Formal education level as a significant marker of clinical status in rheumatoid arthritis.
код для вставкиСкачать1346 FORMAL EDUCATION LEVEL AS A SIGNIFICANT MARKER OF CLINICAL STATUS IN RHEUMATOID ARTHRITIS LEIGH F. CALLAHAN and THEODORE PINCUS Clinical status was assessed in 385 patients with rheumatoid arthritis, according to erythrocyte sedimentation rate, joint count, grip strength, walking time, and other quantitative measures. All measures indicated substantially poorer clinical status in patients who did not complete high school, compared with those who had completed high school. In general, the poorest results were seen in patients with only a grade school education. Progressively better results were seen in patients with some high school education, high school graduates, and patients with some college education. No differences in clinical status were seen among patients who had attended college, graduated from college, or had postgraduate education. Although patients seen at the Veterans Administration Medical Center had lower levels of formal education than those seen at a university clinic and private practices, trends in clinical status according to formal education level were similar in all three Presented in part at the Southeastern Regional Meeting of the American Rheumatism Association and the Arthritis Health Professions Association, Savannah, GA, December 1985, and at the 50th Annual Meeting of the American Rheumatism Association and the 21st Annual Meeting of the Arthriti? Health Professions Association, New Orleans, LA, June 1986. From the Division of Rheumatology and Immunology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee. Supported in part by the Arthritis Foundation, the Jack C. Massey Foundation, the Robert Wood Johnson Foundation, NIH grant AM-21393 to the American Rheumatism Association Medical Information System, and NIH General Clinical Research Centers grant 5M-01-RR-00095. Leigh F. Callahan, BS; Theodore Pincus, MD. Address reprint requests to Leigh F. Callahan, BS, Division of Rheumatology and Immunology, B-32 19 Medical Center North, Department of Medicine, Vanderbilt University, Nashville, TN 37232. Submitted for publication January 18. 1988; accepted in revised form June 6, 1988. Arthritis and Rheumatism, Vol. 31, No. 11 (November 1988) clinical settings. Differences in clinical status according to formal education level are not explained by age, sex, duration of disease, clinical setting, or multiple comparisons. Formal education level may identify an important marker of clinical status in rheumatoid arthritis. Morbidity and mortality rates in patients with rheumatoid arthritis (RA) have been reported to vary significantly according to level of formal education (1). These findings are consistent with reports indicating that lower formal education levels are associated with increased mortality in cardiovascular disease ( 2 4 , and that lower socioeconomic status is associated with higher disease prevalence and increased mortality in patients with RA (5-9), cardiovascular disease (10-201, cancer (21), pulmonary disease (22), and in the general population (23-37). Formal education level appears to be a composite or surrogate variable for overall socioeconomic status, although many studies indicate associations between education and health that are independent of other socioeconomic variables (2,9,26, 28,34). The magnitude of associations between poor health and lower socioeconomic status suggests that further studies may lead to useful insights into mechanisms of pathogenesis, morbidity, and mortality of specific chronic diseases. However, few reports in medical journals have included markers of socioeconomic status in relation to clinical status (see ref. I), and most studies of socioeconomic status and health are based on self-report rather than physical or laboratory measures. Our own previous studies (1) were limited by the absence of laboratory data, as well as by a relatively small number of patients. The findings did suggest that there might be value in studies of larger EDUCATION LEVEL AND RA numbers of patients which would include both socioeconomic status a n d clinical status variables. T h e s e considerations led us to analyze laboratory, physical, a n d functional measures of clinical status in 385 RA patients whose socioeconomic status was known. Formal education level w a s chosen a s a study marker for socioeconomic status, both because it is more easily and accurately measured than other markers such a s occupation a n d income, and because it is much less likely to be affected by disease beginning in adult life than are these other markers. We report evidence of m o r e severe clinical status in RA patients with lower educational attainment, according to all available quantitative measures. PATIENTS AND METHODS Patients. The study population included 385 patients seen at the Division of Rheumatology and Immunology at Vanderbilt University (VUC), the Nashville Veterans Administration Medical Center Clinic (VAMC), or in the private practices of four rheumatologists in Nashville (Drs. Joseph W. Huston, J. Thomas John, John S. Johnson, and John S. Sergent). An effort was made to obtain data on consecutive patients in the course of a routine visit to a rheumatologist, without designation of a “study” situation; it was estimated that more than 95% of consecutive patients seen during the data collection periods were included. All 201 patients seen at the VUC and VAMC met American Rheumatism Association (ARA) criteria for definite RA (38). The other 184 patients were designated by their rheumatologists as having RA. Data were reviewed on a sample of 30 of these patients, and all met ARA criteria for definite RA. The mean age of the patients was 56 years. Eightyseven percent of the population were white, 63% were female, and 71% were married. The mean duration of disease was 1 1 .O years. On the basis of these measures, the patients appear to be a representative group of RA patients (9). Measures of clinical status. In the absence of agreement regarding optimal measures by which to assess clinical status in RA, many types of measures were determined for these patients. Erythrocyte sedimentation rate (ESR) was determined using the Westergren method (39). Joint counts were assessed according to a modified Ritchie index, recording tenderness or pain on motion and swelling in 66 joints (40,41), i.e., the temporomandibular (2 joints), sternoclavicular (2 joints), acromioclavicular (2 joints), shoulder (2 joints), elbow (2 joints), wrist (2 joints), metacarpophalangeal(l0 joints), proximal interphalangeal (PIP) of the hands (10 joints), distal interphalangeal (8 joints), knee (2 joints), ankle (2 joints), tarsus (2 joints), metatarsophalangeal (10 joints), and PIP of the feet (10 joints). Three physical measures of functional capacity were obtained using standard techniques (42), as follows: 1) Grip strength. A blood pressure cuff was inflated to 20 mm Hg, and the patient was asked to squeeze as hard as he or she could (43). The score recorded was the mean mm Hg of three 1347 measures of each hand, i.e., six measures. 