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The independent contributions of socioeconomic status and health practices to health status
PREVENTIVE
MEDICINE
14, 372-378 (1985)
The Independent Contributions and Health Practices
of Socioeconomic to Health Status
CARL H. SLATER, M.D.,'R~NALD AND DAVID R. LAIRSON, Health
Services Organization,
University of Texas School Houstojz, Texas 77025
Status
J. LORIMOR, PH.D., PH.D.
qf Public Health, P.O. Box 20186,
The objective of this study was to determine whether the much-repeated finding of a relationship between socioeconomic status and health status is explained by individuals’ health practices. The investigation was carried out using data tapes from the 1977 Health Interview Survey in which a one-third subsample of adults was asked a series of questions related to the seven nonmedical health practices identified in the Alameda County Study. The group selected for analysis comprised 15,892 white, responding adults. With age controlled statistically, perceived health status was found to be associated with socioeconomic status, whether the indicator was educational level, family income, or occupation, and to number of positive health practices. When number of health practices, in addition to age and other socioeconomic indicators was controlled for, the association was still positive and significant. The finding of an independent contribution by socioeconomic status to health status emphasizes that individual health habits are not the only influence on health status. 0 1985 Academic Press. Inc.
INTRODUCTION One of the most enduring and least explained relationships found in the literature on health is the association between socioeconomic status (SES) and health status. Whether SES is measured by education, income, occupation, or a composite index, and whether health status is measured by mortality (I, 14, 24, 25), morbidity (7-10, IX, 25), or perceived health status (3, 11, 20, 21, 23), higher SES repeatedly has been shown to be associated with better health status. One question that has been asked is: do health practices explain this relationship? Two early studies demonstrate a relationship between number of positive health practices and health status (22, 23), while the Alameda County Study showed both physical health status (3, 27) and subsequent mortality (4, 6) to be related to seven health habits practiced by a sample of nearly 7,000 California adults. Other studies (15, 23) also have shown the expected relationship between SES and reported health practices and have examined the interrelationships among SES, health practices, and health status (3, 22, 23). Pratt (23) showed the rclationship between health practices and health status to be significant only among mothers at the lowest SES level. The health practice reports (3, 27) emanating from the Alameda County Study demonstrated that neither income nor education altered the basic relationship. The present study was undertaken to determine which of these interrelations would be found in a national data set. ’ To whom reprint requests should be addressed. 372 0091-7435/85 $3.00 CopyrIght U 1985 by Academic Press. Inc. All rights of reproduction in any form reserved.
HEALTH
PRACTICES,
SES,
AND
HEALTH
STATUS
373
METHODS The Subsample
In 1977, the Health Interview Survey of the National Center for Health Statistics (NCHS) included a series of questions related to the seven health practices studied in the Alameda County Study (17). These questions were asked of a onethird subsample of adults in the general survey. Since the Health Interview Survey included 41,000 households with approximately 121,000 persons and 70,000 adults, the one-third subsample included 22,824 eligibles of whom 9.4% chose not to respond to this set of questions. For purposes of this analysis, only white eligibles were selected (n = 20,381). Those with incomplete demographic data were excluded, leaving a group of 18,286 white eligibles with complete demographic data. Nonrespondents (7.8% of the remaining eligibles) and then those missing either health status or some health practice responses (5.7% of the remaining respondents) were eliminated. The successive application of these selection criteria yielded a final group of 15,892 white respondent adults with complete socioeconomic, health status, and health practices data. Table 1 compares the demographic characteristics of those with complete data to those missing the health practices and health status data. There were some small differences between the two groups. For the missing value group of 959 respondents, there was a slight excess of women, older persons, people with the least amount of education, persons in the lowest income bracket, and those not in the labor force. The Variables
