Sot. Sci. Med. Vol. 29. No. 6, pp. 761-768. 1989 Printed in Great Britain. All rtghts reserved

0277-9536189 $3.00 + 0.00 Copyright r, 1989 Maxwell Pergamon Macmillan plc

AN EMPIRICAL ANALYSIS OF THE DIMENSIONS

OF HEALTH STATUS MEASURES

JORGE SEGOVIA, ROY F. BARTLETT and ALISON C. EDWARDS

Division of Community Medicine and Behavioural Sciences, Faculty of Medicine, Memorial University of Newfoundland, St JohnIs, NF, Canada AIB 3V6

Abstract--The objective of this study is to verify empirically the existence of separate dimensions in the overall concept of health status by analyzing 10 variables included in a questionnaire that was applied to all adults in a simple random sample of households in St John’s, Newfoundland. The response rate was 85% for a total of 3300 subjects.

These data were analyzed by frequencies and by associations with sex, age and education. Nonparamet- ric correlation. factor and cluster analyses on variables were used to verify if health status had identifiable dimensions. All these methods produced similar results showing five distinct factors. The first factor is composed of variables related lo disease (disability/chronic conditions/worry about health); the second, to happiness (happiness/emotional); the third. to subjective appraisal of health (physical condi- tion/comparative level of energy/self-rated health status). Finally, the fourth and fifth factors were single variables: restriction of normal activities and social contacts. An interesting finding was that self-rated health status was distributed with almost equal weight in both the first and third factors.

A validation of the IO variables and the 5 factors was undertaken by studying their association with health care utilization. Two measures of utilization were used; number of physicians’ visits in a year and number of hospital days in a 4-year period. Number of chronic conditions, disability and self-rated health status were associated with both measures of utilization; factor I was the only summary construct showing association with utilization.

This paper demonstrates that self-rated health status is valid as a single measure of overall health status in this sample, being associated with both disease and subjective assessment components.

Key a,ords--health status, health indicators, health measurement, health survey

INTRODUCTION In countries of the industrialized world, substantial changes in social and economic conditions, which in most cases are associated with greater access to a more effective medical care system, have produced major changes in morbidity and mortality patterns. As a consequence traditional concepts and measures of morbidity and mortality are inadequate to measure the state of health of individuals and populations.

Health as a multidimensional concept is now widely accepted. The traditional medical or disease dimension has been complemented-and for many, superseded-by the psychological and sociological dimensions. In an article by Sanazaro [I], Elinson enunciated these ideas with his, by now universal, five D’s: death, disease, disability, discomfort, and dis- satisfaction. Articles which illustrate the progression of ideas in this field are: Bauer [2], Berg [3], Elinson [4] and Siegman [5]. Early attempts to conceptualize and quantify health as defined by WHO were made by Fanshell [6] and Breslow [7]; these were followed by more elaborate mathematical modeling ap- proaches [8,9]. Maddox, as early as 1964 [lo], published an interesting study on the importance of health self-assessment. A recent review of research in health indicators includes an interesting conceptual approach to their uses and limitations [l I]. A recent chapter by Ware [12] reviews the current state of the art in health status measurement and postulates six dimensions: physical, mental, social, role, general perceptions, and symptoms. Four of these dimen- sions-physical. mental, social. and general health

perceptions-were extensively studied for their vari- ability, reliability, and validity in the Rand Health Insurance Study [13]. Most of the more generalized instruments used to assess health status such as the Sickness Impact Profile [14], the Quality of Well- being Scale [15] and the Nottingham Health Profile [ 16, 171, incorporate questions designed to obtain information from a variety of domains. The concep- tual framework used to develop these scales is ex- tremely important in order to understand their uses and limitations [ 181.