2) Walk time, The patient was asked to walk at a normal pace for 25 feet. The score was recorded in seconds (44). 3) Button test. A standard board (JA Preston, Clifton, NJ) was used. The patient was asked to unbutton 5 buttons and then button them as quickly as he or she could accomplish this task. The score was recorded in seconds, and the mean for both hands was recorded (45). Self-report questionnaires for assessment of difficulty, dissatisfaction, and pain in activities of daily living (ADL) were administered as previously described (46,47). Four responses are possible on these ADL scales, analogous to ARA functional class (48), with a higher score indicating poorer clinical status (46,47). A standard 10-cm visual analog scale was used to quantitate pain (49,50). Global self-assessment of functional status was determined using a 1 4 scale (1 = no limitations, 2 = some limitations, 3 = many limitations, 4 = totally disabled), again analogous to ARA functional class (48). Analyses according to formal education level and other demographic variables. The patients were initially classified into six groups according to formal education level (Table I), including grade school (24%), some high school (19%), high school graduate (29%), some college (20%), college graduate (5%), and postgraduate education (3%). The proportion of patients with 5 1 1 years of education (43%) is higher than the 25% of individuals in the general population with this level of education. These data reflect evidence that RA occurs more frequently in individuals with lower educational attainment (5-9), as is the case with most chronic diseases (8,37). The proportions of RA patients according to level of formal education are similar to those derived from a survey designed to be representative of the United States population, in which, among all individuals with symmetric polyarthritis, 31% had 8 or fewer years of formal education, 20% had e l l years, 31% had 12 years, and 18% had more than 12 years of formal education (9). The patients seen in private practice settings and at the VUC were similar in educational attainment, although both groups showed a higher level of formal education than patients seen at the VAMC (Table 1). Therefore, analyses were performed to control and adjust for clinical setting. Initial analyses according to six educational categories indicated differences among patients in the lowest four categories, i.e., grade school, some high school, high school graduate, and some college, but no differences among patients with some college, college graduates, and those with postgraduate education (Table 2). Therefore, the three categories beyond high school graduate (some college, college graduate, and postgraduate education) were collapsed into one group, and the next set of analyses was performed with the patients grouped into four categories, i.e., grade school, some high school, high school graduate, and at least one year of college. This classification appeared advantageous also in that only 8% of the study population had graduated from college, which was again consistent with other published studies (5,9). Patients in the four education categories did not differ in terms of race, marital status, or duration of disease, but did differ significantly in age, age at disease onset, and sex. The mean ages of the subjects by education category CALLAHAN AND PINCUS 1348 Table 1. Distribution of 385 rheumatoid arthritis patients studied, according to clinical setting and level of formal education* Grade school Some high school High school graduate Some college College graduate Postgraduate University clinic Veterans Administration hospital clinic Private practice Total 21 43 20 18 27 14 22 21 8 2 2 2 47 13 22 24 18 19 37 29 17 20 4 5 3 3 40 100 Distribution of individuals with symmetric polyarthritis in a population-based survey? Distribution of all individuals in US population§ 31 20 31 18$ NA NA 100 10 15 38 37$ NA NA 100 Total * Values are percentages. NA = not available. 9. $ Includes some college, college graduate, and postgraduate education. 0 Data from ref. 37. t Data from ref. high school, 73% in high school graduates, and 67% in those with more than 12 years of formal education (P< 0,001). As a result of these findings, all analyses included statistical adjustments to control for age and sex. Furthermore, analyses to determine clinical status by the study measures according to formal education level were conducted with patients grouped into four categories according to sex and age, i.e., males 5 6 0 years, males >60 years, females 5 6 0 years, and females >60 years. In these analyses, because there already existed four categories according to age and sex, patients were classified into two groups according to formal education level, i.e., individuals with were 62.2, 53.4, 53.6, and 54.7 in those who had been to grade school only, those with some high school education, high school graduates, and those with more than 12 years of formal education, respectively ( P < 0.001, those with grade school only versus the other three groups). The reported age at onset of disease was 49.7, 42.2, 43.2, and 42.6 in the four groups, respectively; this was consistent with the data showing a similar duration of disease in all four groups. The age at disease onset in the group with only a grade school education was significantly (P < 0.001) higher than in the other groups. The percentage of females was 41% in the group with a grade school education, 59% in those with some Table 2. Mean values for laboratory, physical, and self-report measures of disease status in 385 rheumatoid arthritis patients, classified according to level of formal education Patients classified according to formal education level ~ Disease status measure* Laboratory measures ESR (mmlhour) Painful joint count Physical measures Grip strength (mm Hg) Walk time (seconds) Button test (seconds) Self-report measures ADL difficulty scale ( 1 4 ) ADL pain scale (11)) ADL dissatisfaction scale ( 1 4 ) Visual analog pain scale (0-10) Global self-assessment ( 1 - 4 ) All patients Grade school Some high school 40.1 12.1 48.3 16.3 49.4 15.1 98.8 10.3 62.5 93.7 11.2 80.5 92.1 10.0 61.3 1.97 2.37 2.26 5.12 2.68 2.26 2.62 2.54 5.75 3.09 2.04 2.56 2.41 5.85 2.70 High school graduate Some college College graduate Postgraduate 34.7 9.1 29.3 10.2 41.8 9.3 26.6 8.5 0.002$ O.Ool§ 97.9 10.6 60.8 111.6 9.6 46.9 101.6 10.2 58.1 110.9 6.5 54.8 0.079 0.424 0.003f 1.86 2.26 2.12 4.89 2.55 1.73 2.06 2.03 4.26 2.43 * ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living. t By analysis of covariance, after controlling for age, sex, clinical setting, and disease duration. $ P < 0.05 after adjustment for multiple comparisons. P P < 0.01 after adjustment for multiple comparisons. 