The health status variable analyzed was self-assessed health status as excellent, good, fair, or poor (EGFP). It derived from the respondent’s answer to the question of how he or she would compare his or her health with that of others in the same age group. Several of the previous studies of health practices (20, 22, 23) have used a similar question. Ware et al. (26) reviewed the literature on this selfassessed health status measure and confirmed its association with a wide range of other health status measures, especially those that tap the general health status domain. The skewed distribution of this variable, however, differs from the more normally distributed physical health status variable used in the Alameda County Study (2, 16). While a variety of activities have been called health practices, ranging from as few as the three examined in Palmore’s study (22), the Health Interview Survey (17) focused on the same seven health practices included in the Alameda County Study, namely, sleep, breakfast, snacks, physical exercise, smoking, drinking, and weight control. Previous research (3, 27) has shown that a simple sum of these health practices can predict both health status and mortality and has been used as the basis for the construction of a cumulative index of positive health practices. The health practices in this study were scored in a similar fashion with each of the following being given one point: sleeping 7-8 hr a night, eating breakfast every day, rarely or never snacking between meals, being more physically
374
SLATER,
LORIMOR, TABLE
AND
LAIRSON
1
WITH AND WITHOUT COMPLETE DATA IN THE 1977 HEALTH INTERVIEW SURVEY HEALTH PRACTICE SUBSAMPLE
COMPARISONS
BETWEEN WHITE,
Variable
ELIGIBLE
RESPONDENTS
White, eligible respondents with complete data (N = 15,892)
White, eligible respondents with missing data (N = 959)
Gender M F
45.9% 54.1
45.2% 54.8
Age 20-24 25-34 35-44 45-54 55-64 65-74 75+
13.4 23.8 16.7 16.5 14.4 10.1 5.1
11.8 24.0 14.8 16.5 14.7 11.3 6.8
Education O-8 9-11 12 13+
15.4 14.3 37.9 32.3
19.9 14.3 35.3 30.5
Family income Less than $5,000 5,000-9,999 10,000-14,999 15,000-24,000 25,000
12.8 20.2 21.1 28.5 17.4
14.8 19.3 21.4 28.4 16.2
Occupation Not in labor force Blue collar White collar
35.6 29.6 34.8
37.4 29.9 32.7
Perceived health status (EGFP) Poor Fair Good Excellent Missing
3.2 11.2 38.8 46.7 0
4.7 10.2 38.8 40.9 5.4
Positive health practices 0 1 2 3 4 5 6 7 Missing
1.2 5.8 15.6 24.4 26.0 18.3 7.2 1.4 0
0.4 0.6 1.7 1.7 0.5 0 0 95.1
0
HEALTH
PRACTICES,
SES, AND
HEALTH
STATUS
375
active than other persons the same age, never having smoked, and not having had more than five drinks at one sitting anytime in the past year. While the Alameda County Study used the Metropolitan Life Tables (3) as the basis for determining weight control, body mass index (weight in kilograms divided by height in meters squared) was used in this study. This index has been demonstrated in other studies (12, 13) to be associated with body fat. Bray (5) states that the normal range of the body mass index is 20-25 for males and 19-24 for women. A score of + 1 on the health practices index was given if the respondent was within the normal range for his or her gender. The number of positives a respondent had on these practices was summed to yield a health habit score ranging from 0 to 7. The respondent’s reported number of years of schooling, after recoding into four categories (see Table l), was used as the education variable. Family income was reported by the respondent by the intervals, shown in Table 1. Occupation, from a list of 90 items, was recoded into the three categories, also displayed in Table 1. ANALYSIS
AND RESULTS
The Health Interview Survey was accomplished through a multistage probability design. In this design, a different sample of households is selected for interview each week in such a way that the weekly samples will be additive over the year. This complex, multistage, probability design affects the estimates derived from such a sample in two ways. First, the crude estimates are not representative of the U.S. population. This problem can be corrected by applying a weighting factor which adjusts for the nonrepresentativeness as well as for the nonresponses among the households selected. This weighting factor, calculated by NCHS, is available on the public-use data tape and was used to adjust all of the estimates in Tables 1 and 2. Such estimates, however, also have larger sampling errors than those derived from a true random sample. Some statistical packages, such as SPSS, assume a simple random sample. As a result, the test statistics reported from such a program will be underestimated. For this reason, the acceptable significance level for the F tests reported in Table 2 was set at the level of P < 0.001, but the level of significance is larger in all cases. As with past studies, the EGFP health status variable was treated as a continuous variable. The respondents were skewed to the right for this variable, with 85% reporting their health as good or excellent. Means were calculated for each category of the independent variables and comparisons between respondents grouped by gender, age, and health practices as well as by education, family income, and occupation were then made. The crude mean health status, where excellent was scored as 4, was 3.32 for men and 3.26 for women. Health status was found, as expected, to vary inversely with age, ranging from 3.51 for the youngest age group (20-24) to 2.99 for the oldest (75 +), and the differences were significant. Variation in unadjusted mean health status by socioeconomic indicator is displayed in the first column of Table 2. Education, family income, and occupation each has a positive and significant association with self-assessed health status.
