Season of birth and schizotypy in a sample of undergraduate students

doi:10.21203/rs.3.rs-4094630/v1

Purpose

In line with the psychotic continuum theory, the study of psychometric schizotypy in non-clinical samples has been proposed as a convenient yet powerful method for studying the etiology of psychosis. Based on this paradigm, several studies explored the association between season of birth (SoB) and schizotypy but led to inconsistent results. Building on the analysis of the previous studies, in the present study, we aimed to advance our understanding by improving the methodology (using a homogeneous group, eliminating unreliable respondents, taking into account potential confounders) and the reporting.

Methods

Subjects were recruited among undergraduate students from 3 Romanian Universities. To limit the potential influence of invalid response, we applied methods for detecting unreliable and/or biased questionnaires and excluded subjects with unreliable/ biased answers from the analyses. Schizotypal dimensions were measured using the Romanian translation of the 22-items Schizotypal Personality Questionnaire-Brief (SPQ-B). The association between schizotypy scores and season of birth was explored using linear regression.

Results

In a sample of 484 undergraduate students from Romania, we found that being born in late winter/early spring (February and March) was associated to higher total schizotypy score and disorganization. Furthermore, we found that restricting the sample to subjects born in an urban environment increased the strength of the association.

Conclusion

This study is consistent with an association between SoB and the risk of psychotic disorders.

Schizophrenia

psychosis

urbanicity

risk factors

Psychotic disorders are major causes of suffering for patients and their families, are major contributors to years of life lost and years lost due to disability and have huge individual and societal costs [1, 2]. As a consequence, significant efforts to understand their etiology and to develop adequate treatments and preventive measures have been deployed. This is reflected in the growing number of publications on the subject over the last decades.

However, to date, the research has been only (very) partially successful. As our understanding progressed, two paradigms emerged for the research. First, that of a psychotic continuum at both clinical and etiological levels [3–5]. At clinical level, this means that differences between severe psychotic disorders (e.g. schizophrenia), mild and subclinical psychosis (e.g. schizotypy) and isolated psychotic manifestations in nonclinical populations are all different degrees of a qualitatively similar category. On the other hand, the etiological continuum implies the existence of similar etiologies all along the psychotic continuum, with differences being of degree and not of nature.

The second, more general, paradigm is that of risk factors (RF) which are at the origins of complex, and most often chronic disorders among which psychotic disorders. By contrast to simpler direct causes, as seen for example in infectious diseases, risk factors are neither necessary (are not present in all ill subjects) nor sufficient (they might be present, without leading to the disorder) [6].

There are several advantages of studying subclinical, quantitative psychotic traits in the general population instead of case-control studies of schizophrenia, e.g. no interference from post-diagnosis factors like treatment and hospitalization, less risk of errors of classification/diagnosis, better stability and better statistical power (see also Barrantes-Vidal et al. [7]).

Because of these advantages, and based on the two paradigms enunciated above, a quantitatively significant research of the association of a schizotypal dimensions with putative RF for schizophrenia emerged.

One such factor that has been associated with risk for schizophrenia is season of birth (SoB) [8–10]. Indeed, birth in winter has been associated with a slight increase in the risk for developing schizophrenia (RR = 1.04–1.07).

Two points are important in understanding the link between SoB and schizophrenia. First: a sizeable part of the population is exposed to the potential risk period which makes it an important risk factor at population level. The second point is that, as is the case for several “risk factors” for schizophrenia, the SoB is only a marker for one (or several) yet unknown risk factor(s). Several hypotheses of what these effective risk factors might be have been proposed: seasonal infectious diseases, sun exposure and vitamin D deficit, temperature variation, etc. [11]. These hypotheses have not yet been tested in case-control studies involving subjects with schizophrenia because of the practical and statistical (i.e. power) limitations alluded before. Thus, the demonstration of a similar association with schizotypy (or specific schizotypal dimensions) in the general population might pave the road to test these hypotheses.

Although at least 8 studies of the association of SoB with psychometric schizotypy have been published to date (see Konrath et al. [12] for a summary of the published studies - to which the study by Mimarakis et al. [13] has to be added), a clear picture has not yet emerged.

There were positive findings either for total schizotypy [12, 14] or specific dimensions/traits [12, 13, 15, 16] but also negative findings (i.e. no differences in scores according to SoB) [17, 18]. Furthermore, the significant associations were not always the same i.e. in different studies increased scores were not always associated with the same season or month.