1.99 2.46 2.29 4.94 2.61 1.70 2.06 1.81 3.86 2.25 Pi O.OOO§ 0.0019 0.006 0.074 o.oOo(i 1349 EDUCATION LEVEL AND RA Table 3. Odds ratios of more severe findings on laboratory, physical, and self-report measures of clinical status in 385 rheumatoid arthritis patients, classified according to level of formal education* Years of formal education Grade school Unadjusted odds ratios for clinical status measures ESR Joint count Grip strength Walk time Button test ADL difficulty scale Visual analog pain scale Global health self-assessment Age (>60 years vs. 5 6 0 years) Sex (male vs. female) Disease duration (>11 years vs. < 11 years) Clinical setting (VAMC vs. non-VAMC) Odds ratios for clinical status measures adjusted for age, sex, clinical setting, and duration of disease ESR Joint count Grip strength Walk time Button test ADL difficulty scale Visual analog pain scale Global health self-assessment 1.Y1: 2.11 I .8$ 4.41: 10.7$ 3.11: 2.0$ 4.7$ 3.2$ 2.8t I .41 Some high school 2.31: 1.41 1.71: 2.41: 3.11: High school graduate 1.1 1.41: 1.61: 2.7$ 2.01: Some college 1 .o 1 .o 1.o 1.o 1 .o 1.o 2.11: 2.01: I .o 1.41: 1.2 1.41: 1.4$ 1.1 1.31: 0.7 I .2$ 2.31: 0.7 0.5 1 .o 1.4 1.61: 1.8$ 2.71: 4.01: 2.41: I S1: 2.8$ 1.6$ 1.1 0.9 0.8 I .3 2.11 1 .5 1.2 1 .o 1 .o 1.o 1.81: I .61: 2.01: 2.11: 1.4 f .4 I .6$ 1.1 1 .o 1.o 1.o 1.o 1.o Trendt 2.4P 2.3T 2.08 4.06 6.41 3.75 2.49 4.89: 3.48 3.69 1 .o 3.09 1 .o 1.o I .o I .o I .o 1 .o * ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living; VAMC = Veterans Administration Medical Center. t AnalyLed by the S* statistic. 1: Ninety-five percent confidence limits for these point estimates are > I . I , indicating a 95% probability that the comparison group differs from the referent group. An odds ratio of 1.3 may show a confidence limit of > 1.1 if there is little variance and a large number of patients, while in another analysis, a similar odds ratio may include a confidence limit of 1.0 if there is greater variance or fewer patients. d P < 0.01. n P < 0.05. 5 11 years of formal education versus those with 2 12 years of formal education. This classification was based on the finding that the most substantial differences in clinical status according to years of formal education were seen between those who had and those who had not completed high school (Tables 2 and 3). An additional analysis was done with patients classified according to the clinical setting in which they were seen. All patients were grouped according to one of three clinical settings, i.e., the VUC, private practice, and the VAMC. The clinical status of patients within each of these three groups was analyzed according to the two categories of formal education used in the studies of age and sex. Statistical methods. The data were analyzed using the Statistical Package for the Social Sciences (SPSSx) software package (5 1). Pearson product moment correlations were computed initially between all available measures. All measures generally correlated with one another, indicating that poorer status according to one measure is usually associated with poorer status according to other measures. However, other than high correlations among the A D L scores, the correlations (r) of 0.2-0.5 d o not explain more than 25% of the variation between two measures. Therefore, it appeared reasonable t o analyze possible associations between formal education level and clinical status according to many different types of measures. Associations between formal education level and various clinical status measures were analyzed by comparing mean values of each measure for patients classified into six groups: grade school, some high school, high school graduate, some college, college graduate, and postgraduate education (Table I). The statistical significance of differences 1350 among all six categories was computed using one-way analysis of variance, as well as analysis of covariance controlling for clinical setting, age, sex, and duration of disease. The significance of differences between any two individual groups among the six education categories was determined in post hoc analyses according to Duncan’s multiple range test (52). Analyses were controlled for multiple comparisons by multiplication of the observed P value by the number of observations, i.e., Bonferroni adjustment (53). The likelihood that an individual would have more severe or less severe clinical status according to his or her formal education level was analyzed by classification of patients into four education groups, i.e., grade school, some high school, high school graduate, and some college education, based on analysis of variance (Table 2 ) . The odds that individuals in the three categories of lower levels of formal education would have poorer clinical status compared with those with some college education were then computed as odds ratios (54). The odds ratio represents the level of risk for an event associated with the presence or absence of a study variable. The variables in these analyses are different measures of clinical status. For each quantitative measure, more severe clinical status was defined as a value greater than the median value for that measure (other than grip strength, for which the definition was reversed because a high value indicates better clinical status). When the odds ratio is 1.0, the risk is the same in a study group as in a referent group, and when it is > 1.O, an estimate of the odds for more severe clinical status according to that variable is given. Confidence limits for odds ratios are computed as 95% Taylor series precisionbased confidence limits for each point estimate (55). If the confidence limits include 1.0, a difference in an odds ratio may be secondary to chance; if the lower confidence limit is 2 1.1, a higher odds ratio is significant in 95% of instances. The confidence limit is a function of the point estimate, variance, and the number of subjects, so that one odds ratio may be higher than another but may not exclude a confidence limit of 1.0 if there is greater variance or a smaller number of patients. Odds ratios were computed for three sets of analyses of all the patients. In the first set of analyses, all patients were classified into four educational groups, i.e., grade school, some high school, high school graduate, and some college education, based