This Article Abstract Figures Only Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Neeleman, J. Articles by Bijl, R. V. Search for Related Content PubMed PubMed Citation Articles by Neeleman, J. Articles by Bijl, R. V. Related Collections Social Class Personality Somatoform

The Distribution of Psychiatric and Somatic Ill Health: Associations With Personality and Socioeconomic Status

From the Department of Social Psychiatry (J.N., J.O.), University of Groningen, Groningen; and the Netherlands Institute of Mental Health and Addiction (R.V.B.), Utrecht, Netherlands.

Address reprint requests to: J. Neeleman, MSc, MD, PhD, MRCPsych, Department of Social Psychiatry, University of Groningen, P.O. Box 30.001, 9700 RB Groningen, Netherlands. Email: j.neeleman{at}med.rug.nl

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 
OBJECTIVE: Psychiatric and somatic disorders frequently co-occur in the same individuals. We examined whether this happens because these types of morbidity share risk factors or because they are risk factors for each other.

METHODS: Negative binomial regression was used to examine, in a random sample of Dutch adults (N = 7076), cross-sectional associations of sociodemographic and personality variables like income and neuroticism with the presence, over 1 year, of 30 somatic and 13 psychiatric disorders, with the latter diagnosed by structured interview. We examined to what extent the links of these variables with these two morbidity types were independent of each other.

RESULTS: This population experienced 5050 somatic and 2438 psychiatric disorders during the preceding year. Subjects reporting more somatic disorders had more psychiatric disorders. Neuroticism, followed closely by low educational attainment, was the strongest correlate of both morbidity types. After adjustment for all other covariates including somatic morbidity, the number of psychiatric diagnoses rose 1.84-fold (95% confidence interval = 1.74–1.94) per standard deviation increase in neuroticism. Likewise, adjusted for all other covariates including psychiatric diagnoses, 1.42 (95% confidence interval = 1.35–1.50) times more somatic disorders were reported per standard deviation increase in neuroticism.

CONCLUSIONS: Personal features like neuroticism and low educational attainment are linked with psychiatric and with somatic morbidity. These links are largely independent. Although this study was cross-sectional, the results suggest that these different types of morbidity may have overlapping etiologies.

Key Words: Comorbidity • multimorbidity coefficient • negative binomial regression • epidemiology • neuroticism • social class.

Abbreviations: CI = confidence interval; DSM-III-R = Diagnostic and Statistical Manual of Mental Disorders, third edition, revised; LR = likelihood ratio test; LRI = likelihood ratio test for interaction; NEMESIS = Netherlands Mental Health Survey and Incidence Study; OR = odds ratio; RR = relative rate.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 
Episodes of ill health are unevenly distributed over populations (1). Thus, some individuals experience more disorders than others (2). Disorders might concentrate in individuals if the disorders caused, precipitated, or maintained one another directly or indirectly. Numerous examples exist of such morbidity-dependent mechanisms. Psychiatric patients’ increased risk of cardiovascular disease may arise because they often smoke (3). Physical illness may act as a threatening life event that raises the risk of psychiatric disorder many-fold (4). Thus, it has been stated that the relation between depression and physical illness is self-perpetuating and mutually reinforcing (5). Findings of somatic-psychiatric comorbidity are generally attributed to morbidity-dependent mechanisms (6, 7). However, even if psychiatric and somatic disorders do not affect the risk of each other, they may still concentrate in individuals if they share determinants.

Terms such as "general propensity to ill health" (2) have been used to refer to the possibility of an etiological overlap between various disorders. However, the contribution of this overlap to the tendency of psychiatric and somatic disorders to co-occur remains unquantified. To qualify as generic determinants of somatic and psychiatric morbidity, variables should fulfill two conditions:

1. They should be prospectively linked with psychiatric and somatic disorders. This applies, for instance, to low socioeconomic status (8–10), perinatal complications (11), temperamental features like neuroticism, hopelessness (10, 12, 13), and impulsivity (10), and perceived lack of parental care (14, 15).

2. Links to both types of morbidity should be direct ( Figure 1, A). As Figure 1,B, illustrates, a risk factor and a given morbidity type may appear to be linked while in fact only an indirect association, mediated by another disorder, exists between them. In this case, comorbidity is attributable to morbidity-dependent and not generic effects.

