Gender and procreation among patients with schizophrenia

Schizophrenia Research 68 (2004) 387 – 394 www.elsevier.com/locate/schres

Gender and procreation among patients with schizophrenia T. Bhatia a, M.A. Franzos b, J.A. Wood c, V.L. Nimgaonkar a,c,d,*, S.N. Deshpande e a Indo-US Project on Schizophrenia Genetics, New Delhi, India Uniformed Services University of the Health Sciences, Bethesda, MD, USA c Department of Psychiatry, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA 15213, USA d Department of Human Genetics, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, PA 15213, USA e Department of Psychiatry, Dr. R.M.L. Hospital, New Delhi, India b

Received 14 April 2003; received in revised form 30 July 2003; accepted 12 August 2003

Abstract Objective: Reduced procreation among men with schizophrenia has been reported consistently when compared with female patients, but the cause is unknown. Reports on Caucasian individuals predominate in the published literature. Therefore, analyses were conducted concurrently among independent Indian and US samples in the present study. Method: Individuals with schizophrenia or schizoaffective disorder (DSM-IV criteria) were ascertained and interviewed at New Delhi and in the northeastern United States using identical procedures (n = 224 and 144, respectively). Selected indices of fertility and fecundity were compared among men and women at each site. Results: In the smaller US sample, male cases were significantly more likely to be single and childless compared with female cases. They also had fewer children. In contrast, there were no significant gender differences in the larger Indian sample with regard to the reproductive indices. Multivariate analyses revealed that the indices of reproduction were associated with different variables in the US and Indian samples. Fertility (the presence or absence of offspring) was associated with gender and age in the US sample while in the Indian sample conjugal status and age were significant predictors. Fecundity (the number of offspring) was associated with gender, conjugal status and educational status in the US sample while in the Indian sample conjugal status and educational status were both significant. Conclusions: The reproductive deficit observed among US males was not observed among the Indian men. Conjugal status was a significant covariate for reproduction in both samples. The reproductive deficit may be due to difficulties in establishing long-term conjugal relationships among the US men. The differences may also reflect underlying cultural variations related to marital practices in these two countries. Our analyses suggest that the male reproductive deficit in schizophrenia is variable and may be overcome. D 2003 Elsevier B.V. All rights reserved. Keywords: Schizophrenia; Fertility; Fecundity; India; Culture

1. Introduction * Corresponding author. Western Psychiatric Institute and Clinic, UPMC Health System, 3811 O’Hara St. Room 443, Pittsburgh, PA 15213, USA. Tel.: +1-412-624-0823; fax: +1-412624-0446. E-mail address: [email protected] (V.L. Nimgaonkar). 0920-9964/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2003.08.009

Two basic aspects of procreation have been investigated widely in schizophrenia, namely fertility (the ability to reproduce) and fecundity (the number

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of offspring) (Ritsner et al., 1992; Nanko and Moridaira, 1993; Bassett et al., 1995; Fananas and Bertranpetit, 1995; Hutchinson et al., 1999; McGrath et al., 1999; Avila et al., 2001). There is near unanimity with regard to reduced fertility in schizophrenia, leading early researchers to suggest that innate factors are responsible (Myerson, 1917; Jablensky and Kalaydjieva, 2003). The results are less consistent for fecundity. The magnitude of the case-control differences also vary, probably due to a complex interplay of biological, clinical and socioeconomic factors (Lane, 1971; Haverkamp et al., 1982; Erlenmeyer-Kimling et al., 1969; Odegaard, 1980; Nimgaonkar, 1998). Recent studies have continued to reveal procreational deficits among patients, suggesting that improvement in treatment has not concomitantly improved this important function (Hutchinson et al., 1999; Howard et al., 2002; Haukka et al., 2003). Numerous studies in economically advanced European nations, as well as Japan and the USA have reported on gender-related variations in procreation among individuals with schizophrenia. It has been noted consistently that the unmarried state and childlessness (the latter, a proxy for infertility) are more common among male patients, but the results are variable with regard to fecundity (Myerson, 1917; Nanko and Moridaira, 1993; Nimgaonkar et al., 1997; Nimgaonkar, 1998; Haukka et al., 2003). Fertility, as well as fecundity is likely to be influenced by the frequency of conjugal relationships, institutionalization, and inherent infertility in addition to other cultural factors. The reduced likelihood for male patients to establish conjugal relationships has been suggested as one of the major causes for the male deficits (Odegaard, 1980). Since Indian men with schizophrenia are often married, India may be an obvious setting to test this hypothesis. We are aware of only one other study of schizophrenia related fertility in India. Reduced fertility was observed among 100 individuals with schizophrenia in comparison with their relatives in this study, but gender-related effects were not examined (Thara and Srinivasan, 1997). Though a substantial amount of research has been conducted in this field, relatively few studies have addressed the role of gender in relation to marital and socioeconomic status. Therefore, we investigated

