An empirical analysis of the dimensions of health status measures

Sot. Sci. Med. Vol. 29. No. 6, pp. Printed in Great Britain. All rtghts

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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. Nonparametric 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 condition/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,

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 dissatisfaction. 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 approaches [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 dimensions-physical. mental, social. and general health 761

health survey

perceptions-were extensively studied for their variability, 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 Wellbeing Scale [15] and the Nottingham Health Profile [ 16, 171, incorporate questions designed to obtain information from a variety of domains. The conceptual framework used to develop these scales is extremely 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 questionnaire 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. Previous 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

762

JORGESEWVIA et

verify whether this decision was supported by empirical 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 directory was used as the sample frame. Telephone coverage 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 (questions 48, 49); label: Social. The complete questions are included in the Appendix 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 assigned 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 information was used to validate some of the health status indicators and constructs testing them against

al.

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 utilization 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 analysis factor scores were computed by the regression 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 Table

I. Percentages

for health

related

variables

by sex and age group

Male All

2044

28 54 16 2

30 55 14 I

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

53 23 21 4

CHRONIC CONDITIONS None One Two Three or more

Female 65+

All

2&44

25 51 21 3

20 SO 26 4

28 54 I7 I

31 56 I2 I

24 50 23 2

17 51 29 3

54 24 I9 3

50 18 26 6

46 27 21 7

39 27 29 5

40 29 27 4

38 21 35 6

35 26 29 10

50 29 13 8

59 28 IO 4

35 31 19 I5

19 29 27 25

39 28 19 14

47 28 I5 IO

26 31 21 22

I7 25 32 26

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

I4 26 55 4 I

I3 26 57 3 I

I4 25 55 4 2

19 32 39 8 2

II 22 61 6 I

IO 21 63 5 I

I3 22 56 8 I

II 25 56 6 I

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

I9 61 19 2

17 61 20 I

19 59 I8 3

32 57 9 2

I7 66 I5 2

16 66 16 2

20 66 13 I

22 66 IO I

EMOTIONAL Very good Good Fair Poor

48 43 7 I

45 47 7 I

5s 35 9 1

62 31 6 I

45 4s 9 I

42 48 9

51 39 9 2

49 39 IO 2

HAPPINESS Very happy Fairly happy Not too happy Unhappy

33 62 4 I

31 64 4 I

36 59 5 0

43 52 5 0

33 61 5 I

33 62 5

34 59 6 2

35 58 7 0

DISABILITY No disability Temporary disability Permanent disability

91 2 7

95 2 3

85 2 I3

78 4 I8

92 2 7

96 3

87 2 II

79 2 19

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

57 21 IS 7

52 22 I8 8

66 20 8 6

76 19 2 3

52 21 17 10

47 20 21 12

60 23 9 8

63 23 5 8

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

29 27 31 8 5

28 28 32 8 4

32 23 28 9 8

32 29 26 8 5

21 31 39 8 I

20 34 38 7

20 2s 42 II 2

23 26 39 II I

SELF-RATED EXCelle”t Good Fair Poor

45-64

454

65+

HEALTH

STATUS

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

Self-rated Health Worry over Health Chronic Conditions Comparable Energy Physical Condition Emotional Status Happiness Disability Restr~cuo” Social Contacts

763

Sex,

Sex

Age (grouped)

Education

0.005 0.226 0.229 0. I36 -0.031 0.072 0.013 -0.034 0.103 0.079

0.248 0.132 0.430 -0.026 -0.141 -0.136 -0.053 0.554 -0.277 0.048

-0.320 0.003 -0.169 -0.109 0.097 -0.046 0.086 -0.268 0.212 -0.046

I

I I

I

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 sample 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 components 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

JORGE SEGOVIA er al. Table 3. Spearman Correlation Matrix-IO SRHealth 0.4034 0.3147 0.2774 0.3551 0.2278 0. I786 0.2491 0.1010 0.0434

