#
2002 Martin Dunitz Ltd
International Journal of Psychiatry in Clinical Practice 2002 Volume 6 Pages 147 ± 153
147
Relative humidity and affective disorders EMAD SALIB1 AND NICOLA SHARP2 1
Int J Psych Clin Pract Downloaded from informahealthcare.com by Freie Universitaet Berlin on 11/12/14 For personal use only.
Hollins Park Hospital, Warrington and 2 Liverpool University; Specialist Registrar in Psychiatry, Liverpool
Looking at specific weather parameters over a period of time prior to hospital admissions may provide evidence of a link between weather conditions and some psychiatric conditions such as affective disorders. We examined the association between relative humidity (as well as other parameters such as sunshine hours, diurnal variations in temperature and rainfall) and psychiatric admissions in North Cheshire, UK. INTRODUCTION:
The daily numbers of all psychiatric admissions in North Cheshire in a specified year were analysed in relation to meteorological data, which were measured at the meteorological office nearest to the study population. METHOD:
We found a significant inverse relationship (with time lag) between admissions for affective disorders and relative humidity in the week preceding admission. Changes in diurnal variations in temperature, sunshine hours and rainfall a few days before admission were also noted, but the findings did not achieve statistical significance for any diagnostic category. RESULTS:
Correspondence Address Emad Salib, MSc, MRCPI, FRCPsych, Hollins Park Hospital, Warrington, WA2 8W1 Tel: 01925 664123 Fax: 01925 664145
Received 7 February 2001; revised 12 November 2001; accepted for publication 18 December 2001
The effect of weather parameters on mental health is likely to be influenced by other seasonal factors, as well as non-climatic factors, predominantly social, that may have contributed to the study findings. Psychiatric admissions reflect the behaviour of patients, carers and medical professionals. The complexity of this behaviour and the day-ofthe-week periodicity may have confounded variations associated with the weather. (Int J Psych Clin Pract 2002; 6: 147 ± 153) CONCLUSION:
Keywords weather parameters affective disorders relative humidity
INTRODUCTION
T
he influence of weather conditions on general wellbeing, psychiatric symptoms, hospital admissions and suicide has been reported in several studies. Hot dry winds (such as the foehn in central Europe, the Santa Ana of Southern California, and the sharav in the Near East) have been implicated as the cause of various symptoms such as depression, lassitude, irritability, nausea and migraine in exposed populations.1 Some psychiatric symptoms have been attributed to the presence of high concentrations of small positive ions in the atmosphere, and studies of subjects exposed to the sharav have shown a marked increase of urinary serotonin at the time of the symptoms. 2 Inhalation of air containing large numbers of
meteorological effects psychiatric admissions
small negative ions was said to reduce the symptoms. Atmospheric humidity is one of the factors influencing the production and availability of small ions, which in some cases are generated by shearing of the water droplets. A negative correlation between relative humidity and hospital 3 admissions for mania has also been reported. Seasonal variations have been reported in a number of measures of serotonin function, including CSF 5H1 AA, platelet 5HT uptake, platelet 3H imipramine binding, plasma L-tryptophan, plasma 5HT and prolactin, with general levels being low in spring and peak in summer/ 4 autumn. Depressive symptoms, measured using the Zung self-rating depression score, were found to vary with the season in patients suffering from depressive disorder, with a peak in April and May and a trough in August and
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5
6
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September. Both Maes and colleagues and Salib and Gray reported an increase in the incidence of suicide in fine weather conditions and with more hours of sunshine. Carney and colleagues suggested that increasing amounts of sunlight facilitate affective relapses, and that patients with an affective predisposition are more sensitive to the effects of light on decreasing nocturnal melatonin levels.8 These authors also reported that admission rates for mania in Galway were higher in sunnier months and in months with days of greater than average length. In Scotland, Myers and Davies reported that admissions for mania correlated with the current month’s mean daily temperature and the previous month’s mean day length and mean daily sunshine hours.9 Sayer and colleagues reported significant associations in New Zealand between mean daily tempera10 ture and day length and number of admissions for mania. Parker and Walter found seasonal variations in admission for manic depressive psychosis depression in New South Wales (with a peak in August [winter]) and for reactive depressive psychosis (peak in November), but not for 11 neurotic depression. Eastwood and Stiasny reported a seasonal variation in admission frequency for neurotic depression (autumn peak) and endogenous depression (spring peak).1 2 Variations in the occupancy of psychiatric beds have been found to be related to composite effects of weather 13 variables in the preceding weeks. Temporal variations in human psychological and physiological characteristics may be influenced by composite effects of past and present atmospheric activity.1 4 An inverse relationship between dementia admissions and relative humidity has been 15 reported, which was attributed to possible affective changes, in either patients or their carers, leading to admissions. The admissions rate of bipolar depressed patients was found to be negatively correlated with the average monthly hours of daylight with an increased 16 admission rate in winter months. Based on the available literature regarding a possible link between the incidence of some psychiatric disorders, including suicide, and weather conditions, we believe that looking at specific weather parameters over a period of time prior to hospital admissions may provide further evidence of such a link. The relationship, if any, may be of interest to clinicians and researchers in this field. In this study we explored the association between relative humidity, as well as other weather parameters such as sunshine hours, diurnal variations in temperature and rainfall, and psychiatric admissions in North Cheshire.
