This study analysis of a community cohort at the age of 35 focused on the effects of gender and multimorbidity on quality of life and subjective distress. Consistent with an earlier analysis, quality of life decreased with increasing numbers of concurrent psychiatric diagnoses. Women generally reported lower quality of life and higher distress than men. Relative to men, well-being in women was subject to more diagnostic (alcohol abuse/dependence, depression, generalized anxiety disorder, bulimia) and social influences (partner, promotion). The same factors predicted women's psychological and physical well-being, indicating a more holistic experience in women. Men's physical well-being did not correlate with any of the diagnostic or social variables measure

Angst, Jules

Gamma, Alex

RERO DOC Digital Library

Fur Arch Psychiatry Clin Neurosci (2001) 251: Suppl. 2,11/43-11/46 9 Steinkopff Verlag 2001 Alex Gamma. Jules Angst Concurrent psychiatric comorbidity and multimorbidity in a community study: gender differences and quality of life Abstract This study analysis of a community cohort at the age of 35 focused on the effects of gender and mul- timorbidity on quality of life and subjective distress. Consistent with an earlier analysis, quality of life decreased with increasing numbers of concurrent psy- chiatric diagnoses. Women generally reported lower quality of life and higher distress than men. Relative to men, wen-being in women was subject to more diagnos- tic (alcohol abuse/dependence, depression, generalized anxiety disorder, bulimia) and social influences (partner, promotion). The same factors predicted women's psy- chological and physical well-being, indicating a more holistic experience inwomen. Men's physical well-being did not correlate with any of the diagnostic or social vari- ables measured. Key words Quality of life 9 distress, prevalence, gen- der. comorbidity, multimorbidity It was a great pleasure to write this paper in honour of my friend Per Bech, in gratitude for his longlasting friendship and his untiring efforts as President and past-President of the European Association of Psychi- atrists and in recognition of his outstanding contribution to research on clinical methodology and quality of life. Introduction Comorbidity or co-occurrence ofpsychiatric diagnoses has become a important issue in classification: comor- bidity is frequent and is associated with greater treatment Prof. J. Angst (l~l) 9 A. Gamma Zurich University Psychiatric Hospital Lenggstr. 31, 8029 Zurich, Switzerland Phone: +41 1 384 26 11 Fax: +41 1 384 24 46 E-Mail: jangst@bli.unizh.ch needs, severity and chronicity. Comorbidity denotes pri- marily the association of two disorders, but many patients receive multiple diagnoses (multimorbidity). This problem has been dealt with mainly in the field of somatic disorders (Gijsen et al. 2001, Xuan et al. 1999). In an earlier analysis we presented data on psychiatric mul- timorbidity as an indicator of clinical severity on the basis of longitudinal cumulative associations assessed by repeated interviews (Angst et al., submitted). That study showed the association of the number of diagnoses with overall severity, work/social impairment, suicide attempt, reatment and prescribed medication rates, and with distress and quality of life. Associations within diag- nostic spectra (for instance between major depression and dysthymia) were more common than between spec- tra. This paper presents additional data on the impact of concurrent multimorbidity (1, 2 and 3+ diagnoses) on distress and quality of life (QoL) in subjects aged 35 years from the community and gives special consideration togender differences. Methods The Zurich study is a longitudinal epidemiological inves- tigation carried out in a community sample from the can- ton of Zurich, Switzerland. In 1978, 4457 young adults (19-20 years old) were screened using the Symptom Checklist 90-Revised (SCL-90R) of Derogatis (1977). For the Zurich study, 591 subjects were selected from this larger sample, with 396 having an SCL-R score above, and 195 below the 85th percentile. This weighting scheme was ttsed to enrich the sample with potential risk cases. Five interview