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Keywords:

  • epidemiology;
  • schizophrenia;
  • season of birth;
  • seasonality

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Aims

The aim of this study was to examine the correlations of birth seasonality in schizophrenia, considering influences of gender and income status.

Methods

The sample consisted of 1 000 000 people in the general population randomly selected from the Taiwan National Health Insurance Research Database. Data for the birth-year period 1950–1989 were extracted for analysis (n = 631 911; 306 194 male, 325 717 female). Subjects with schizophrenia (2796 male, 2251 female) were compared with the general population. Subgroups divided by birth-year periods (10-year interval), gender, and income status (low, medium, high) were analyzed using both the Walter and Elwood seasonality and chi-squared tests.

Results

The winter/spring birth excess in schizophrenia was 5.3% when compared with the general population. There was a statistically significant excess in winter/spring births than summer/autumn births inschizophrenia patients (relative risk [RR], 1.12; 95% confidence interval [CI]: 1.06–1.18). This winter/spring birth excess in schizophrenia was observed only in female subjects (RR, 1.20; 95%CI: 1.10–1.30), not in male subjects (RR, 1.03; 95%CI: 0.98–1.14), in all subgroups of income status, but was most pronounced in the low income subgroup (RR, 1.20, 1.09, 1.13; 95% CI: 1.05-1.37, 1.01–1.17, 1.02–1.25 for low, medium, and high income status, respectively).

Conclusion

A gender difference with female predominance of the effect of birth seasonality in schizophrenia, and a more pronounced effect in low income status were noted.

BIRTH SEASONALITY IN schizophrenia generally refers to the higher number of winter and spring births among individuals who later develop schizophrenia. Many studies have reported this effect among schizophrenia patients. Review articles have concluded that among schizophrenia patients worldwide there are 5–8% more births during the winter and spring months compared with the general population.[1] The peak months of schizophrenia births are between January and April in the northern hemisphere, and between July and September in the southern hemisphere.[1-4] Possible explanations include season-specific factors, such as infectious agents, vitamin D deficiency, physical and psychological development affected by sunlight, nutritional deficiencies, external toxins, procreational habits, prenatal and perinatal insults, a decreased biological capability for those persons to survive season-specific insults, and numerous meteorological influences.[1, 5-8]

The latitude effect for birth seasonality in schizophrenia has also been described. This refers to birth seasonality in schizophrenia varying by latitude bands, with a small but significant positive correlation between effects of birth seasonality and latitude in the northern hemisphere. There is a more significant seasonality effect in high-latitude regions, but no such effect has been observed in equatorial regions, where seasons are absent.[2, 9] Therefore, effects of seasonality in low-latitude areas are thought to vary between significant and absent. Nevertheless, most seasonality studies were conducted in high-latitude regions and in Western countries. Few studies have been conducted in low-latitude regions or in Asian countries, where findings have been inconsistent: some studies reported a slight increase in schizophrenia births in the winter/spring months, but others found no such increase.[10, 11]

There have been attempts, however, to correlate effects of birth seasonality in schizophrenia with gender, social class, urbanicity, race, family history, marital status, chronicity, severity, subtypes, as well as neurological, neuropsychological, and neurophysiological features.[1, 12, 13] Perhaps due to the small sample size used in many of these correlation studies, the results have not been conclusive. The purpose of this study was to examine the birth seasonality of schizophrenia in Taiwan, a low-latitude, subtropical area (latitude 25°03′N), based on a large, population-based sample. Additionally, we classified the schizophrenia patients according to gender and income in order to identify any correlations with birth seasonality.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Data source

All citizens and foreign residents who have lived in Taiwan for at least 4 months are required to be insured by the National Health Insurance (NHI) program. The NHI program is a single-payer compulsory social insurance plan that centralizes the disbursement of health-care funds, and the population coverage had reached 99% by the end of 2003. The National Health Research Institute (NHRI) routinely transfers health insurance claims data from the NHI bureau and makes the National Health Insurance Research Database (NHIRD) available for research purposes.