METHOD HOSPITAL ADMISSIONS Data were obtained from the Information Department of Winwick Hospital on all dementia admissions from within North Cheshire on each day of 1993. North
Cheshire has a population of 350 000 in two districts, Warrington and Halton. Winwick Hospital, a large psychiatric hospital which was closed two years ago, accommodated all psychiatric inpatients from the two districts during 1993. Details of total daily admissions for all ICD-9 diagnostic categories (re-coded into five groups) were collected from the computerized hospital records. They included: 1. affective disorders (ICD-9 296.0-296.9), re-coded in the study as ICD-9 296; 2. schizophrenia, paranoid disorders and related psychosis (ICD-9 295, ICD-9 297 and ICD-9 298); 3. alcohol- and drug-related (ICD-9 291, ICD-9 292, ICD-9 304 and ICD-9 305); 4. behavioural, reactive and mixed neurosis (ICD-9 300, ICD-9 310, ICD-9 308, ICD-9 309, ICD-9 311 and ICD-9 312). The fifth group, covering dementia and other organic aetiology (ICD-9 290, ICD-9 294, ICD9 293 and ICD-9 310), was excluded.
WEATHER DATA The nearest weather station to North Cheshire is at Manchester Airport, 30 miles from the study population. The Meteorological Office collects daily information on maximum and minimum temperatures, total rainfall, hours of sunshine and maximum relative humidity. Each daily figure is the mean of eight 3-hourly recordings. Values were obtained for each day of the year 1993 (excluding days of admission for dementia and other organic disorders, as above).
STATISTICAL METHOD The data for each day of the year were coded for the day of the week, day of the month and month of the year. The proportion of all psychiatric admissions for each diagnostic category (other than organic) of total hospital admissions provided a rough test as to whether there was inequality between day-of-the-week variations. After assessing a dayof-the-week variation in admissions, the data were therefore considered in respect of 13 28-day periods instead of calendar months (which contain an uneven number of days 3 of the week). Daily mean values of humidity and hours of sunshine, and daily numbers of admissions for different diagnoses as defined in ICD-9, were used as units in the analysis. The cross-correlation between pairs of series of data was computed. This involved calculating the correlation between a pair of variables with one of the series of 365 observations lagged at 0, 1, 2, etc, days behind the other. Thus it represents the correlation of all admissions with the weather variable in the previous 14 days. A similar analysis was carried out for each diagnostic category. The ``correlogram’’ is the graph of the cross-correlations thus obtained, plotted in sequence for the positive and negative values 1, 2, 3, etc, by days. Statistical analysis was carried out using SPSS (version 8).