waves have been conducted so far using the semi-structured SPIKE interview administered by trained clinical psychologists and psychiatrists oassess psychiatric and somatic syndromes, ubstance abuse, social factors, and quality of life. Symptoms, frequency and duration, degree of distress, treatment history, social 11/44 and work impairment, personal and family history were evaluated for each syndrome. The present analysis is restricted to the participants of the fifth wave conducted in 1993 (215 females and 192 males, aged 34 and 35 respectively). Psychiatric diagnoses were based on the following cri- teria: DSM-III: panic disorder, generalized anxiety dis- order; DSM-III-R: major depression, dysthymia nd bulimia; DSM-IV: bipolar disorder, substance abuse/ dependence; ICD-10: neurasthenia. The number of psychiatric diagnoses occurring over the previous twelve months was calculated for each sub- ject, considering the following 14 diagnoses: unipolar major depressive disorder (N = 40), unipolar dysthymia (9), bipolar disorder I/II (15), generalized anxiety disor- der (37), panic disorder (6), agoraphobia (6), social pho- bia (6), simple phobia (25), alcohol abuse/dependence (40), drug abuse/dependence (9), benzodiazepine dependence (5), obsessive-compulsive disorder (5), bulimia (4), and neurasthenia (15). Drug abuse/depend- ence includes abuse/dependence of opiates, hallucino- gens, cocaine, amphetamines and cannabis. Quality of life was evaluated in ten domains: work, finances, family of origin, friends, physical well-being, psychological well-being, spouse/partner, own family, childhood, and total distress. A five-point scale (5 = excellent, 4 = good, 3 = satisfactory, 2 = poor, 1 = very poor) was used to rate quality of life satisfaction or well- being in these domains (Bech and Angst 1996). As a fur- ther measure, for each of the 14 diagnoses total distress was computed as the sum of the individual distress cores (0-100). Statistical analyses were carried out with SAS for Win- dows Version 8.01. Effects of gender and the number of individual diagnoses on the quality of life were assessed non-parametrically with the Kruskal-Wallis Test. Back- ward multiple linear regression analyses (SAS: PROC REG) were used to compute diagnostic and social pre- dictors of quality of life by gender. All regressions con- trolled for risk status (stratified sampling of high-risk cases). Tab. 1 Frequency and prevalence rates as a function of the number of psychiatric diagnoses N of diagnoses 0 1 2 3+ N (total = 407) 271 83 36 17 Women (%)* 51.3 56.6 47.2 70.6 Prevalence (%) 75.4 16.9 5.0 2.7 Women (%) 76.5 17.9 3.1 2.5 Men (%) 74.2 15.4 6.9 2.9 *p=0.35 Tab.2 Mean scores for different domains of quality of life as a function of gender Quality of Life related to: Men Women pa* N mean N mean Physical well-being 155 3.45 197 3.25 .03 Psychological well-being 154 3.56 197 3.26 .003 Family of origin {parents, siblings) 156 3.47 195 3.31 .09 Own family 87 3.86 130 3.71 .06 Childhood/adolescence 153 3.32 195 3.02 .005 Relationship with spouse/partner 129 3.68 169 3.31 .002 Relationships in general 156 3.61 195 3.62 .99 Work 156 3.54 196 3.34 .02 Financial situation 156 3.37 197 3.34 .87 Total distress 137 74.23 177 112.54 .001 * Kruskal-Wallis Test, bold figures are statistically significant Results :J Frequency and prevalence rates As expected, frequency and prevalence decreased with an increasing number of individual diagnoses: 242 subjects of the sample received no psychiatric diagnosis, 83 one diagnosis, 36 two diagnoses and 17 three or more diag- noses (Table 1). Overall there was no gender difference incomorbidity, but females predominated in the group with three or more diagnoses. At the age of 35, the weighted total one-year prevalence of morbidity was 24.6%, with 5% of the sample receiving two diagnoses and 2.7 % three or more. In contrast to the raw figures the weighted rates demonstrate an equal distribution of co- and multimorbidity between males and females. :~ Quality of life and gender Gender-related differences in quality of life ratings are shown in Table 2. Women rated their quality of life con- sistently lower than men in most domains examined. These