on analysis of variance (Table 2 ) . Odds ratios were computed for the grade school, some high school, and high school graduate categories compared with the some college education category as the referent group (odds ratio = 1) each time. The statistical significance of trends according to the education strata was tested using the S* statistic described by Brown and Hollander (56). In the second set of analyses, odds ratios were computed for all patients categorized by clinical setting, and in the third set patients were categorized by age and sex, according to formal education level. As noted above, only two education level categories were included in the second and third sets: 5 1 1 years and 2 1 2 years, based on the findings shown in Tables 2 and 3. This approach was used to identify six subsets in analyses by clinical setting (VUC, VAMC, and private practice, each divided into two groups according to education level), and eight subsets in analyses CALLAHAN AND PINCUS by age and sex (males age 5 6 0 , males age >60, females age 560, and females age >60, each divided into two groups according to formal education level). In analyses of associations between two variables, the possibility of confounding by a third variable must be considered. For example, if an association between lower formal education level and higher ESR is observed, it is possible that the associations between formal education level and ESR are simply explained by associations of both variables with age, since both higher ESR and lower formal education levels are seen in individuals who are older. There are two approaches to analyses of possible confounding of associations between two variables by a third variable: stratification and regression modeling. Stratified analyses were performed by computing odds ratios according to formal education level, adjusted for age, using the MantelHaenszel chi-square statistic (57), as presented in Table 3. In regression analyses, possible confounding of associations between two variables by associations between both variables and a third variable is approached by analysis of all three (or more) variables simultaneously. Regression analyses allow identification of the variable that provides the most explanation of variation in a given variable, known as a dependent variable, among a group of other variables, known as independent variables. Furthermore, in these analyses, the degree to which variation in the dependent variable is explained by all of the independent variables can be computed. Multiple logistic regression analyses were performed in which formal education level served as the dependent variable and various clinical status measures as the independent variables, using the maximum likelihood ratio method (54). A logistic regression allows use of a dichotomous (yes/ no) dependent variable (rather than a continuous variable), and dichotomous as well as continuous independent variables. Formal education level, the dependent variable, was dichotomized to 1 1 1 years and 2 1 2 years. Eight separate logistic regressions were computed, with each containing age, sex, clinical setting, and a single clinical status variable as independent variables. A partial odds ratio depicting the probability of having 511 years of formal education if a measure indicated poorer clinical status was calculated for the dependent variable in each regression, controlling for age, sex, and clinical setting. To evaluate possible multicollinearity among the various types of clinical status measures, a logistic regression was computed which included single laboratory, physical, and questionnaire measures of disease status (ESR, button test, and ADL difficulty scale) as independent variables and formal education level as the dependent variable. Partial odds ratios are reported for the single laboratory, physical, and questionnaire measures, after controlling for the other measures in the equation. To evaluate associations of three invariant measures (formal education level, age, and duration of disease) with clinical status measures, eight stepwise multiple linear regressions were computed. The dependent variables in each of the eight regressions were the eight clinical status measures: ESR, joint count, grip strength, walk time, button test, ADL difficulty scale, visual analog scale, and global health self-assessment scale. The independent variables EDUCATION LEVEL AND RA were the three invariant measures. A P \.ralue less than 0.05 was considered significant for a variable to be entered into the regression model. RESULTS Clinical status measures according to formal education level. All measures analyzed indicated poorer clinical status in patients with fewer years of formal education (Table 2). Among traditional disease activity measures, the mean ESR was 48.3 mm/hour in patients with a grade school education, 49.4 in those with some high school, 34.7 in high school graduates, 29.3 in those with some college, 41.8 in college graduates, and 26.6 in those who had attended graduate school. These differences were statistically significant after controlling for age, sex, duration of disease, clinical setting, and multiple comparisons. Mean values for the number ofjoints with tenderness or pain on motion were 16.3, 15.1,9.1, 10.2,9.3, and 8.5 in the six groups, respectively. Patterns of statistical significance were similar to those seen for the ESR, and these patterns also remained significant after controlling for age, sex, duration of disease, clinical setting, and multiple comparisons. Among physical measures of functional capacity, mean values for walk time and grip strength indicated poorer clinical status in patients with fewer years of formal education, although these differences were not statistically significant. Results of the button test, i.e., the time required to button and unbutton 5 buttons on a standard board, indicated values of 80.5 seconds in patients with a grade school education, 61.3 in those with some high school, 60.8 in high school graduates, 46.9 in those with some college, 58.1 in college graduates, and 54.8 in those with postgraduate education. These differences were statistically significant after controlling for age, sex, duration of disease, clinical setting, and multiple comparisons. All self-report questionnaire measures also indicated significantly poorer clinical status in patients with lower formal education levels. The general pattern was similar to that for the physical measures. The highest scores, indicating greater difficulty, dissatisfaction, and pain in activities of daily living, were seen in patients with a grade school education. Scores were progressively lower in the next three groups, i.e., those with some high school, high school