Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 Aug 27.
Published in final edited form as: Schizophr Res. 2013 Mar 13;146(0):132–137. doi: 10.1016/j.schres.2013.01.032

Changes in Academic and Social Premorbid Adjustment across Sex and Developmental Period in Individuals with Schizophrenia

Daniel N Allen 1,*, Gregory P Strauss 2, Kimberly A Barchard 1, Mary Vertinski 1, William T Carpenter 2,3, Robert W Buchanan 2,3
PMCID: PMC3752849  NIHMSID: NIHMS499160  PMID: 23490759

Abstract

The assessment of premorbid adjustment in schizophrenia has received considerable attention because of models suggesting that schizophrenia is a neurodevelopmental disorder characterized by abnormalities in functioning prior to onset of the disorder. Some studies suggest that premorbid adjustment is best viewed as a multidimensional construct where different areas of functioning might be differentially impacted by the illness and sex. The current study examined these matters using of Premorbid Adjustment Scale (PAS) in a sample of 421 individuals with schizophrenia. Confirmatory factory analyses conducted for three developmental periods (childhood, early adolescence, late adolescence) and for males and females separately, indicated that the PAS consists of academic and social factors that are invariant across developmental period and sex. Differences in severity of academic and social premorbid impairment were also present between males and females across developmental periods. Findings suggest important differences between males and females in the course of premorbid deterioration prior to onset of schizophrenia.

Keywords: Schizophrenia, Premorbid Adjustment Scale, Social, Academic, Neurodevelopmental

1.0. Introduction

Deficits in premorbid adjustment support neurodevelopmental models of schizophrenia (Murray et al., 1992; Weinberger, 1995). There is now strong evidence in schizophrenia of premorbid abnormalities in motor and intellectual abilities (Caspi et al., 2003; Marcus et al., 1993; Rosso et al., 2000; Schiffman et al., 2004, 2009; Walker, 1994; Walker et al., 1996), affective responsivity (Walker et al., 1996), and social and academic adjustment (Allen et al., 2001, 2005; Monte et al., 2008; Reichenberg et al., 2002; Schiffman et al., 2004; Strauss et al., 2012). Poor premorbid adjustment has been linked to a number of negative outcomes such as increased negative symptoms, worse global and cognitive functioning, poor psychomotor abilities, and decreased quality of life (MacBeth &Gumley, 2008; Malla & Payne, 2005). However, a relevant question is whether premorbid abnormalities are best characterized as a generalized deficit that equally affects many different premorbid abilities, or if there is evidence for multiple premorbid dimensions that show differences in severity and course of deterioration prior to schizophrenia onset, and demonstrate different patterns of association with relevant outcomes. Studies of the Premorbid Adjustment Scale (PAS; Cannon-Spoor et al., 1982) provide some support for this latter point of view by providing factor analytic evidence for separate social and academic functioning domains (Allen et al., 2001; Cannon et al., 1997; van Kammen et al., 1994) and demonstrating that these domains are differentially associated with a number of important outcomes after diagnosis of schizophrenia. For example, higher levels of academic adjustment in childhood are associated with more education, more work and better working memory, while a stable course of social adjustment was related to shorter duration of untreated psychosis, more friends, and fewer negative symptoms in adulthood (Larsen et al., 2004). There is also evidence that a differential decline in academic relative to social functioning occurs as the onset of psychosis approaches (Allen et al., 2005; Monte et al., 2008), and that this decline may be more pronounced in males than females (Monte et al., 2008). Individuals who meet criteria for “deficit schizophrenia” (i.e., primary and enduring negative symptoms; Carpenter et al., 1988; Kirkpatrick et al., 2001) show poorer overall premorbid adjustment (Buchanan et al., 1990), as well as more severe deterioration in social functioning compared to those with “nondeficit schizophrenia” (Strauss et al., 2012). Thus, prior research supports the validity of distinct but related academic and social dimensions of premorbid functioning in schizophrenia. However, a review has highlighted the lack of studies examining academic and social premorbid adjustment separately and stressed the importance of increasing this type of investigation (MacBeth & Gumley, 2008).