376
SLATER, LORIMOR,
UNADJUSTED
AND ADJUSTED OCCUPATION
AND LAIRSON
TABLE 2 MEAN SELF-ASSESSED HEALTH FOR WHITE,
ELIGIBLE
STATUS BY EDUCATION, RESPONDENTS (N = 15,892)
INCOME,
AND
Mean health status Socioeconomic indicator
Unadjusted
Ageadjusted
Age-habits” adjusted
Age-habitsaother SESadjusted
Education O-8 yrs. 9-11 12 13+
2.80b 3.06 3.35 3.56
2.91 3.08 3.33 3.52
2.93 3.11 3.33 3.50
3.03 3.15 3.31 3.45
Family income Less than $5,000 5,000-9,999 10,000- 14,999 15,000-24,999 25,000 +
2.88 3.09 3.31 3.44 3.55
2.98 3.12 3.28 3.40 3.54
2.99 3.13 3.28 3.40 3.53
3.10 3.18 3.29 3.36 3.44
Occupation Not in labor force Blue collar White collar
3.03 3.33 3.51
3.11 3.29 3.47
3.11 3.31 3.46
3.17 3.36 3.43
a Habits means number of positive health practices. b All differences between means for each indicator are significant (P < 0.001).
The range of mean health status from the lowest to the highest category within each SES indicator is reassuringly consistent. Approximately 70% of the respondents reported from three to five positive health practices with the percentage reporting as positive on any individual health practice ranging from 34.1% (for not snacking) to 68.8% (for not drinking excessively). Health practices also had a positive and significant association with perceived health status. The range was from 3.09 for those with no positive health practices to 3.62 for those reporting all seven positive health practices. Analysis of covariance (19) was used to adjust mean health status successively for the effects of age, habits, and other SES indicators. Adjustment for gender is not reported because there was only a modest relationship between gender and health status. Furthermore, controlling for age reduced the difference between men and women on mean health status and controlling for gender did not alter any of the relationships, unadjusted and adjusted, analyzed. Since health status was found to vary inversely with age, analysis of covariance with age as a continuous variable was used to adjust the means for this negative relationship. The age-adjusted mean health status for each category of SES indicator is displayed in the second column of Table 2. The association between SES and health status remained positive and significant when the relationships were adjusted for age; the range from the lowest to the highest level within each variable, however, was slightly attenuated by the age adjustment. Because health status was related to number of health practices as well as to
HEALTH
PRACTICES,
SES, AND
HEALTH
STATUS
377
each of the SES indicators, the age-adjustment was coupled with adjustment for the level of health practices and for the level of each of the other two SES indicators. The third and fourth columns of Table 2 show the relationship between the SES indicator and health status when number of health practices, age, and the other SES indicators were controlled. The association between SES and health status is seen to hold for each indicator, with the health practices adjustment having almost no effects. The range of variation from the lowest to the highest category of each indicator for the complete SES adjustment is further attenuated. These results show that age and the other SES variables operate, when uncontrolled, to alter slightly the association between any one SES variable and self-assessed health status, but that the level of health practices does not alter the SES to health status relationship. DISCUSSION
This analysis of the 1977 Health Interview Survey data has demonstrated the expected positive associations between SES and health status and between health practices and health status. Furthermore, the associations are not substantially influenced by one another. These results make it clear that health practices do not explain the association between SES and health status, and confirm the Alameda County Study results (3, 27). The cross-sectional design limits the conclusions that can be reached about the nature and, in particular, the sequencing of the relationships among these variables. The design does allow the