graduates, and those with some college education. Again, no meaningful differences were seen between those with some college, college graduates, and those with grad- 1351 uate education. The similarity of patterns between observer-reported and self-reported measures reflects the general validity of functional status questionnaires in depicting clinical status in RA (46,50,58). Relative frequencies (odds ratios) of more severe disease status according to formal education level. Relative frequencies for age, sex, duration of disease, clinical setting, and more severe disease determined by laboratory, physical, and self-report measures of clinical status were calculated as odds ratios according to level of formal education (Table 3). The three groups with formal education levels of some college. college graduate, or postgraduate education were collapsed into a referent group having >12 years of formal education, on the basis of findings shown in Table 2. The highest odds ratios, indicating the most severe clinical status, were generally seen in patients with grade school education, compared with the referent group having >12 years of formal education (Table 3). Progressively lower odds ratios were seen in patients with some high school education and high school graduates. Tests for trend indicated statistical significance at the P < 0.01 level for six of the eight comparisons (all except grip strength and joint count, which were significant at P < 0.05). Odds ratios for age, sex, duration of disease, and clinical setting indicated that patients with only a grade school education were more likely to be older, male, VA patients, and have a longer duration of disease (Table 3). Therefore, the odds ratios for the disease status measures were calculated controlling for age, sex, clinical setting, and disease duration. Adjusted odds ratios were lower than unadjusted results (Table 3), as expected, but remained significantly different according to formal education level. Further analyses to control for age, sex, and clinical setting were conducted by classifying all patients into subsets for analyses according to formal education level. On the basis of the results reported above, patients were grouped according to two education levels, 5 1 I years of formal education and 2 12 years of formal education, to determine the probability of poorer status according to formal education. In the first set of analyses, patients were classified into three subsets on the basis of clinical setting: VUC, VAMC, or private practice (Table 4). Almost all odds ratios were >1.5 for the patients with 5 1 1 years of formal education, indicating that the odds of poor clinical status were substantially greater for patients with lower education within all three clinical settings. CALLAHAN AND PINCUS 1352 Table 4. Odds ratios of having more severe disease status in patients with 5 I I years of formal education versus patients with 2 1 2 years of formal education, in subsets of rheumatoid arthritis patients grouped by clinical setting" Private practice VAMC vuc 2.63 3.4f l.9$ 1.0 1.0 2.3f 1.0 2.31 1.3 1.0 2.0f 1.0 1.0 2.0f 1.0 1.0 1.0 1.0 2.61; 2.81: l.3$ 1.0 6.5f 1.0 1.0 16.01 1.0 1.0 l.Yf 1.0 1.Yf 3.1$ 2.0f 1.0 1.0 1.0 2.5f 2.5f l.4$ 1.0 2.5$ 1.0 1.0 8.1$ 2.3f 1.0 1.0 1.0 * ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living; VAMC = Veterans Administration Medical Center: VUC Vanderbilt University Clinic. i 5 1 1 and 2 1 2 refer to years of formal education. 1 Ninety-five percent confidence limits for these point estimates are > I . I (see Table 3 for explanation). = including the 5 for which partial odds ratios were >2.0 (i.e., ESR,joint count, grip strength, button test, and global self-assessment). These regression models complemented the stratified analyses reported above, further indicating that associations between lower formal education level and poorer clinical status are not explained by age, sex, or clinical setting. Another consideration was the possible confounding effect of multicollinearity in associations between lower formal education level and poor clinical status, i.e., that the eight measures of clinical status studied might be so highly correlated with one another that individual analyses of each measure would be merely depicting the same phenomenon, This consideration was addressed in a logistic regression model which included three different types of clinical status measures, i.e., laboratory, physical, and questionnaire measures (Table 6). The results indicated that clinical status ratings according to ESR,button test, and ADL difficulty scale score all vary significantly and independently with formal education level, i.e., associations In a second set of analyses, patients were classified into four subsets: females and males, older and younger than 60 years of age (Table 5). Odds ratios in all subsets again indicated higher probabilities of poor status among individuals with 5 1 1 years versus those with 2 1 2 years of formal education. Odds ratios according to formal education level were generally higher for patients in the subsets younger than 60 years of age, indicating higher relath,e associations of formal education level and clinical status in younger patients. Only the ESR, which is known to increase with aging (39), showed meaningfully higher odds ratios in older patients. Regression analysis for multiple variables according to formal education level. Additional studies to adjust for possible confounding variables were conducted using multiple logistic regression analysis. Initial regression models were computed for each measure of clinical status together with age, sex, and clinical setting (Table 6 ) . Odds ratios were higher for all measures adjusted for age, sex, and clinical setting, Table 5. Odds ratios of having more severe disease in patients with 5 1 1 years of formal education versus patients with 2 1 2 years of formal education. in subsets of rheumatoid arthritis patients grouped by sex and age* Males age 560 Males age >60 Females age 560 Females age >60 All patients 1.8t 1.0 2.8f 1.0 2.8f 1.0 1.0 2.4f 1.5 l.3f 3.21; 1.4$ 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 3.51 1.5 1.0 1.0 1.0 5.6f 1.0 7.2f 1.1 1.0 1.0 1.0 1.2 1.0 1.0 1.0 5.0f 2.6f 1.6 1.0 1.0 4.6f 0.9 1.0 1.0 2.0$ 1.0 1.4$ 1.0 1.2 2.9f 1.8$ l.9$ 1.0 3.8f 1.0 2.4f 1.0 3.2$ 2.1$ * ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living. t 5 1 1 and 2 1 2 refer to years of formal education. f Ninety-five percent confidence limits for these point estimates are >1.1 (see Table 3 for explanation). 