Relative humidity and affective disorders
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RESULTS During 1993 there was a total of 2070 admissions to Winwick Hospital, representing all psychiatric admissions in North Cheshire. The mean age for the total sample was 50 years (sd 20), ranging from 18 to 101 (median 43 years); 989 males (47%) and 1081 females (53%). The mean monthly number of admissions for all psychiatric diagnoses, excluding dementia and other organic aetiology, was 150, with a mean age of 40 years (sd 16), ranging from 18 to 90 years. Of the total admissions, 266 (13%) were cases of dementia and other organic aetiology (ICD-9 290, ICD-9, ICD-9 294, ICD-9 293 and ICD-9 310). These were excluded, leaving 1804 admissions, divided into: 508 (28%) affective disorders (ICD-9 296.0-296.9); 263 (15%) schizophrenia, paranoid disorders and related psychosis (ICD-9 295, ICD-9 297 and ICD-9 298); 544 (30%) alcohol- and drug-related (ICD-9 291, ICD-9 292, ICD-9 304 and ICD-9 305); 301 (17%) behavioural, reactive and mixed neurosis (ICD-9 300, ICD9 310, ICD-9 308, ICD-9 309, ICD-9 311 and ICD-9 312); and 188 (10%) cases with other, or no, diagnosis. The reason for the rather high proportion of alcohol- and drug-related cases was the fact that at that time the hospital housed a subregional unit serving a much larger population. Table 1 shows the mean daily values of all available weather parameters for each month and the numbers of psychiatric admissions. Rough inspection of the table may suggest that the proportion of admissions appeared to be slightly lower when there was higher relative humidity, lower diurnal variations in daily temperature and fewer hours of sunshine. Daily hospital admissions provided a rough test as to whether there was inequality between dayof-the-week variations that could confound the study’s positive or negative findings. Table 2 shows the pattern of psychiatric admissions for each day of the week. There was a significant variation in the number of admissions for all diagnoses (P50.05), with 36% of
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admissions being reported during weekends. No information was available as to the immediate causes of the admissions. Table 3 shows monthly admissions for the main diagnostic categories, grouped under five main headings: affective disorders, schizophrenia and related disorders, behavioural and reactive states, alcohol and drugs, and unspecified or other diagnoses. Table 4 shows the Spearman rank correlations for the daily mean values of weather parameters and psychiatric admissions for male and female patients on the same day, and with average values during the 2 weeks preceding admission. A small but significant negative correlation was found for female patients only within the 2 weeks preceding admission, maximum on days 5, 6 and 7 before admission (r70.262, P50.05). Table 5 is a summary of the cross-correlation between daily admissions and weather parameters during the 14 days preceding the admission date. For comparison, correlation with weather parameters on the same admission day was also calculated. A small but significant correlation was found between the number of psychiatric admissions and the relative humidity. The ``cross-correlogram’’ was calculated by computing the correlation between the number of admissions on a given day and the previous days’ mean weather parameters. Table 2 Psychiatric admissions on different days of the week in 1993 Day
No. of admissions
% of total
Mon Tue Wed Thu Fri Sat Sun
165 152 315 241 283 326 322
9 8 17 13 16 18 19
Table 1 Weather conditions and psychiatric admissions (mean values) during 1993
Month
Psychiatric admissions
Max. temp. 8C
Min. temp. 8C
Diurnal variation, 8C temp.
Daily mean humidity, %
Rainfall, mm
Sunshine, h
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
173 163 157 128 137 146 170 148 143 156 153 130
6.95 7.50 10.89 12.74 16.39 18.54 20.03 19.23 17.54 12.01 9.34 7.23
0.70 1.15 4.20 5.16 7.73 9.93 12.6 10.95 9.95 6.13 3.1 1.57
6.25 6.35 6.69 7.57 8.67 8.61 7.44 8.28 7.59 5.93 6.24 5.66
94.1 94.2 91.4 90.5 90.6 92.3 93.9 93.7 92.3 92.9 93.9 95.7
1.81 1.22 1.85 1.87 1.39 1.67 2.56 2.02 2.23 2.93 2.13 2.83
2.13 2.17 2.69 4.2 5.64 6.13 4.84 5.55 4.36 2.62 1.79 1.46
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Table 3 Monthly psychiatric admissions in North Cheshire in 1993, by diagnostic categories.
Table 4 Correlation between weather parameters and number of psychiatric admissions.