This paper presents the results of analyzing several indicators of health status included in a survey ques- tionnaire used to study possible associations between health status, health practices, and medical care utilization. Health status was one of the aspects to be explored in this survey, and it was the decision of the research team to use self-assessed health status-see question 41 in the Appendix-as the main indicator for overall health status. We reached this decision because we wanted to use a measure that was related to information and perceptions as understood and expressed by the subject with minimal ‘interference’ from symptoms and medical interpretations. Previ- ous analysis of our results (presented elsewhere) showed that there was good association between self-assessed health status and individual health practices [19] and also with various additive health practice scores (unpublished results). These analyses confirmed the findings of previous studies [20,21] and corroborated that self-assessed health status was acceptable as the dependent variable. To further

761

762 JORGE SEWVIA et al.

verify whether this decision was supported by empiri- cal data we included in our instrument nine questions which were taken from other studies (Canada Health Survey [22], and National Study of Health Practices and Consequences [21]). For practical reasons, i.e. length of the interview and total costs, we were not able to include a larger set of questions which could have measured all aspects of health status. For the same reason we did not attempt to use previously extensively tested instruments such as the Sickness Impact Profile, as they were difficult to apply within our methodology.

3lETHODOLOGY ’

A questionnaire was applied by telephone to all adults (20 years and older) in a probabilistic sample of households from metropolitan St John’s, Newfoundland, Canada. The local telephone direc- tory was used as the sample frame. Telephone cover- age is loo%, of which less than 3% are unlisted numbers; the directory, which is updated every year, was considered to be the most accurate and complete available sample frame. Questionnaires were obtained from 3300 subjects with a response rate of 85%. More details about the methodology have been published elsewhere [23].

There were 10 variables to measure health status. A brief explanation of domain, the question number for each variable, and the abbreviated label used in subsequent analysis follows:

I. Health self-rating (question 41); label: SRHealth.

2. Worry over health last year (question 42); label: Worry.

3. Number of chronic conditions (question 43); label: Chronic.

4. Comparative level of energy (question 44); label: Energy.

5. Satisfaction with overall physical condition (question 45); label: PhyCond.

6. Emotional status (question 46), simplified Bradburn scale, label: Emotional.

7. Current self-assessed happiness (question 47); label: Happiness.

8. Temporary/permanent disability (questions 25, 26); label: Disability.

9. Restrictions of normal activities (questions 34, 35, 36); label: Restrict.

10. Number of relatives and close friends (ques- tions 48, 49); label: Social.

The complete questions are included in the Ap- pendix and the categories used are shown in Table 1.

One significant methodological aspect of this study was that subjects responding to the questionnaire were matched with computer records of hospital and physician use, using a unique number which is as- signed for health insurance purposes; 2994 subjects (90.7%) provided us with this personal number. This linkage eliminated problems of recall and, due to the characteristics of the provincial health insurance plan, practically all contacts with physicians and all hospitalizations were available for analysis. This in- formation was used to validate some of the health status indicators and constructs testing them against

medical care utilization. For this analvsis. utilization variables were treated as dichotomies (following analysis of their distributions). Physicians’ visits (for a one-year period) were divided into <5 visits. and 2 6 visits in a year: hospitalizations were classified as none, and I day or more (over a period of 4 years) [24]. For hospitalizations all episodes related to delivery and pregnancy were omitted but for doctors’ visits, ambulatory visits of all types were included. Because of obvious differences in patterns of utiliza- tion by gender, analyses were done separately by sex.

Analyses were performed using the SPSS-X (version 2.2) package; the different methods used included:

-ross-tabulation using Gamma as a measure of association;

-nonparametric (Spearman) correlations; -factor analysis (using principal components with

oblique rotation); for some sections of the anal- ysis factor scores were computed by the regres- sion method from the factors extracted by principal components after oblique rotation and categorized by quartiles;

-cluster analysis of variables; the agglomeration schedule was used to identify clusters, although for simplicity only the icicle plot is included in the Results section.

RESb LTS

The dimensions of health status measures 763

Table I. Percentages for health related variables by sex and age group

Male Female

All 2044 45-64 65+ All 2&44 454 65+

SELF-RATED HEALTH EXCelle”t Good Fair Poor

WORRY OVER HEALTH No worry at all Hardly any worry Some worry A lot of worry

CHRONIC CONDITIONS None One Two Three or more

COMPARABLE ENERGY Much more energy Somewhat more Average amount Somewhat less Much less energy

PHYSICAL CONDITION Very satisfied Satisfied Not too satisfied Not at all satisfied