3.91 2.61; 2.0 1.1 2.0* 1.0 1.0 1.0 1.0 1.0 7.3$ 1.91 1.0 2.71 1.0 1.0 1.0 l.6f 2.95: 1.0 EDUCATION LEVEL AND RA 1353 Table 6. Logistic regression analyses comparing patients with 5 1 1 years of formal education versus patients with 212 years of formal education* Results of 8 models including each disease status measure, as well as age, sex, and clinical setting Clinical status measure Coefficient Standard error ESR Joint count Grip strength Walk time Button test ADL difficulty scale Visual analog pain scale Global self-assessment 0.835 0.789 0.709 0.501 1.27 0.667 0.530 0.742 0.258 0.250 0.236 0.240 0.254 0.229 0.270 0.241 Single model including 1 laboratory, 1 physical, and I questionnaire measure of disease status Partial odds ratiot Coefficient 2.318 2.200 2.036 1.657 3.57# 1.958 1.707 2.57# 0.655 I .04 0.740 - Standard ertor 0.344 0.351 0.349 - Partial odds ratiof 1.9311 - 2.820 2.107 - - - * In all regressions, formal education level served as the dependent variable. ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living. t Odds ratio for each disease status measure after controlling for other independent variables in the model (age, sex, and clinical setting). t Odds ratio for the laboratory, physical, and questionnaire measure of disease status after controlling for the other measures of disease status in the model. 0 P < 0.01. n P < 0.05. # P< 0.001. between disease status and formal education level according to the three different types of measures are not explained by correlations between the measures themselves. A final consideration involved a comparison of three invariate measures, age, duration of disease, and formal education level, to explain variation in the various clinical status measures studied (Table 7). These analyses were conducted because age and duration of disease are generally included in all clinical Table 7. Analysis of age, duration of disease, and formal education level as explanations of variation in clinical status measures* Clinical status measure (dependent variable) ESR Joint count Grip strength Walk time Button test ADL difficulty scale Visual analog pain scale Global self-assessment Independent variable First step Second step Formal education level Formal education level Disease duration Disease duration Age -t Formal education level Disease duration Formal education level Third step Multiple r R2 0.23 0.05 0.20 0.04 0.21 0.22 0.37 0.04 0.05 0.14 0.30 0.09 Formal education level Disease duration Disease duration Formal education level Age Age 0.25 0.06 Disease duration 0.30 0.09 * Eight stepwise multiple regressions were computed with each clinical status variable serving as the dependent variable. The independent variables were age, disease duration, and formal education level. If the variables met the criterion of P 5 0.05, they were included in the model in the order depicted. ESR = erythrocyte sedimentation rate; ADL = Activities of Daily Living. t - indicates no other variables met the level of statistical significance to enter the model. 1354 CALLAHAN AND PINCUS studies of RA, while formal education level generally is not included. None of the invariant measures explained more than 14% of the variation in any of the clinical status measures. However, formal education level was more explanatory than age or duration of disease for four clinical status measures, including the two traditional laboratory and physical measures, ESR and joint count. These data suggested that studies of clinical status measures in RA should include, among the invariant measures considered, formal education level or some measure of socioeconomic status. DISCUSSION Our findings in this cross-sectional study indicated more severe clinical status in RA patients with lower levels of formal education, according to all measures studied, including laboratory, physical. functional, and questionnaire measures. These associations are not explained by associations of formal education level or clinical status measures with clinical setting, age, sex, or duration of disease, or by correlations among various types of clinical status measures. Although individuals with lower education levels tended to be older, the odds ratios for poorer clinical status in individuals with lower education levels were generally higher in younger patients (for measures other than ESR, which is known to increase with aging 1391). The data reported herein may, in part, explain the observation that lower formal education level is associated with substantially higher mortality and morbidity in RA (1). In our earlier study, patients were found to not differ significantly at baseline according to formal education level, although many measures indicated somewhat poorer status in individuals with <8 years of education (1). Those differences were not statistically significant, in part because the number of patients was small. The most significant associations between clinical status and formal education level were seen in the diflerences between baseline and 9-year review, rather than in baseline and 9-year review values. These significant differences with small numbers emphasize the magnitude of changes in clinical status according to formal education level, and suggest that patients in our previous study may have been evaluated when their disease was relatively mild. One important consideration may be that patients with lower levels of formal education may delay seeing a physician because of financial considerations, ignorance, limited access, or other variables. How- ever, differences in status among patients according to education levels remained significant after controlling for duration of disease, and no significant differences were found among the four educational strata according to duration of disease. Furthermore, most measures of clinical status in RA (other than radiographic measures) are correlated only weakly or not at all with duration of disease (58,59). Therefore, associations between formal education levels and disease severity cannot be explained by differences in duration of disease, although the possibility cannot be excluded in these studies that there may be differences in percepfiori of onset of disease according to education level. Another consideration in explaining our findings is the fact that the patients were seen at referral settings, rather than a primary care setting. Although patients seen in the VAMC had less formal education than patients seen in the VUC or private practice settings, the trends of more severe clinical status in patients with fewer years of formal education remained when clinical setting was controlled for using stratification and modeling analyses. In the patient populations from all three settings, the proportion of individuals with <12 years of education was higher than that in the general population, confirming reports that RA is more frequent in individuals of lower socioeconomic strata (5-9). It is possible that