View larger version (17K): [in this window] [in a new window]      Fig. 1. (A) Clustering of psychiatric morbidity with somatic morbidity due to the effect of common shared risk factors. (B) Clustering of psychiatric morbidity with somatic morbidity due to the effect of the disorders on each other. The dashed line indicates a spurious association.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 
Sample
Data were used from the 1996 wave of NEMESIS, which was set up to chart the incidence and prevalence of mental disorders (16, 17) and use of psychiatric services (18, 19) in the general population. Ninety municipalities were selected on the basis of size and geographic location. The number of households sampled was proportional to the size of each municipality’s population. In these households, the person between 18 and 64 years of age with the most recent birthday was asked to participate. The response rate was 64.2%, giving a total sample of 7076 individuals. There were no demographic differences between those who declined to participate and those who participated. Individuals between 18 and 24 years of age were underrepresented, but otherwise the age and gender composition of the sample was representative of the Dutch population. Participants were interviewed at home by trained interviewers using the computerized version of the Composite International Diagnostic Interview (20). Somatoform disorders were not diagnosed. The present analyses used 1-year prevalences of 13 DSM-III-R diagnosed conditions (Table 1). Subjects completed well-validated questionnaires designed to measure neuroticism (21), self-esteem (22), psychological symptoms (23), recall of parental care and control during childhood (Parental Bonding Instrument) (24), and a checklist concerning their experience during the previous year of 30 somatic disorders (Table 1). If a participant experienced any of the somatic disorders (ie, answered yes), he or she was asked whether medication had been prescribed or another medical treatment had been obtained for these (25). Only treated and/or medicated somatic disorders were analyzed. Details on socioeconomic status, marital status, religious affiliation, and perceived importance of religion in subjects’ daily life (religious salience) were also obtained ( Table 2).

General Approach
Figure 1,A and B, illustrates our analytical approach. A link between a given variable and a certain morbidity type may be direct (Figure 1,A) or indirect when another disorder mediates it (Figure 1, B). Statistical adjustment for this mediating disorder (type 1 in Figure 1,B) results in removal of the indirect part of the link. Thus, a variable that retains links with somatic morbidity after adjustment for psychiatric disorder and with psychiatric morbidity after controlling for somatic ill health must be directly associated with both. Because our data are cross-sectional, adjustment for the mediating disorder effectively results in removal of comorbid conditions from the analysis of the separate morbidity types. Because part of the comorbid conditions may also be directly linked with the variables of interest, the procedure underestimates the strength of generic links. This is inevitable in cross-sectional data (26).

Quantification of Multimorbidity
Patterns of comorbidity are commonly expressed by means of ORs. However, ORs capture associations between a maximum of two disorders; thus, they are unsuited to examine 30 somatic and 13 psychiatric disorders simultaneously. We therefore used multimorbidity coefficients (27). The multimorbidity coefficient is the ratio between observed and expected numbers of persons with a given number of disorders. When disorders are randomly distributed (ie, neither cause each other nor share risk factors), the Poisson distribution gives the expected number of persons. However, when disorders cluster in individuals, their distribution is a negative binomial (1), and more individuals than expected under the Poisson assumption of independence have multiple morbidities. In this case multimorbidity coefficients are larger than unity. LR tests were used to examine departure of the distribution of psychiatric and somatic disorders from the Poisson distribution. This was also assessed graphically because with as many as 7076 individuals, LR tests might yield significant results even when extra-Poisson variation is trivial (1). Because there was strong evidence of multimorbidity in a minority of the study population, negative binomial regression was used to examine variables associated with this phenomenon. Negative binomial regression is comparable to Poisson regression but is more appropriate with nonrandomly distributed outcomes. It yields RRs comparing numbers of disorders between subjects according to their (degree of) exposure (expressed as SDs for standardized variables) to the risk factors in question.

Multimorbidity Associated With Separate Conditions
For the separate 30 somatic and 13 psychiatric conditions, we calculated RRs indicating how much they raised the numbers of other somatic or psychiatric disorders. We examined whether conditions with psychiatric multimorbidity were also those with more somatic disorders. This was done using linear regression with RRs for psychiatric disorders as dependent variables and RRs for somatic disorders as independent variables.

Crude Associations of Multimorbidity
The numbers of somatic and psychiatric disorders subjects had experienced during the preceding year were examined in relation to gender, age, marital status, country of birth, educational attainment, net income, religious affiliation, religious salience, hours spent on physical exercise weekly, neuroticism, self-esteem, and recall of parental care and control during childhood. Crude associations between somatic and psychiatric ill health were also examined and expressed as RRs indicating how many more somatic disorders are present per additional psychiatric one and vice versa.