conclusion that health practices are not the sole explanatory variable, but it does not allow confirmation of SES and health practices as causes of health status. Some additional cross-sectional studies are needed, such as replication with other health status measures and other race and ethnic groups. Equally important, however, are longitudinal studies to determine the chronological ordering of the relationships among these three variables. The Alameda County Study confirmed that earlier health practices are related to subsequent physical health status even when education or income was controlled (27). Other studies, however, have shown that changes in health status themselves may lead to decreased income and withdrawal from the labor force. The directionality question among these three sets of variables-SES, health practices, and health status-thus require careful and detailed analyses in further studies. If the findings from this study hold for longitudinal studies using other health status measures, then we may have a better understanding of the separate roles of SES and health practices in the determination of health status and can develop further a theory of health and health practices. The questions are to what extent they are determined by social and biological structures beyond the control of the individual and to what extent they are a matter of individual rational choice. Clearly, both forces operate, and there may be a way to integrate the two approaches to achieve a better overall understanding of the interrelations. ACKNOWLEDGMENTS The authors thank the National Center for Health Statistics (NCHS) for the provision of the Health Interview Survey used in this study via the Health Services Data Library (HSDL) of the University
378
SLATER, LORIMOR, AND LAIRSON
of Texas School of Public Health. We also acknowledge the assistance of Mr. Bill Spears of the HSDL for assistance with the retrieval and part of the programming. It is understood that the NCHS and the HSDL take neither responsibility nor credit for the analysis or interpretation done in the study.
REFERENCES I. Antonovsky, A. Social class, life expectancy, and overall mortality. Milbank Mem. Fund Q. 4.5, 31-73 (1967). 2. Belloc, N. B., Breslow, L., and Hochstim, J. R. Measurement of physical health in a general population survey. Amer. J. Epidemiol. 93, 328-336 (1971). 3. Belloc, N. B., and Breslow, L. Relationship of physical health status and health practices. Pvev. Med. 1, 409-421 (1972).
4. Belloc, N. B. Relationship of health practices and mortality. Prev. Med. 2, 67-81 (1973). 5. Bray, G. A. Definition, measurement and classification of the syndromes of obesity. Int. J. Obes. 2, 99-112 (1978). 6. Breslow, L., and Enstrom, J. R. Persistence of health habits and their relationship to mortality. Prev. Med. 9, 469-483 (1980). 7. Cassel, J. The contribution of the social environment to host resistance. Amer. J. Epidemiol. 104, 107-123 (1976).
8. Conover, P. W. Social class and chronic illness. Int. J. Heulth Serv. 3, 357-368 (1973). 9. Graham, S.. and Reeder, L. A. Social factors in the chronic diseases, in “Handbook of Medical Sociology” (E. Freeman, S. Levine, and L. A. Reeder, Eds.), pp. 63-107. Prentice-Hall, Englewood Cliffs, N.J., 1972. 10. Harkey, J., Miles, D. L.. and Rushing, W. A. The relationship between social class and functional status: A new look at the drift hypothesis. J. He&h Sot. Behuv. 17, 194-204 (1976). Il. Johnston, S. A., and Ware, J. E. Income differences in relationships among survey measures of physical and mental health. He&h Serv. Res. 11, 416-429 (1976). 12. Keys. A., et ul. Indices of relative weight and obesity. J. Chronic Dis. 25, 329-343 (1972). 13. Keys, A. Overweight, obesity. coronary heart disease, and mortality. Nutr. Ret). 38, 297-307 (1980). 14. Kitagawa, E., and Hauser, P. “Differential Mortality in the United States: A Study in Socioeconomic Epidemiology.” Harvard Univ. Press, Cambridge, Mass., 1973. 15. Langlie, J. R. Social networks, health beliefs, and preventive health behavior. J. Heulth Sot. Behnv. 18, 244-260 (1977).
