RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 1

Transcription

1 AMERICAN JOURNAL or EPIDEMIOLOGY Copyright 1981 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved Vol. 114, No. 4 Printed in RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 1 WILLIAM W. EATON AND LARRY G. KESSLER Eaton, W. W., and L. G. Kessler (Dlv. of Biometry and Epidemiology, National Institute of Mental Health, Rockville, MD 20857). Rates of symptoms of depression in a national sample. Am J Epidemiol 1981 ;114: This paper Is an analysis of data on symptoms of depression In a nationwide sample of adults, collected during the National Center for Health Statistics' first Health and Nutrition Examination Survey. The data are analyzed by a statistical adjustment procedure which assesses the Impact of the major soclodemographlc variables simultaneously, Instead of one by one, which avoids a former major source of confusion. The measure of depression Is the Center for Epidemlologic Studies Depression (CES-D) Scale. With the exception of race, the study replicates earlier findings. The adjustment procedure facilitates generalization of the results to other populations which are not demographically comparable, such as to smaller locales in the United States, or to populations defined by some criterion variable. Comparison of results is made with earlier work that used the Identical measure of depression in two separate locales. depression; health surveys; socioeconomlc factors With the development of reliable casefinding techniques, the high prevalence of mental disorder among the noninstitutionalized US population has become increasingly apparent. Recent epidemiologic studies have found depressive disorders to be the most common among the mental disorders in the community (1). The present study is an analysis of symptoms Received for publication December 1980, and in final form April 6, Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; HANES, Health and Nutrition Examination Survey. 1 From the Division of Biometry and Epidemiology, National Institute of Mental Health, Rockville, MD. Reprint requests to Dr. Eaton, Room 18C-05, CES-NIMH, 5600 Fishers Lane, Rockville, MD The authors thank Ben Z. Locke for originating the idea for the basic framework of the paper, and the staff of the National Center for Health Statistics, particularly Robert Murphy, Dale Hitchcock, and Neil Fleming, who helped with a number of comments on an earlier draft. Formulas 1 3 are from the PhD thesis of Donna Strobino. Michael Yuhas assisted with many of the analyses reported within the paper. of depression from a nationwide sample of adults collected during the National Center for Health Statistics' first Health and Nutrition Examination Survey (HANES). These data are analyzed by a statistical adjustment procedure that controls for multiple independent variables in relationship to symptoms of depression, the dependent variable. The effects of multiple sociodemographic variables on depression can be examined explicitly, and spurious effects can be readily detected. This approach, which is an extension of standard multiple linear regression techniques, also allows rapid generalization of HANES data to estimate the prevalence of symptoms of depression in smaller locales. MATERIALS AND METHODS Depression. Depression can vary in severity from mild swings in mood to severe psychosis. In the latest Diagnostic and Statistical Manual of Mental Disorders (2) there are two basic types of depressive diagnoses: major depressive disorder 528