Independent Associations of Multimorbidity
The extent to which the independent variables were associated with ill health of one type, irrespective of and unmediated by their links with the other, was examined by entering them jointly as covariates into negative binomial regression models and removing them, starting with those contributing least (assessed by LR tests) to the model until its fit started to decline. In the final models, LR tests for interaction (LRI tests) were used to test whether associations of disorder occurrence with the main risk factors varied by age or gender.

Reporting Bias
To examine possible effects of contemporaneous distress on models containing reported somatic disease as an outcome or covariate, effects of adjustment for General Health Questionnaire scores (23) were assessed.

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 
Somatic and Psychiatric Multimorbidity
Overall, 5050 treated somatic disorders and 2438 psychiatric disorders were reported. Neither the somatic ( Figure 2) nor the psychiatric disorders ( Figure 3) were randomly distributed; instead they were concentrated in a minority of individuals (somatic disorder, LR test vs. Poisson: ²(1) = 956.0, p < .001; psychiatric disorder, LR test vs. Poisson: ²(1) = 992.1, p < .001). The steep rise of multimorbidity coefficients illustrates this best. An implication is that fewer individuals than expected have one disorder only.

View larger version (20K): [in this window] [in a new window]   Fig. 2. Clustering of 5050 somatic disorders as indicated by departure of their distribution from the Poisson distribution.

View larger version (20K): [in this window] [in a new window]   Fig. 3. Clustering of 2438 psychiatric disorders as indicated by departure of their distribution from the Poisson distribution.

Crude Associations of Psychiatric and Somatic Multimorbidity
Of most of the variables examined, associations with somatic and psychiatric ill health were in the same direction (Table 2). More disorders of either type occurred in individuals from lower social strata (indicated by relatively low income and educational attainment) than in persons from higher socioeconomic groups, in divorced and widowed than in married people, in subjects not born in the Netherlands compared with natives, and in women than in men. High neuroticism, low self-esteem, and perceived exposure to affectionless parenting were associated with multimorbidity, more strongly for the psychiatric than the somatic type. The pattern of comparability of associations of somatic and psychiatric multimorbidity did not apply to indices of religiousness and single marital status. Religiousness had a positive association with somatic disorder and a negative one with psychiatric disorder. The reverse was true for single people compared with married people. This was attributable to an age effect, with older people having more somatic but fewer psychiatric disorders. Single subjects were younger (mean age = 32.6 years, SD = 10.6 years) than married subjects (mean age = 44.6 years, SD = 10.6 years) (t = 35.7, p < .001), as were participants without a religious affiliation (mean age = 40.5 years, SD = 11.8 years) compared with others (mean = 43.7 years, SD = 12.4 years) (t = 11.0, p < .001).

Per additional psychiatric disorder, 1.23 (95% CI = 1.19–1.28, p < .001) more somatic disorders were present and vice versa.

Independent Associations of Somatic Multimorbidity
Given the opposite associations with age, somatic and psychiatric morbidity were modeled separately. Country of birth, parental bonding, religious salience and affiliation, marital status, income level, self-esteem, physical exercise, and, importantly, psychiatric morbidity did not contribute to the fully adjusted model for somatic disorders; LR tests for each of these covariates yielded nonsignificant results. The final model, containing gender, age, educational attainment, and neuroticism only, performed as well as the full model (LR test: ²(11) = 17.7, p = .089). Age did not interact with the other retained variables, but the effect of neuroticism differed by gender (LRI test: ²(1) = 8.4, p = .004). Increasing age, lower educational level, and higher neuroticism were associated with somatic multimorbidity. The effect of neuroticism was more pronounced in men than in women ( Table 3).

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 
Morbidity is concentrated in a socioeconomically and temperamentally disadvantaged minority of the population (1, 2). Our study confirms that this is true for psychiatric as well as somatic disorders. Published evidence indicates that neuroticism and low educational attainment are prospectively associated with both types of ill health (8, 9, 12, 13). This study adds to this knowledge by indicating that the links of these two variables with somatic ill health exist independently of their associations with psychiatric disorder. Conversely, most of the links of low educational attainment and neuroticism with psychiatric disorder are not mediated by their simultaneous associations with somatic morbidity. This supports the hypothesis that these two prospective risk factors for psychiatric and somatic disorder are indeed generic associations and thus likely components of a general propensity to ill health.