Month
Affective disorders
Jan Feb Mar April May June July Aug Sept Oct Nov Dec
37 38 48 60 55 45 42 36 35 40 34 34
Numbers of admissions Behavioural Schizophrenia and reactive
Weather
26 22 21 17 29 21 22 20 21 24 20 20
42 24 20 25 21 27 30 16 25 28 21 22
Male
P
Correlation coefficent Min. temp. Same day Preceding 2 weeks Max. temp. Same day Preceding 2 weeks Diurnal variation in temp. Same day Preceding 2 weeks Humidity Same day Preceding 2 weeks Rainfall, mm Same day Preceding 2 weeks Sunshine, h Same day Preceding 2 weeks
Alcohol and drugs
No diagnosis
51 44 55 33 42 48 42 50 48 47 51 33
17 13 19 17 10 10 21 14 11 23 16 17
Female
P
Correlation coefficent
0.062 0.051
40.05 40.05
70.022 70.031
40.05 40.05
0.082 0.073
40.05 40.05
70.015 70.014
40.05 40.05
0.031 0.333
40.05 50.05*
0.016 0.034
40.05 50.05*
70.013 70.103
40.05 50.05*
70.063 70.262
40.05 50.05*
70.012 70.025
40.05 40.05
70.086 70.055
40.05 40.05
0.044 0.043
40.05 40.05
0.023 0.032
40.05 40.05
*Significant
Table 5 Correlation between weather parameters and numbers of daily psychiatric adult admissions in North Cheshire in 1993.
Weather parameter Max. temp. Min. temp. Dirunal variation in temp. Humidity Rainfall, mm Sunshine, h
*Significant
Same day Preceding Same day Preceding Same day Preceding Same day Preceding Same day Preceding Same day Preceding
2 weeks 2 weeks 2 weeks 2 weeks 2 weeks 2 weeks
Correlation coefficient
P
0.035 0.044 0.063 0.062 0.011 70.124 70.037 70.198 70.091 0.087 70.024 70.122
40.05 40.05 40.05 40.05 40.05 40.05 40.05 50.05* 40.05 40.05 40.05 40.05
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Cross correlation function (CCF)
0.1 0.05 0 ± 0.05 ± 0.1 ± 0.15 ± 0.2
1
2
3
4
5
6
8
9
10
11
12
13
14
Figure 1 C h a rt o f c r o s s-c o r re la tio n s b e tw e en p s y c h ia tric a d m iss io n s a nd r ela tiv e h u m id ity ( m a x im u m n eg a tiv e c o r re la tio n b e tw e e n p s y c h ia tr ic a d m is s io n s a nd re la tiv e h u m id ity 3 d a y s p re v io u s ly ) 96 95
40
94 30 93 20 92 10
91
2
3
4
5
6
DISCUSSION
7
8
9
10
11
12
ICD-9 296 admissions
50
90 1
0 13
Period relative humidity + % ICD-9 296 admissions
METHODOLOGICAL LIMITATIONS
Dotted lines: % affective disorders admissions
Figure 2 M e a n d a ily v a ria tio n s in r ela tiv e h u m id ity a n d a d m iss io n s c a te g o r iz e d a s IC D -9 29 6 a s a p e rc e n ta g e o f to ta l a d m iss io n s d ur in g th e 1 3 2 8 -d a y p e rio d s 100
96 95
80
94 60 93 40 92 20
91 90
1
2
3
4
5
6
7
8
9
10
11
12
13
ICD-9 296 admissions
1. Methodological problems encountered in this study are mainly related to the reliance on static weather codes to describe the weather changes in relation to psychiatric admissions. 2. The catchment area of the hospital is geographically distant from the point at which weather data was collected. The nearest meteorological office where weather data are recorded is about 30 miles away from the study population, which could have reduced the degree of accuracy of weather data in relation to this population. Also, residents of North Cheshire may perhaps be less exposed to weather changes than some other populations living under different conditions. 3. The ICD-9 diagnosis used in 1993 was included regardless of whether it was recorded as a primary or secondary diagnosis, to ensure that no admissions were missed. All subtypes of affective disorders (ICD-9 296.0-296.9) were re-coded into a single diagnostic category (ICD-9 296). This may have affected the accuracy of the data in some cases, and may have detracted from more subtle possible associations. 4. ICD coding was only included on the termination of an inpatient episode; some patients (albeit a very small number), who remained in hospital, were not included in the data collection. 5. The accuracy of ICD-9 coding by various doctors, and also the re-coding of other diagnoses into fewer but
7
Lag (days)
Relative humidity
A separate analysis was carried out for the different diagnoses. Figure 1 shows that the maximum negative correlation between psychiatric admissions and relative humidity occurred with a lag of 3 days, gradually increasing till admission day. A negative correlation was also found between psychiatric admissions and diurnal variation in temperature and hours of sunshine, but a positive correlation with the amount of rainfall; however, this was not significant (Table 5). Weather conditions on the same admission day did not appear to have any association with the number of psychiatric admissions. Because of the day-of-the-week variation, the patterns of relative humidity and number of psychiatric admissions for all affective disorders (ICD-9 296), and all psychiatric diagnoses other than affective disorders, were plotted on 3 the basis of 13 28-day periods. Figure 2 shows the negative relationship between relative humidity and number of admissions for affective disorders, particularly between periods 3 and 6 (spring). Figure 3 shows no such association between relative humidity and daily admissions for other than affective disorders. Figures 2 and 3 suggest that the small but significant correlation between the number of psychiatric admissions and the relative humidity is mainly confined to the affective disorders.