EMOTIONAL STATUS Very good Good Fair Poor

HAPPINESS Very happy Fairly happy Not too happy Unhappy

DISABILITY No disability Temporary disability Permanent disability

RESTRICTION No days at home No days in bed 1-3 days in bed 4+ days in bed

SOCIAL CONTACTS I I + contacts 7-10 con1acts 34 contacts l-2 cO”lactS 0 contacts

28 30 25 20 28 31 24 17 54 55 51 SO 54 56 50 51 16 14 21 26 I7 I2 23 29 2 I 3 4 I I 2 3

53 54 50 46 39 40 38 35 23 24 18 27 27 29 21 26 21 I9 26 21 29 27 35 29

4 3 6 7 5 4 6 10

50 59 35 19 39 47 26 I7 29 28 31 29 28 28 31 25 13 IO 19 27 19 I5 21 32 8 4 I5 25 14 IO 22 26

I4 I3 I4 19 II IO I3 II 26 26 25 32 22 21 22 25 55 57 55 39 61 63 56 56

4 3 4 8 6 5 8 6 I I 2 2 I I I I

I9 17 19 32 I7 16 20 22 61 61 59 57 66 66 66 66 19 20 I8 9 I5 16 13 IO 2 I 3 2 2 2 I I

48 45 5s 62 45 42 51 49 43 47 35 31 4s 48 39 39

7 7 9 6 9 9 9 IO I I 1 I I I 2 2

33 31 36 43 33 33 34 35 62 64 59 52 61 62 59 58

4 4 5 5 5 5 6 7 I I 0 0 I I 2 0

91 95 85 78 92 96 87 79 2 2 2 4 2 I 2 2 7 3 I3 I8 7 3 II 19

57 52 66 76 21 22 20 19 IS I8 8 2 7 8 6 3

47 60 63 20 23 23 21 9 5 12 8 8

29 28 32 32 27 28 23 29 31 32 28 26

8 8 9 8 5 4 8 5

52 21 17 10

21 31 39

8 I

20 20 23 34 2s 26 38 42 39

7 II II I 2 I

Table 2. Association (gamma) between health status variables. Sex, Age (grouped) and Education

Age Sex (grouped) Education

Self-rated Health 0.005 0.248 -0.320 Worry over Health 0.226 0.132 0.003 Chronic Conditions 0.229 0.430 -0.169 Comparable Energy 0. I36 -0.026 -0.109 Physical Condition -0.031 -0.141 0.097 Emotional Status 0.072 -0.136 -0.046 Happiness 0.013 -0.053 0.086 Disability -0.034 0.554 -0.268 Restr~cuo” 0.103 -0.277 0.212 Social Contacts 0.079 0.048 -0.046

nor Restrict correlate with any other variable and remain isolated.

The next step in studying possible patterns was to use factor analysis. Separate analyses were carried out first on the aforementioned randomly split sam- ple while controlling for sex and then, as the results for each sex were very similar, on the whole study population not controlling for sex. Several methods of extraction of factors including principal compo- nents and maximum likelihood were used on the data set [25,26]. Principal component extraction with oblique rotation was chosen as the most suitable method. The results must be interpreted with caution

764

Worry Chronic Energy PhyCond Emotional Happiness Disability Restrtct Social

SRHealth

0.4034 0.3147 0.2774 0.3551 0.2278 0. I786 0.2491 0.1010 0.0434

JORGE SEGOVIA er al.

Table 3. Spearman Correlation Matrix-IO health status variables

worry Chronic Energy PhyCond Emouonal Happiness

0.4165 0.1658 0.0923 0.2887 0.1734 0.3156 0.2586 0.1478 0.2003 0.2049 0. I788 0.0759 0.1573 0.2305 0.4 106 0.2505 0.3085 0.1 192 0.1359 0. I200 0.0875 0.2399 0.1681 0.0616 0. I590 0.1023 0.0545 0.0831 0.0586 0.0677 0.0600 0.1225 0.1035

Dtsabtlity

0.1135 0.0201

Restrict

-0.0268

Table 4. Pattern matrix with eiaenvalue and % of vartance

Factor I Factor 2 Factor 3 Factor 4 Factor 5

Chronic Disability Worry

Happiness Emotional

Energy PhyCond SRHealth

Restrict

Social

Eigenvalue % of var.