Our approach to analyzing comorbidity is unusual. ORs are mostly used to quantify links between disorders (17). However, when relatively prevalent disorders are studied, ORs overestimate effect sizes because real associations are confounded with the random chance that conditions will co-occur (28). Thus, highly prevalent disorders will have large ORs for their association merely by chance, whereas multimorbidity coefficients and negative binomial regression adjust for this. ORs are also a limited means of exploring the concept of propensity to ill health because they can capture associations between two single disorders only.

Fewer people than expected had one health problem only, and many more than expected had multiple morbidities. Persons with a psychiatric diagnosis are much more likely than persons without one to report multiple somatic disorders. This may be compatible with either a morbidity-dependent or generic mechanism of morbidity accumulation, but it may also reflect overreporting of somatic symptoms in the presence of psychological distress. However, the possibility that overreporting may have inflated associations of somatic morbidity with psychiatric disorder, neuroticism, or low educational attainment must be offset against the fact that our analytical method underestimates generic associations because it assumes that comorbid conditions are wholly due to morbidity-dependent effects.

In considering the validity of self-reported somatic morbidity, reports of single disorders must be distinguished from the main outcome measure, which was the sum of all somatic disorders per individual. Comparison of the prevalences of separate somatic disorders in the present sample with data from ongoing health surveys of the Dutch Central Bureau of Statistics (N = 16,293) (29) indicates good concordance (correlation coefficient = 0.96, p < .001), with relative differences being largest for rare conditions like uterine prolapse (NEMESIS, 0.6%; Central Bureau of Statistics, 1.8%) and cirrhosis (NEMESIS, 0.4%; Central Bureau of Statistics, 0.2%). Indeed, self-reports of rare, relatively unknown, or ill-defined conditions (like arthritis, which is frequently confused with osteoarthritis) are the least accurate (30). There is no consistent association between levels of psychological distress and under- or overreporting of somatic disorders (31). Thus, although it cannot be ruled out that associations of certain specific somatic disorders like arthritis or osteoarthritis with psychiatric ill health are inflated, the sum of reported somatic disorders, the main somatic outcome measure, is unlikely to be a systematic overestimate. In general population samples, underreporting of specific conditions contributes more than overreporting to low validity of total scale scores (32). Overestimates of the total sum score are also unlikely because conditions for which no medical help is sought or medication taken were excluded (30) and because respondents would have been simply unaware of certain conditions like hypertension or incipient cancers. Thus, in the least optimistic scenario, underreporting and overreporting are likely to have partialled out in the total somatic morbidity score. However, the strongest argument against overreporting as the sole explanation for the results stems from the fact that adjustment for levels of contemporaneous distress and psychiatric disorder did not obliterate links of somatic morbidity with neuroticism and low educational attainment. Unfortunately, somatoform disorders were not diagnosed. However, somatization rarely occurs in isolation of other psychiatric diagnoses and is strongly linked with raised scores on the General Health Questionnaire (33), for which we adjusted. All in all, biased reporting of somatic symptoms is unlikely to be the sole explanation for the patterns found.

Cross-sectional studies cannot indicate directions of causality. It cannot be excluded, on the basis of these data, that illness makes people more neurotic and less confident, biases memories of their childhood (24, 34), and precedes marital difficulties, income loss, and religious disaffiliation. However, a fair amount of evidence in the literature indicates the existence of prospective links of high neuroticism (10), low self-esteem (35), divorce (36), widowhood (37), low income levels (9), lack of religious affiliation (38), and negative childhood experiences (15) with future ill health. Moreover, neuroticism, although liable to fluctuations over longer time periods, is relatively uncontaminated by current symptom status (39). Morbidity was associated not only with neuroticism but also with low educational attainment. Most disorders we studied, but especially the somatic ones, surface after completion of education. Moreover, chronic childhood illness has not been shown to impair educational achievement (40). Thus, although some of the reported associations may be subject to confounding by reverse causality, this is unlikely to fully explain them.

Alcohol dependence or abuse was the only psychiatric disorder negatively associated with somatic morbidity. This reflected confounding by age. Problem drinking is more prevalent at younger ages, when somatic disorders are less prevalent, than later in life. Accidents were associated with psychiatric as well as somatic morbidity. This fits the notion that liability to accidents and disease is related to one underlying continuum (10, 41). Painful conditions like arthritis were among the strongest associations with psychiatric and somatic ill health, and hypertension and diabetes were among the weakest. This supports similar findings in two studies of elderly Dutch samples (42, 43) and an American population study (44).