Relative humidity
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Relative humidity and affective disorders
0
Period relative humidity + all other diagnoses Dotted lines: all other diagnoses excluding ICD-9 296
Figure 3 M e a n d aily relativ e h u m id ity a nd nu m be r o f a dm issio ns fo r a ll o th e r IC D -9 d ia g n o se s, e xc lu ding a ffec tive d iso rd e rs ( IC D -9 2 96 ) , d u rin g th e 1 3 28 -da y p er io d s
larger groups, may have influenced some of the negative (no association) findings, leading to statistical Type II error. 6. Some significant correlations may have been the product of multiple testing, which could limit the findings and the inferential value of the study.
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INTERPRETATION OF THE FINDINGS The study found a statistically significant time-lagged inverse relationship between the admissions for affective disorders and the relative humidity in the week preceding admission. The inverse relationship between relative humidity and admissions for affective disorders was consistent when the data were expressed in terms of monthly variation, or as 13 28-day periods (removing the day-of-the-week effect), or when admissions for affective disorders were shown as a percentage of the total hospital admissions. Many social factors influence the availability of hospital beds, or accessibility of persons who can arrange admission, although in some instances the condition would have been severe enough to over-ride such factors. In virtually all cases, a time lapse between the onset of a psychiatric disorder and admission would be expected. This time lapse is likely to be least for psychotic disorders (including affective disorders) because of their relative severity. In many cases admission will correspond with a worsening of symptoms, although some other factors may be important in determining whether or not the patient is admitted, including social factors such as availability of support at home, provision of community mental health services and the number of beds available in the admitting hospital. When a hospital fills most or all of its psychiatric beds, a negative feedback effect will occur, i.e. admission rates will fall and the threshold for admission will rise. Any changes in mental state in response to variations in the weather may vary with different populations, predictability of the climate, occupation, the amount of exposure of the population to the weather, etc. Social factors may also vary for individuals with the time of year, e.g. variation in workload of doctors and rate of referrals. In psychiatry, it is not uncommon that a patient may be admitted because of the mental health of a distressed carer, not because of the worsening of an already existing psychiatric condition. In other instances, sudden changes in behaviour, aggression, mood swings and other affective features may determine the timing of admission. The inverse relationship between admissions for some 3 affective (manic) disorders and relative humidity is not dissimilar to the results of this study. A large twin study has shown seasonal variations in mood and behaviour to have a genetic contribution,1 7 which would apply both to psychiatric patients and their carers. The inverse relationship between dementia admissions and relative humidity was attributed to possible affective changes in either patients or their carers which might lead to admissions.1 5
CONCLUSION The effect of weather parameters on mental health is likely to be influenced by seasonal as well as non-climatic factors
(predominantly social), which may have contributed to the study findings. Psychiatric admissions reflect the behaviour of patients, carers and medical professionals. The complexity of this behaviour and the day-of-the-week periodicity may have confounded variations associated with the weather. Although the study provides somewhat interesting but largely inconclusive findings, we hope that future studies of this intriguing possibility, using longer time series to control seasonal effects, may overcome the various methodological limitations we encountered. One approach would be to examine the patterns over several years, thus excluding the effects of using data for a single year only, which might have been the result of some particular idiosyncracy. It may also be interesting for future studies to look at a part of the world where daily weather variations are much more pronounced than in the climatically equable, Atlantic weather zone of Cheshire, e.g. the eastern seaboard of the USA, or a mid-continental location. Annual seasonality of admissions and day-of-theweek effects should be adjusted for, while attempting to interpret subtle daily weather variations.