Worry Chronic Energy PhyCond Emotional Happiness Disability Restrtct Social

worry

Chronic

0.4165 0.1658 0.2887 0.2586 0. I788 0.2505 0.2399 0.0831

0.0923 0.1734 0.1478 0.0759 0.3085 0.1681 0.0586

Energy

health status variables

PhyCond

Emouonal

0.2049 0.2305 0.1359 0. I590 0.0600

0.4 106 0. I200 0.1023 0.1225

0.3156 0.2003 0.1573 0.1 192 0.0616 0.0677

Happiness

Dtsabtlity

Restrict

0.0875 0.0545 0.1035

0.1135 0.0201

-0.0268

Table 4. Patternmatrixwith eiaenvalue and % of vartance Factor I

Factor2

Factor3

Factor4

Factor5

0.77824 0.77181 0.54733

-0.03069 -0.00425 0.12787

0.06879 0.03268 -0.13073

-0.11936 0.14903 -0.28809

0.07035 -0.05436 0.05757

Happiness Emotional

-0.08418 0.05812

0.88781 0.83279

-0.00029 0.02802

0.02954 -0.00022

-0.02435 0.02894

Energy PhyCond SRHealth

-0.08530 0.01003 0.46728

-0.03219 0.05185 0.06688

-0.85535 -0.71586 -0.47466

0.09447 -0.16604 0.06080

0.01127 0.02263 -0.04241

-0.95757

-0.04612

Chronic Disability Worry

Restrict

-0.01246

-0.02169

Social

-0.00015

-0.00028

Eigenvalue % of var.

2.83614 28.4

1.25865 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 normal 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 Emotional), and a third related to subjective opinions about level of energy and physical condition. Cluster analysis undertaken on the 10 variables also pro-

__ WORRY

-0.01008 1.02431 10.2

When analyzing the 10 indicators of health status included in our instrument, a clear pattern emerged s REHPEDCWS OEMAHN C SOPYESRRH I TTPCRAORE ARIIOG L I ONNYII C N E D T A S

ENERGY --------------C

__ SOCIAL __ RESTRICT Fig

I. Pattern

from nonparametric correlations health status variables.

between

IO

0.89711 9.0

DISCUSSION

---- SRHEALTH

/ EMOTIONAL

0.99494

0.98266 9.8

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.

/ CHRONIC , DISABILITY ---

__ PHYCOND , __ HAPPINESS

0.05135

L u S T E R S

1 2 3 4 5 6 7 8 9

I

H

0

R

B

N

Y

A L T H

L c I T Y I/l rfis /I xxxxxxxxxxxxxxxxxxxxxxxxxxxx x xxxxxxxxxxxxxxxxxxxxxxxxx x x xxxxxxxxxxxxxxxxxxxxxx x x xxxx xxxxxxxxxxxxxxxx x x xxxx xxxx xxxxxxxxxx x x xxxx xxxx x xxxxxxx x x xxxx x x x xxxxxxx x x xxxx x x x x xxxx x x xxxx x x x x x x

Fig. 2. Cluster

analysis-icicle

plot.

The dimensions of health status Table 5. Assoclatmn

(gamma)

between

health status

variables,

doctors’

visits and hostxtahzations

Male Doctors’ wits Self-Rated Health Worry over Health Chronic Conditions Comoarable Enerev Physical Conditioi Emotional Status Happtness Disability Restrict& Social Contacts Factor Factor Factor Factor Factor

I 2 3 4 5

765

measures

Hospitalizations

Doctors’ visits

Hospitahzations

0.423 0.502 0.540 0.123 0.036 0.150 0.106 0.596 0.247 0.061

0.299 0.349 0.408 0.237 0.131 0.148 0. I36 0.743 0.205 0.104

0.288 0.373 0.405 0.079 0. I54 0. I35 0.059 0.407 0.282 0.033

0.295 0.328 0.322 0.1 I8 0.175 0.085 0.060 0.502 0. I76 0.085

OS25 -0.047 0.058 -0.146 0.052

0.366 -0.021 0.000 -0.01 I 0.055

0.360 -0.027 0.018 .0.222 0.044

0.355 0.022 0.041 _ 0.122 0.068

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 factor’. The question regarding worry about health during the past year is also included in this factor. Because this variable is also associated with utilization (measured concurrently with the survey), it may be measuring the level of concern produced by diseases or infirmity which was present around the time of the survey. A second factor is composed of selfrated happiness and an emotional score-a ‘happiness 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 utilizationboth doctors’ visits and hospitalizations-provides a confirmation of the validity of this factor. Conceptually it is licit to assume that an indicator of health status should be associated with medical care utilization. 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 framework 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-

bv sex

Female

tion and Expenditure Survey. His findings are very complex and due to some specific design and methodological 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 indicators 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. Studies from the U.S.A. tend to include variables related to cost or to include health care costs in the measurement 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 population 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

JORGESEGOVIAet al.