gender differences in a systematically ascertained Indian sample. Detailed clinical and demographic data were gathered. A US sample recruited concurrently using the same procedures was used for comparison.

2. Method The study was conducted simultaneously at New Delhi, India and Pittsburgh, PA. Participants were sought among inpatients and outpatients at publicly funded hospitals and private clinics in both cities, so as to sample from the spectrum of care available at each site. 2.1. New Delhi The primary recruitment site was Dr. Ram Manohar Lohia Hospital (RML), a publicly funded tertiary care center providing inpatient and outpatient care. In addition, all major hospitals and psychiatric rehabilitation facilities in New Delhi were approached regularly. Though most patients at such facilities resided in the metropolitan limits, approximately one third are also drawn from rural areas surrounding New Delhi. 2.2. Pittsburgh Recruitment occurred primarily at Western Psychiatric Institute and Clinic, a University affiliated tertiary care center which also serves as a catchment area hospital for a defined region of Allegheny County, PA. Inpatients and outpatients were also sought at 35 University hospitals, nonacademic community centers, hospitals, and state facilities located within a 500-mile radius of Pittsburgh. Thus, a variety of urban and rural areas were sampled. Clinical information was obtained at both sites using the Hindi or English versions of the ‘Diagnostic Interview for Genetic Studies’ (DIGS), as well as medical records and supplemental information from relatives as appropriate (Nurnberger et al., 1994; Deshpande et al., 1998). Consensus diagnoses were established by two psychiatrists/psychologists at each site, using DSM-IV criteria. Inter-rater

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reliability between the Indian and US research staff was examined throughout the study (kappa > 0.80). Three indices of procreation were examined. Fertility was assessed indirectly as the proportion of patients who were childless. The ability to have children is restricted by age among women. Hence, for the purposes of gender-based comparisons of procreation, analyses were conducted only among individuals in the female reproductive age group, defined here as 15 –45 years. Total Reproduction Rate (TRR), defined as the mean number of children per case, is the most popular measure of fecundity. TRR estimates may be distorted by the presence of individuals who have never been in conjugal relationships. Therefore, we also included marital fertility, defined as the mean number of children among individuals who have been married (Nimgaonkar, 1998). Since procreation is affected by diverse economic and clinical factors, multivariate analysis was conducted in addition to standard gender based comparisons (Nimgaonkar et al., 1997). To evaluate the socioeconomic status of the participants, we used the occupation of the respective heads of households (HOH). This was necessary because many of the female patients listed their occupation as ‘housewives’, making it difficult to classify their socioeconomic status. Moreover, most participants were economically dependent on their HOH. All participants provided written informed consent, as approved by the Institutional Review Boards at Dr. R.M.L. Hospital and the University of Pittsburgh. 2.3. Statistical analysis The Mann Whitney U-test and Student’s t-test were used to compare continuous variables. The chi square test was used for categorical variables. Multivariate analyses were also performed, utilizing the Statistical Package for Social Sciences (SPSS, version 10.0 for Windows). 2.4. Power analysis The computer program GPOWER was used for power analysis (Faul and Erdfelder, 1992). The Indian sample has over 98% power to detect the gender-

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related differences in childlessness or marital fertility observed in the US sample.