2 RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 529 (which is severe but not necessarily psychotic); and dysthymia, which is less severe. In both cases there are associated somatic features such as psychomotor retardation, loss of appetite, and insomnia. A depressive mood alone is not sufficient to classify an individual into either of these categories. The Center for Epidemiologic Studies Depression Scale (CES-D) was designed as a measure of the symptoms of clinical depression. It has 20 items which ask for the frequency with which a given symptom was experienced during the previous week. Either self-report or interview procedures can be used to complete the scale. The scale has been widely used and extensively validated (3-5). The internal consistency (alpha) reliability of the scale has been found to be about 0.85, and the two-week test-retest correlation has been estimated at about 0.5 (3). The CES-D correlates with other measures of depressive affect, and with variables which theoretically are thought to relate to depression, such as the number of stressful life events experienced, or the assessment of need for help (3). Mean CES-D scores are much higher for clinically depressed persons: for example, acutely depressed psychiatric patients had a mean score of 38 compared to 9 for the general population (4). In this paper we adopt the standard cutoff point of 16 or more symptoms (5). The population with scores above 15 contains a high proportion of individuals with major depressive disorder and dysthymia, as well as some persons who suffer from several symptoms of clinical depression but do not meet the criteria for diagnosis. The HANES survey. In , the National Center for Health Statistics conducted the first Health and Nutrition Examination Survey (HANES). This study had as its major purpose the collection of health and nutrition data on a sample representative of the coterminous United States. The HANES design was that of a multistage, stratified, probability sample of loose clusters of persons in landbased segments. At each site for data collection, called a stand, three mobile trailers were set up for the examination portion of the study. Prior to the health examination, households were visited and a variety of questionnaires were completed by the respondent. The original design of the HANES in 1974 was augmented by an additional collection period in 1975 including 35 additional stands. These latter stands included the completion, in the household, of the CES-D scale (6). The augmented sample was designed to have two properties: 1) it was to complete the earlier subsample of adults aged to include certain specifications; and 2) by itself it was to be a representative sample of the US population. All data in this report are derived from the augmentation survey and, with appropriate weighting, represent a clusterprobability sample of the noninstitutionalized adult US population aged Our purpose was not to estimate parameters on a national basis but rather to study relationships in a large national sample which included sufficient numbers of important sociodemographic subgroups in the US population. Therefore, we did not weight the data according to sample design specifications. The failure to take into account design effects produces estimates of variances that are biased low, by a factor of about one-half to one-quarter and, thus, our analyses are more likely to achieve significance than otherwise and the reader should be aware of this fact. The original sample from the HANES augmentation included 3059 persons; however, missing data on certain items reduced the number to 2867 persons who have complete data on all variables included in these analyses (93.7 per cent of respondents). The general characteristics of this population for blacks and whites are shown in table 1. The population is

3 530 EATON AND KESSLER TABLE 1 Distribution of independent variables, HANES* augmentation sample, 1975 Characteristic Total Sex Male Female Age Marital status Single Married Widowed Other Years of education *13 Employment status Working Housework Retired/student Other Household income $0-$3,999 $4,000-$5,999 $6,000-$14,999 s=$15,000 Household size (persons) 1 2 3»4 Residence City Suburban Town Rural Blacks: % (n - 242) 10O Whites:* (n = 2626) * Health and Nutrition Examination Survey, National Center for Health Statistics. predominately white (91.6 per cent), and slightly more than half is female (56.3 per cent). The other variables included in these analyses are age, marital status, years of schooling, employment status, household income, number living at home, and urban-rural location of home. The analytic approach in the present study follows previous work by Comstock Total: % (n = 2867) and Helsing (5). However, there are several significant differences in the present approach, which will be noted while describing the details of the analysis. Figure 1 shows the frequency distribution of CES-D score by race. As in the Comstock and Helsing study, the frequency distribution of CES-D scores is heavily skewed right, but unlike their study, the

4 RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 531 I B S 7 I I FIGURE 1. Percentage distribution of Center for Epidemiologic Studies Depression Scale (CES-D) score by race of a Health and Nutrition Examination Survey (HANES) augmentation sample in the United States, present distributions are not artificially smoothed. Both studies show the peculiar peaking pattern at a score of 3 and relatively low frequencies at 1 and 2. Blacks show generally lower frequencies than whites at the low symptom portion of the distribution, but considerably higher proportions between scores of 13 and 22. The difference between the two distributions is significant atp < by the Kolmogorov-Smirnov test (7). Method of analysis. The focus of the analysis is on differences in CES-D values between subcategories of explanatory variables after adjustment for the remaining independent variables. The procedure chosen is the Feldstein binary variable multiple regression approach, which is, in effect, a dummy variable regression with dichotomous dependent variable (8). It is appropriate here because of our focus on relatively severe depression as the sole dependent variable. As Comstock and Helsing observe, in the Feldstein approach, "the adjusted values obtained by this procedure indicate the relative importance of the various subcategories of each independent variable when the confounding effects of the other independent variables are removed" (5). We also analyzed the data with the dependent variable of depressive symptomatology treated in continuous fashion, in the more conventional fashion of the general linear model. These results were very similar to the Feldstein procedure and are not presented, except for the effect of race (discussed below). Adjusted means of the sample were computed in the following manner: dummy variables were created, for all explanatory variables, with one category omitted per variable, and least squares regression estimates computed with the binary depression score as the dependent variable. The grand mean (in this case, a proportion with scores higher than 15 on the CES-D scale) is related to the estimated 6 0 as follows: P=a+ n W u (1) i-i j-i where p is the grand mean of the sample, N is the total number of persons in the sample, k is the number of explanatory