16. Meltzer, J. W.. and Hochstim, J. R. Reliability and validity of survey data on physical health. Public Heulth Rep. 85, 1075-1086 (1971). 17. National Center for Health Statistics. Health practices among adults: United States. 1977. Adv. Data 64, l-11 (November 4, 1980). 18. Newachek, P. W. et ul. Income and illness. Med. Cure 18, 1165-1176 (1980). 19. Nie. N. H., et a/. “Statistical Package for the Social Sciences.” McGraw-Hill, New York, 1975. 20. Osborn, R. W. Social rank and self-health evaluation of older urban males. Sot. Sci. Med. 7, 209-218 (1973). 21. Palmore, E., and Luikart, C. Health and social factors related to life satisfaction. J. Health Sot. Behav. 13, 68-80 (1972).
22. Palmore, E. Health practices and illness among the aged. Gerontologist 10, 313-316 (1970). 23. Pratt, L. The relationship of socioeconomic status to health. Amer. J. Public He&h 61, 281291 (1971).
24. Stockwell, E. G. Socioeconomic status and mortality in the United States. Public Heulth Rep. 76, 1081-1086 (1961). 25. Syme, S. L., and Berkman, L. F. Social class, susceptibility, and sickness. Amer. J. Epidemiol. 104, l-8 (1976). 26. Ware, J. E., Davies-Avery, A., and Donald, C. A. “Conceptualization and Measurement of Health for Adults in the Health Insurance Study: Volume V. General Perceptions.” Rand. Santa Monica, Calif., 1978. 27. Wiley, J. A., and Camacho, T. C. Life-style and future health: Evidence from the Alameda County Study. PI-ev. Med. 9, l-21 (1980).
MEDICINE
14, 372-378 (1985)
The Independent Contributions and Health Practices
of Socioeconomic to Health Status
CARL H. SLATER, M.D.,'R~NALD AND DAVID R. LAIRSON, Health
Services Organization,
University of Texas School Houstojz, Texas 77025
Status
J. LORIMOR, PH.D., PH.D.
qf Public Health, P.O. Box 20186,
The objective of this study was to determine whether the much-repeated finding of a relationship between socioeconomic status and health status is explained by individuals’ health practices. The investigation was carried out using data tapes from the 1977 Health Interview Survey in which a one-third subsample of adults was asked a series of questions related to the seven nonmedical health practices identified in the Alameda County Study. The group selected for analysis comprised 15,892 white, responding adults. With age controlled statistically, perceived health status was found to be associated with socioeconomic status, whether the indicator was educational level, family income, or occupation, and to number of positive health practices. When number of health practices, in addition to age and other socioeconomic indicators was controlled for, the association was still positive and significant. The finding of an independent contribution by socioeconomic status to health status emphasizes that individual health habits are not the only influence on health status. 0 1985 Academic Press. Inc.
INTRODUCTION One of the most enduring and least explained relationships found in the literature on health is the association between socioeconomic status (SES) and health status. Whether SES is measured by education, income, occupation, or a composite index, and whether health status is measured by mortality (I, 14, 24, 25), morbidity (7-10, IX, 25), or perceived health status (3, 11, 20, 21, 23), higher SES repeatedly has been shown to be associated with better health status. One question that has been asked is: do health practices explain this relationship? Two early studies demonstrate a relationship between number of positive health practices and health status (22, 23), while the Alameda County Study showed both physical health status (3, 27) and subsequent mortality (4, 6) to be related to seven health habits practiced by a sample of nearly 7,000 California adults. Other studies (15, 23) also have shown the expected relationship between SES and reported health practices and have examined the interrelationships among SES, health practices, and health status (3, 22, 23). Pratt (23) showed the rclationship between health practices and health status to be significant only among mothers at the lowest SES level. The health practice reports (3, 27) emanating from the Alameda County Study demonstrated that neither income nor education altered the basic relationship. The present study was undertaken to determine which of these interrelations would be found in a national data set. ’ To whom reprint requests should be addressed. 372 0091-7435/85 $3.00 CopyrIght U 1985 by Academic Press. Inc. All rights of reproduction in any form reserved.