3. Results 3.1. Demographic and clinical variables The first set of analyses involved comparisons between male and female patients and were conducted separately at each site. At New Delhi as well as Pittsburgh, there were no significant differences between male and female cases with respect to age, years of education, and socioeconomic status based on occupation of the head of the household. No significant gender-related differences were detected for the course of illness or Global Assessment of Function (GAF) at either site (Table 1). There were nonsignificant trends for gender differences in age of onset (AOO) in both the samples. The AOO was earlier among male patients in the US sample and was later among female patients in the Indian samples (all estimates as mean F standard deviation; US sample, men 19.40 F 6.25, women 21.88 F 9.04, p = 0.064; Indian sample, men 23.63 F 7.87, women 22.29 F 6.52, p = 0.177). The AOO was delayed among the Indian patients compares with the US patients (Indian patients, 23.0 F 7.3 years; US patients, 20.9 F 10.2; p < 0.05). 3.2. Variables related to procreation There were no significant differences in conjugal status among men and women in India. In contrast, the male US cases were much less likely to have established conjugal relationships, in comparison with female US cases. The proportion of individuals who were childless did not differ by gender in India. On the other hand, the male US cases were more likely to be childless and had significantly fewer children compared with their female compatriots. There were no significant gender-based differences in marital fertility in India; i.e., among individuals who had ever been in conjugal relationships, male and female patients did not differ with respect to the number of children. However, the US male cases had significantly lower marital fertility.

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Table 1 Demographic variables and reproductive indices Variable

Mean age (S.D.) Age 15 – 45 years Age < 15 Age > 45 Years of education (0 – 8/9 – 12/13 – 15/ >15) Occupation of head of householda Course of illnessb Global Assessment of function (GAF) Conjugal state (married/separated, divorced or widowed/never married) Proportion of childless individuals (%)c Total Reproduction Rate (TRR) Marital fertility

Indian cases

US cases

Men (n = 116)

Women (n = 108)

Men (n = 90)

Women (n = 54)

32.6 (10.8) 103 0 13 12/57/37/9 18%/49%/32%/8% 29/42/13/24

32.8 (11.3) 92 1 15 18/52/27/10 17%/48%/25%/9% 33/34/9/20

38.27 (10.1) 66 0 23 7/41/28/13 8%/46%/31%/14% 17/9/5/41

40.6 (9.3) 38 1 15 5/23/19/6 9%/43%/36%/11% 11/7/3/21

41/8/49/12/4/0 28.7 F 13.6

48/11/37/5/4/0 25.9 F 11.5

20/12/42/4/0/1 43.5 F 14.3

15/2/27/1/0/0 41.8 F 18.0

34/13/69 29%/11%/60%

30/23/54 28%/21%/50%

4/14/72* 4%/16%/80%

4/18/32 7%/33%/60%

73/101 (72.3%)

67/89 (75.3%)

53/63 (84.1%)**

21/38 (55.3%)

0.67 F 1.15

0.76 F 1.40

0.35 F 0.82***

1.26 F 1.68

1.64 F 1.22

1.57 F 1.66

1.06 F 1.12

2.45 F 1.97#

GAF scores refer to the worst point during the most recent episode of illness. Marital fertility: number of children per patient among persons with one or more children. TRR: number of children per case. S.D.: standard deviation. Data for education, conjugal state and socioeconomic status were unavailable for some cases. a Occupations were considered under the following four categories, derived from the DIGS: managerial and professional specialty occupations; technical, sales and administrative support occupations; service occupations (household, protective); and all other occupations (farming, forestry, fishing, mechanic, construction, transportation, laborers, armed services, homemaker, student, unemployed, retired). b The course of the illness was classified under six groups listed in the DIGS: episodic with inter-episodic residual symptoms; episodic with no inter-episodic residual symptoms; continuous; single episode in partial remission; single episode in full remission; and other or unspecified pattern. c Only cases aged 15 – 45 years were used. * Significantly different from female cases of the same nationality: v2 = 7.34, p < 0.025, 1 df. ** Significantly different from female cases of the same nationality: v2 = 17.32, p < 0.0001, 1 df. *** Significantly different from female cases of the same nationality: p < 0.0001, Mann – Whitney U-test. # Significantly different from female cases of the same nationality: p < 0.02, Mann – Whitney U-test.