5 532 EATON AND KESSLER variables in the equation, n u is the number of persons in the jth subclass of the ith explanatory variable, by is the estimated regression coefficient for thejth subclass of the ith explanatory variable, a is both the constant of the regression equation and is the mean of the dependent variable for the total of the k explanatory variable subclasses omitted from the equation, and r ( is the total number of subclasses for the ith explanatory variable. The adjusted mean, a^ for thejth subclass of the ith explanatory variable is given by: a u = p + b u -jy (2) The adjusted mean for the omitted subclass of the ith explanatory variable is given by equation 2 with b u = 0. For any other given population measured on the same k variables, a predicted mean for the dependent variable can be computed by: 1 v = a + jj 2. i-i j-i m u b u (3) where p is the estimated mean of the new population, M is the total number of persons in the population, and m lj represents the total number of persons in the jth subclass of the ith explanatory variable. RESULTS Adjusted rates of depression. Most reviews of literature on depression consider sociodemographic variables one by one (e.g., 9). The variables considered here are all highly correlated with at least one other variable, however, so that it is difficult to compare the relative magnitudes of association of the different variables with depression. The Feldstein procedure allows the researcher to assess the impact of a single variable, unconfounded with its relationship with other variables. Table 2 presents the raw percentages with scores above 15, and a replication of Comstock and Helsing's (5) results on blacks and whites. The column headed "With race" incorporates race explicitly into the model, and the column headed "With household size and residence" gives adjustments including variables eliminated by Comstock and Helsing in their models due to lack of statistical significance, but included here because they do reach significance atp < 0.05 (household size and residence). The influence of sex on depression is very strong (20 per cent of the females above the cutting score and 10 per cent of the males in the column headed "Whites," which most closely replicates Comstock and Helsing). It is unaffected by controlling for other demographic factors. This finding is consistent with the literature, and with the Comstock-Helsing results. The association of age with depression is not well established because the literature has produced conflicting results (9). One major problem is the fact that older persons are much more likely than young people to be widowed, divorced, or separated, and slightly more likely to have lower education, and these variables are highly associated with depression. The conflicting results in the literature may have come about because of differences in the proportions of the various marital status, education and income groups in the different samples reported. In table 2 there is a slight tendency for young persons to have more depression, when one considers age alone. When other variables are controlled, there is a strong linear tendency for the young to be more depressed (18 per cent depressed among those under 45 compared to 6 per cent of the population over 65 years of age, in the column headed "Whites"). Again this finding is consistent with Comstock and Helsing's work. Although the relationship to age is strengthened when one removes the effect of other variables, the association of marital status and depression is slightly weakened, in that the adjusted rates for

6 RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 533 TABLE 2 Raw and adjusted percentages with a CES-D* score over 15, HANESt augmentation sample, 1975 Sex Male Female Age Marital status Single Married Widowed Other Years of education »13 Employment status Working Housework Retired or student Other Household income $0-$3,999 $4,000-$5,999 $6,000-$14,999 *$15,000 Race White Black Household size (persons) *4 Residence City Suburban Town Rural r 1 Raw % * Center for Epidemiologic Depression Scale. t Health and Nutrition Examination Survey. Replication of Comstock-Helsing: adjusted % Blacks Whites With new variables added : adjusted % With household sixe With race and residence divorce, separated and widowed are not quite so high as the unadjusted rates. In fact, in the full model, marital status is not a statistically significant predictor of depression. In the column headed "Raw %," housewives and persons in the "other" employment category have much higher rates of depression (20.6 and 28.1, respectively). The "other" category consists mostly of