ACKNOWLEDGEMENTS The authors are grateful to Miss Bernadet Hayes, Chief Librarian, Hollis Park and Miss Kate Spencer (Beckett Unit) for their help with this paper. The authors would also like to thank Mrs Janet Davies (Pfizer) for her support.
KEY POINTS . The study reports a time-lagged inverse relationship between hospital admissions for affective disorders and relative humidity in the week preceding admission. The undetermined study power may limit its inferential value . It is possible that the ``no association’’ between weather conditions and other psychiatric diagnoses may have been the result of a Type II error, due to the small number of admissions . The reliance on static weather codes and the distance of the meteorological office from the study population could have influenced the study findings . The study highlights that for affective disorders, hospital admissions may reflect the behaviour of patients, carers and medical professionals . The complexity of this behaviour and the day-ofthe-week periodicity may have confounded variations associated with the weather. Longer time series are needed in order to control seasonal effects
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REFERENCES 1. Krueger AP, Reed EJ (1976) Biological impact of small air ions. Science 193: 1209 ± 13. 2. Sluman FG, Levy D, Peeifer Y et al (1974) Air ionometry of hot, dry desert winds (Sharav) and treatment with air ions of weather-sensitive subjects. Int J Biometeorol 18: 313 ± 8. 3. Mawson D, Smith A (1981) Relative humidity and manic admissions in the London area. Br J Psychiatry 138: 134 ± 8. 4. Brewerton TD (1989) Seasonal variation of serotonin function in humans. Research and clinical implications. Ann Clin Psychiatry 1: 153 ± 64. 5. Maes M, Meltzer HY, Suy E, De Meyer F (1993) Seasonality in severity of depression: relationships to suicide and homicide occurrence. Acta Psychiatr Scand 88: 156 ± 61. 6. Maes M, De Meyer F, Thompson P et al (1994) Synchronised annual rhythm in violent suicide rate, ambient temperature and light-dark span. Acta Psychiatr Scand 90: 191 ± 6. 7. Salib E, Gray N (1997) Weather conditions and fatal self harm in North Cheshire 1989-1993. Br J Psychiatry 170: 473 ± 7. 8. Carney PA, Fitzgerald CT, Monaghan CE (1988) Influence of climate on the prevalence of mania. Br J Psychiatry 152: 820 ± 3. 9. Myers DH, Davies P (1978) The seasonal incidence of mania and its relationship to climatic variables. Psychol Med 8: 433 ± 40. 10. Sayer HK, Marshall S, Melsopp GW (1991) Mania and seasonality in the southern hemisphere. Affect Disorders 23: 151 ± 6.
11. Parker G, Walter S (1982) Seasonal variation in depressive disorders and suicidal deaths in New South Wales. Br J Psychiatry 140: 626 ± 63. 12. Eastwood MR, Stiasny S (1978) Psychiatric disorder, hospital admission and season. Arch Gen Psychiatry 35: 769 ± 71. 13. Maes M, De Meyer F, Meltzer HY (1993) The periodicities in and biometeorological relationships with bed occupancy of an acute psychiatric ward in Antwerp, Belgium. Int J Biometeorol 37: 78 ± 82. 14. Maes M, De Meyer F, Meltzer HY (1992) Seasonal variations and meteotropism in various self-rated psychological and physiological features of normal people. Int J Biometeorol 36: 195 ± 200. 15. Salib E, Sharp N (1999) Does the weather influence dementia admissions? Int J Geriatric Psychiatry 14: 925 ± 935. 16. Modai I, Kikinzon L, Valevski A (1994) Environmental factors and admission rates in patients with major psychiatric disorders. Chronobiol Internat 11: 196 ± 9. 17. Madden PAF, Heath AC, Rosenthal NE, Martin MG (1996) Seasonal changes in mood and behaviour. The role of genetic factors. Arch Gen Psychiatry 53: 47 ± 55.