3.3. Multivariate analyses The concurrent effects of selected demographic and clinical variables on fertility and fecundity were next evaluated using multivariate analyses in each sample. The analyses were initially restricted to individuals between the ages of 15 and 45 years, in order to take account of the restricted reproductive period for women. Fertility was examined first among the cases using logistic regression analysis with backward elimination. Presence or absence of offspring was considered as the dependent variable. The covariates included age, gender, conjugal status, education,

course of illness, GAF score, and occupation of HOH. Linear regression with backward elimination was used next to evaluate the predictive value of the same covariates, using number of children as the dependent variable. When fertility was examined among the Indian cases using logistic regression with backward elimination and the set of covariates listed above, only a history of conjugal relationship predicted presence or absence of offspring, along with age (b coefficient, married status = 5.60, p < 0.0001; widowed, divorced or separated, b coefficient = 3.57, p < 0.002; age: b coefficient = 0.107, p < 0.05). All the other variables

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were sequentially removed from the regression analysis, without significantly altering the goodness of fit. Linear regression with backward elimination was next used to evaluate the relationship of the same covariates to fecundity, using number of children as the dependent variable. Conjugal status and educational status were retained as significant predictors in the presence of GAF scores and age (standardized b coefficients; conjugal status: 0.57, p < 0.0001, educational status: 0.21, p < 0.0001; age: 0.116, p < 0.1; GAF score: 0.11, p < 0.06). Multivariate analyses were also conducted among the US cases with the same covariates as used in the Indian sample. Logistic regression with backward elimination and presence or absence of offspring as the dependent indicator variable for fertility revealed associations with age and gender in the presence of educational status (b coefficients; age: 0.17, p < 0.02; gender: 2.22, p < 0.006, educational status, p < 0.23). Secondary analyses were also conducted with ethnicity as an additional covariate, since the US sample included 135 Caucasians, 22 African-Americans, and 3 individuals from other ethnic groups. These analyses continued to reveal age as significant predictors, in the presence of gender, ethnicity, conjugal status, and course of illness (data not shown). With respect to fecundity, linear regression analysis of the US sample with number of children as the outcome variable and backward elimination of the above covariates suggested significant correlations with gender, conjugal status, and educational status in the presence of age (standardized b coefficients; gender: 0.35, p < 0.003; conjugal status: 0.28, p < 0.02; educational status: 0.25, p < 0.03; age: 0.19, p < 0.1). Ethnicity did not emerge as a significant predictor variable in these analyses. These analyses reveal predictable associations with age and socioeconomic indices. In the Indian sample, conjugal status had significant predictive value. In contrast, gender was the predominant factor in the US sample. Hence we explored the distribution of fertile individuals by conjugal status among male and female patients from each sample (Table 2). Consistent with the multivariate analyses, a significant association between conjugal status and fertility was present among women from India and the USA, as well as Indian males. This association was not significant among the US male patients, among whom a striking

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Table 2 Conjugal status and fertility Group

US male patients with children US male patients with no children Indian male patients with children*** Indian male patients with no children*** US female patients with children* US female patients with no children* Indian female patients with children** Indian female patients with no children**

Married or previously married

Never married

3

7

6

47

27

1

7

66

11

6

3

19

22

0

15

51

Analyses were restricted to those between 15 and 45 years of age. Individuals with or without children were compared with respect to fertility separately among the Indian and US male and female patients. * v2 = 10.9, p < 0.001 1 df. ** v2 = 40.4, p < 0.0001, 1 df. *** v2 = 68.3, p < 0.0001.

excess of unmarried, individuals without children were present. Although restricting analysis to patients aged 15 – 45 may effectively approximate the period of reproductive parity between women and men, some concerns remain. Leaving women over 45 out of the analyses may lead to erroneous conclusions regarding fecundity and may also underestimate the lifetime fecundity. In addition, the Indian sample is evidently younger. It is also possible that Indian men and women have reproductive parity early in their reproductive lives, but the Indian women may be more productive at later years. The pertinence of the age difference is further underscored by the trend towards increasing maternal age observed particularly in the US population. Births to US women over the age of 35 have increased from 8.4% in 1990 to 12.6% in 1996 (Tough et al., 2002). These concerns were addressed by completing subset analyses of all patients 35 years of age and older, 45 years of age and older, and all ages (Table 3). The results were consistent with the earlier findings:

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Table 3 Significant variables in analyses of additional age subsets Analysis

Age 35 years and older

Age 45 years and older

All ages

Indian (n = 67)

US (n = 69)

Indian (n = 21)

US (n = 32)

Indian (n = 186)

US (n = 90)

Fertility (backward logistic regression)

Conjugal status ( p < 0.0001) Education ( p < 0.010) GAF ( p < 0.012)

Conjugal status ( p < 0.0001) Gender ( p < 0.007)

No significant covariates

Conjugal status ( p < 0.005) Gender ( p < 0.0018)

Conjugal status ( p < 0.0001) Age ( p < 0.034)

Fecundity (backward linear regression)

Conjugal status ( p < 0.0001) Age ( p < 0.003) Education ( p < 0.05)

Conjugal status ( p < 0.0001) Gender ( p < 0.002) HOH occupation ( p < 0.023)

Conjugal status ( p < 0.001) Education ( p < 0.058)

Conjugal status ( p < 0.002) HOH occupation ( p < 0.032) Gender ( p < 0.042)

Age ( p < 0.0001) Conjugal status ( p < 0.0001) Education ( p < 0.003) Course ( p < 0.078)

Gender ( p < 0.002) Conjugal status ( p < 0.002) Age ( p < 0.033) HOH occupation ( p < 0.055) Conjugal status ( p < 0.0001) Gender ( p < 0.001) HOH occupation ( p < 0.002)

gender was a significant variable for fertility and fecundity for the US sample, but was not significant in the Indian sample.

4. Discussion Though relatively small, our US sample revealed lower indices for procreation among male patients in comparison with female patients (Table 1). The differences may not be attributable only to under-reporting of children among US male patients, as proportionately more unmarried male patients reported children, compared with the female US patients (Table 2). The gender differences may thus reflect lowered rates among US male patients or elevated rates among US female patients. We favor the former explanation, as it is consistent with a large body of prior publications (Nimgaonkar, 1998; Haukka et al., 2003). In contrast to the US patients, the gender differential for childlessness and fecundity was less obvious in the larger Indian sample. Indeed, the marital fertility rate was numerically larger among Indian men compared with Indian women. Consequently, none of the gender-based comparisons attained statistical significance in the Indian sample, unlike the US sample. The failure to detect significant differences in the Indian sample are unlikely to represent a type II error as it

had adequate power to detect differences of the magnitude noted in the US sample. Thus, the severe procreational deficit noted previously among male patients may not be an invariable feature of the disorder. There are several possible reasons for the improved parity among Indian male patients. We employed multivariate analyses in an effort to dissect the role of available demographic and clinical variables. The multivariate analyses revealed conjugal status as an important correlate of procreation among the Indian as well as the US samples, along with standard indices such as age, education and socioeconomic status. While gender emerged as an additional significant predictor in the US sample, it did not have significant predictive value in the Indian sample. These results were also supported by univariate analyses, which revealed that individuals who had not been married were also unlikely to have children (Table 2). A history of conjugal relationships was comparable among male and female patients in India, in contrast to the US patients. Approximately 40% of male Indian patients and 50% of Indian women had ever been in conjugal relationships. In the US sample, only 20% of male US cases reported having been in such relationships, compared with 40% of US women. Indeed, proportionately more Indian patients