The health status of immigrants has caused concern (45). However, in this study, most of their crude increased risk of ill health was attributable to other factors, such as low socioeconomic position. Adjusted for all else, immigrants are psychiatrically more healthy than natives. This supports the idea that fitness to emigrate indicates good psychological health (46). Religious affiliation was negatively associated with psychiatric morbidity. Negative links have also been reported between religiousness and somatic ill health (38), but in this study, this did not hold after full adjustment. It has indeed been argued that these links are largely spurious and mediated by other factors like psychiatric disorder, age, and gender (38). In line with previous research (14), poor parental rearing style was independently associated with psychiatric disorder. However, this did not apply to somatic morbidity. This probably indicates that the reported links of this variable with somatic disorders (15) depend on the presence of psychiatric ill health.

The results are in line with findings that low educational level and personality features like neuroticism are predictors of major disorders such as myocardial infarction (12) and with evidence that personality features mediate some, but not all, of the links between childhood social circumstances and future ill health (9). This study cannot reveal the biological mechanisms underlying proneness to psychiatric and somatic disease. The link between neuroticism and liability to ill health has been attributed to impaired reactivity of the sympathetic-adrenomedullary and hypothalamic-pituitary-adrenocortical systems, which place people under stress, through a variety of metabolic, immunological, and endocrinological mechanisms, at risk of a host of disorders (47). Low social class, of which low educational attainment is an aspect, may link with these mechanisms in a number of ways. It may increase exposure to stressful experiences and to more disorder-specific risk factors (48). However, early childhood adversity may also contribute to vulnerability in the form of neuroticism and its neurobiological substrates (9). The fact that low educational attainment, a proxy for childhood adversity, and neuroticism contributed independently to the risk of ill health suggests that both mechanisms are operative.

The results of this study suggest that substantial proportions of all morbidity in populations are attributable not to disorder-specific risks but rather to a few generic liability factors applicable to many disorders, both somatic and psychiatric. Attempts to remedy such generic determinants of ill health at an early age will yield benefits not only in the psychiatric but also in the somatic domain (49).

Received for publication March 7, 2000.

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

 