This is not so surprising given the fact that definition of the SoB varied widely (e.g. meteorological or astronomical season, half years with different starting points, or months) and also did the measures of schizotypy (different versions of the SPQ, one or several of the Chapmans’ questionnaires of magical ideation, perceptual aberrations or physical anhedonia). Also, the dimensions analyzed and reported varied from study to study (total score, positive or negative dimension, etc.). Furthermore, important characteristics of the samples – that might confound the association – differed between studies, e.g. age of subjects (mean and range), latitude and climate of birth place etc. Finally, important confounders (including substance abuse data, unreliable style of responding, urbanicity of place of birth [19–21]) were not always measured or used in modeling the link between SoB and schizotypy.

An additional problem was that even when some of the variables of interest were available (e.g. the different dimensions of the SPQ), they were not always reported thus making comparison of the results impossible (e.g. Cohen & Najolia [18]).

In this context, the study by Konrath et al. [12], represents, in our view, a step forward. Indeed not only this is – by far – the largest study but, by reporting results for total and dimension scores, by month and also using different definitions of SoB, it allows for comparison. In this study - of more than eight thousand subjects and using different definitions of SoB - the only group that showed significantly increased scores for total schizotypy was that of subjects born in late winter and early spring (i.e. during the months of February and March). Positive (i.e. cognitive-perceptual) and disorganization dimensions also were significantly increased in this group. All comparisons were adjusted for sex and age.

Among the limitations of this study were the age range of participants which was very large (18–104), the absence of a test to identify invalid responding and the fact that no other potential confounders (substance use, education etc.) were used.

Thus, in the present study, our aim was to replicate and extend the findings from Konrath’s et al. study [12] by collecting similar variables of interest and using a similar reporting procedure but also improving the design by taking into account the risk of inaccurate responding and by reducing the heterogeneity introduced by a large range of ages, years of birth and education. As some of the explanatory hypotheses of the association between SoB and schizotypy suggest an interaction between SoB and urbanicity (e.g. sun exposure and vitamin D deficit, infectious disease risk), we also repeated the analyses in a sample restricted to subjects born in an urban setting.

Subjects

Subjects were recruited among undergraduate students from 3 Romanian Universities: Bucharest Medical School, Craiova Medical School and Brasov (Sociology). Details about the recruitment process are provided elsewhere [22]. In short, the subjects were informed of the aim of the study (i.e. to assess personality traits and their relation to demographic variables and substance consumption), of the fact that the questionnaires were anonymous, and of the necessity of answering all the questions as best and as honestly that they can. They were also informed that the study has been approved by an ethics committee and that they were free to participate in the survey or to decline.

Among the available subjects, we operated a selection in order to enhance homogeneity and limit statistical noise.

A negative correlation between age and schizotypy scores has been previously described [23]. In order to enhance homogeneity of the sample and limit the variability in schizotypy scores for reasons other than the season of birth, we decided to limit the analyses to subjects younger than 30 years.

To limit the potential influence of invalid response, we applied methods for detecting unreliable and/or biased questionnaires and excluded subjects with unreliable/ biased answers from the analyses. To this aim, we added 3 validity items and 5 social desirability items randomly distributed among the 22 SPQ-B original items (see details below).

Subjects with one (or more) invalid answer(s) were classified as unreliable and were thus excluded from the analyses as were the subjects showing a social desirability score that exceeded the sample mean plus two standard deviations.

Data recorded

Sociodemographic data collected included gender, age, date and place (urban vs. rural) of birth.

Several questions explored consumption of psychoactive substances (no details on the specific substance), alcohol and tobacco. The corresponding variables were dichotomized according to frequency of consumption. Answers were considered as positive when corresponding to at least once a week for alcohol, daily consumption for tobacco and at least “occasional” for psychoactive substances.

Several variables related to the concept of “season of birth” (see introduction) have been derived from the date of birth: month of birth, astronomical season of birth (i.e. winter from December 21st to March 20th, spring from March 21st to June 20th, summer from June 21st to September 20th and fall from September 21st to December 20th), meteorological season of birth (i.e. winter from December 1st to end of February, spring from March 1st to May 31st, summer from Jun 1st to August 31st and fall from September 1st to December 31st) and a high risk period (February & March) based on the study by Konrath et al [12].