Subjects

The study was based on the Longitudinal Health Insurance Database 2005 (LHID2005). The LHID2005 contains all the original claim data from 1 000 000 beneficiaries, randomly sampled from the year 2005 Registry for Beneficiaries (ID) of the NHIRD. There is no significant difference in the gender or age distribution or average insured payroll-related amount between the patients in the LHID2005 and the original NHIRD.[14] Data for the birth-year period 1950–1989 were extracted for analysis in this study. Subjects with schizophrenia were identified according to the following steps. First, subjects with disease codes 295.x (other than those only with 295.7) of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) as the primary diagnosis during 2000–2005 were included. Second, for subjects who had been included in the first step but had other previous diagnoses of 290.x or 294.x (dementia and other organic psychotic condition) were excluded. Finally, subjects who had been included in the first step but had a diagnosis of 296.x were included only when the last diagnoses were 295.x. A total of 631 911 subjects (306 194 male, 325 717 female) aged from 21 to 60 years were included in this study, among whom 5047 were schizophrenia patients (2796 men, 2251 women).

Seasons and months

The definition of the four seasons in the northern hemisphere used in the literature was adopted, as follows: spring, March–May; summer, June–August; autumn, September–November; and winter, December–February.

Subgroups

To examine correlations of effects of birth seasonality in schizophrenia with age, gender, and income status, both the schizophrenia patients and the general population were divided into subgroups by age, gender, and income status. Age subgroups were divided into 10-year birth-year periods as follows: 1950–59, 1960–69, 1970–79, and 1980–89. Each of these subgroups was further divided by gender. Income status subgroups included low, medium, and high yearly income (USD 0–1007, USD 1007–20 000, and USD >20 000).

Statistical analysis

We used SAS (SAS System for Windows, version 9.2; SAS Institute, Cary, NC, USA) to perform all analyses in this study. To assess differences between schizophrenia patients and the general population in terms of effects of birth seasonality and birth month variation in gender and income status, we used both the Walter and Elwood seasonality[15] and chi-squared tests. Two-sided P < 0.05 was considered statistically significant in this study. Relative risk (RR) and its 95% confidence interval (CI) for schizophrenia were estimated for each month (using the month with the lowest risk as reference) to compare the risk of winter/spring births to summer/autumn births.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Effects of birth seasonality in schizophrenia

Figure 1 plots the monthly birth distributions of births for the schizophrenia patients and the general population. There was an obvious peak in February and a trough from June to July, but in the general population, no such patterns can be observed. The distributions across the 12 months were significantly different between the schizophrenia patients and the general population (Walter and Elwood test, P = 0.0001; Fig. 1).

figure

Figure 1. Proportion of births vs month (1950–89) for (image) the general population and (image) schizophrenia patients.

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Table 1 lists the monthly distributions of births in schizophrenia patients and in the general population for the whole sample and for each of the four birth-year subgroups (1950–59, 1960–69, 1970–79, 1980–89). The differences between the observed and expected numbers of patients are expressed as a percentage of excess or deficit for each month. The expected number of schizophrenia births was calculated based on the expected proportion observed in the general population. An excess of schizophrenia births in winter/spring of 5.3% and a deficit in summer/autumn of 5.6% in comparison with the general population was observed. A comparison of the winter/spring schizophrenia birth excess with the summer/autumn deficit showed that the magnitude of the deficit births was greater than that of the excess births (T test, P = 0.007, SE = 2.66).

Table 1. Distribution of births (1950–1989)
Period/ageJanFebMarAprMayJunJulAugSepOctNovDecTotal
Schizophrenia             
Birth-year period/age
1950–59/50–591131341009094918485871131021291 222
1960–69/40–491611571401181261061101251361551551421 632
1970–79/30–39148144131114104931041331301121221291 464
1980–89/20–29647854624865576361645955729
Total (observed number)4865134253833723553554064144444384555 047
Expected number445442411384374382385428439461458438 
Percentage excess or deficit (+/−) (%)+9.31+16.01+3.39−0.14−0.64−6.99−7.80−5.09−5.60−3.72−4.44+3.98 
All births             
Birth-year period/age
1950–59/50–5913 53313 58412 23811 08710 37010 25410 04111 83312 52512 97713 48412 859144 784
1960–69/40–4915 14115 55514 33513 11112 60812 67312 26014 19214 06115 33715 35314 991169 617
1970–79/30–3914 79014 30413 63213 05313 15213 57414 00015 10015 41116 19615 82814 965174 005
1980–89/20–2912 20311 87911 26110 81810 74811 35111 90712 43712 95913 23212 74011 971143 506
Total55 66755 32251 46648 06946 87847 85248 20853 56254 95657 74257 40554 786631 911

To confirm the effects of birth seasonality, we performed chi-squared analysis to examine the risks of each month relative to the month with fewest schizophrenia births. We used July as the reference month and calculated the RR (95%CI) for each month. The risks were significantly greater in January and February, with RR of 1.19 (95%CI: 1.03–1.36) and 1.26 (95%CI: 1.10–1.44) separately. When the RR was compared by seasons, there was a significant excess in winter/spring schizophrenia births compared to summer/autumn (RR, 1.12; 95%CI: 1.06–1.18; P < 0.0001).