differences were statistically significant for physi- cal and psychological well-being, relationship with spouse/partner, work, childhood and total distress; in addition there was a trend (p < 0.1) towards alower qual- ity of life in relationships with family members (both family of origin and own family). Considering only the subset of subjects without a psy- chiatric diagnosis (139 females, 132 males) there was a trend towards lower psychological well-being in women (F: mean 3.45; M: mean 3.65; p = 0.07), but there was no significant gender difference in mean physical well-being scores (F 3.44; M 3.51; p = 0.38). Women without any psychiatric diagnoses rated their QoL in childhood and adolescence much lower than men (F 3.13; M 3.42; p = 0.02). Tab. 3 Physical and psychological well-being as a function of gender and the number of psychiatric diagnoses N of diagnoses 0 1 2 3+ well-being sex mean SD mean SD mean SD mean SD p* physical F 3.44 0.74 3.05 0.93 2.87 1.06 2.45 1.13 0.001 M 3.52 0.77 3.23 0.86 3.33 0.99 3.00 1.0 0.39 F vs M p** 0.38 0.36 0.20 0.37 - psychological F 3.45 0.89 3.07 0.91 3.00 0.93 2.09 1.04 0.001 M 3.65 0.75 3.42 0.70 2.92 1.08 4.00 0.00 0.01 FvsM p** 0.07 0.13 0.72 0.02 - * Kruskal-Wallis Test for well-being across different numbers of diagnoses, bold figures are statistically significant; ** Kruskal-Wallis Test for well-being across gender, bold figures are statistically significant; 5D standard deviation Quality of life, multimorbidity and social factors Both physical and psychological well-being decreased with an increasing number of concurrent diagnoses (Table 3). In order to analyze the contribution of specific diagnoses and social factors, separate backward multiple linear regression analyses were carried out by gender. Independent predictors were entered into the model as follows: a) social variables: living alone, having a spouse/ partner, number of children, level of training, crucial changes at work, workload, income, regular financial obligations, conflicts at work, promotion, difficulties in relationship with spouse/partner, improvement in rela- tionship with spouse/partner and birth of a child; b) the psychiatric diagnoses for unipolar major depressive dis- order, bipolar disorder I/II, generalized anxiety disorder, simple phobia, alcohol abuse/dependence, bulimia and neurasthenia; (the remaining diagnoses were excluded due to insufficient numbers of subjects), and c) risk sta- tus as a consequence ofthe sample stratification. In women significant predictors of poorer psycholog- ical well-being were alcohol dependence (standardized beta = -0.28; p < 0.001), unipolar depression (beta = -0.22; p = 0.003), bulimia (beta = -0.21; p = 0.003), diffi- culties in relationships with spouse/partner (beta = -0.15; p = 0.04) whereas promotion exercised a positive effect (beta = 0.20; p = 0.01). Women's physical well-being was also significantly predicted by bulimia (beta = -0.24; p < 0.001), alcohol dependence (beta = -0.21; p = 0.005) and difficulties in relationships with spouse/partner (beta = -0.15; p = 0.04) but also by generalized anxiety disorder (beta = -0.16; p = 0.02); promotion again was a positive factor (beta = 0.15; p = 0.03). In men the significant predictors of psychological well-being were income (beta = 0.21; p = 0.01), simple phobia (beta = -0.19; p = 0.02) and sampling stratification (beta = -0.23; p = 0.007). Men's physical well-being was not predicted by any of the variables examined. 11/45 Discussion The large epidemiological surveys (Kessler et al. 1994, Bijl et al. 1998) found no gender differences in the over- all lifetime prevalence rates of psychiatric disorders (mood disorders, anxiety disorders and substance abuse/dependence) but reported higher female preva- lence rates for comorbid and multimorbid cases. Our one-year prevalence rates for 1, 2 and 3+ diagnoses (16.9 %, 5.0% and 2.7%) are similar to those reported by Bijl (1998) for the Netherlands (15.3 %, 4.4 %, 3.8 %). Our analysis of the raw data showed a significant female pre- ponderance among subjects with three or more diag- noses, a difference which disappeared after weighting for the stratified sampling. The relatively small group sizes