HEALTH
PRACTICES,
SES,
AND
HEALTH
STATUS
373
METHODS The Subsample
In 1977, the Health Interview Survey of the National Center for Health Statistics (NCHS) included a series of questions related to the seven health practices studied in the Alameda County Study (17). These questions were asked of a onethird subsample of adults in the general survey. Since the Health Interview Survey included 41,000 households with approximately 121,000 persons and 70,000 adults, the one-third subsample included 22,824 eligibles of whom 9.4% chose not to respond to this set of questions. For purposes of this analysis, only white eligibles were selected (n = 20,381). Those with incomplete demographic data were excluded, leaving a group of 18,286 white eligibles with complete demographic data. Nonrespondents (7.8% of the remaining eligibles) and then those missing either health status or some health practice responses (5.7% of the remaining respondents) were eliminated. The successive application of these selection criteria yielded a final group of 15,892 white respondent adults with complete socioeconomic, health status, and health practices data. Table 1 compares the demographic characteristics of those with complete data to those missing the health practices and health status data. There were some small differences between the two groups. For the missing value group of 959 respondents, there was a slight excess of women, older persons, people with the least amount of education, persons in the lowest income bracket, and those not in the labor force. The Variables
The health status variable analyzed was self-assessed health status as excellent, good, fair, or poor (EGFP). It derived from the respondent’s answer to the question of how he or she would compare his or her health with that of others in the same age group. Several of the previous studies of health practices (20, 22, 23) have used a similar question. Ware et al. (26) reviewed the literature on this selfassessed health status measure and confirmed its association with a wide range of other health status measures, especially those that tap the general health status domain. The skewed distribution of this variable, however, differs from the more normally distributed physical health status variable used in the Alameda County Study (2, 16). While a variety of activities have been called health practices, ranging from as few as the three examined in Palmore’s study (22), the Health Interview Survey (17) focused on the same seven health practices included in the Alameda County Study, namely, sleep, breakfast, snacks, physical exercise, smoking, drinking, and weight control. Previous research (3, 27) has shown that a simple sum of these health practices can predict both health status and mortality and has been used as the basis for the construction of a cumulative index of positive health practices. The health practices in this study were scored in a similar fashion with each of the following being given one point: sleeping 7-8 hr a night, eating breakfast every day, rarely or never snacking between meals, being more physically
374
SLATER,
LORIMOR, TABLE
AND
LAIRSON
1
WITH AND WITHOUT COMPLETE DATA IN THE 1977 HEALTH INTERVIEW SURVEY HEALTH PRACTICE SUBSAMPLE
COMPARISONS
BETWEEN WHITE,
Variable
ELIGIBLE
RESPONDENTS
White, eligible respondents with complete data (N = 15,892)
White, eligible respondents with missing data (N = 959)
Gender M F
45.9% 54.1
45.2% 54.8
Age 20-24 25-34 35-44 45-54 55-64 65-74 75+
13.4 23.8 16.7 16.5 14.4 10.1 5.1
11.8 24.0 14.8 16.5 14.7 11.3 6.8
Education O-8 9-11 12 13+
15.4 14.3 37.9 32.3
19.9 14.3 35.3 30.5
Family income Less than $5,000 5,000-9,999 10,000-14,999 15,000-24,000 25,000
12.8 20.2 21.1 28.5 17.4
14.8 19.3 21.4 28.4 16.2
Occupation Not in labor force Blue collar White collar
35.6 29.6 34.8
37.4 29.9 32.7
Perceived health status (EGFP) Poor Fair Good Excellent Missing
3.2 11.2 38.8 46.7 0
4.7 10.2 38.8 40.9 5.4
Positive health practices 0 1 2 3 4 5 6 7 Missing
1.2 5.8 15.6 24.4 26.0 18.3 7.2 1.4 0
0.4 0.6 1.7 1.7 0.5 0 0 95.1
0
HEALTH
PRACTICES,
SES, AND
HEALTH