0.77824 -0.03069 0.77181 -0.00425 0.54733 0.12787

-0.08418 0.88781 0.05812 0.83279

-0.08530 -0.03219 0.01003 0.05185 0.46728 0.06688

-0.01246 -0.02169

-0.00015 -0.00028

2.83614 1.25865 28.4 12.6

and should used indicators the subsumed our variables; fact analyses

different produced similar strengthens conclusions. components

produced eigenvalue which that five-factor may the suitable.

five account 70.0% the Table shows pattern (loadings) the

rotation. first (which 28.4% the loads Chronic, and the factor of is

of and the has mainly Energy, and

But also in first in health is identically between I and 3. The fourth and fifth factors are composed of single variables: restriction of nor- mal activity (Restrict) and social contacts (Social). Therefore factor analysis reveals three factors which summarize information from several variables: one which is composed of variables related to disease (Chronic, Disability, and Worry), a second related to a psychological dimension (Happiness and Emo- tional), and a third related to subjective opinions about level of energy and physical condition. Cluster analysis undertaken on the 10 variables also pro-

__ WORRY / CHRONIC , DISABILITY ---

---- SRHEALTH

__ PHYCOND , ENERGY ---------------

__ HAPPINESS / EMOTIONAL

__ SOCIAL __ RESTRICT

Fig I. Pattern from nonparametric correlations between IO health status variables.

0.06879 0.03268

-0.13073

-0.00029 0.02802

-0.85535 -0.71586 -0.47466

-0.01008

1.02431 10.2

-0.11936 0.14903

-0.28809

0.02954 -0.00022

0.09447 -0.16604 0.06080

-0.95757

0.05135

0.98266 9.8

0.07035 -0.05436 0.05757

-0.02435 0.02894

0.01127 0.02263

-0.04241

-0.04612

0.99494

0.89711 9.0

duced similar results. The icicle plot provides an informative approach to the way in which variables are joined at each step and shows that, at the five cluster level, the clusters have the same pattern as shown by factor analysis (Fig. 2).

One way to validate these dimensions is to test them against medical care utilization. Table 5 shows gammas for all 10 health status variables plus the five factor scores for doctors visits and hospitalizations by sex. The differences in associations with utilization are clear. Factor 1 shows good association with utilization especially for males and doctors’ visits; the other factors are not associated with utilization. But it should be noted that single variables such as Chronic and Disability are more strongly associated with utilization than factor I. Self-rated Health is also associated with utilization especially for doctor visits in males.

DISCUSSION

When analyzing the 10 indicators of health status included in our instrument, a clear pattern emerged

s REHPEDCWS OEMAHN I H 0 R C SOPYESRRH I TTPCRAORE ARIIOG B N Y A L I ONNYII L

C N E D L c T T A S I H

rfis /I I/l T Y

1 xxxxxxxxxxxxxxxxxxxxxxxxxxxx C 2 x xxxxxxxxxxxxxxxxxxxxxxxxx L 3 x x xxxxxxxxxxxxxxxxxxxxxx u 4 x x xxxx xxxxxxxxxxxxxxxx S 5 x x xxxx xxxx xxxxxxxxxx T 6 x x xxxx xxxx x xxxxxxx E 7 x x xxxx x x x xxxxxxx R 8 x x xxxx x x x x xxxx S 9 x x xxxx x x x x x x

Fig. 2. Cluster analysis-icicle plot.

The dimensions of health status measures 765

Table 5. Assoclatmn (gamma) between health status variables, doctors’ visits and hostxtahzations bv sex

Self-Rated Health

Doctors’ wits

0.423

Male

Hospitalizations

0.299

Doctors’ visits

0.288

Female

Hospitahzations

0.295 Worry over Health 0.502 Chronic Conditions 0.540 Comoarable Enerev Physical Conditioi

0.123 0.036

Emotional Status 0.150 Happtness 0.106 Disability 0.596 Restrict& 0.247 Social Contacts 0.061

Factor I OS25 Factor 2 -0.047 Factor 3 0.058 Factor 4 -0.146 Factor 5 0.052

0.349 0.373 0.328 0.408 0.405 0.322 0.237 0.079 0.1 I8 0.131 0. I54 0.175 0.148 0. I35 0.085 0. I36 0.059 0.060 0.743 0.407 0.502 0.205 0.282 0. I76 0.104 0.033 0.085