differences according to formal education level might not be seen in primary care locales, although other studies indicate higher frequencies of symmetric polyarthritis in a population-based data set (8,9,37). The present study indicates that among individuals seen in referral center settings, those with lower educational attainment have more severe clinical status regardless of age, sex, or clinical setting. However, the possibility cannot be excluded that patients seen in referral centers and those seen in primary care locales might differ according to clinical status or formal education level. Associations between greater disease severity and formal education level do not appear to be unique to RA: Social class may be an important predictor of the occurrence and course of disease in general (1-37). It has been documented that mortality from coronary artery disease over 7 years in 17,530 civil service workers in London was highly associated with employment grade, and the relative risk of death was explained more by employment grade than by the sum of recognized risk factors including cholesterol level, smoking, and hypertension (16,17). Furthermore, evidence has been presented that the prevalence of all EDUCATION LEVEL AND RA common chronic diseases is higher in individuals with lower levels of formal education (37). The mechanisms whereby health and formal education level are associated remain poorly understood. Formal education level appears to be a surrogate or composite for many variables including income, diet, compliance with medical care, general health habits, efficiency in consuming medical services, and overall lifestyle (1,23). Associations between formal education level and health may be viewed as resulting from biologic, social, or methodologic sources. The finding of associations between all types of measures and formal education level in this study, as well as the associations between education and health documented in numerous other studies (1-37), would appear to exclude a methodologic explanation. While social class is highly correlated with formal education level, the number and strength of the associations also suggest biologic, as well as social, explanations. Our finding that ESR and formal education level are associated may provide initial clues to underlying biologic mechanisms to explain the observed association between education and health. Our findings do provide strong evidence that, if the number of years of education is known, variation in clinical status is not likely to be random in a population of RA patients, even after adjusting for age, sex, clinical setting, and duration of disease. Indeed, formal education level appears to explain variation in clinical status measures as effectively as do age and duration of disease, and might be routinely included in clinical studies as a significant demographic variable associated with clinical status. Formal education level is an easily measured socioeconomic variable which is less likely to be affected by chronic disease than are variables such as income and occupation. The findings may have pragmatic implications for clinical studies and formulation of health policies. For example, the use of Medicaid data to analyze drug toxicities or other phenomena might yield results that are not necessarily applicable to the entire population. In contrast, the unexpectedly good outcomes in chronic diseases reported by several individuals (6& 62) may be related to the high education level of the patient-authors. Finally, most health policies are based on the premise that risk of disease and responses to therapy are likely to be equal in individuals of a given age and sex. A need for some reassessment may be indicated on the basis of associations between formal education level and health status. 1355 ACKNOWLEDGMENTS We thank Raye H. Brooks for careful evaluation of patients and collection of data, Drs. Alan N. Baer, Howard A. Fuchs, Joseph W. Huston, J. Thomas John, John S. Johnson, Nancy J. Olsen, and John S . Sergent for encouraging their patients to participate in the study, Carolyn Burnette and Ginnie Farley for assistance in data processing, and Marilyn Welch-Fava for expert editorial assistance. REFERENCES 1. Pincus T, Callahan LF: Formal education as a marker for increased mortality and morbidity in rheumatoid arthritis. J Chronic Dis 38:973-984, 1985 2. Weinblatt E, Ruberman W, Goldberg JD, Frank CW, Shapiro S, Chaudhary BS: Relation of education to sudden death after myocardial infarction. N Engl J Med 299:60-65, 1978 3. Ruberman W, Weinblatt E, Goldberg JD, Chaudhary BS: Education, psychosocial stress and sudden cardiac death. J Chronic Dis 36:151-160, 1983 4. Ruberman W, Weinblatt E , Goldberg JD, Chaudhary BS: Psychosocial influences on mortality after myocardial infarction. N Engl J Med 311552-559, 1984 5. Cobb S, Kasl SV, Chen E, Christenfeld R: Some psychological and social characteristics of patients hospitalized for rheumatoid arthritis, hypertension, and duodenal ulcer. J Chronic Dis 1831259-1278, 1965 6. Engel A: National health examination survey: rheumatoid arthritis: comments, Population Studies of the Rheumatic Diseases. Edited by PH Bennett, PHN Wood. Amsterdam, Excerpta Medica, 1968 7. Wolfe AM: The epidemiology of rheumatoid arthritis: a review. I. Surveys. Bull Rheum Dis 19518-523, 1968 8. Pincus T, Callahan LF: Taking mortality in rheumatoid arthritis seriously: predictive markers, socioeconomic status and comorbidity. J Rheumatol 132341-845, 1986 9. Mitchell JM, Burkhauser RV, Pincus T: The importance of age, education, and comorbidity in the substantial earnings losses of individuals with symmetric polyarthritis. Arthritis Rheum 31:348-357, 1988 10. Syme SL, Hyman MM, Enterline PE: Some social and cultural factors associated with the occurrence of coronary heart disease. J Chronic Dis 17:277-289, 1964 11. Lehman EW: Social class and coronary heart disease: a sociological assessment of the medical literature. J Chronic Dis 20:381-391, 1967 12. Hinkle LE, Whitney LH, Lehman EW, Dunn J, Benjamin B, King R, Plakun A, Flehinger B: Occupation, education, and coronary heart disease. Science 161:238246, 1968 13. Antonovsky A: Social class and the major cardiovascular diseases. J Chronic Dis 21:65-106, 1968 14. Jenkins CD: Recent evidence supporting psychologic 1356 and social risk factors for coronary disease. N Engl J Med 294:987-994, 1976 15. Holme 1, Helgeland A, Hjermann I, Leren P, LundLarsen PG: Coronary risk factors in various occupational groups: the Oslo study. Br J Prev SOCMed 3 1 :96100, 1977 16. Marmot MG, Rose G , Shipley M, Hamilton PJS: Employment grade and coronary heart disease in British civil servants. J Epidemiol Community Health 32:244249, 1978 17. Rose G, Marmot MG: Social class and coronary heart disease. Br Heart J 45:13-19, 1981 18. Keil JE, Sandifer SH, Loadholt CB, Boyle E Jr: Skin color and education effects on blood pressure. Am J Public Health 71:532-534, 1981 19. Jenkins CD: Psychosocial risk factors for coronary heart disease. Acta Med Scand [Suppl] 660: 123-136, 1982 20. Dobson AJ, Gibberd RW, Leeder SR, O’Connell DL: Occupational differences in ischemic heart disease mortality and risk factors in Australia. Am J Epidemiol 122: 283-290, 1985 21. Jenkins CD: Social environment and cancer mortality in men. N Engl J Med 308:395-398. 