STATUS
375
active than other persons the same age, never having smoked, and not having had more than five drinks at one sitting anytime in the past year. While the Alameda County Study used the Metropolitan Life Tables (3) as the basis for determining weight control, body mass index (weight in kilograms divided by height in meters squared) was used in this study. This index has been demonstrated in other studies (12, 13) to be associated with body fat. Bray (5) states that the normal range of the body mass index is 20-25 for males and 19-24 for women. A score of + 1 on the health practices index was given if the respondent was within the normal range for his or her gender. The number of positives a respondent had on these practices was summed to yield a health habit score ranging from 0 to 7. The respondent’s reported number of years of schooling, after recoding into four categories (see Table l), was used as the education variable. Family income was reported by the respondent by the intervals, shown in Table 1. Occupation, from a list of 90 items, was recoded into the three categories, also displayed in Table 1. ANALYSIS
AND RESULTS
The Health Interview Survey was accomplished through a multistage probability design. In this design, a different sample of households is selected for interview each week in such a way that the weekly samples will be additive over the year. This complex, multistage, probability design affects the estimates derived from such a sample in two ways. First, the crude estimates are not representative of the U.S. population. This problem can be corrected by applying a weighting factor which adjusts for the nonrepresentativeness as well as for the nonresponses among the households selected. This weighting factor, calculated by NCHS, is available on the public-use data tape and was used to adjust all of the estimates in Tables 1 and 2. Such estimates, however, also have larger sampling errors than those derived from a true random sample. Some statistical packages, such as SPSS, assume a simple random sample. As a result, the test statistics reported from such a program will be underestimated. For this reason, the acceptable significance level for the F tests reported in Table 2 was set at the level of P < 0.001, but the level of significance is larger in all cases. As with past studies, the EGFP health status variable was treated as a continuous variable. The respondents were skewed to the right for this variable, with 85% reporting their health as good or excellent. Means were calculated for each category of the independent variables and comparisons between respondents grouped by gender, age, and health practices as well as by education, family income, and occupation were then made. The crude mean health status, where excellent was scored as 4, was 3.32 for men and 3.26 for women. Health status was found, as expected, to vary inversely with age, ranging from 3.51 for the youngest age group (20-24) to 2.99 for the oldest (75 +), and the differences were significant. Variation in unadjusted mean health status by socioeconomic indicator is displayed in the first column of Table 2. Education, family income, and occupation each has a positive and significant association with self-assessed health status.
376
SLATER, LORIMOR,
UNADJUSTED
AND ADJUSTED OCCUPATION
AND LAIRSON
TABLE 2 MEAN SELF-ASSESSED HEALTH FOR WHITE,
ELIGIBLE
STATUS BY EDUCATION, RESPONDENTS (N = 15,892)
INCOME,
AND
Mean health status Socioeconomic indicator
Unadjusted
Ageadjusted
Age-habits” adjusted
Age-habitsaother SESadjusted
Education O-8 yrs. 9-11 12 13+
2.80b 3.06 3.35 3.56
2.91 3.08 3.33 3.52
2.93 3.11 3.33 3.50
3.03 3.15 3.31 3.45
Family income Less than $5,000 5,000-9,999 10,000- 14,999 15,000-24,999 25,000 +
2.88 3.09 3.31 3.44 3.55
2.98 3.12 3.28 3.40 3.54
2.99 3.13 3.28 3.40 3.53
3.10 3.18 3.29 3.36 3.44
Occupation Not in labor force Blue collar White collar
3.03 3.33 3.51
3.11 3.29 3.47
3.11 3.31 3.46
3.17 3.36 3.43
a Habits means number of positive health practices. b All differences between means for each indicator are significant (P < 0.001).