In the current study, we aimed to extend the literature on premorbid adjustment by addressing a number of unresolved questions regarding the stability of the latent structure of the PAS across developmental periods and sex, and by examining differences between males and females on the PAS factors. Regarding latent structure, prior studies have examined the factor structure of the PAS by collapsing across items from the childhood, early adolescence, and late adolescence developmental periods, even though stability of the PAS factor structure across developmental periods cannot be assumed, given the evidence for differences in rate of premorbid decline across developmental periods. Also, although there are well-documented sex differences in premorbid decline, which generally indicate better functioning in females with schizophrenia, there is some evidence that these differences are not uniformly reflected across all of the premorbid domains subscales (Childers and Harding 1990; Gittelman-Klein and Klein 1969; Hoff et al. 1998; Larsen et al. 1996; however, see Schmael et al., 2007). Therefore, the current study evaluated stability of the PAS factor structure across each of the three developmental periods (childhood, early adolescence, late adolescence) in males and females, and further compared developmentally related changes in academic and social functioning for males and females. Based upon previous factor analytic studies of the PAS (Allen et al., 2001; Cannon et al., 1997; van Kammen et al., 1994), we hypothesized that a model consisting of academic and social factors would provide the best fit of the data at each developmental period for both males and females, and that there would be sex related differences in social and academic developmental trajectories that generally favored females.

2.0. Methods

2.1. Participants

Participants included 421 adults (male = 284, female = 137) who met DSM criteria for schizophrenia (n = 382) or schizoaffective disorder (n = 39). There were 257 outpatients and 164 inpatients recruited through the research programs at the Maryland Psychiatric Research Center (MPRC). Demographic and clinical characteristics are presented in Table 1 for the entire sample, and for males and females separately. One-way ANOVAs and chi square analyses comparing the males and females indicated that the groups did not significantly differ on race, inpatient/outpatient status, or schizophrenia/schizoaffective diagnosis. Males were significantly younger than females at the time of the evaluation, had an earlier age of onset, fewer years of education, and poorer premorbid adjustment overall and for every developmental period.

Table 1.

Demographic and Clinical Characteristics for Females, Males and the Entire Sample.

Female (n=137) Male (n=284) Total (n=421) F df p
Mean sd Mean sd Mean SD
Age 40.8 9.0 35.4 9.1 37.2 9.4 32.17 1, 418 <.001
Age Onset 26.3 7.1 23.8 5.3 24.6 6.1 16.46 1, 419 <.001
Education* 12.9 2.0 12.6 2.0 12.7 2.0 3.39 1, 411 .07
PAS Childhood 1.73 1.03 1.98 1.07 1.90 1.06 5.07 1, 419 .03
PAS Early Adolescence 1.82 1.00 2.22 1.07 2.09 1.06 13.22 1, 419 <.001
PAS Late Adolescence 1.92 1.12 2.60 1.22 2.38 1.23 30.02 1, 419 <.001
PAS Total 1.84 0.94 2.30 1.00 2.15 1.01 20.10 1, 419 <.001
% % % χ2 df P
Race 0.817 2 .67
 White 51.1 54.9 53.7
 African American 42.3 40.1 40.9
 Other 6.6 4.9 5.5
Treatment Program 2.00 1 .16
 Inpatient 43.8 36.6 39.0
 Outpatient 56.2 63.4 61.0
Diagnosis 0.012 1 .91
 Schizophrenia 90.5 90.8 90.7
 Schizoaffective 9.5 9.2 9.3
Open in a new tab
*

Total N = 413, Females = 135, Males = 278.

Schizophrenia onset was operationally defined as the first time at which full criteria for schizophrenia were met according to the DSM that was in use at the time of the evaluation. The presence of symptoms meeting criteria for schizophrenia was established using the Structured Clinical Interview for DSM diagnoses (SCID). For this study, only individuals whose prodrome or illness onset occurred entirely in the adult period (age 19 or after) were included. Prodrome was operationally defined as presence of symptoms that met criteria for one of the Criteria A symptoms for schizophrenia as established by the SCID. We did not include data collected for the adulthood period because the PAS items differ from the earlier developmental periods, and because many participants were already in the prodromal phase of the illness by early adulthood. Finally, only individuals with complete data for each developmental period were included.

2.2. Measures

The PAS is widely used to assess premorbid functioning in schizophrenia and other disorders and has excellent psychometric properties (Allen et al., 2001; Alvarez et al., 1987; Brill et al., 2008; Cannon-Spoor et al., 1982; Krauss et al., 1998). The PAS was designed to assess premorbid functioning across developmental periods and across a number of domains. The developmental periods include childhood (up to 11 years), early adolescence (12 to 15 years), late adolescence (16 to 18 years), and adulthood (19 years and above). PAS items to assess premorbid domains from childhood to late adolescence include 1) sociability and withdrawal, 2) peer relationships, 3) scholastic performance, 4) adaptation to school, and 5) social-sexual functioning (social-sexual functioning is not assessed during childhood). Each domain is rated on a 0 to 6 point scale, with 0 indicating normal adjustment and 6 indicating severe impairment. The “premorbid” period for PAS purposes is the period ending six months prior to the first episode.

