.8% prevalence of hypertension, and an 8.3 vs 9.4% prevalence of diabetes. Also, the comorbidity was similar in that 20.7% of the hypertensives also had diabetes in the present study compared with 18.1% in the MOS study, and 63.2% of the diabetics also had hypertension compared with 56.9% in the MOS. The two studies, in which two different scales were used to measure functional health, reported the very similar finding that 27 vs 24% of the variance of physical function was explained by regression models in which illness was a principal predictor of health [5]. Also, the present study replicated many of the results found previously by the same investigators in a different primary care site concerning the relationships of family stress and support, and severity of illness with functional health outcomes [7-91. More research is needed to answer certain questions raised by the findings of this study, such as: why the support of non-family members was a statistically significant predictor of mental health, social health, general health, and self-esteem, while family support was a predictor of only self-esteem; why black patients had higher self-assessed quality of life and mental health function than whites; and why no more than 17% of quality of life and 3 1% of functional health was explained by all the factors which were studied in these patients (Table 3). Even without answers to these important questions, the present study provides strong support for the value of quality of life and functional health status as outcomes of medical care, and for the importance of measuring family stress and other social factors along with severity of illness and disability as predictors of these outcomes. The data indicate that clinicians should pay much closer attention to the perceptions of their patients concerning quality of life

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Quality of Life

and to the family factors with which their patients are struggling, regardless of which illnesses they may have, or how severely ill they may seem to be from the traditional medical perspective. Acknowledgements-Completion of the DUSOI Checklists at the time of patient visits was performed by W. E. Broadhead, M.D., Ph.D., James W. R. Harding, III, M.D., M.P.H., Janet Jezsik P.A.-C., Todd Shapley-Quinn, M.D., and Bret C. Williams, M.D., M.P.H. Funding was provided by Glaxo, Inc., Research Triangle Park, NC, and the Department of Community and Family Medicine, Duke University Medical Center.

REFERENCES 1. Patrick DL, Erickson P. What constitutes quality of life? Concepts and dimensions. Clln Nutr 1988; 7: 53-63. Bergner M. Quality of life, health status, and clinical research. Med Care 1989; 27: Sl48-Sl56. World Health Organization. The First Ten Years of the World Health Orgaaization. Geneva: WHO; 1959: 459. Parkerson GR Jr. Health as the outcome of medical care. Southeru Med J 1984; 77: 807-808. Stewart AL. Greenfield S. Havs _ RD et al. Functional status and well-being of patients with chronic conditions. JAMA 1989; 262: 907-913. 6. Greenfield S, Aronow HU, Elashoff RM et al. Flaws in mortality data. JAMA 1988; 260: 2253-2255. 7. Parkerson GR Jr, Michener JL, Wu LR er al. Associations among family support, family stress, and personal functional health status. J CIln Epidemiol 1989; 42: 217-229. 8. Parkerson GR Jr, Michener JL, Wu LR et al. The effect of a telephone family assessment intervention on the functional health of patients with elevated family stress. Med Care 1989: 27: 680693. 9. Parkerson GR Jr,. Broadhead WE, Tse CKJ. Validation of the Duke Social Support and Stress Scale. Fam Med 1991: 23: 357-360. 10. Holmes TH, Rahe RH. The social readjustment rating scale. J Psychosom Rea 1967; 11: 213-218. 11. Broadhead WE, Kaplan BH, James SA et al. The epidemiologic evidence for a relationship between social support and health. Am J Epidemiol 1983; 117: 521-537. 12. Campbell TL. Family’s Impact 011Health, A Critkal Review aad Annotated Bibliography. National Institute of Mental Health. Series DN No. 6, DHHS Publ. No. (ADM) 86-1461. Washington, DC: Supt. of Dots., U.S. Government Printing G&e; 1986. 13. Nelson E, Conger B, Douglass R et al. Functional health status levels of primary care patients. JAMA 1983; 249: 3331-3338. 14. Parkerson GR Jr, Broadhead WE, Tse C-KJ. The

15. 16

17.

18. 19.

20. 21. 22.

23. 24. 25. 26.

27.

28. 29. 30. 31. 32. 33.

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Duke Health Profile, a 17-item measure of health and dysfunction. Med Care 1990, 28: 1056-1072. Spitzer WO, Dobson AJ, Hall J et al. Measuring the quality of life of cancer patients, a concise QL-Index for use by physicians. J Chrw MS 1981; 34: 585-597. McCubbin HI, Patterson JM, Wilson L. Family Inventory of Life Eveats aad changes (FILE) Form A. St Paul: Family Social Science; 1980. Spielberger CF, Gorsuch RL, Lushene RE. Manual for tbe State-Trait Auxlety Inventory (self-evaluation questionnaire). Palo Alto, CA: Consulting Psychologists Press; 1970. Radloff LS. The CES-D Scale: A self-report depression scale for research in the general population. Appl Fkivchol Meas 1977: 1: 38wl. Broadhead WE, G&lbach SH, deGruy FV et a/. The Duk&JNC Functional Social Support Questionnaire: Measurement of social support in family medicine patients. Med Care 1988; 26: 709-723. Rosenberg M. Sod&y and the Adolescent Self-Image. Princeton, NJ: Princeton University Press; 1965. Green LW. Manual for scoring socioeconomic status for research on health behavior. Public Health Rep 1970; 85: 815-827. Parkerson GR Jr, Broadhead WE, Tse C-KJ. The Duke Severity of Illness Checklist (DUSOI) for measurement of severity and comorbidity. (In review for publication). Cronbach LJ. Coefficient alpha and the internal structure of tests. Psyehometrles 1951; 16: 297-334. Maxwell AE, Pilliner AEG. Deriving coefficients of reliability and agreement for ratings. Br J Math Stat Psydtol 1968; 21: 105-116. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Blometrles 1977; 33: 159-174. Coates A, Gebski V, Bishop JR et al. Improving the quality of life during chemotherapy for advanced breast cancer. N Emil J Mod 1987: 317: 1490-1495. Churchill DN, Torrance GW, Taylor DW et al. Measurement of quality of life in end-stage renal disease.: the time trade off approach. Cllu Invest Med 1987; lo: 14-20. Parfrey PS, Vavasour HM, Henry S ef al. Clinical features and severity of nonspecific symptoms in dialysis patients. Nephron 1988; 50: 121-128. Wolcott DL, Namir S, Fawxy FI et al. Illness concerns, attitudes towards homosexuality, and social support in gay men. Gen Hosp Psyehlat 1986; 8: 395403. Sage WM, Meyer AB, Rosenthal H et al. Is intensive care worth it? An assessment of input and outcome for the critically ill. Crlt Care Med 1986; 14: 777-782. Querido A. ‘Ibe Elllcleuey of Me&al Care. Leiden: H.E. Stenfert Kroese; 1963: 46-52. Stewart MA, Buck CW. Physicians’ knowledge of and response to patients’ problems. Med Care 1977; 15: 578-585. Brody DS. Physician recognition of behavioral, psychological, and social aspects of medical care. Arch Intern Med 1980; 140: 12861289.