766

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 extended family subsists despite increasing urbanization. Therefore social contacts may not interact with variables which attempt to measure health status. The fourth factor which loads only on the variable restriction of normal activity is not easy to explain; conceptually 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 absenteeism from work which is likely to respond to different causes unrelated to health. This interpretation 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 subjective appraisal. The dimensions suggested by the analysis 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 concerning 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.

6 7

8.

9.

IO.

I I. 12.

13.

14.

15.

16.

17.

18.

19.

20.

21. 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.

22.

23 REFERENCES

1. Sanazaro P. J. Seminar on research in patient care. Med. Care 4, 43, 1966. R. A. (Ed.) Social Indicators. MIT Press, 2. Bauer Cambridge, Mass., 1966. 3. Berg R. L. (Ed.) Health Status Indexes, pp. 243-247. Hospital Research and Educational Trust, Chicago, Ill.. 1973. 4. Elinson J. Toward sociomedical health indicators. Sot. Indicar. Res. 1, 59, 1974. 5. Siegmann A. E. A classification of sociomedical health indicators; perspectives for health administrators and health planners. In Socio-Medical Healrh Indicators

24.

25

26

(Edited by Elinson J. er al.). p. 197. Baywood. Farmingdale. N.J.. 1979. Fanshel S. A meaningful measure of health for epidemiology. Inr. J. Eprdem. 1, 319. 1972. Breslow L. A quantitative approach to the world health organization detinition of health: physical, mental and social well-being. Inr. J. Epidem. 1, 347-350. 1972. Patrick D. L.. Bush J. W. and Chen M. M. Toward an operational definition of health. J. Hlth sot. Behac. 14, 6-23, 1973. Chen M. M. and Bush J. W. Maximizing health system output with political and administrative constraints using mathematical programming. Inquiry 13, 215. 1976. Maddox G. L. Self-assessment of health status-A longitudinal study of selected elderly subjects. J. chron. Dis. 17, 449460. 1964. Mootz M. Health indicators. Sot. Sci. Med. 22, 255-263, 1986. Ware J. E. Jr. The assessment of health status. In Applications of Social Science to Clinical Medicine and Health Policy (Edited by Aiken L. H. er al.), p. 204. Rutgers University Press, New Jersey, 1986. Brook R. H., Ware J. E. Jr. Davies-Avery A., Stewart A. L.. et al. Overview of adult health status measures fielded in Rand’s Health Insurance Study. Med. Care Suppl. 17, No. 7, 1979. Bergner M., Bobbitt R. A.. Kressel S., Pollard W. E., Gilson B. S. and Morris J. R. The sickness impact profile: conceptual formulation and methodology for the development of a health status measure. fnr. J. Hlth Serv. 6, 393, 1976. Kaplan R. M. and Bush J. W. Health related quality of life measurement for evaluation research and policy . _ analysis. HIrh Psychol. 1, 61, 1982. Hunt S. M., McEwen J. and McKenna S. P. Measuring health status: a new tool for clinicians and enidemiologists. J. R. Coil. Cen. Pratt. 35, 185, 1985. _ McDowell I. W.. Martini C. J. and Waugh W. A method for self-assessment of disability before and after hip replacement operations. Br. med. J. 2, 857, 1978. McDowell I. and Newell C. Measuring Health-A Guide IO Rating Scales and Ouestionnaires. Oxford University Press, New York, 1987. Seeovia J.. Bartlett R. F. and Edwards A. C. The association between self-assessed health status and individual health practices. Can. J. publ. Hlth 80, 32-37. 1989. Berkman L. F. and Breslow L. Health and Ways of Living; The Alameda County Study. Oxford University Press, New York, 1983. Wilson R. W. and Elinson J. National survey of personal health practices and consequences: background, conceptual issues and selected findings. Publ. Hhh Rep., Wash. %, 218. 1981. Canada Health Survey: The Health of Canadians. Health and Welfare and Statistics Canada, Ottawa, 1981. Segovia J., Bartlett R. F., Edwards A. C. and Veitch B. Lifestyle, health practices and utilization of health services-Final Report. Memorial University of Newfoundland, St John’s, Canada, 1987. Segovia J., Bartlett R. F., Veitch B. and Edwards A. C. The St John’s Study of health practices and medical care utilization. American Public Health Association, Medical Care Section, Contributed Papers V; 114th Annual Meeting. Las Vegas, 1986. Johnson R. A. and Wichern D. W. Applied Multivariate Sfaristical Anai_vsis. Prentice-Hall, ‘Englewood Cliffs, N.J.. 1983. Cattell R. B. The Scientific Use of Factor Analysis in Behavioural and Life Sciences. Plenum Press, New York, 1978.