makes this result inconclusive. In contrast, the National comorbidity survey (Kessler et al. 1994), the Nemesis study (Bijl et al. 1998) and our earlier longitudinal analy- sis (Angst et al. 2001) all showed consistently higher ates of comorbidity and multimorbidity for females than males. We found important gender differences in the sub- jects' rating of their quality of life: women rated their QoL in most domains ignificantly lower and their distress (as a consequence of psychiatric disorders) significantly higher than men; this result is in agreement with Bech et al.'s (1996) finding that men scored higher on the WHO (Ten) Well-Being Index. Childhood/adolescence received especially low QoL ratings, particularly from women. The main findings refer to gender differences in psy- chological and physical well-being, which would not have been expected on the basis of a factor analysis of Ruggeri et al. (2001) where the two loaded on the same factor. Confirming the results of our earlier analysis (Angst et al. 2001), we found that psychological well-being tended to decrease systematically with an increasing number of diagnoses in both men and women. However, for phys- ical well-being this correlation was found only in women. In the logistic regression, the same social and psychi- atric factors predicted women's psychological nd phys- ical well-being and the predictive power of those factors was much higher in women than in men. In fact, men's physical well-being did not correlate at all with any of the measures, and their psychological well-being correlated only with two variables (simple phobia and income). These findings can be interpreted as an expression of women's more holistic experience of well-being, whereas men experience physical well-being as independent from psychiatric disorders and social factors. The obvious limitations of this study are the relatively small sample size and the selection of a cohort at the age of 35. A further limitation is the relatively short instru- ment, which measures quality of life in 10 domains. Acknowledgment This work was supported by grant 32-33980-92 ofthe Swiss National Science Foundation. 11/46 References Angst J, Sellaro R, Merikangas KR (2001) Multimorbidity of psychiatric disorders as an indicator of clinical severity. Submitted to Eur Arch Psychiatry Clin Neurosci Bech P, Angst J (1996) Quality of life in anxiety and social phobia. Int Clin Psychopharmacol 1 I: 97-100 Bech P, Staehr-lohansen K, Gudex C (1996) The WHO (Ten) Well- Being Index: validation in diabetes. Psychother Psychosom 65: 138-190 Bijl RV, Ravelli A, van Zerssen G (1998) Prevalence of psychiatric dis- order in the general population: results of the Netherlands mental health survey and incidence study (NEMESIS). Soc Psychiatry Psy- chiatr Epidemio133:587-595 Derogatis LR (1977) SCL-90. Administration, Scoring and Procedures Manual-I for the R (revised) version and other instruments of the Psychopathology Rating Scales Series. Johns Hopkins University School of Medicine, Chicago Gijsen R, Hoeymans N, ScheUevis FG, Ruwaard D, Satarino WA, van den Bos GA (2001) Causes and consequences of comorbidity: a review. J Clin Epidemio154:661-674 Kessler RC, Mcgonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, Wittchen H-U, Kendler KS (1994) Lifetime and 12-month preva- lence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Arch Gen Psychia- try51: 8-19 Ruggeri M, Warner R, Bisoffi G, Fontecedro L (2001) Subjective and objective dimensions of quality of life in psychiatric patients: afac- tor analytical approach. (The South Verona Outcome Project 4). Br J Psychiatry 178:268-275 Xuan J, Kirchdoerfer LJ, Boyer JG, Norwood GJ (1999) Effects ofcomor- bidity on health-related quality-of-life scores: an analysis of clinical trial data. Clin Therapeutics 21:383-403 

Concurrent psychiatric comorbidity and multimorbidity in a community study: gender differences and quality of life

http://doc.rero.ch/record/318715/files/406_2009_Article_BF03035126.pdf

Concurrent psychiatric comorbidity and multimorbidity in a community study: gender differences and quality of life

Abstract

Similar works

Full text

Available Versions

RERO DOC Digital Library