Schizotypal dimensions were measured using the Romanian translation of the 22-items Schizotypal Personality Questionnaire-Brief (SPQ-B), in a Likert format (from 1 = completely disagree to 5 = completely agree) (Ladea et al, 2020) to which we added 3 validity and 5 social desirability items.

Two of the validity items were items for which some of the responses are very improbable (infrequent answers). The first one was “Sometimes I feel tired or sleepy” (infrequent answer “completely disagree”) and the second one “I have answered this questionnaire the best I could” (infrequent answers - “completely disagree” or “disagree”). Such items have previously been shown to capture invalid responses [24, 25].

The third validity item, “I find it easy to communicate clearly what I want to say to people”, has the exact opposite meaning of one of the SPQ items (i.e., “I find it hard to communicate clearly what I want to say to people”). Answers of the same polarity (i.e., scores of 1 or 2, or scores of 4 or 5) to both of those items were considered indicative of unreliable responding.

To eliminate biased questionnaires due to social desirability we used 5 items extracted from the Eysenck’s Lie scale (see Ladea et al. [22] and the supplementary material for details). A desirability score was calculated by summing the 5 items and questionnaires for which this score exceeded the sample mean plus two standard deviations were considered biased and excluded from further analyses.

Statistical analyses

The main analyses concerned the total schizotypy score, the whole sample and the risk period described by Konrath et al. [12] (February-March).

We conducted secondary analyses for the three schizotypy dimensions (positive, negative, disorganization), using the alternative definitions of the season of birth and also in a sample restricted to subjects born in an urban environment.

Descriptive statistics

To describe our sample, we used the following variables: demographic, substance consumption, SPQ scores. For continuous variables, we calculated mean and standard deviation and for categorical variables number and percentage.

We also calculated the mean and standard deviation for the SPQ score according to month of birth.

Univariable analyses

The association between the different definitions of season of birth and schizotypy scores was assessed using Student’s t (for two groups) or ANOVA statistics.

We also investigated the association of schizotypal scores with (putative) confounders i.e. demographic variables (sex, age, place of birth) and substance consumption using Student’s t or ANOVA statistics (for categorical variables) and correlations (Pearson’s) for continuous variables.

Multivariable analyses

The association between schizotypy scores and season of birth was explored using linear regression in two different models that included potential confounders. In the first model, only age and gender were included. In the second model, to these demographic variables, data on tobacco and psychoactive substance consumption were added.

Descriptive statistics

The initial sample consisted of 580 subjects. Of these 10 had incomplete data for SPQ or birth date and 29 were not considered because they were older than 30. Of those eligible, 47 had at least one invalid answer and 10 had desirability scores exceeding the threshold, and were thus excluded of the subsequent analyses.

After applying all these exclusion criteria, our sample consisted of 484 subjects. Among those most were women, urban born and single. The mean age was 24. Detailed demographic data, data about substance consumption and SPQ-B scores are provided in Table 1.

Table 1

Description of the sample
Total	N	484
Gender (F)	N (%)	374 (77.27%)
Age	Mean (SD)	23.76 (1.83)
Tobacco	N (%)	235 (48.6%)
Alcohol	N (%)	156 (32.2%)
Psychoactive substances	N (%)	58 (12.0%)
Urban birth	N (%)	453 (93.6%)
Single	N (%)	318 (65.7%)
SPQ scores Total	Mean (SD)	54.85 (11.75)
Positive	Mean (SD)	25.39 (6.28)
Negative	Mean (SD)	15.87 (4.40)
Disorganization	Mean (SD)	13.59 (4.11)

Total scores according to month of birth are shown in Table 2 and in Fig. 1.