1. Kilpatrick SJ. An empirical study of the distribution of episodes of illness recorded in the 1970–1971 national morbidity survey. Appl Stat 1977; 26: 26–33.
2. Hinkle LE, Wolff HG. The nature of man’s adaptation to his total environment and the relation of this to illness. Arch Intern Med 1957; 99: 422–60.
3. Sims A. Why the excess mortality from psychiatric illness? BMJ 1987; 294: 986–7.
4. Finlay-Jones R, Brown GW. Types of stressful life event and the onset of anxiety and depressive disorders. Psychol Med 1981; 11: 803–16.[Medline]
5. Aneshensel CS, Frerichs RR, Huba GJ. Depression and physical illness: a multiwave, nonrecursive causal model. J Health Soc Behav 1984; 25: 350–71.[Medline]
6. Stansfeld SA, Davey Smith G, Marmot M. Association between physical and psychological morbidity in the Whitehall II study. J Psychosom Res 1993; 37: 227–38.[Medline]
7. Hotopf M, Mayou R, Wadsworth M, Wessely S. Temporal relationships between physical symptoms and psychiatric disorder. Br J Psychiatry 1998; 173: 255–61.[Abstract/Free Full Text]
8. Davey Smith, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ 1998;316:1631–5.
9. Bosma H, Dike van de Mheen H, Mackenbach JP. Social class in childhood and general health in adulthood: questionnaire study of the contribution of psychological attributes. BMJ 1999; 318: 18–22.[Abstract/Free Full Text]
10. Neeleman J, Wessely S, Wadsworth M. Predictors of suicide, accidental death, and premature natural death in a general-population birth cohort. Lancet 1998; 351: 93–7.[Medline]
11. Sayer AA, Cooper C, Barker DJ. Is lifespan determined in utero? Arch Dis Child 1997; 77: 162–4.
12. Eaker ED, Pinsky J, Castelli WP. Myocardial infarction and coronary death among women: psychosocial predictors from a 20-year follow-up of women in the Framingham study. Am J Epidemiol 1992; 135: 854–64.[Abstract/Free Full Text]
13. Rodgers B. Behaviour and personality in childhood as predictors of adult psychiatric disorder. J Child Psychol Psychiatry 1990; 31: 393–414.[Medline]
14. Rodgers B. Reported parental behaviour and adult affective symptoms. 1. Associations and moderating factors. Psychol Med 1996; 26: 51–61.[Medline]
15. Russek LG, Schwartz GE. Perceptions of parental caring predict health status in midlife: a 35-year follow-up of the Harvard Mastery of Stress Study. Psychosom Med 1997; 59: 144–9.[Abstract/Free Full Text]
16. Bijl RV, Van Zessen G, Ravelli A, de Rijk C, Langendoen Y. The Netherlands Mental Health Survey and Incidence Study (NEMESIS): objectives and design. Soc Psychiatry Psychiatr Epidemiol 1998; 33: 581–6.[Medline]
17. Bijl RV, Ravelli A, Van Zessen G. Prevalence of psychiatric disorder in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Soc Psychiatry Psychiatr Epidemiol 1998; 33: 587–95.[Medline]
18. Bijl RV, Ravelli A. Psychiatric morbidity, service use and need for care in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Am J Public Health 2000; 90: 602–7.[Abstract/Free Full Text]
19. Algeria M, Bijl RV, Lin E, Walter EE. Income differences in persons seeking outpatient treatment for mental disorders: a comparison of the United States with Ontario and the Netherlands. Arch Gen Psychiatry 2000; 57: 383–91.[Abstract/Free Full Text]
20. Smeets RMW, Dingemans PMAJ. Composite International Diagnostic Interview (Dutch version 1.1). Amsterdam: World Health Organization; 1993.
21. Eysenck HJ. A short questionnaire for the measurement of two dimensions of personality. J Appl Psychol 1958; 43: 14–7.
22. Rosenberg M. The measurement of self-esteem. Princeton (NJ): Princeton University Press; 1965.
23. Goldberg D. The detection of psychiatric illness by questionnaire. London: Oxford University Press; 1972.
24. Parker GB, Barrett EA, Hickie IB. From nurture to network: examining links between perceptions of parenting received in childhood and social bonds in adulthood. Am J Psychiatry 1992; 149: 877–85.[Abstract/Free Full Text]
25. Central Bureau of Statistics. Gezondheidsenquete vragenlijst [Health survey inquiry]. Voorburg, Netherlands: Central Bureau of Statistics; 1989.
26. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology 1992; 3: 143–55.[Medline]
27. van den Akker M, Buntinx F, Metsemakers JFM, Roos S, Knottnerus AJ. Multimorbidity in general practice: prevalence, incidence and determinants. J Clin Epidemiol 1998; 51: 367–75.[Medline]
28. Greenland S, Thomas DC. On the need for the rare disease assumption in case-control studies. Am J Epidemiol 1982; 116: 547–53.[Abstract/Free Full Text]
29. Central Bureau of Statistics. Netherlands Health Interview Survey 1981–1991. The Hague: Staatsdrukkerij; 1992.
30. van de Bos GAM. Zorgen voor chronisch zieken [Caring for the chronically ill]. Amsterdam: Bohn, Scheltema & Holkema; 1989.
31. Kriegsman DMW, Penninx BWJH, van Eijk JTM, Boeke JP, Deeg DJH. Self-report and general practitioner information on the presence of chronic disease in community dwelling elderly: a study on the accuracy of self-reports and the determinants of inaccuracy. J Clin Epidemiol 1996; 49: 1407–17.[Medline]
32. Trussell RE, Elinson J, Levin ML. Comparisons of various methods of estimating the prevalence of chronic disease in a community: the Hunterdon County study. Am J Public Health 1956; 46: 173–82.
33. Lobo A, Garcia-Campayo J, Campos R, Marcos G, Perez-Echeverra MAJ. Somatization in primary care in Spain. 1. Estimates of prevalence and clinical characteristics. Br J Psychiatry 1996; 168: 337–43.[Abstract/Free Full Text]
34. Duggan CF, Sham P, Lee AS, Murray RM. Does recurrent depression lead to a change in neuroticism? Psychol Med 1991; 21: 985–90.[Medline]
35. Seeman TE, Berkman LF, Gulanski BI, Robbins RJGSL, Charpentier RJW. Self-esteem and neuro-endocrine response to challenge: MacArthur studies of successful ageing. J Psychosom Res 1995; 39: 69–84.[Medline]
36. Brown GW, Harris T. Social origins of depression: a study of psychiatric disorder in women. London: Tavistock; 1978.
37. Kaprio J, Koskenvuo M, Rita H. Mortality after bereavement: a prospective study of 95,647 widowed persons. Am J Public Health 1987; 77: 283–7.[Abstract/Free Full Text]
38. Levin JS, Vanderpool HY. Is religion therapeutically significant for hypertension? Soc Sci Med 1989; 29: 69–78.
39. Duncan Jones P, Fergusson DM, Ormel J, Horwood LJ. A model of stability and change in minor psychiatric symptoms: results from three longitudinal studies. Psychol Med Monogr Suppl 1990; 18: 1–28.[Medline]
40. Pless IB, Cripps HA, Davies JMC, Wadsworth MEJ. Chronic physical illness in childhood: psychological and social effects in adolescence and adult life. Dev Med Child Neurol 1989; 31: 746–55.[Medline]
41. Neeleman J. Continuum of premature death: meta-analysis of competing mortality in the psychosocially vulnerable. Int J Epidemiol 2001; 30: 154–62.[Abstract/Free Full Text]
42. Penninx BW, Beekman AT, Ormel J, Kreigsman D, Boeke JP, van Eijk JT, Deeg DJ. Psychological status among elderly people with chronic diseases: does type of disease play a part? J Psychosom Res 1996; 40: 521–34.[Medline]
43. Ormel J, Kempen GIJM, Deeg DJH, Brilman EI, van Sonderen E, Relyveld J. Functioning, well-being and health perception in late middle aged and older people: comparing the effects of depressive symptoms and chronic medical conditions. J Am Geriatr Soc 1998; 46: 39–48.[Medline]
44. Wells KB, Golding JM, Burnham MA. Psychiatric disorder in a sample of the general population with and without chronic medical conditions. Am J Psychiatry 1988; 145: 976–81.[Abstract/Free Full Text]
45. Balarajan R, Soni Raleigh V. Hospital care among ethnic minorities in Britain. Health Trends 1991; 23: 90–3.[Medline]
46. Neeleman J, Wessely S. Density effects on ethnic minority suicide: a small area geographical study in south London. Psychol Med 1999; 29: 429–36.[Medline]
47. Brunner E. Socioeconomic determinants of health: stress and the biology of inequality. BMJ 1997; 314: 1472–6.[Abstract/Free Full Text]
48. Power C, Hertzman C. Social and biological pathways linking early life and adult disease. Br Med Bull 1997; 53: 210–21.[Abstract/Free Full Text]
49. Syme LS. Rethinking disease: where do we go from here? Ann Epidemiol 1999; 6: 463–8.