Gender and effects of birth seasonality

Effects of birth seasonality in schizophrenia births were observed only in female subjects but not in male subjects (P < 0.00001 in women but non-significant in men; Walter and Elwood test). Excess winter/spring schizophrenia births were observed only in female subjects (RR, 1.20; 95%CI: 1.10–1.30) but not in male subjects (RR, 1.03; 95%CI: 0.98–1.14; P = 0.15). Also, the monthly distributions of schizophrenia births were significantly different between male and female subjects. May, August, and November showed a marked excess of male schizophrenia births, while December had a marked deficit. The birth-month-specific birth rate of schizophrenia in both genders, and the male to female ratio of monthly births in schizophrenia and the general population are plotted in Figure 2. The pattern of male to female birth ratio in schizophrenia patients fluctuated greatly compared to that in the general population, which was almost identical throughout the year (Fig. 2).

figure

Figure 2. Gender-specific prevalence and male : female ratio of prevalence vs birth month for (image) schizophrenia patients and (image) the general population.

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Income and effects of birth seasonality

Effects of birth seasonality of schizophrenia were noted in all income status (data not shown). Low income status was associated with the most significant effects of birth seasonality, followed by medium and high income status (P = 0.002, 0.006, and 0.047 for low, medium, and high income status, respectively; Walter and Elwood test). The winter/spring to summer/autumn RR in the three groups were 1.20 (95%CI: 1.05–1.37), 1.09 (95%CI: 1.01–1.17), and 1.13 (95%CI: 1.02–1.25), respectively.

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This study identified a statistically significant winter/spring schizophrenia birth excess of 5.3% in a population-based sample in a low-latitude region (Taiwan). This result was consistent with findings in previous large studies that showed that schizophrenia patients worldwide have a 5–8% excess of winter/spring births compared with that of the general population.[1]

To better understand the effect of latitude on schizophrenia birth excess/deficit, we compared the present findings to the results of a systematic review and meta-analysis of schizophrenia birth studies conducted in the northern hemisphere.[2] That study indicated an existence of latitude effects for the disorder. There was a positive correlation between the odds ratios for the winter/spring births to summer/autumn births in schizophrenia and latitude (a nearly 0.02% increase in odds per 10° increase in latitude), and the seasonality trend in schizophrenia births varied according to the latitude band: the trough gradually shifted from March to September and the magnitude of monthly variation was found to fluctuate more markedly as latitude increased. We matched the RR of winter/spring to summer/autumn births from the present study to the latitude–RR map obtained in the systematic review.[2] On the x-coordinate latitude 25°03’ where Taiwan lies, the RR 1.12 (95%CI: 1.06–1.18) found in the present study was moderately consistent with this. In contrast, the within-year variations were based on the ratio of observed number to the expected number of schizophrenia births in each month. In the present study, there was a peak of 1.16 in February and a trough of 0.92–0.93 from June to July. These findings correspond well to those of the systematic review, which suggests that the trough appears to gradually shift from spring towards autumn as latitude bands increase (Fig. 3).[2] The present findings were drawn from data for a region with less temperature variation than the countries studied in the aforementioned meta-analysis, and they generally matched well with the two corresponding figures in that study. Therefore, the present findings may help shed light on the roles of latitude and the effects of birth seasonality in schizophrenia.

figure

Figure 3. Latitude vs ratio of observed/expected number of schizophrenia births per month. Reproduced with permission from McGrath J and Oxford University Press. Davies G, Welham J, Chant D, Torrey EF, McGrath J. A systematic review and meta-analysis of Northern Hemisphere season of birth studies in schizophrenia. Schizophr Bull. 2003; 29: 587–93.