0.366 0.360 -0.027 0.018

.0.222 _

0.044

0.355 0.022 0.041 0.122 0.068

-0.021 0.000

-0.01 I

0.055

from the nonparametric correlation matrix and the factor and cluster analyses. The first factor includes the variables associated with diseases such as number of chronic conditions and presence or absence of temporary or permanent disability-a ‘disease fac- tor’. The question regarding worry about health during the past year is also included in this factor. Because this variable is also associated with utiliza- tion (measured concurrently with the survey), it may be measuring the level of concern produced by dis- eases or infirmity which was present around the time of the survey. A second factor is composed of self- rated happiness and an emotional score-a ‘happi- ness factor’. A third factor includes questions relating to subjective opinions about energy and physical condition as well as most of the loading for self-rated health-a ‘subjective’ factor. It is important to note that self-rated health loads almost equally between the first (‘disease’) and third (‘subjective’) factors, which indicates that this variable cuts across two significant dimensions of health status in our data.

The association of the first factor with utilization- both doctors’ visits and hospitalizations-provides a confirmation of the validity of this factor. Conceptu- ally it is licit to assume that an indicator of health status should be associated with medical care utiliza- tion. This association will not be perfect because many aspects of health status may not be amenable to medical care services. In addition, subjects vary in their predisposition to use health services. In Newfoundland as for all of Canada, an important barrier to access-cost-has been eliminated but social and cultural barriers are likely to persist.

Several authors have related health indicators with utilization. Martini er al. [27] studied the sensitivity of a battery of traditional health indexes to medical care variation and proved that traditional indicators were not good predictors of utilization. Siegman and Elinson [28] compared social versus physiological health indicators and advanced a conceptual frame- work for new sociomedical indicators to estimate health care needs and utilization. Pope [29] recently analyzed several indicators of health status (self-rated health status. role limitations, restricted activity days and functional limitations) together with 42 medical conditions. using data from the Medical Care Utiliza-

tion and Expenditure Survey. His findings are very complex and due to some specific design and method- ological features must be interpreted with care. It was found that perceived or self-rated health status reflects serious chronic conditions but it does not measure acute transitory morbidity. The health indi- cators also predicted ambulatory utilization for some conditions. Roos et al. [30] in a recently published paper made a comparison between administrative data (computerized utilization data banks) and survey data (including more traditional indicators of health status) for a sample of elderly residents of Manitoba. They reported that both survey and administrative data were similar for predicting entry into nursing homes; administrative data was better for predicting hospitalization and death. However self-assessed health status was included in all logistic regression models with a significant probability; in models including survey data only, self-assessed health status was either the best predictor or the second best predictor. Manning [31] reported results from the Health Insurance Study and concluded that a battery of health indicators is better at predicting utilization than simple self-assessed health status. Other authors [32] have reported that self-assessed health status by elderly tenants in public housing is a good predictor of hospital admission and nursing home placements. In general most of these studies have been carried out on selected populations, i.e. the elderly, or are related to specific interventions. Stud- ies from the U.S.A. tend to include variables related to cost or to include health care costs in the measure- ment of utilization [29]; therefore straightforward comparison with the Newfoundland and Canadian scenes could be incorrect. We consider our validation of the health status factors against utilization a method which continues and reinforces the findings of previous studies, with the advantage that our data were obtained from a sample of the general popula- tion and combines it with utilization data which is complete and not subjected to problems of recall. We consider these findings as preliminary, however, because the cross-sectional design of this study does not yet allow for a correct time sequence between health status variables and utilization variables.

The fact that social contacts is uncorrelated with

766 JORGE SEGOVIA et al.

the other variables in our sample is not surprising. Other studies have shown that Newfoundlanders tend to report less stress and larger number of relatives and close friends than in other areas of Canada [33]. Primary relations are still very important, the ex- tended family subsists despite increasing urbaniza- tion. Therefore social contacts may not interact with variables which attempt to measure health status. The fourth factor which loads only on the variable restric- tion of normal activity is not easy to explain; concep- tually it could have been a part of either factor 1 or 3. This apparent discrepancy may be related to poor phrasing of the question which is not adequate for subjects working at home or not working. Also these questions may be measuring a dimension of absen- teeism from work which is likely to respond to different causes unrelated to health. This interpreta- tion is supported by the fact that the percentage of subjects reporting some restriction is slightly greater for the age group 20-44, and more so in females (Table 1).