The PAS was completed by raters trained to reliably administer and score the instrument based on standardized procedures. This included a semi-structured interview completed with participants and their family members to gain information pertaining to the premorbid period (Cannon-Spoor et al., 1982). Family members selected for the interview were determined to be reliable informants on the basis of having substantial contact with the participant during childhood, adolescence, and early adulthood. In addition, collateral information was also obtained from hospital and academic records when available. This extensive background information collected on these participants was necessary to ensure accurate completion of the PAS because many of the participants had been diagnosed with schizophrenia for many years at the time of the current evaluation.

2.3. Procedure

The Premorbid Adjustment Scale (PAS; Cannon-Spoor et al., 1982) was administered as part of a battery of measures that were given to participants on their index admission into the MPRC, from the years 1988 to 2010. In addition to the PAS, this battery included demographic and clinical history interviews, a family history interview, the Structured Clinical Interview for DSM, symptom interviews, neuropsychological tests, and functional outcome measures.

2.4. Data Analysis

Confirmatory factor analyses were conducted using EQS (Bentler, 2004). Four goodness-of-fit statistics were used to determine the adequacy of fit for the models, including the maximum-likelihood chi-square test, comparative fit index (CFI; Bentler, 1990), root mean square error of approximation (RMSEA; Steiger & Lind, 1980), and Akaike information criterion (AIC; Akaike, 1987). These indexes were selected because they examine several different and important aspects of model fit (Byrne, 2006; Kline, 2005). A significant chi-square provides an indication of poor fit between the hypothesized model and the sample data. The CFI is an incremental fit index that ranges from 0 to 1 and indicates relative fit of the model as compared to the baseline independence model, with values greater than .95 indicating good fit (Hu & Bentler, 1999). The RMSEA ranges from 0 to 1 and is considered a parsimony index because it also takes into account model complexity, with a good fit indicated by a value of 0.05 or less (Jöreskog & Sörbom, 1993). The AIC is a predictive fit index because it estimates how well the model would fit in a hypothetical replication sample. The AIC is not scaled between 0 and 1, so interpretation of AIC is entirely comparative, with a smaller AIC indicating better fit.

Because maximum likelihood methods require multivariate normality, we used Mardia’s statistic (1970) to evaluate whether this assumption was met. In cases where Mardia’s statistic was greater than 3.0, robust statistics were used (Bentler, 2004). Additionally, we checked for the presence of outliers by examining the five cases that made the greatest contribution to Mardia’s statistic, as recommended by Bentler (2004). There was no evidence of outliers in this data.

Two CFA models were examined. The first model was based on a conceptualization of premorbid functioning as a unitary construct best represented on the PAS by one general premorbid adjustment summary score. This one-factor model specified that at each developmental level (childhood, early adolescence, late adolescence), the five PAS items would all load on a single factor. This model was designated PAS12345, for early and late adolescence and PAS1234 for childhood (where the social-sexual functioning item is not rated). The second model is based on studies suggesting that PAS scores are better conceptualized as measuring two separate but related factors that reflect social and academic premorbid adjustment (Allen et al., 2001; Cannon et al., 1997; van Kammen et al., 1994). For this model, PAS items 1, 2 and 5 (sociability and withdrawal, peer relationships, and social-sexual) were specified to load on a Social premorbid adjustment factor, while PAS items 3 and 4 (scholastic performance and adaptation to school) were specified to load on an Academic premorbid adjustment factor. This model was designated SO125AC34 for early and late adolescence and SO12AC34 for childhood.

After the best fitting model for each combination of sex and developmental period had been determined, a series of two-group models were fit to determine whether the factor structure was the same for males and females. In each analysis, the factor loadings and the correlation between factors were set to be equal for males and females. If this model fits the data, it indicates that a single model for males and females fits the data just as well as separate models, and the factor structure can be considered identical across the two sexes.

Finally, given potential premorbid differences between males and females across developmental periods and premorbid domain, we used a mixed-model ANOVA to compare the males and females on the social and academic domains across the three developmental periods. The domain scores should provide a more reliable indication of function than single items.

3.0. Results

3.1. CFA Results

Goodness-of-fit indices for the CFA models at each developmental period for males and females are presented in Table 2. As hypothesized, the two-factor model provided a better fit of the data than the one-factor model at each developmental level for both males and females. At each developmental period and for each sex, the one-factor model yielded significant chi-squares, and the CFI and RMSEA indicated marginal to poor fit. In contrast, the two-factor models consistently yielded non-significant chi-squares, and the CFI and RMSEA indicated moderate to excellent fit. Moreover, the AICs were consistently smaller for the two-factor models than the one-factor models. Thus, the two-factor modelwas selected as the best model for each sex and for each developmental level.

Table 2.