167

The dimensions of health status measures 27. Martini C. J.. Allan G. J.. Davison J. and Backett E. M. Health indexes sensitive to medical care variation. In Socio-Medical Healrh Indicators (Edited by Elinson J. et al.), p. 145. Baywood, Farmingdale, N. J., 1979. 28. Siegmann A. E. and Elinson J. Newer sociomedical health indicators-implications for evaluation of health services. Med. Care Suppl. 15, No. 5, 1977. 29. Pope G. C. Medical conditions, health status, and health services utilization. Hlrh Sero. Res. 22, No. 6, 1988. 30. Roos N. P., Roos L. L., Mossey J. and Havens 9. Using administrative data to predict important health outcomes; entry to hospital, nursing home, and death. Med. Care 26, No. 3, 1988.

31. Manning W. G., Newhouse J. P. and Ware J. E. Jr. The status of health in demand estimation; or, beyond excellent, good. fair, and poor. In Economic Aspecfs of Health (Edited by Fuchs V. R.), p. 143. The University of Chicago Press, Chicago, Ill., 1982. 32. Weinberger M., Darnell J. C., Tierney W. M., Martz 9. L., et a/. Self-rated health as a predictor of hospital admission and nursing home placement in elderly public housing tenants. Am. J. publ. Hlth 76, No. 4, 1986. 33. The Active Health Report: The Health Promotion Survey in Neyfoundiand. Department of Health, Government of Newfoundland, St John’s, 1987.

APPENDIX SRHealrh:

QUESTION

41.

Would you say that your health is. Excellent-Good-Fair-Poor-

42.

Over the past year, has your health caused you. No worry at all-Hardly any worrySome worry-A great deal of worry-

Worry:

QUESTION Chronic:

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 01 High blood pressure Anemia 02 Kidney disease (stones etc.) Allergy (of any kind) 03 Mental illness Arthritis, rheumatism 04 Missing arm(s) or leg(s) Asthma Missing finger(s) toe(s) Cancer 05 06 Paralysis of any kind Cerebral Palsy MALES: Prostate disease 07 Diabetes Recurring backaches FEMALES: Dysmenorrhea 08 Recurring headaches (menstrual problems) Stomach ulcer 09 Emphysema Thyroid trouble or goitre 10 Epilepsy Tuberculosis Heart Disease 11 Hemorrhoids (piles) 12 OTHER Specify_ None QUESTION

43.

Energy:

QUESTION

44.

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

45.

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

46.

During the past few weeks, how often have you felt. . Often Sometimes On top of the world Lonely That things were going your way Restless Depressed. or unhappy

PhyCond:

QUESTION Emotional:

QUESTION

Never

Happiness:

QUESTION

47.

All in all. how happy are you these days? Would you say. Very happy-Fairly happyNot too happy-unhappy-

QUESTION

25.

QUESTION

26.

Are you now suffering from any disability? (PROBE: A condition that stops you from doing your routine activities) Yes-NoIs it a temporary conditions? (PROBE: A condition that will disappear in a few weeks) Yes-No--Don’t know_

Disabilir),:

13 14 I5 16 17 18 19 20 21 22 23 24 25 88

JORGE SEGOVIAet al.

768 Restrict:

QUESTION

34.

QUESTION

35.

QUESTION

36.

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-NoHow many days did you stay in bed? CODE DIRECT-I-I-/

Social:

QUESTION 48.

QUESTION 49.

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_/ ~