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were married at the time of the study (men: 29%, women: 28%), than male US patients (men: 4%, women: 7%). Thus, the rates for conjugal relationships tend to be more comparable between male and female patients in India, while greater gender disparity exists among US male and female patients. The increased likelihood for Indian male patients to be in conjugal relationships may be a major factor for the gender parity among patients in India, though other explanations such innate biological factors cannot be excluded. The elevated rates of conjugal relationships among the Indian patients may be related to cultural norms, as arranged marriages are common in India. Premorbid asociality is a plausible reason for celibacy among male patients (Odegaard, 1980; Kendler et al., 1984). Such hurdles evidently hamper male patients in other cultures, but arranged marriages may help Indian males circumvent personal and social hurdles to conjugal relations. It is also possible that asociality is less common among Indian male patients compared with female Indian patients, thus enabling them to establish conjugal relationships at comparable rates. Data regarding premorbid asociality among Indian patients are currently unavailable. Male Indian patients may also have children because they might form lasting conjugal relationships at rates comparable to Indian women. As detailed sexual histories were unavailable for our sample, important information such as onset and frequency of sexual activity could not be evaluated in either sample. These issues need to be investigated. We elected not to conduct exhaustive cross-national comparisons in the present study, as they are difficult to interpret in the absence of more comprehensive data. For example, the Indian samples had lower GAF scores than the US cohort. The Indian patients also had later AOO than the US patients. The difference may represent variations in treatment or ascertainment. We also anticipate other differences between the samples, such as treatment availability. Absence of data from unaffected persons precludes comparison with population norms in either country. In common with all published studies, characteristics of the patients’ spouses were not available at either site. The results from the Indian sample reported here require scrutiny because they differ from most published studies with regard to a gender-related differ-

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ence in procreation. It has been suggested that organic etiological factors may confound the diagnosis of schizophrenia in India (Collins et al., 1999; Mojtabai et al., 2000). Care was taken in the present studies to ensure reliable interview and diagnostic procedures, which were conducted in tandem at the US and Indian sites. Participants were recruited from a variety of treatment sites in both countries to sample a wide range of treatment facilities. Though ascertainment bias may have occurred in the Indian sample, it is unlikely to have extended to gender. The multivariate analyses were restricted to individuals between the ages of 15 and 45 years, in order to define a period during which both men and women could be considered to be capable of reproduction. The lower age was used to arbitrarily define the onset of puberty for both genders. The upper age of 45 years was chosen as a typical age for menopause. Beyond this age, women could be considered to be at a reproductive disadvantage compared with men. Even so, separate analyses involving older patients continued to reveal the same trends observed using the individuals between 15 and 45 years of age. This study revealed some intriguing differences related to AOO, apart from the later AOO in the Indian sample vis a vis the US sample. The age of onset was earlier among male patients in the US sample and was later among female patients in the Indian. These differences were not statistically significant, possibly reflecting relatively small samples. We anticipate that different cultural factors and treatment related variables may influence these estimates across the nations. In both the samples, the age of onset was correlated with current age. As the latter had already been included in the multivariate analyses, we elected not to use AOO in our multivariate analyses. In conclusion, gender-related differences in fertility and fecundity recorded previously in samples from developed countries were detected at different levels among patients with schizophrenia from India and the USA. It is possible that cultural factors, such as elevated marriage rates contribute to increased procreation among Indian male patients with schizophrenia, though other factors may also play a role. Unlike prior studies, the present report investigated the role of demographic and socioeconomic factors in procreation. Significant correlations with such factors were identified.

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Acknowledgements This study was funded in part by grants from the National Institute of Mental Health, the Fogarty International Center and the US India fund (#K02 MH 01489, #R03 TW00730 and Indo-US Project Agreement #N-443-645). We thank the following physicians and colleagues for advice and help with ascertainment: S.P. Aggarwal, C.P. Singh, N. Bohra, D. Mohan, B.R. Agnihotri, R. Rastogi, R.K. Singh, R. Rastogi, R.C. Jiloha, S. Mittal, H. Matai, R.K. Chadha; K. Kumar, A. Lal, P.L. Chawla, D.N. Mandekar, P.K. Shrivastva, A.K. Sharma, R. Sagar, M. Batra, A. Kumar, H.C. Raheja, M.N.L. Mathur, A.K. Das, S.K. Das, U. Goswami, U. Khastgir, R. Nagpal, Suneel, M. Pahwa, Ms. Sushma, Ms. V. Sood, Mrs. M. Zutshi.

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