7 534 EATON AND KESSLER unemployed persons. Adjustment for the housewife/sex identity relationship eliminates the high rate for housewives (to 15.6, identical to those working). The high rate for "other" is also lowered somewhat (down to 25.6), a likely effect of the adjustment for education and income. Finally, income and education have strong inverse effects on scores for depression which persist after other variables are controlled. For blacks the effect of income is very strong, and much stronger than reported in Comstock and Helsing: almost 50 per cent of blacks with less than $4000 income are above the cutoff score, compared to only 18 per cent of those in the upper income category. In general, we replicate the patterns of findings reported by Comstock and Helsing for adjusted rates of depression for variables of sex, age, marital status, employment, education and income. The effects of race on depression. The influence of race on depression has been examined, but interpretation of univariate results is clouded by the fact that blacks tend to have less education and lower incomes, factors which are also positively correlated with depression. Comstock and Helsing analyzed blacks and whites separately, and our replication of their analyses is shown in table 2. Blacks and whites show the same pattern of relationship to depression for sex, age, and income, as was found by Comstock and Helsing (except that in their results black males had slightly higher rates than black females). For marital status, black single and widowed persons have much lower adjusted rates than white single and widowed persons. This finding is distinctly opposite to Comstock and Helsing's and we think it is probably due to sampling fluctuations and the small number of single and widowed blacks in both studies (e.g., 30 single blacks and 17 widowed, in our study). Our results for blacks and whites are also similar to the Kansas City study for employment status, with the exception of the retired or student category, which again was small (e.g., 20 in our study). In summary, these results indicate no strong interaction between race, other sociodemographic variables, and depression. What remains is to consider the main effect of race on depression. In an important study from Florida, Warheit et al. (10) showed that race had little effect on measures of mental disorder when other sociodemographic variables were controlled. Comstock and Helsing attempted to replicate this result by computing a total adjusted rate for blacks, apparently using the regression results from their white sample (see equation 3 for the computational procedure). The total adjusted percentage for blacks derived by this procedure was about equal to the crude percentage for whites, and they concluded that their results were consistent with those of Warheit et al. In the HANES data, the total adjusted percentage for blacks, using the adjustment figures from the whites in our sample with equation 3, was 21.3 per cent, much higher than the crude figure for whites of 15.3 per cent. Since there is no important interaction of variables with race, it was entered into the regression procedure to generate the adjusted rates in the column headed "With race." This analysis again reveals a different result than that found by Comstock and Helsing: an effect of race even when other variables are controlled. The large crude difference between races under "Raw %" in table 2 (28.5 per cent depressed among blacks, 15.3 per cent among whites) is reduced, but not eliminated: a difference of 7 per cent remains (23.0 per cent of blacks depressed versus 15.8 per cent of whites) when only race is added. This difference is statistically significant at the level (F = 8.4; 1,2850 degrees of freedom). This is reduced to a difference of 5 per cent (21.3 per cent versus 16.0 per cent) when both household size and residence variables are added. Our results are very similar to those

8 RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 535 reported in another study examining race (11), although in that study global distress was the focus rather than depression. However, we suggest our data contrast with those of Warheit et al. (10) because of the different methods used. When we analyzed the data with the depression score as a continuous variable, there was a higher mean CES-D score in blacks (10.9) than in whites (8.4), but there was no significant effect of race on depression with other variables controlled, exactly as in the analysis by Warheit et al. The significant difference we found with the binary score indicates that blacks do indeed have more depression, but only if one considers the more severe end of the depression continuum. This result is consistent with the differences in the tails of the distribution of depression scores for blacks and whites (figure 1). Household size and residence. In this national sample two variables contribute significantly to the regression model that failed to do so in the work by Comstock and Helsing. Two-person households are less likely to have individuals with high CES-D scores 13 per cent versus 17, 18, or 19 per cent for the other categories. Note that marital status is controlled in this analysis and the two-person family still has an effect. It may be true that parentchild families or nonmarried couples living together have an advantage over larger or smaller units, as well as the known better mental health of married persons. On the other hand, it is certainly true that at least some of the variance accounted for by household size is due simply to its association with marital status, and the sharing of variance explained by the two variables together. The level of depression for the 110 individuals in twoperson families in this sample who are not married is not as low as it is for married two-person families, but due to the small number of two-person families which are not married couples, it is impossible to shed further light on this point with these data. The place of residence was not included in the Comstock-Helsing analysis because there was so little variation in their samples. In the present work we divided the sample into four groups (table 2): residents of center city areas of standard metropolitan regions ("city"); residents of metropolitan regions living outside center city areas ("suburban"); residents of urban areas (over 2500 population) outside the metropolitan regions ("town"); and residents of rural areas outside metropolitan regions ("rural"). A recent review of the differences between rural and urban areas as to levels of psychiatric disorder concluded that the evidence suggested that depression was more prevalent in urban areas (12). These data are consistent with that evidence in that the adjusted rate for rural areas (12.9), in the column headed "With household size and residence" (table 2), is much lower than for the other areas (18.8, 16.4, and 18.8). These data are an important addition to the literature because of the presence of an explicit comparison of urban with rural areas using the same methodology, and because other sociodemographic variables are statistically controlled. We know of no prior studies with this combination of features. Comparison of local with national samples. The Feldstein procedure of adjustment (8) or others similar to it, has considerable potential to aid in the generalization of research results. Adjusted total rates from one sample can be potentially extrapolated to areas where no sample survey has been conducted. In particular, the coefficients of adjustment for a national sample may be applied to known demographic characteristics of a local area to yield a predicted rate of disorder. This method is an indirect (or "social indicators") approach to needs assessment (13, 14). The availability of local and national samples, using the identical measure of depression, gives us the opportunity to experiment with the technique. If the extrapolation is fruitful, the assess-