Goodness of Fit Indices for All Models at Each Developmental Level for Males and Females

Developmental Period Sex Model Fit Indices
χ2 df p CFI RMSEA [90% CI] AIC
Childhood Male PAS1234 70.71 2 .00 .82 .348 [.280, .418] 66.71
SO12AC34 0.30 1 .59 1.00 .000 [.000, .125] −1.71
Female PAS1234 10.28 2 .01 .94 .175 [.080, .286] 6.28
SO12AC34 0.12 1 .73 1.00 .000 [.000, .160] −1.88
Early Adolescent Male PAS12345 116.17 5 .00 .80 .280 [.236, .326] 106.13
SO125AC34 5.65 4 .23 1.00 .038 [.000, .104] −2.35
Female PAS12345 33.90 5 .00 .85 .207 [.144, .274] 23.90
SO125AC34 3.88 4 .42 1.00 .000 [.000, .128] −4.12
Late Adolescent Male PAS12345 171.07 5 .00 .73 .343 [.299, .386] 161.07
SO125AC34 3.39 4 .50 1.00 .000 [.000, .083] −4.61
Female1 PAS12345 54.12 5 .00 .78 .270 [.206, .334] 44.12
SO125AC34 6.74 4 .11 .99 .071 [.000, .161] −1.26
Open in a new tab

Note. CFI = comparative fit index; RMSEA = Root mean square error of approximation; CI = Confidence interval; AIC = Akaike information criterion; PAS1234 and PAS12345 = One-factor model; SO12AC34 and SO125AC34 = Two-factor model based with on results from the CTMT Normative Sample.

1

Robust statistics are reported for females because Mardia’s statistic was 3.88, indicating lack of normality.

Multigroup analyses of the two-factor models were conducted for males and females at each developmental period (see Table 3). All of these multi-group models fit the data well indicating that at all ages, the structure of the test is comparable for males and females. Males and females were therefore combined into a single analysis at each age level and the factor loadings and inter-factor correlations were very similar to the previous multi-group analysis and are presented in Figure 1. As can be seen from the figure, all of the PAS items had strong loadings on their respective factors at each of the three age levels.

Table 3.

Goodness of Fit Indices for Multi-Sample Analyses at Each Developmental Level

Developmental Period Model Fit Indexes
χ2 df p CFI RMSEA [90% CI]
Childhood1 SO12AC34 9.98 7 .79 .99 .045 [.000, .103]
Early Adolescence2 SO125AC34 17.95 14 .21 1.00 .037 [.000, .080]
Late Adolescence3 SO125AC34 18.28 14 .17 1.00 .038 [.000, .081]
Open in a new tab

Note. CFI = comparative fit index; RMSEA = Root mean square error of approximation; CI = Confidence interval; AIC = Akaike information criterion. SO12AC34 and SO125AC34 = Two-factor model based with on results from the CTMT Normative Sample.

1

Chi-square for independence model = 530.85; df = 12.

2

Chi-square for independence model = 755.98; df = 20.

3

Chi-square for independence model = 815.625; df = 20. Robust statistics are reported because Mardia’s statistic was 3.84 in females, indicating lack of normality.

Figure 1. Best Fitting Models at Each of the Three Developmental Levels.

Figure 1

Open in a new tab

Note. In confirmatory factor analyses, rectangles represent measured variables (in this case, the PAS items), while circles represent latent constructs. This model includes two types of latent constructs: the factors (Academic Adjustment, Social Adjustment) and the random error components (E).

3.2. Sex Differences across PAS domains and Developmental Levels

To examine sex differences in academic and social PAS scores (Monte et al., 2008), we conducted a Sex (Male, Female) X developmental period (childhood, early adolescence, late adolescence) X PAS domain (social, academic) mixed-models ANOVA. There were significant main effects for sex, developmental period, and PAS domain (p’s<.001), as well as significant interactions for developmental period X sex, F(2,418)=10.27, p<.0001, ηp2=.047, and developmental period X PAS domain, F(2,418)=10.36, p<.0001, ηp2=.047. As can be seen in Figure 2, interaction effects reflect that females had better premorbid adjustment than males for both Social and Academic adjustment but unlike males, did not demonstrate a steep decline in premorbid social adjustment from childhood to late adolescence. However, both groups demonstrated poorer academic compared to social adjustment by late adolescence, suggesting that decline in academic premorbid function is a consistent indicator of schizophrenia that is unaffected by sex.

Figure 2.