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Few studies have examined gender differences in effects of birth seasonality in schizophrenia, and the findings are inconsistent.[1] Some studies suggested that the seasonality effect was confined only to one gender, but some did not find any gender differences. A Japanese study found a significant seasonality effect for schizophrenia in male subjects, but not in female subjects.[16] In the present study, a seasonality effect for schizophrenia was found only in female, but not in male subjects. Many factors have been shown to be associated with gender differences in schizophrenia, such as incidence, prevalence, disease course, positive, negative and affective symptoms, premorbid function, social function, neuropsychological function, brain structure, outcomes, and family history.[17-26] Such gender differences may imply distinct pathophysiological processes. Additionally, the present results also produced several other interesting findings with regard to gender differences. Overall, the annual birth rate of schizophrenia in the present study was 0.8% (male, 0.91%; female, 0.69%), male subjects 1.32-fold greater than female subjects (Table 2). The overall birth rate was different from rates reported in previous Taiwanese epidemiological studies,[27, 28] but similar to that published by the World Health Organization (WHO), which showed that schizophrenia affects approximately 0.7% of the adult population. Whether schizophrenia is more prevalent in male or female subjects, however, remains controversial. In the WHO Ten Country Study, male subjects tended to have a higher incidence of schizophrenia in the European countries, while elsewhere a gender difference was not observed.[29] A recent review of gender difference in schizophrenia incidence showed a male : female ratio of approximately 1.4,[20] and another study among Han Chinese subjects found a male : female ratio of 1.22 for schizophrenia prevalence,[25] which is close to the present finding of 1.32.

Table 2. Birth rate of schizophrenia in Taiwan (2005)
Birth year/age(years)Birth rate (%)
TotalMaleFemaleM/F ratio
1950–59/50–590.840.830.860.96
1960–69/40–490.961.090.831.32
1970–79/30–390.841.000.691.46
1980–89/20–290.510.650.391.65
Total0.800.910.691.32

In terms of gender differences in each birth-year period subgroup, the male : female ratios were 0.96, 1.32, 1.46, and 1.65 in the 1950–59, 1960–69, 1970–79 and 1980–89 subgroups, respectively (Table 2). Male birth rate was predominant in all subgroups except for the 1950–59 subgroup (50–59 years old). The results suggest that male schizophrenia patients have an earlier onset. As for the exception of the 1950–59 subgroup, possible explanations include (i) a second peak of the disease onset in middle age for women; (ii) a longer lifespan in women; and (iii) an underreported rate for male subjects in the past years because of the traditionally conservative society.

There were only few studies examining the correlation between social class and effects of birth seasonality in schizophrenia, with inconclusive results;[30-32] to our knowledge, however, the correlation between effects of birth seasonality in schizophrenia and income status has never been investigated. In the present study, all income status subgroups had seasonal variations in schizophrenia births, but there was a more pronounced effect in the lower income subgroup. This finding might provide some insights for the virus hypothesis of effects of seasonality in schizophrenia. People from a low socioeconomic background might be more ignorant of disease prevention and might be more vulnerable to infection during the prenatal and postnatal periods. Further analysis, however, to consider confounding such as educational and occupational status, is necessary to test this possibility.

The primary advantages of the present database include a purer ethnic composition with few immigrants and emigrants; accuracy of birth data and subject count; a compatible control group from the general population; large sample size; and geographic centralization. This study made up a deficiency in previous seasonality studies at lower latitude. It also provides further evidence in issues of gender and socioeconomic effects, which have not been extensively studied in the seasonality literature. Some limitations should also be considered. Because Taiwan's NHI program was launched in 1995 and more comprehensive data were collected after 2000, the available data did not allow for the applications for analysis of period effect, cohort effect, and possible shifts of birth seasonality over time.

Conclusion

The present study has confirmed the well-replicated finding of a winter–spring excess that varies with latitude, and provides comprehensive subgroup analyses with gender and socioeconomic status. A significant winter/spring birth excess in schizophrenia at low latitude, a gender difference with regard to female predominance, and a more pronounced effect in low income status were found in this study. Further follow-up studies are required to explore the underlying mechanisms, combining analyses of the potential risk variables in birth seasonality and development of schizophrenia, such as vitamin D deficiency, prenatal and perinatal insults, viral exposure, meteorological influences, social factors, and so on. These analyses can be done using data for temperature, rainfall, sunlight, ultraviolet, ozone, pregnancy and birth complications, and vaccine records. If we identify these underlying risks, clinical implications of primary prevention in schizophrenia may be practicable in the future.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This study was based in part on data from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health and managed by the National Health Research Institutes, Taiwan. The study was funded by Taichung Veteran General Hospital, Taichung Taiwan (Grant number TCVGH-100-3105). We acknowledge the help of the Biostatistics Task Force of Taichung Veterans General Hospital, Taiwan. All authors declare no conflicts of interest.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References