1983 22 Lebowitz MD: The relationship of socio-environmental factors to the prevalence of obstructive lung diseases and other chronic conditions. J Chronic Dis 30599-61 1 , I977 23, Stockwell EG: A critical examination of the relationship between socioeconomic status and mortality. Am J Public Health 53:95&964, 1963 24. Fisher S: Relationship of mortality to socioeconomic status and some other factors in Sydney in 1971. J Epidemiol Community Health 32:4146, 1978 25. Auster RD, Leveson I, Sarachek D: The production of health: an exploratory 5tudy. J Human Resources 4:411436. 1969 26. Kitagawa E, Hauser P: Differential mortality in the U.S. Cambridge. Harvard University Press, 1973 27. Nagi MH, Stockwell EG: Socioeconomic differential in mortality by cause of death. Health Serv Rep 88:449465, 1973 28. Grossman M: The correlation between health and schooling, Production and Consumption. Edited by NE Terleckyj. New York, National Bureau of Economic Research, 1975 29. Syme SL, Berkman LF: Social class susceptibility and sickness. Am J Epidemiol 104: 1-8, 1976 30. Rosen S, Taubman P: Changes in the impact of education and income on mortality in the U.S., Statistical Uses of Administrative Records with Emphasis on Mortality and Disability Research. Edited by L DelBene, F Scheuren. Social Security Administration, 1979 31. Wiley JA, Comacho TC: Life-style and future health: evidence from the Alameda County study. Prev Med 9: 1-21. 1980 I CALLAHAN AND PINCUS 32. Fuchs VR: Time preference and health: an exploratory study, Economic Aspects of Health. Edited by VR Fuchs. Chicago, University of Chicago Press, 1982 33. Wingard DL, Berkman LF, Brand RJ: A multivariate analysis of health-related practices: a nine-year rnortality follow-up of the Alameda County study. Am J Epidemiol 116:765-775, 1982 34. Leigh JP: Direct and indirect effects of education on health. SOCSci Med 17:227-234, 1983 35. Slater C, Carlton B: Behavior, lifestyle, and socioeconomic variables as determinants of health status: implications for health policy development. Am J Prev Med 1:25-33. 1985 36. Kleinman JC, Madans JH: The effects of maternal smoking, physical stature, and educational attainment on the incidence of low birth weight. Am J Epidemiol 121:843-855, 1985 37. Pincus T, Callahan LF, Burkhauser RV: Most chronic diseases are reported more frequently by individuals with fewer than 12 years of formal education in the age 18-64 United States population. J Chronic Dis 402365874, 1987 38. Ropes MW, Bennett GA, Cobb S , Jacox R, Jessar RA: 1958 revision of diagnostic criteria for rheumatoid arthritis. Bull Rheum Dis 9:175-176, 1958 39. Baum J, Ziff M: Laboratory findings in rheumatoid arthritis, Arthritis and Allied Conditions. Tenth edition. Edited by DJ McCarty. Philadelphia, Lea & Febiger, 1985 40. Ritchie DM, Boyle JA. Mclnnes JM, Jasani MK, Dalakos TG, Grieveson P, Buchanan WW: Clinical studies with an articular index for the assessment of joint tenderness in patients with rheumatoid arthritis. Q J Med 147:393406, 1968 41. Camp AV: An articular index for the assessment of rheumatoid arthritis. Orthopedics 4:3945, 1971 42. Pincus T, Callahan LF. Brooks RH: Quantitative nonlaboratory measures to monitor and predict the course of rheumatoid arthritis, Rehabilitation Management of Rheumatic Conditions. Second edition. Edited by GE Ehrlich. Baltimore, Williams & Wilkins, 1986 43. Lee P, Baxter A, Dick WC, Webb J: Assessment of grip strength measurement in rheumatoid arthritis. Scand J Rheumatol 3:17-23, 1973 44. DeCeulaer K, Dick WC: The clinical evaluation of antirheumatic drugs, Textbook of Rheumatology. Edited by WN Kelley, ED Harris Jr, S Ruddy, CB Sledge. Philadelphia, WB Saunders, 1981 45. Clawson DK, Souter WA, Carthum CJ, Hymen ML: Functional assessment of the rheumatoid hand. Clin Orthop 77203-210, 1971 46. Pincus T, Summey JA. Soraci SA Jr, Wallston KA. Hummon NP: Assessment of patient satisfaction in activities of daily living using a modified Stanford Health EDUCATION LEVEL AND RA 47. 48. 49. SO. 51. 52. 53. 54. Assessment Questionnaire. Arthritis Rheum 26: 13461353, 1983 Callahan LF, Brooks RH, Summey JA, Pincus T: Quantitative pain assessment for routine care of rheumatoid arthritis patients, using a pain scale based on activities of daily living and a visual analog pain scale. Arthritis Rheum 30:630-636, 1987 Steinbrocker 0, Traeger CH, Batterman RC: Therapeutic criteria in rheumatoid arthritis. JAMA 140:659-662, 1949 Scott J , Huskisson EC: Graphic representation of pain. Pain 2: 175-184. 1976 Fries JF, Spitz P, Kraines RG, Holman HR: Measurement of patient outcome in arthritis. Arthritis Rheum 23: 137-145, 1980 SPSSx User’s Guide. Second edition. New York, McGraw Hill, 1986 Godfrey K: Comparing the means of several groups. N Engl J Med 313:145&1456, 1985 Cupples LA, Heeren T, Schatzkin A, Colton T: Multiple testing of hypotheses in comparing two groups. Ann Intern Med 100:122-129, 1984 Kleinbaum DG, Kupper LL, Morgenstern H, editors: Epidemiologic Research, Principles and Quantitative 1357 55. 56. 57. 58. 59. 60. 61. 62. Methods. Belmont, CA, Lifetime Learning Publications, 1982 Greenland S: Interpretation and estimation of summary ratios under heterogeneity. Stat Med 1:2 17-227. 1982 Brown BW Jr, Hollander M: Statistics: A Biomedical Introduction. New York, John Wiley & Sons, 1977 Mantel N. Haenszel W: Statistical aspects of the analysis of data from retrospective studies of disease. JNCI 22:719-748, 1959 Pincus T, Callahan LF, Sale WG, Brooks AL, Payne LE, Vaughn WK: Severe functional declines, work disability, and increased mortality in seventy-five rheumatoid arthritis patients studied over nine years. Arthritis Rheum 27:864-872, 1984 Pincus T, Callahan LF, Vaughn WK: Questionnaire, walking time and button test measures of functional capacity as predictive markers for mortality in rheumatoid arthritis. J Rheumatol 14:240-251, 1987 Alsop S: Day of Execution. New York. Harper & Row, 1973 Cousins N: Anatomy of an Illness as Perceived by the Patient. New York, Bantam, 1979 Abram M: The Day is Short: An Autobiography. New York, Harcourt Brace Jovanovich, 1982
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