Figure 2

Open in a new tab

Changes in Social and Academic Adjustment across Developmental Levels for Males and Females

4.0. Discussion

Results of this study extend prior research in several important ways by finding that the PAS social and academic factors are the same in males and females, and remain stable across childhood, early adolescence, and late adolescence developmental periods. Given the stability of these dimensions across developmental periods, the use of scores reflecting social and academic adjustment is preferred over the use of a general score that averages across the PAS items or the scores from individual items. Outside of these methodological considerations, identification of the two distinct domains may have important theoretical implications. A number of studies interested in the heterogeneity of schizophrenia have used premorbid adjustment scores to develop classification schemes (Haas & Sweeney, 1992; Cole et al., 2012). These studies are particularly relevant to the current results because they used PAS scores averaged across social and academic items. Both studies identified three subtypes that could be characterized as good adjustment, declining adjustment, and poor adjustment. The good adjustment group evidenced good academic and social adjustment prior to onset, the declining adjustment group had good adjustment in childhood but demonstrated decline in functioning as onset of the disorder approached, and the poor group had poor adjustment in childhood, which remained stable, or declined further as onset approached. Both studies report that good premorbid adjustment was associated with more positive outcomes while poor premorbid adjustment was associated with increased negative outcomes. However, neither study considered the distinction between the academic and social domains. Whether considering academic and social adjustment separately within a subtyping system would provide more meaningful subtypes awaits further investigation, as do the environmental and biological developmental influences that cause the differences in trajectory across these two domains.

We also found that males exhibit more severe premorbid impairment than females, and that the decline of academic adjustment is evident in both sexes. Interestingly, females did not demonstrate any meaningful decline in social functioning from childhood to late adolescence, in contrast to academic functioning, which did decline. Sparing of social functioning in females may be linked to neuroprotective effects of estrogen which is associated with older age of onset, milder negative symptoms, and better response to antipsychotics (Pregeli, 2009; Häfner 2003; Kulkarni, Gavrilidis, Hayes, Heaton, Worsley, 2012). Consistent with this suggestion, Strauss et al. (2012) reported poorer premorbid social adjustment in those with primary persisting negative symptoms, which also occur at higher rates among males. These findings also parallel sex differences in functional outcome and recovery that occur much later in life among individuals with schizophrenia (Grossman et al., 2008). It may be that some of the contradictory findings regarding relationships between sex and premorbid adjustment (for review see Schmael et al., 2007) can be accounted for by a failure to consider academic and social domains separately.

Deterioration in premorbid academic functioning may be a unique neurodevelopmental marker for schizophrenia and a number of studies also suggest that deterioration accelerates from early adolescence to late adolescence (Allen et al., 2005; Fuller et al., 2002; Gunnell et al., 2002; Jones et al., 1994; Monte et al., 2008; van Oel et al., 2002). The current results comparing academic functioning to social functioning extend this research by revealing that accelerated deterioration in premorbid academic functioning from childhood to late adolescence is evident for both males and females, although males demonstrate an overall greater deficit in this domain.

The current study had a number of limitations, including the use of retrospective interviews and chart reviews to establish premorbid functioning. This limitation is mitigated to some extent by studies demonstrating robust correlations between the PAS scores and, for example, draft board scores obtained before schizophrenia onset (e.g., Brill et al., 2008). Despite this limitation, the current results suggest that premorbid adjustment should be viewed as multidimensional in nature consisting of at least two domains (academic and social), and that the developmental trajectory of these domains are differentially affected in males and females. Future studies may further investigate the multidimensional nature of premorbid adjustment by using multiple methods and instruments capable of assessing the multi-faceted abnormalities known to occur during the premorbid phase of schizophrenia (e.g., affective and motor dysfunction).

References

  1. Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–332. [Google Scholar]
  2. Allen DN, Frantom LV, Strauss GP, van Kammen DP. Differential patterns of premorbid academic and social deterioration in patients with schizophrenia. Schizophrenia Research. 2005;75:398–97. doi: 10.1016/j.schres.2004.11.011. [DOI] [PubMed] [Google Scholar]
  3. Allen DN, Kelley ME, Miyatake RK, Gurklis JA, Jr, van Kammen DP. Confirmation of a two-factor model of premorbid adjustment in males with schizophrenia. Schizophrenia Bulletin. 2001;27:39–46. doi: 10.1093/oxfordjournals.schbul.a006858. [DOI] [PubMed] [Google Scholar]
  4. Alvarez E, Garcia-Ribera C, Torrens M, Udina C, Guillamat R, Casas M. Premorbid adjustment scale as a prognostic predictor for schizophrenia. British Journal of Psychiatry. 1987;150:411. doi: 10.1192/bjp.150.3.411. [DOI] [PubMed] [Google Scholar]
  5. Bentler PM. EQS 6 Structural Equations Program Manual. California: Multivariate Software, Inc; 2004. [Google Scholar]
  6. Bentler PM. Comparative fit indexes in structural models. Psychological Bulletin. 1990;107:238–246. doi: 10.1037/0033-2909.107.2.238. [DOI] [PubMed] [Google Scholar]
  7. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin. 1980;88:588–606. [Google Scholar]
  8. Brill N, Reichenberg A, Weiser M, Rabinowitz J. Validity of the premorbid adjustment scale. Schizophrenia Bulletin. 2008;34:981–983. doi: 10.1093/schbul/sbm128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Buchanan RW, Kirkpatrick B, Heinrichs DW, Carpenter WT. American Journal of Psychiatry. 1990;147:290–294. doi: 10.1176/ajp.147.3.290. [DOI] [PubMed] [Google Scholar]
  10. Byrne BM. Structural equation modeling with EQS: basic concepts, applications, and programming. 2. New Jersey: Lawrence Erlbaum Associates Publishers; 2006. [Google Scholar]
  11. Cannon M, Jones P, Gilvarry C, Rifkin L, McKenzie K, Foerster A, et al. Premorbid social functioning in schizophrenia and bipolar disorder: similarities and differences. American Journal of Psychiatry. 1997;154:1544–1550. doi: 10.1176/ajp.154.11.1544. [DOI] [PubMed] [Google Scholar]
  12. Cannon-Spoor HE, Potkin SG, Wyatt RJ. Measurement of premorbid adjustment in chronic schizophrenia. Schizophrenia Bulletin. 1982;8:470–484. doi: 10.1093/schbul/8.3.470. [DOI] [PubMed] [Google Scholar]
  13. Carpenter WT, Jr, Heinrichs DW, Wagman AM. Deficit and nondeficit forms of schizophrenia: the concept. American Journal of Psychiatry. 1998;145:578–83. doi: 10.1176/ajp.145.5.578. [DOI] [PubMed] [Google Scholar]
  14. Caspi A, Reichenberg A, Weiser M, Rabinowitz J, Kaplan Z, Knobler H, et al. Cognitive performance in schizophrenia patients assessed before and following the first psychotic episode. Schizophrenia Research. 2003;65:87–94. doi: 10.1016/s0920-9964(03)00056-2. [DOI] [PubMed] [Google Scholar]
  15. Childers SE, Harding CM. Gender, premorbid social functioning, and long-term outcome in DSM-III schizophrenia. Schizophrenia Bulletin. 1990;16:309–318. doi: 10.1093/schbul/16.2.309. [DOI] [PubMed] [Google Scholar]
  16. Cole VT, Apud JA, Weinberger DR, Dickinson D. Using latent class growth analysis to form trajectories of premorbid adjustment in schizophrenia. Journal of Abnormal Psychology. 2012;121:388–395. doi: 10.1037/a0026922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fuller R, Nopoulos P, Arndt S, O’Leary D, Ho BC, Andreasen NC. Longitudinal assessment of premorbid cognitive functioning in patients with schizophrenia through examination of standardized scholastic test performance. American Journal of Psychiatry. 2002;159:1183–9. doi: 10.1176/appi.ajp.159.7.1183. [DOI] [PubMed] [Google Scholar]
  18. Gittelman-Klein R, Klein DF. Premorbid asocial adjustment and prognosis in schizophrenia. Journal of Psychiatric Research. 1969;7:35–53. doi: 10.1016/0022-3956(69)90010-7. [DOI] [PubMed] [Google Scholar]
  19. Grossman LS, Harrow M, Rosen C, Faull R, Strauss GP. Sex differences in schizophrenia and other psychotic disorders: a 20-year longitudinal study of psychosis and recovery. Comprehensive Psychiatry. 2008;49:523–9. doi: 10.1016/j.comppsych.2008.03.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gunnell D, Harrison G, Rasmussen F, Fouskakis D, Tynelius P. Associations between premorbid intellectual performance, early-life exposures and early-onset schizophrenia. Cohort study British Journal of Psychiatry. 2002;181:298–305. doi: 10.1192/bjp.181.4.298. [DOI] [PubMed] [Google Scholar]
  21. Haas GL, Sweeney JA. Premorbid and onset features of first-episode schizophrenia. Schizophrenia Bulletin. 1992;18:373–386. doi: 10.1093/schbul/18.3.373. [DOI] [PubMed] [Google Scholar]
  22. Häfner H. Gender differences in schizophrenia. Psychoneuroendocrinology. 2003;28:17–54. doi: 10.1016/s0306-4530(02)00125-7. [DOI] [PubMed] [Google Scholar]
  23. Hoff AL, Wieneke M, Faustman WO, Horon R, Sakuma M, Blankfeld H, et al. Sex differences in neuropsychological functioning of first-episode and chronically ill schizophrenic patients. American Journal of Psychiatry. 1998;155:1437–9. doi: 10.1176/ajp.155.10.1437. [DOI] [PubMed] [Google Scholar]
  24. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55. [Google Scholar]
  25. Jones P, Rodgers B, Murray R, Marmot M. Child development risk factors for adult schizophrenia in the British 1946 birth cohort. Lancet. 1994;344:1398–402. doi: 10.1016/s0140-6736(94)90569-x. [DOI] [PubMed] [Google Scholar]
  26. Jöreskog KG, Sörbom D. LISREL 8 user’s reference guide. Chicago: Scientific Software International; 1993. [Google Scholar]
  27. Kirkpatrick B, Buchanan RW, Ross DE, Carpenter WT., Jr A separate disease within the syndrome of schizophrenia. Archives of General Psychiatry. 2001;58:165–71. doi: 10.1001/archpsyc.58.2.165. [DOI] [PubMed] [Google Scholar]
  28. Kline RB. Principles and practice of structural equation modeling. 2. New York: Guilford Press; 2005. [Google Scholar]
  29. Krauss H, Warwinski K, Held T, Rietchel M, Freyberger HJ. Reliability and validity of the premorbid adjustment scale (PAS) in a German sample of schizophrenic and schizoaffective patients. European Archives of Psychiatry and Clinical Neuroscience. 1998;248:277–281. doi: 10.1007/s004060050050. [DOI] [PubMed] [Google Scholar]
  30. Kulkarni J, Gavrilidis E, Hayes E, Heaton V, Worsley R. Special biological issues in the management of women with schizophrenia. Expert Review of Neurotherapeutics. 2012;12:823–33. doi: 10.1586/ern.12.62. [DOI] [PubMed] [Google Scholar]
  31. Larsen TK, McGlashan TH, Johannessen JO, Vibe-Hansen L. First-episode schizophrenia: II. Premorbid patterns by gender Schizophrenia Bulletin. 1996;22:257–269. doi: 10.1093/schbul/22.2.257. [DOI] [PubMed] [Google Scholar]
  32. Larsen TK, McGlashan TH, Moe LC. First episode schizophrenia: I. Early course parameters Schizophrenia Bulletin. 1996;22:241–256. doi: 10.1093/schbul/22.2.241. [DOI] [PubMed] [Google Scholar]
  33. Larsen TK, Friis S, Haahr U, Johannessen JO, Melle I, Opjordsmoen S, et al. Premorbid adjustment in first-episode non-affective psychosis: distinct patterns of pre-onset course. British Journal of Psychiatry. 2004;185:108–15. doi: 10.1192/bjp.185.2.108. [DOI] [PubMed] [Google Scholar]
  34. Luo D, Petrill SA, Thompson LA. An exploration of genetic g: hierarchical factor analysis of cognitive data from the western reserve twin project. Intelligence. 1994;18:335–347. [Google Scholar]
  35. MacBeth A, Gumley A. Premorbid adjustment, symptom development and quality of life in the first episode psychosis: a systematic review and critical reappraisal. Acta psychiatrica Scandinavica. 2008;177:85–99. doi: 10.1111/j.1600-0447.2007.01134.x. [DOI] [PubMed] [Google Scholar]
  36. Malla A, Payne J. First-episode psychosis: Psychopathology, quality of life, and functional outcome. Schizophrenia Bulletin. 2005;3:650–671. doi: 10.1093/schbul/sbi031. [DOI] [PubMed] [Google Scholar]
  37. Mardia KV. Measures of multivariate skewness and kurtosis with applications. Biometrika. 1970;57:519–530. [Google Scholar]
  38. Marcus J, Hans SL, Auerbach JG, Auerbach AG. Children at risk for schizophrenia: The Jerusalem infant developmental study. II. Neurobehavioral deficits at school age. Archives of General Psychiatry. 1993;50:797–809. doi: 10.1001/archpsyc.1993.01820220053006. [DOI] [PubMed] [Google Scholar]
  39. Monte RC, Goulding SM, Compton MT. Premorbid functioning of patients with first-episode nonaffective psychosis: a comparison of deterioration in academic and social performance, and clinical correlates of premorbid adjustment scale scores. Schizophrenia Research. 2008;104:206–13. doi: 10.1016/j.schres.2008.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Murray RM, Jones P, O’Callaghan E, Takei N, Sham P. Genes, viruses, and neurodevelopmental schizophrenia. Journal of Psychiatric Research. 1992;26:225–35. doi: 10.1016/0022-3956(92)90029-n. [DOI] [PubMed] [Google Scholar]
  41. Pregelj P. Neurobiological aspects of psychosis and gender. Psychiatria Danubina. 2009;21:128–131. [PubMed] [Google Scholar]
  42. Reichenberg A, Weiser M, Rabinowitz J, Caspi A, Schmeidler J, Mark M. A population-based cohort study of premorbid intellectual, language, and behavioral functioning in patients with schizophrenia, schizoaffective disorder, and nonpsychotic bipolar disorder. American Journal of Psychiatry. 2002;159:2027–35. doi: 10.1176/appi.ajp.159.12.2027. [DOI] [PubMed] [Google Scholar]
  43. Rosso IM, Bearden CE, Hollister JM, Gasperoni TL, Sanchez LE, Hadley T, et al. Childhood neuromotor dysfunction in schizophrenia patients and their unaffected siblings: a prospective cohort study. Schizophrenia Bulletin. 2000;26:367–78. doi: 10.1093/oxfordjournals.schbul.a033459. [DOI] [PubMed] [Google Scholar]
  44. Schiffman J, Sorensen HJ, Maeda J, Mortensen EL, Victoroff J, Hayashi K, et al. Childhood motor coordination and adult schizophrenia spectrum disorders. American Journal of Psychiatry. 2009;166:1041–7. doi: 10.1176/appi.ajp.2009.08091400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Schiffman J, Walker E, Ekstrom M, Schulsinger F, Sorensen H, Mednick S. Childhood videotaped social and neuromotor precursors of schizophrenia: a prospective investigation. American Journal of Psychiatry. 2004;161:2021–7. doi: 10.1176/appi.ajp.161.11.2021. [DOI] [PubMed] [Google Scholar]
  46. Schmael C, Georgi A, Krumm B, Buerger C, Deschner M, Nöthen MM, et al. Premorbid adjustment in schizophrenia--an important aspect of phenotype definition. Schizophrenia Research. 2007;92:50–62. doi: 10.1016/j.schres.2007.01.016. [DOI] [PubMed] [Google Scholar]
  47. Schmid J, Leiman JM. The development of hierarchical factor solutions. Psychometrika. 1957;22:53–61. [Google Scholar]
  48. Steiger JH, Lind JC. Statistically based tests for the number of common factors. Paper presented at the meeting of the Psychometric Society; Iowa City, IA. 1980 May. [Google Scholar]
  49. Strauss GP, Allen DN, Miski P, Buchanan RW, Kirkpatrick B, Carpenter WT. Differential patterns of premorbid social and academic deterioration in patients with deficit and nondeficit schizophrenia. Schizophrenia Research. 2012;135:134–138. doi: 10.1016/j.schres.2011.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Tarbox SI, Brow LH, Hass GL. Diagnostic specificity of poor premorbid adjustment: comparison of schizophrenia, schizoaffective disorder and mood disorder with psychotic features. Schizophrenia Research. 2012 doi: 10.1016/j.schres.2012.07.008. epub ahead of time. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. van Kammen DP, Kelley ME, Gilbertson MW, Gurklis J, O’Connor DT. CSF dopamine beta-hydroxylase in schizophrenia: associations with premorbid functioning and brain computerized tomography scan measures. American Journal of Psychiatry. 1994;151:372–8. doi: 10.1176/ajp.151.3.372. [DOI] [PubMed] [Google Scholar]
  52. van Oel CJ, Sitskoorn MM, Cremer MP, Kahn RS. School performance as a premorbid marker for schizophrenia: a twin study. Schizophrenia Bulletin. 2002;28:401–14. doi: 10.1093/oxfordjournals.schbul.a006949. [DOI] [PubMed] [Google Scholar]
  53. Walker EF. Developmentally moderated expressions of the neuropathology underlying schizophrenia. Schizophrenia Bulletin. 1994;20:453–80. doi: 10.1093/schbul/20.3.453. [DOI] [PubMed] [Google Scholar]
  54. Walker EF, Lewine RR, Neumann C. Childhood behavioral characteristics and adult brain morphology in schizophrenia. Schizophrenia Research. 1996;22:93–101. doi: 10.1016/s0920-9964(96)00070-9. [DOI] [PubMed] [Google Scholar]
  55. Weinberger DR. From neuropathology to neurodevelopment. The Lancet. 1995;346:552–557. doi: 10.1016/s0140-6736(95)91386-6. [DOI] [PubMed] [Google Scholar]
  56. Wolff H, Preising K. Exploring item and higher order factor structure with the Schmid-Leiman solution: syntax codes for SPSS and SAS. Behavior Research Methods. 2005;37:48–58. doi: 10.3758/bf03206397. [DOI] [PubMed] [Google Scholar]

RESOURCES