9 536 EATON AND KESSLER ment of mental health needs in communities throughout the country might be greatly facilitated. The ability to extrapolate results also helps in judging whether the characterization of certain groups as high in risk for depression is appropriate. For example, suppose a researcher has a sample of women being treated for alcoholism at an inner-city clinic with demographic characteristics and CES-D scale scores. If tht percentage of the sample with high scores of depression is very large, the researcher may wonder if the high rate is due to the special demographic characteristics of the sample, or to the fact that alcoholism may be associated with depression. The adjustment procedure allows the researcher to minimize the influence of major demographic variables, so that differences "due" to alcoholism can be analyzed more confidently. There are two major sources of error in these attempts at generalization. One is the possibility that adjustment coefficients differ in different groups. When groups are defined by the demographic variables included in the model, this problem is equivalent to nonadditivity, and tests for statistical interactions are appropriate. There is also the possibility that groups defined by variables not included in the model would show different adjustment coefficients, and it is obviously impossible to include all possible variables which might yield these kinds of differences in any given model. In the data we are analyzing, race and size of place of residence are examples of variables not included in the Comstock- Helsing models which might produce this sort of interaction. The second major source of error in extrapolating rates from sample to sample is the various types of study variance. Any difference in method might produce different coefficients of adjustment for different samples. For example, the local samples from the Comstock-Helsing study were given the CES-D by an interview, whereas the National Center for Health Statistics data were gathered by self-report. There are many other types of differences in method that could produce different coefficients of adjustment. Thus, theoretically, at least, extrapolation of results from sample to sample is somewhat problematic. However, our purpose here is quite pragmatic: we are asking if the procedure is valuable at all, even with these possible threats to generalization. The combination of data from two local samples and one national sample allows assessment of the robustness of the adjustment procedure against these potential threats. Our suspicion is that interactions will occur where there are strong cultural differences between groups, which produce entirely different meanings for the variables under study. The cultural difference between the two races is one example that concerned Comstock and Helsing; another probable cultural difference is between urban and rural areas. The analysis above has indicated that race did not interact significantly with other variables. We also considered all other twoway interactions, and none were significant, except those concerning residence. As in other psychiatric epidemiologic studies (e.g., 15), the effects of several socioeconomic variables were attenuated in rural areas. For example, the effect of income was greater in center city areas, as table 3 shows; there is a spread of about 14 per cent in the rural areas, about 24 per cent in towns outside metropolitan areas, about 20 per cent in metropolitan areas outside the center cities, and about 32 per cent in the center city areas. Education, likewise, had a milder effect in rural areas, as did employment status. In spite of these interactions we have calculated predicted rates of depression in the three local samples, using the adjustment figures from our national sample and equation 3. For the Washington