This article has been cited by other articles:

				B. P. Chapman, J. M. Lyness, and P. Duberstein Personality and Medical Illness Burden Among Older Adults in Primary Care Psychosom Med, April 1, 2007; 69(3): 277 - 282. [Abstract] [Full Text] [PDF]

				N. FIEDLER, G. OZAKINCI, W. HALLMAN, D. WARTENBERG, N. T. BREWER, D. H. BARRETT, and H. M. KIPEN Military deployment to the Gulf War as a risk factor for psychiatric illness among US troops The British Journal of Psychiatry, May 1, 2006; 188(5): 453 - 459. [Abstract] [Full Text] [PDF]

				R. Noyes Jr., C. P. Carney, S. L. Hillis, L. E. Jones, and D. R. Langbehn Prevalence and Correlates of Illness Worry in the General Population Psychosomatics, December 1, 2005; 46(6): 529 - 539. [Abstract] [Full Text] [PDF]

				J. Ormel, A. J. Oldehinkel, and W. Vollebergh Vulnerability Before, During, and After a Major Depressive Episode: A 3-Wave Population-Based Study Arch Gen Psychiatry, October 1, 2004; 61(10): 990 - 996. [Abstract] [Full Text] [PDF]

				J. E. Dimsdale Comorbidity Studies: A Core Area for Psychosomatic Research Psychosom Med, March 1, 2001; 63(2): 201 - 202. [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Neeleman, J. Articles by Bijl, R. V. Search for Related Content PubMed PubMed Citation Articles by Neeleman, J. Articles by Bijl, R. V. Related Collections Social Class Personality Somatoform