Table 3

Association of SPQ-B total score with (putative) risk factors and demographic variables
Variable	Category¹	SPQ score (Mean)	p-value²
Season of birth (February -March)	Y	58.20	0.0054
Season of birth (February -March)	N	54.20
Sex	F	54.90	0.96
Sex	M	54.83
Tobacco	Y	56.37	0.0054
Tobacco	N	53.41
Alcohol	Y	54.54	0.69
Alcohol	N	55.00
Psychoactive substances	Y	58.86	0.0055
Psychoactive substances	N	54.30
Urban	Y	54.66	0.186
Urban	N	57.55
Age	-	-0.19³	< 0.0001
¹ Y – (putative) risk factor present/ N – (putative) risk factor absent
² Probability of the null hypothesis (i.e. no difference between categories or correlation coefficient not different from 0)
³ correlation coefficient

Table 4

Association of SPQ-B total score with (putative) risk factors and demographic variables in the two multivariable models
	Age		Sex (ref M)		Tobacco (ref N)		Psychoactive subst. (ref N)		At risk period (ref N)		Model
	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	R²	p-value
Model 1	-1.17	4.8*10^− 5	-0.439	0.73	NA	NA	NA	NA	3.67	0.010	0.043	2.116 * 10^− 5
Model 2	-1.11	2.97*10^− 4	0.332	0.79	1.37	0.22	1.32	0.0046	3.37	0.017	0.060	1.806 * 10^− 6

Main analyses (total SPQ score, whole sample and February-March as risk period )

Univariable analyses

The association between the SPQ-B total score and individual variables of interest i.e. SoB and potential confounders are presented in Table 2.

The subjects born in the “risk period” showed a significantly increased SPQ-B score. The other characteristics that were associated with (significantly) higher scores were psychoactive substances and tobacco use, as well as younger age. No significant differences were associated with sex, alcohol consumption or urban birth.

Multivariable analyses

The results from the 2 models (the 1st including only age and gender and the 2nd including also tobacco and psychoactive substances) are similar to the results from univariate analyses showing a significant increase in schizotypy score for the subjects born in the “at risk period” compared with the rest of the subjects.

Secondary analyses

Detailed data (tables) are available as supplementary material. Here, we only summarize the main results of our study i.e. on the association – or not – between schizotypy (different definitions) and SoB (different definitions)

Analyses of the SPQ dimensions (with the risk period of birth)

All schizotypal dimensions were elevated in the group of subjects born in the risk period (February-March). However, the difference reached significance only for disorganization (in univariate analyses, as well as in the 2 multivariable models) (see supplementary material tables 1S to 4S).

Analysis of the SPQ total score using different definitions of the SoB

We analyzed the data using several alternative definitions for the season/month of birth. We compared groups defined by their month of birth (12 groups), by astronomical season (4 seasons of three months each, beginning with 21st of December for winter), by meteorological season (4 seasons of three months each, beginning with 1st December for winter), or winter-born (astronomical or meteorological) vs. the remaining 9 months (2 groups) (see supplementary material tables 5S to 9S).

The only significant differences observed were for subjects born in February (increased values compared to subjects born in June – the reference month) in univariate and multivariate (both models) analyses.

Analyses repeated in the subsample restricted to subjects born in an urban environment (see Tables 10S to 18S in the supplementary material for details)

In univariable analyses total, positive and disorganization scores were significantly elevated in the sample of subjects at risk (born February-March). Negative dimension was also elevated (and nearly reached significance with p = 0.050).

After adjusting for demographic variables (model 1) and demographic and substance consumption (model 2) only total and disorganization scores retained significance.

When different SoB definitions were used in the sample limited to subjects born in urban settings in univariate analyses, two months (February and March) and winter (astronomic but not meteorologic season) compared with the rest of the year reached significance.

In multivariable analyses only February remained significant in the two models (March was significantly different only in the first model).

The main result of our study is the replication of the earlier finding of increased schizotypy in subjects born in late winter/early spring, i.e. in the months of February and March. Additional analyses showed that this was true not only for the total score of schizotypy but also for the three dimensions (positive, negative, disorganized) although it reached significance only for disorganization. Using different, previously used, definitions for season of birth, we did not find any other significant association. Finally, restricting the sample to subjects born in an urban environment did improve our capacity to detect significant differences. In this restricted sample, in addition to the differences already observed in the total sample, in univariable analyses the positive dimension was significantly increased in the subjects born in the risk period as well as total schizotypy in subject born in astronomic winter.

It is important to note, before any interpretation of results involving season of birth, that date of birth is only a marker/ a proxy for a risk factor (or several risk factors) yet to be discovered. Several hypotheses have been formulated on the nature of this (these) factor(s). Some are about characteristics directly depending on the season like day-light duration or temperature. Others are indirectly linked to such characteristics like vitamin D and nutrition or risk of infectious diseases. Furthermore, these characteristics are not uniformly shared in all populations/places depending on the latitude, but also on the situation of the specific place (altitude, distance from the sea etc.) which influence the climate (see classification of climates in Beck et al. [26]). Finally, even for a given population and place, there might also be yearly variations (for example in monthly average temperature or months of heightened influenza risk, etc.).

Another important point is that, although for convenience we use the date of birth as a variable of interest, it is highly possible that the time at which the specific (direct, effective) risk factor acts is either before (e.g. in the second trimester of gestation) or after birth (e.g. in the first year of life).

Regarding the replication of findings by Konrath et al, we observed a similar pattern of increased schizotypy not only for the same months (February/ March) but also for the total score and (at least in the sample limited to urban born subjects) for positive and disorganized dimensions. As in Konrath’s and al. study the negative dimension was not (significantly) associated with birth in the period of risk [12].

Also, similar to Konrath’s study, in the whole sample, we did not observe significant differences in groups defined by SoB according to classical (i.e. three month long) definitions of seasons.

Regarding this almost perfect replication of an earlier study, it is important to stress that our study took place at a similar latitude and in the same general climate (mainly continental Dfb in the Köppen-Geiger classification – see Beck et al. [26]). This might be an important point explaining the differences in results with other previous studies.

Another factor that might explain our capacity to uncover this association was the fact that we limited the range of birth years (reducing potential variation in some of the characteristics that might change yearly). For the same reason (limiting unwanted variation), we used specific methods to identify and eliminate invalid questionnaires.

Although tobacco and psychoactive substance consumption were associated with scores of schizotypy (which was expected given previous researches [27, 28]) they did not change the association between schizotypy scores and season of birth. By contrast, interestingly, restricting the sample to urban-born subjects seemed to improve our capacity of detecting association between SoB and schizotypy.

Our study has several limitations. Compared to some of the previous studies (e.g. Konrath et al. [12]), our sample size is relatively small. However, this has been in part compensated by measures to improve homogeneity and limit the influence of potential confounders (see above). Although we inquired about psychoactive substance use, there was no objective test and no detail on the specific substance used. The percentage of subjects that used psychoactive substances in the general population of Romania in 2016 was of 7.9% and most of them used cannabis (5.8%) according to National Report for drug consumption in Romania 2017 [29]. For most substances, these figures are almost double in the 15–34 age band (e.g. 10% for life-time cannabis consumption) which suggests that the prevalence we observed (12%) closely reflects the reality.

The exact place of birth (thus latitude) has not been recorded. However, the percentage of foreign-born students (that attended classes in Romanian) is likely to be (very) small. Thus it might be inferred that for most of them the latitude of their place of birth corresponds to that of Romania (i.e. roughly between 43 and 48° N).

Although we can speculate on potential RF that might explain these associations (see introduction) the present study was not designed to distinguish between several alternative hypotheses. However, as a whole our study suggests that urbanicity at birth increases the strength of the association and that climate might be important. Infectious hypotheses as for example those involving seasonal respiratory (e.g. flu) or enteric viruses are compatible with these findings [11]. However, it seems too early to accept this hypothesis.

On the other hand, the present study suggests several methodological improvements that might advance our understanding and shed light on the RF associated with SoB. The use of homogeneous samples (age, urbanicity, latitude at birth) seems important.

Also, future studies might benefit from the recent availability of data to include some of the hypothesized risk factors in the analyses (i.e. monthly records of temperature and days of sunshine, epidemiological data on infectious diseases like influenza etc.)

Conclusion: The impact of SoB on schizotypy (and more generally on psychotic disorders) might be restricted to some specific conditions (latitude, climate). As a better understanding of the effective factors behind the association might advance our knowledge of risk factors for psychotic disorders the study of the association between SoB and schizotypy (and also of the factors that influence it) is worth pursuing.

Declarations of interest

None

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SupplementaryMaterialV3.docx

Season of birth and schizotypy in a sample of undergraduate students

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Methods

Subjects

Data recorded

Statistical analyses

Descriptive statistics

Univariable analyses

Multivariable analyses

Results

Descriptive statistics

Main analyses (total SPQ score, whole sample and February-March as risk period )

Univariable analyses

Multivariable analyses

Secondary analyses

Analyses of the SPQ dimensions (with the risk period of birth)

Analysis of the SPQ total score using different definitions of the SoB

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1