10 RATES OF SYMPTOMS OF DEPRESSION IN A NATIONAL SAMPLE 537 TABLE 3 Percentage CES-D* scores above 15 by income and geographic location, HANESt augmentation sample, 1975 Income $0-3,999 $4,000-6,999 $7,000-14,999 = $15,000 Total City Location} Suburban Town Rural * Center for Epidemiologic Studies Depression Scale. t Health and Nutrition Examination Survey. t City: the center city of a standard metropolitan statistical area (SMSA); suburban: SMSA regions not in the center city; town: urban places outside of a SMSA; rural: all other, including villages with less than 2500 population. County sample, the predicted rate is 17.4 per cent, whereas the actual rate was 17.0 per cent; in the sample of whites in Kansas City, the predicted rate was 18.2 per cent, and the actual rate 19.0 per cent; for the blacks in Kansas City, the predicted rate was 30.8 per cent, and the actual rate 26.4 per cent. The prediction is quite good for the two white subsamples, with the error at less than 5 per cent of the observed proportion. In the black subsample, the error is 16 per cent of the observed rate. DISCUSSION For major variables such as sex, age, marital status, employment, income and education, the patterns of association demonstrated in earlier more localized research are consistent with these national data. The replication of results is important, but the fact that our data are a national sample adds weight to the conclusion that these relationships are stable across quite a variety of populations. But there are exceptions: for example, number of people in the home and residence are significantly related to levels of depression (in contrast to the findings of Cornstock and Helsing). For residence, it is only a sample of a variety of different areas, such as are included in this national probability sample, which can be used to demonstrate the existence of such a relationship. The analysis of race as a predictor of depression shows some inconsistency. Although in our data statistical adjustment procedures show that mean levels of depression are not significantly different for blacks and whites when other sociodemographic variables are controlled (replicating earlier work), there are a greater proportion of blacks who are more depressed at the extremes of the distribution as measured by the CES-D (contrary to earlier work). Also, blacks below the poverty level have the highest adjusted rate of any group. These findings are consistent with the notion that blacks suffer disadvantages not totally accounted for by differences in education, income, and other social class variables. This analysis provides three tests of the ability of the adjustment procedure to estimate local rates of depression indirectly, and the results are disappointing: we would have been nearly as accurate if we had used the overall national crude rate (16.4 per cent for blacks and whites together) as an estimate for the local areas. Apparently the adjusted rates are close to the mark only when the population resembles a national sample. The error in other cases such as the Kansas City blacks is due in part to the small predictive power of the model itself, the relatively small sample size for blacks and associated sampling variability, and cultural differences between blacks and whites. If a larger sample of blacks becomes available in some local area, there should be an attempt to reassess the predictive power of the model. Adding new sociodemographic variables might well weaken the statistical stability of the adjustment coefficients, and, again, increase the error of prediction. In spite of these shortcomings, the indirect approach deserves further research effort, in view of the mounting social, legal, and financial costs of direct collection of data.

11 538 EATON AND KESSLER REFERENCES 1. Weissman MM, Myers JK, Harding, PS: Psychiatric disorders in a U.S. urban community: Am J Psychiat 1978;135: Diagnostic and statistical manual of mental disorders. (3rd edition.) Washington, DC: American Psychiatric Association, Radloff LS. The CES-D Scale: a self-report depressive scale for research in the general population. J Applied Psychol Measurement 1977; 1: Weissman MM, Sholomskas D, Pottenger M, et al. Assessing depressive symptoms in five psychiatric populations: a validation study. Am J Epidemiol 1977;106: Comstock GW, Helsing KJ. Symptoms of depression in two communities. Psych Med 1976;6: National Center for Health Statistics. Basic data on depressive symptomatology. United States Vital and Health Statistics. Series 11, No DHEW publication no. (PHS) , US GPO, Siegel S. Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill, Feldstein MS. A binary variable multiple regression method of analysing factors affecting peri-natal mortality and other outcomes of pregnancy. J Royal Stat Soc 1966;A129: Weissman MM, Boyd JH. The epidemiology of depression: depressive symptoms, non-bipolar depression and bipolar disorders. In: Paykel ES, ed: Handbook of affective disorders. Edinburgh: Churchill Livingstone, Warheit GJ, Holzer CE, Schwab JJ. An analysis of social class and racial differences in depressive symptomatology: a community study. J Health Soc Behav 1973;14: Kessler RC. Stress, social status, and psychological distress. J Health Soc Behav 1979;20: Mueller DP. The current status of urban-rural differences in psychiatric disorder: an emerging trend for depression. J Nerv Ment Dis 1981;169: Bell R, Sundell M, Aponte JA, et al, eds. Needs assessment in health and human services. Louisville, KY, Department of Psychiatry and Behavioral Sciences, Steinberg J, ed. Synthetic estimates for small areas; NIDA Research Monograph 24. Rockville, MD: National Institute on Drug Abuse, Eaton WW. Residence, social class, and schizophrenia. J Health Soc Behav 1974,15: