Hospital comorbidity bias and the concept of schizophrenia

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1 Soc Psychiatry Psychiatr Epidemiol (2005) 40: ORIGINAL PAPER DOI /s Maarten Bak. Marjan Drukker. Jim van Os. Philippe Delespaul Hospital comorbidity bias and the concept of schizophrenia Accepted: 22 June 2005 / Published online: 22 September 2005 Š Abstract Background The comorbidity bias predicts that if disease definition is based on observations of patients in the hospital, spurious comorbidity of psychopathological dimensions that increase the probability of hospital admission will be included in the disease concept, whereas comorbid dimensions that are not associated with admission will be excluded. The direction of any dimensional comorbidity bias in psychotic illness was assessed in a longitudinal analysis of the psychopathology of patients assessed both inside and outside the hospital. Method Four hundred and eighty patients with broadly defined psychotic disorders were assessed between one and nine times (median two times) over a 5-year period with, amongst others, the Brief Psychiatric Rating Scale. Dimensional comorbidities between positive symptoms, negative symptoms, depression/anxiety, and manic excitement were compared, in addition to their associations with current and future admission status. Results Higher Bak and Drukker shared first authorship M. Bak, MD, PhD. M. Drukker, PhD. J. van Os, MD, PhD. P. Delespaul, MA, PhD Dept. of Psychiatry and Neuropsychology South Limburg Mental Health Research and Teaching Network, EURON Maastricht University Maastricht, The Netherlands J. van Os, MD, PhD Division of Psychological Medicine Institute of Psychiatry De Crespigny Park, Denmark Hill London, UK M. Bak, MD, PhD (*) Dept. of Psychiatry and Neuropsychology Maastricht University PO Box 616 (PAR45) 6200 MD Maastricht, The Netherlands Tel.: Fax: m.bak@sp.unimaas.nl levels of psychopathology in all symptom domains were associated with both current and future hospital admissions. Associations between the positive, negative, and manic symptom domains were higher for patients in the hospital than for patients outside the hospital, in particular, between positive symptoms and manic excitement (β=0.28, p<0.001). However, associations between depression and other symptom domains were higher in out-patients as compared to in-patients (positive symptoms and depression, β= 0.26; p<0.002). Conclusion The current analyses suggest that, to the extent that disease concepts of psychosis do not take into account effects of dimensional comorbidity biases occasioned by differential psychopathology according to treatment setting, florid psychotic psychopathology may be overrepresented, whereas depressive symptoms may be spuriously excluded. Š Key words psychosis admission dimension out-patient Introduction Comorbidity bias (also known as Berkson s bias) predicts that if the definition of a disease with multiple psychopathological dimensions is based on observations of patients in the hospital, spurious comorbidity of those psychopathological dimensions that independently increase the probability of hospital admission will become included in the disease concept [1, 2]. Accordingly, associations between the symptoms that occasion admission to the hospital will be much higher in in-patients than in out-patients. That comorbidity bias may operate in schizophrenia was suggested by an earlier general population study showing that the association between positive and negative symptom dimensions of schizophrenia was much higher in patients compared with non-patients with psychotic experiences [2]. SPPE 971

2 818 Traditional concepts of phenomenology in schizophrenia and psychosis are generally based on in-patients, and therefore, may represent only a minor selection of the total phenotypic continuum [3 5]. Admission to the hospital is related to more complex psychopathology, increased levels of distress, and lower levels of functioning [6, 7]. Consequently, the recruitment of in-patients with a diagnosis of schizophrenia for the purpose of research will not be representative for the underlying population, and conclusions may lack external validity. Therefore, results should be interpreted carefully [8]. Similarly, psychopathological differences between in-patients and out-patients not only have implications for the schizophrenia concept but also for service provision [9]. If associations, correlations, and covariance between symptoms are significantly higher in in-patients than in out-patients, the cumulative effect of in-patient treatments on separate outcome dimensions will be interpreted as an enhanced symptom reduction and large intervention effects, while this interpretation may not be valid for out-patient settings. Anecdotal evidence suggests that mental health workers and policy makers are still unaware of these differences and their consequences. On the other hand, some symptoms may preclude admission. For example, when depressive symptoms are prominent, a patient may not be perceived as a risk for his environment, and hospitalisation may not be indicated. By focussing on criteria generated in the current dominant concept of schizophrenia, important cues of prodromal or first-onset schizophrenia may be misconstrued as benign in light of the referenced clinical reality of severe psychopathology observed in hospital settings. In the present paper, it was hypothesised that the clustering of symptom dimensions differs between inpatients and out-patients, with a higher level of clustering expected in in-patients. We studied the direction of any dimensional comorbidity bias in a longitudinal data set of the psychopathology of patients both inside and outside the hospital. Data were collected from a cumulative register of needs of in-patients and outpatients with psychotic illness in the defined geographical area of South Limburg, The Netherlands. First, associations between symptoms and current (cross-sectional) and future admissions (longitudinal) were studied. The cross-sectional analyses assessed whether psychopathology dimensions were susceptible to comorbidity bias, while the longitudinal analyses examined the possible causality of this association in terms of predictive power. Second, correlations between psychopathological dimensions in out-patients and in-patients were analysed, and to what degree these differed between the two groups. Methods Š Cumulative needs registration The local Cumulative Needs for Care Register (CNCR, formerly called the Psychosis Protocol) [10] is an initiative of the mental health service providers, legislators, and local consumers to assess the match between clients need for care and the level of service provision. All patients with a diagnosis of psychotic disorder according to the DSM-IV classification system [11] were included in the CNCR, and their psychiatrists, psychologists, and case managers were trained to administer various clinical scales (such as the Brief Psychiatric Rating Scale (BPRS), see below). Assessments are carried out routinely at intake and as a yearly evaluation. In addition, an assessment is also carried out with every major change in treatment or setting (e.g. hospitalisation, start of a new psychiatric treatment, and discharge). Š Hospitalisation and other measures Admission status (in-patient or out-patient) was the dependent variable in the analyses. All interviews before 24 June 2002 were included. Each CNCR assessment provides information on (1) demographic variables, (2) the BPRS [12, 13], (3) the General Assessment of Functioning (GAF) scales to assess levels of handicap and psychopathology [11], (4) the Camberwell Assessment of Need (CAN) [14], (5) a 5-item quality of life scale, and (6) quality of care (one item). The present paper focusses on dimensions of psychopathology from the BPRS as independent variables. Demographic variables were entered a priori as confounders in the analyses. Based on previous BPRS research [15], a factor analysis was carried out. This confirmed the existence of four underlying constructs (hereafter, BPRS symptom dimensions): negative symptoms, positive symptoms, manic excitement, and depression/anxiety. Blunted affect, motor retardation, emotional withdrawal, and selfneglect loaded on negative symptoms; bizarre behaviour, unusual thought content, disorientation, hallucinations, and suspiciousness on positive symptoms; motor hyperactivity, elevated mood, excitement, distractibility, hostility, and grandiosity on manic excitement; and depression, anxiety, suicidality, and guilt on depression/anxiety. Š Statistical analyses All analyses were performed using Stata (version 7) [16]. First, logistic regression was used to study the association between symptom dimensions (included separately) and admission status. This was done both in cross-sectional analyses, using all observations at each time point in each person, and in longitudinal analyses linking BPRS symptoms at the first time point of each out-patient with hospitalisation at any future time point (yes/no). Second, Pearson correlation coefficients between the four symptom dimensions were calculated in the group of out-patients and in the group of inpatients, using the assessment at the first time point of each patient only. Finally, we performed linear regression analyses (using all observations at each time point in each patient), including an interaction term admission status symptom dimension, to test the statistical significance of differences in associations between symptom dimensions in out-patients and in-patients. Because patients were interviewed at more than one time point, yielding more than one observation per person (i.e. several records in the data set), the assumption of independence of the observations for standard linear and logistic regression analyses was invalid except for the longitudinal analyses (using a data set with baseline variables and future admission in the same record). Therefore, we

3 819 Table 1 Descriptives Ambulant patients, mean (SD) In-patients, mean (SD) Difference between both groups Positive symptoms 8.62 (4.11) 11.7 (5.06) t=7.48, p<0.001 Negative symptoms 6.46 (2.53) 7.99 (3.37) t=6.69, p<0.001 Manic excitement 7.96 (2.58) 11.3 (4.99) t=9.23, p<0.001 Depression/anxiety 7.51 (3.33) 9.44 (4.12) t=5.74, p<0.001 Measurements were first averaged per patient and then for the groups of in-patients and out-patients. Patients having measurements in both ambulant and hospitalised settings are counted in both columns for their ambulant and hospitalised contacts, respectively performed linear and logistic regression methods ideally suited for the analysis of this data structure: multilevel regression analysis [17]. The parameters (βs obtained from multilevel linear regression analyses and odds ratios obtained from multilevel logistic regression analyses) can be interpreted identically to the estimates obtained from standard unilevel analyses. Results Š Descriptives The data included 994 interviews in 480 persons, each person contributing between one and nine assessments between the start of the data collection in 1997 and June Sixty per cent of the population was male. At the first interview, 49.1% of the clients were hospitalised. The mean age of the out-patients was 39.8 years (men 36.3, women 45.3); the mean age of the inpatients was 43.8 years (men 40.3, women 49.2). BPRS Table 2 Cross-sectional logistic regression analyses (multilevel) Odds ratio 95% CI A. Univariate analyses Positive symptoms 1.12 (p<0.001) Negative symptoms 1.12 (p<0.001) Depression/anxiety 1.13 (p<0.001) Manic excitement 1.20 (p<0.001) B. Controlled for age and gender, only Positive symptoms 1.12 (p=0.001) Negative symptoms 1.11 (p<0.001) Depression/anxiety 1.14 (p<0.001) Manic excitement 1.19 (p<0.001) Odds ratios of hospitalisation of the four BPRS symptom dimensions (symptom dimensions were entered separately in the models) symptom levels were 20 25% lower in out-patients, and these differences were statistically significant (Table 1). and hospitalisation Both the univariate analyses and the analyses controlling for age and gender showed that all four BPRS symptom dimensions were associated with in-patient status (Table 2). Age was positively associated with hospitalisation (OR=1.02, p=0.02), but there was no association with gender (OR=0.99). Longitudinal analyses showed that manic excitement in out-patients at baseline was a predictor for in-patient status at follow-up (Table 3). Positive symptoms also predicted future admission, albeit statistically imprecise by conventional alpha. in two settings In in-patients, correlations between symptom dimensions were generally stronger than in out-patients (Table 4), reaching statistical significance for the association between positive symptoms and manic excitement (β=0.28, p<0.001; not in table). Notable exceptions were the correlation between positive symptoms and depression/anxiety (β= 0.16, p= 0,002), and the correlation between manic excitement and depression/anxiety (β= 0.20, p=0.004), which was significantly lower in in-patients. Table 3 Analyses using longitudinal data Odds ratio 95% CI A. Univariate analysis (n=239, of which 30 were hospitalised during follow-up) Positive symptoms 1.09 (p=0.04) Negative symptoms Depression/anxiety Manic excitement 1.20 (p=0.001) B. Controlled for age and gender (measured at baseline) n=209 Positive symptoms 1.08 (p=0.07) Negative symptoms Depression/anxiety Manic excitement 1.20 (p=0.002) Odds ratios of hospitalisation during follow-up of patients who were out-patients at baseline (symptom dimensions were entered separately in the models)

4 820 Table 4 Correlations between BPRS factors in out-patients and in-patients, first interview of each Positive symptoms Negative symptoms Depression/anxiety Manic excitement Positive symptoms Out-patient 0.13, p= , p< , p<0.001 In-patient 0.29, p< , p= , p<0.001 Negative symptoms Out-patient 0.13, p= , p= , p=0.35 In-patient 0.29, p< , p= , p=0.08 Depression/anxiety Out-patient 0.48, p< , p= , p= In-patient 0.18, p= , p= , p=0.44 Manic excitement Out-patient 0.38, p< , p= , p=0.001 In-patient 0.51, p< , p= , p=0.44 Figures in bold indicate that the association in in-patients was significantly different from the association in out-patients (based on multilevel analyses including an interaction term between the psychopathology dimension presented horizontally and hospitalisation) Discussion Associations between symptom dimensions were generally higher in in-patients than in out-patients, suggesting the possible influence of dimensional comorbidity bias [2]. However, only one association (between positive symptoms and manic excitement) was significantly higher in in-patients than in out-patients. The correlation between positive symptoms and depression/anxiety, as well as the correlation between manic excitement and depression/anxiety, were higher in outpatients than in in-patients, although absolute symptom levels of both manic excitement and depression/ anxiety were lower in out-patients. The results therefore suggest that the occurrence of mania and positive symptoms has a more depressive colouring in patients who are not admitted. Treatment trials and prevention studies should take into account this excess depression comorbidity associated with these symptom domains in out-patients. Thus, the concept of psychosis, in particular schizophrenia, needs cautious interpretation in non-clinical settings. Previously, both positive and negative symptom dimensions were independent predictors of mental health care use, indicating higher associations between positive and negative symptom dimensions in patient groups as opposed to non-patients with sub-threshold psychotic experiences [2]. The present study, which did not compare patients and non-patients but in-patients and out-patients, did not show significant differences in associations between positive and negative symptoms in in-patients and out-patients. Instead, the associations between positive symptoms and manic excitement were significantly different in the comparison between hospitalised and out-patient groups. The different findings in the two studies were likely due to the fact that two different populations were studied. The general population study indicated that positive and negative symptom dimensions were most important for the initiation of any treatment [2], whereas the present study indicated that positive symptoms and manic excitement were important dimensions for the decision of admitting an already-treated psychotic patient to the hospital. In addition, the present analyses showed that all four symptom dimensions were as relevant with regard to admission status. This makes the assumption that the concept of schizophrenia is a hospital-based diagnosis even more plausible. From a clinical perspective, it has been shown that differences in psychosocial needs are related to the dimensions of schizophrenia. More needs are associated with the disorganised and excitatory subtypes [18]. This is in agreement with our finding that manic excitement in subjects with a diagnosis of schizophrenia is associated with hospitalisation. The longitudinal analyses also showed an association between manic excitement and future hospitalisation. In addition, there was also some evidence that a higher level of positive symptoms was associated with future hospitalisation status, albeit statistically inconclusive by conventional alpha. This can be due to a lack of power; only 30 of the 239 included out-patients were hospitalised during follow-up. Thus, results showed strong evidence that manic excitement is not only a reason for being admitted but also predicts future admission, and future analyses including more cases may also identify positive symptoms as a predictor of future admission. Š Implications for intervention studies Current research in psychosis focusses on the decrease of the duration of untreated psychosis, relapse prevention by early intervention programmes, medication compliance, cognitive therapy, and functional biological anomalies in patients with schizophrenia [19 22]. While many interventions focus on positive and negative symptoms, the present study suggests that comorbidity of positive symptoms and manic excitement may be relevant for admission to the hospital. Similarly, the assessment of change in positive and manic symptoms in a sample mixing out-patients and in-

5 821 patients should take into account the comorbidity of these domains with depression/anxiety in out-patients, which may introduce underlying heterogeneity in response due to differential treatment effects across inpatient and out-patient groups. Prevention research and first-onset programmes should be particularly aware of the differences in diagnostic symptom patterns in clinical samples and the target population for these interventions. Š Methodological issues The longitudinal data collected with the CNCR interview provided more insight in the longitudinal admission risk of psychotic patients and the interaction between psychopathology and admission status. This is important both for treatment and research. However, this data set has some limitations. First, interviews should have been carried out at intake, as part of the yearly evaluation, and at every mutation in the patient s situation. However, due to pragmatic and logistic reasons, professional carers do not always comply exactly. In some patients, two interviews were more than 1 year apart. In addition, the CNCR assessment tended to be omitted when patients were admitted repeatedly or stayed for periods of less than 2 weeks. Since reminders are now routinely sent to all interviewers who do not turn in the yearly reassessment, the logistics of the CNCR have improved. This will affect the compliance rate for the yearly reassessments, but not for every change in treatment or setting. Second, interviews were administered by different psychiatrists and psychologists in various settings. Although this can be regarded as a strength of the data set, it also introduced potential problems of reliability. To minimize assessment bias, (1) a manual was developed [10], (2) all participant interviewers were trained extensively, and (3) repeated booster training sessions were organised. Nevertheless, a subjective interpretation of the patients complaints and morbidity can never be completely avoided. It is difficult to envisage, however, how this would have biased the current results. Š Acknowledgements We thank all the participants for their contribution to the CNCR data collection. References 1. Berkson J (1950) Are there two regressions? J Am Stat Assoc 45: Maric N, Myin-Germeys I, Delespaul P, de Graaf R, Vollebergh W, Van Os J (2004) Is our concept of schizophrenia influenced by Berkson s bias? Soc Psychiatry Psychiatr Epidemiol 39: Van Os J, Hanssen M, Bijl RV, Ravelli A (2000) Strauss (1969) revisited: a psychosis continuum in the general population? Schizophr Res 45: Van Os J, Hanssen M, Bijl RV, Vollebergh W (2001) Prevalence of psychotic disorder and community level of psychotic symptoms: an urban rural comparison. Arch Gen Psychiatry 58: Verdoux H, van Os J (2002) Psychotic symptoms in nonclinical populations and the continuum of psychosis. Schizophr Res 54: Jablensky A, Kendell RE (2002) Criteria for assessing a classification in psychiatry. In: Mai M, Gaebel W, López-Ibor JJ, Sartorius N (eds) Psychiatry diagnosis and classification. Wiley, Chichester, pp Ustun TB, Chatterji S, Andrews G (2002) International classifications and the diagnosis of mental disorders: strengths, limitations and future perspectives. In: Mai M, Gaebel W, López-Ibor JJ, Sartorius N (eds) Psychiatric diagnosis and classification. Wiley, Chichester, pp Carr VJ, Lewin TJ, Barnard RE, Walton JM, Allen JL, Constable PM, Chapman JL (2002) Comparisons between schizophrenia patients recruited from Australian general practices and public mental health services. Acta Psychiatr Scand 105: Castle DJ, Phelan M, Wessely S, Murray RM (1994) Which patients with non-affective functional psychosis are not admitted at first psychiatric contact? Br J Psychiatry 165: Delespaul P, Bak M, Van Os J (2002) Handleiding Maastrichtse Psychoseprotocol. Maastricht University, Maastricht 11. American Psychiatric Association A (1994) DSM-IV: diagnostic and statistical manual of mental disorders. APA, Washington, D.C. 12. Overall JE, Gorham D (1962) The Brief Psychiatric Rating Scale. Psychol Rep 10: Ventura J, Green MF, Shaner A, Liberman RP (1993) Training and quality assurance with the Brief Psychiatric Rating Scale: the drift Busters. Int J Methods Psychiatr Res 3: Slade M, Phelan M, Thornicroft G, Parkman S (1996) The Camberwell Assessment of Need (CAN): comparison of assessments by staff and patients of the needs of the severely mentally ill. Soc Psychiatry Psychiatr Epidemiol 31: Ventura J, Nuechterlein KH, Subotnik KL, Gutkind D, Gilbert EA (2000) Symptom dimensions in recent-onset schizophrenia and mania: a principal components analysis of the 24-item Brief Psychiatric Rating Scale. Psychiatry Res 97: StataCorp (2002) STATA. College Station, Texas 17. Everitt BS (1995) Statistical design, analysis and correspondence. Br J Psychiatry 166: Ochoa S, Haro JM, Usall J, Autonell J, Vicens E, Asensio F (2005) Needs and its relation to symptom dimensions in a sample of outpatients with schizophrenia. Schizophr Res 75: Carr V, Halpin S, Lau N, O Brien S, Beckmann J, Lewin T (2000) A risk factor screening and assessment protocol for schizophrenia and related psychosis. Aust N Z J Psychiatry 34 (Suppl):S170 S Garety PA, Kuipers E, Fowler D, Freeman D, Bebbington PE (2001) A cognitive model of the positive symptoms of psychosis. Psychol Med 31: McGlashan TH (2000) Treating schizophrenia earlier in life and the potential for prevention. Curr Psychiatry Rep 2: McGorry P (2001) Rationale for and the substantial potential benefits linked to early recognition and optimal treatment of psychotic disorders, specifically schizophrenia. Acta Psychiatr Scand 103:

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