2002 Martin Dunitz Ltd
International Journal of Psychiatry in Clinical Practice 2002 Volume 6 Pages 147 ± 153
147
Relative humidity and affective disorders EMAD SALIB1 AND NICOLA SHARP2 1
Int J Psych Clin Pract Downloaded from informahealthcare.com by Freie Universitaet Berlin on 11/12/14 For personal use only.
Hollins Park Hospital, Warrington and 2 Liverpool University; Specialist Registrar in Psychiatry, Liverpool
Looking at specific weather parameters over a period of time prior to hospital admissions may provide evidence of a link between weather conditions and some psychiatric conditions such as affective disorders. We examined the association between relative humidity (as well as other parameters such as sunshine hours, diurnal variations in temperature and rainfall) and psychiatric admissions in North Cheshire, UK. INTRODUCTION:
The daily numbers of all psychiatric admissions in North Cheshire in a specified year were analysed in relation to meteorological data, which were measured at the meteorological office nearest to the study population. METHOD:
We found a significant inverse relationship (with time lag) between admissions for affective disorders and relative humidity in the week preceding admission. Changes in diurnal variations in temperature, sunshine hours and rainfall a few days before admission were also noted, but the findings did not achieve statistical significance for any diagnostic category. RESULTS:
Correspondence Address Emad Salib, MSc, MRCPI, FRCPsych, Hollins Park Hospital, Warrington, WA2 8W1 Tel: 01925 664123 Fax: 01925 664145
Received 7 February 2001; revised 12 November 2001; accepted for publication 18 December 2001
The effect of weather parameters on mental health is likely to be influenced by other seasonal factors, as well as non-climatic factors, predominantly social, that may have contributed to the study findings. Psychiatric admissions reflect the behaviour of patients, carers and medical professionals. The complexity of this behaviour and the day-ofthe-week periodicity may have confounded variations associated with the weather. (Int J Psych Clin Pract 2002; 6: 147 ± 153) CONCLUSION:
Keywords weather parameters affective disorders relative humidity
INTRODUCTION
T
he influence of weather conditions on general wellbeing, psychiatric symptoms, hospital admissions and suicide has been reported in several studies. Hot dry winds (such as the foehn in central Europe, the Santa Ana of Southern California, and the sharav in the Near East) have been implicated as the cause of various symptoms such as depression, lassitude, irritability, nausea and migraine in exposed populations.1 Some psychiatric symptoms have been attributed to the presence of high concentrations of small positive ions in the atmosphere, and studies of subjects exposed to the sharav have shown a marked increase of urinary serotonin at the time of the symptoms. 2 Inhalation of air containing large numbers of
meteorological effects psychiatric admissions
small negative ions was said to reduce the symptoms. Atmospheric humidity is one of the factors influencing the production and availability of small ions, which in some cases are generated by shearing of the water droplets. A negative correlation between relative humidity and hospital 3 admissions for mania has also been reported. Seasonal variations have been reported in a number of measures of serotonin function, including CSF 5H1 AA, platelet 5HT uptake, platelet 3H imipramine binding, plasma L-tryptophan, plasma 5HT and prolactin, with general levels being low in spring and peak in summer/ 4 autumn. Depressive symptoms, measured using the Zung self-rating depression score, were found to vary with the season in patients suffering from depressive disorder, with a peak in April and May and a trough in August and
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Int J Psych Clin Pract Downloaded from informahealthcare.com by Freie Universitaet Berlin on 11/12/14 For personal use only.