The range of mean health status from the lowest to the highest category within each SES indicator is reassuringly consistent. Approximately 70% of the respondents reported from three to five positive health practices with the percentage reporting as positive on any individual health practice ranging from 34.1% (for not snacking) to 68.8% (for not drinking excessively). Health practices also had a positive and significant association with perceived health status. The range was from 3.09 for those with no positive health practices to 3.62 for those reporting all seven positive health practices. Analysis of covariance (19) was used to adjust mean health status successively for the effects of age, habits, and other SES indicators. Adjustment for gender is not reported because there was only a modest relationship between gender and health status. Furthermore, controlling for age reduced the difference between men and women on mean health status and controlling for gender did not alter any of the relationships, unadjusted and adjusted, analyzed. Since health status was found to vary inversely with age, analysis of covariance with age as a continuous variable was used to adjust the means for this negative relationship. The age-adjusted mean health status for each category of SES indicator is displayed in the second column of Table 2. The association between SES and health status remained positive and significant when the relationships were adjusted for age; the range from the lowest to the highest level within each variable, however, was slightly attenuated by the age adjustment. Because health status was related to number of health practices as well as to
HEALTH
PRACTICES,
SES, AND
HEALTH
STATUS
377
each of the SES indicators, the age-adjustment was coupled with adjustment for the level of health practices and for the level of each of the other two SES indicators. The third and fourth columns of Table 2 show the relationship between the SES indicator and health status when number of health practices, age, and the other SES indicators were controlled. The association between SES and health status is seen to hold for each indicator, with the health practices adjustment having almost no effects. The range of variation from the lowest to the highest category of each indicator for the complete SES adjustment is further attenuated. These results show that age and the other SES variables operate, when uncontrolled, to alter slightly the association between any one SES variable and self-assessed health status, but that the level of health practices does not alter the SES to health status relationship. DISCUSSION
This analysis of the 1977 Health Interview Survey data has demonstrated the expected positive associations between SES and health status and between health practices and health status. Furthermore, the associations are not substantially influenced by one another. These results make it clear that health practices do not explain the association between SES and health status, and confirm the Alameda County Study results (3, 27). The cross-sectional design limits the conclusions that can be reached about the nature and, in particular, the sequencing of the relationships among these variables. The design does allow the conclusion that health practices are not the sole explanatory variable, but it does not allow confirmation of SES and health practices as causes of health status. Some additional cross-sectional studies are needed, such as replication with other health status measures and other race and ethnic groups. Equally important, however, are longitudinal studies to determine the chronological ordering of the relationships among these three variables. The Alameda County Study confirmed that earlier health practices are related to subsequent physical health status even when education or income was controlled (27). Other studies, however, have shown that changes in health status themselves may lead to decreased income and withdrawal from the labor force. The directionality question among these three sets of variables-SES, health practices, and health status-thus require careful and detailed analyses in further studies. If the findings from this study hold for longitudinal studies using other health status measures, then we may have a better understanding of the separate roles of SES and health practices in the determination of health status and can develop further a theory of health and health practices. The questions are to what extent they are determined by social and biological structures beyond the control of the individual and to what extent they are a matter of individual rational choice. Clearly, both forces operate, and there may be a way to integrate the two approaches to achieve a better overall understanding of the interrelations. ACKNOWLEDGMENTS The authors thank the National Center for Health Statistics (NCHS) for the provision of the Health Interview Survey used in this study via the Health Services Data Library (HSDL) of the University
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of Texas School of Public Health. We also acknowledge the assistance of Mr. Bill Spears of the HSDL for assistance with the retrieval and part of the programming. It is understood that the NCHS and the HSDL take neither responsibility nor credit for the analysis or interpretation done in the study.
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