It should be remembered that these data were collected by a cross-sectional survey and therefore we can only assume association between variables. Some of the possible interpretations at this point are close to falling into the post factum fallacy. Nevertheless these are results which we consider to be clear and meaningful. Self-rated health is a good summary indicator of genera1 health status, having correlations with disease-oriented variables and with more subjec- tive appraisal. The dimensions suggested by the anal- ysis of the correlation matrix and by factor and cluster analyses are plausible. Our objective-to verify if self-reported health status was a good single indicator of health status-has been achieved. A second conclusion is that a relatively small number of questions is sufficient to obtain information con- cerning health status which includes most of the dimensions postulated by other authors.

A 4-year longitudinal design is planned for the study. This second phase will make it possible to test these variables and the factors as predictors of medium-term health status, utilization of health services and of mortality. More conclusive evidence on the validity of the dimensions discovered in these measures of health status is the goal of the next phase.

Acknowledgemenr-This research was supported by grant 6601-1079-46 from the National Health Research and Development Program, Health and Welfare Canada, and by the Faculty of Medicine of Memorial University of Newfoundland.

1.

2.

3.

4.

5.

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APPENDIX

SRHealrh: QUESTION 41. Would you say that your health is.

Excellent-Good-Fair-Poor- Worry: QUESTION 42. Over the past year, has your health caused you.

No worry at all-Hardly any worry- Some worry-A great deal of worry-

Chronic: QUESTION 43. Do you have any of the following chronic conditions?

(Chronic means the condition has been present for 3 months or more) Read list: Circle codes that correspond

Anemia 01 High blood pressure Allergy (of any kind) 02 Kidney disease (stones etc.) Arthritis, rheumatism 03 Mental illness Asthma 04 Missing arm(s) or leg(s) Cancer 05 Missing finger(s) toe(s) Cerebral Palsy 06 Paralysis of any kind Diabetes 07 MALES: Prostate disease FEMALES: Dysmenorrhea Recurring backaches

(menstrual problems) 08 Recurring headaches Emphysema 09 Stomach ulcer Epilepsy 10 Thyroid trouble or goitre Heart Disease 11 Tuberculosis Hemorrhoids (piles) 12 OTHER

Specify_ None

Energy: QUESTION 44.

PhyCond: QUESTION 45.

Emotional: QUESTION 46.

Happiness: QUESTION 47.

Disabilir),: QUESTION 25.

QUESTION 26.

Compared with other people your age, would you say you have.. Much more energy-Somewhat more (energy)_ Average amount of energy- Somewhat less (energy)-Much less energy-

In general, how satisfied are you with your overall physical condition.. Are you very satisfied-satisfied- Not too satisfied-Not at all satisfied--

During the past few weeks, how often have you felt. .

On top of the world Lonely

Often Sometimes Never

That things were going your way Restless Depressed. or unhappy

All in all. how happy are you these days? Would you say. Very happy-Fairly happy- Not too happy-unhappy-

Are you now suffering from any disability? (PROBE: A condition that stops you from doing your routine activities)

Yes-No- Is it a temporary conditions?

(PROBE: A condition that will disappear in a few weeks) Yes-No--Don’t know_

13 14 I5 16 17 18 19 20 21 22 23 24

25 88

768 JORGE SEGOVIA et al.

Restrict: QUESTION 34.

QUESTION 35.

QUESTION 36.

Social: QUESTION 48.

QUESTION 49.

Within the last year (from-1984) have you stayed at home because of an illness. or not feeling well? Yes-No-- Did you stay in bed? Yes-No- How many days did you stay in bed? CODE DIRECT-I-I-/

How many close relatives do you have? These are people that you feei at ease with. can talk to about private matters, and can call on for help. (DO NOT INCLUDE SPOUSE) CODE DIRECT-I - How many close friends do you have? These are people that you feel at ease with, can talk to about private matters and can call on for help. CODE DIRECT_/ ~