5
6
7
September. Both Maes and colleagues and Salib and Gray reported an increase in the incidence of suicide in fine weather conditions and with more hours of sunshine. Carney and colleagues suggested that increasing amounts of sunlight facilitate affective relapses, and that patients with an affective predisposition are more sensitive to the effects of light on decreasing nocturnal melatonin levels.8 These authors also reported that admission rates for mania in Galway were higher in sunnier months and in months with days of greater than average length. In Scotland, Myers and Davies reported that admissions for mania correlated with the current month’s mean daily temperature and the previous month’s mean day length and mean daily sunshine hours.9 Sayer and colleagues reported significant associations in New Zealand between mean daily tempera10 ture and day length and number of admissions for mania. Parker and Walter found seasonal variations in admission for manic depressive psychosis depression in New South Wales (with a peak in August [winter]) and for reactive depressive psychosis (peak in November), but not for 11 neurotic depression. Eastwood and Stiasny reported a seasonal variation in admission frequency for neurotic depression (autumn peak) and endogenous depression (spring peak).1 2 Variations in the occupancy of psychiatric beds have been found to be related to composite effects of weather 13 variables in the preceding weeks. Temporal variations in human psychological and physiological characteristics may be influenced by composite effects of past and present atmospheric activity.1 4 An inverse relationship between dementia admissions and relative humidity has been 15 reported, which was attributed to possible affective changes, in either patients or their carers, leading to admissions. The admissions rate of bipolar depressed patients was found to be negatively correlated with the average monthly hours of daylight with an increased 16 admission rate in winter months. Based on the available literature regarding a possible link between the incidence of some psychiatric disorders, including suicide, and weather conditions, we believe that looking at specific weather parameters over a period of time prior to hospital admissions may provide further evidence of such a link. The relationship, if any, may be of interest to clinicians and researchers in this field. In this study we explored the association between relative humidity, as well as other weather parameters such as sunshine hours, diurnal variations in temperature and rainfall, and psychiatric admissions in North Cheshire.
METHOD HOSPITAL ADMISSIONS Data were obtained from the Information Department of Winwick Hospital on all dementia admissions from within North Cheshire on each day of 1993. North
Cheshire has a population of 350 000 in two districts, Warrington and Halton. Winwick Hospital, a large psychiatric hospital which was closed two years ago, accommodated all psychiatric inpatients from the two districts during 1993. Details of total daily admissions for all ICD-9 diagnostic categories (re-coded into five groups) were collected from the computerized hospital records. They included: 1. affective disorders (ICD-9 296.0-296.9), re-coded in the study as ICD-9 296; 2. schizophrenia, paranoid disorders and related psychosis (ICD-9 295, ICD-9 297 and ICD-9 298); 3. alcohol- and drug-related (ICD-9 291, ICD-9 292, ICD-9 304 and ICD-9 305); 4. behavioural, reactive and mixed neurosis (ICD-9 300, ICD-9 310, ICD-9 308, ICD-9 309, ICD-9 311 and ICD-9 312). The fifth group, covering dementia and other organic aetiology (ICD-9 290, ICD-9 294, ICD9 293 and ICD-9 310), was excluded.
WEATHER DATA The nearest weather station to North Cheshire is at Manchester Airport, 30 miles from the study population. The Meteorological Office collects daily information on maximum and minimum temperatures, total rainfall, hours of sunshine and maximum relative humidity. Each daily figure is the mean of eight 3-hourly recordings. Values were obtained for each day of the year 1993 (excluding days of admission for dementia and other organic disorders, as above).
STATISTICAL METHOD The data for each day of the year were coded for the day of the week, day of the month and month of the year. The proportion of all psychiatric admissions for each diagnostic category (other than organic) of total hospital admissions provided a rough test as to whether there was inequality between day-of-the-week variations. After assessing a dayof-the-week variation in admissions, the data were therefore considered in respect of 13 28-day periods instead of calendar months (which contain an uneven number of days 3 of the week). Daily mean values of humidity and hours of sunshine, and daily numbers of admissions for different diagnoses as defined in ICD-9, were used as units in the analysis. The cross-correlation between pairs of series of data was computed. This involved calculating the correlation between a pair of variables with one of the series of 365 observations lagged at 0, 1, 2, etc, days behind the other. Thus it represents the correlation of all admissions with the weather variable in the previous 14 days. A similar analysis was carried out for each diagnostic category. The ``correlogram’’ is the graph of the cross-correlations thus obtained, plotted in sequence for the positive and negative values 1, 2, 3, etc, by days. Statistical analysis was carried out using SPSS (version 8).
Relative humidity and affective disorders
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RESULTS During 1993 there was a total of 2070 admissions to Winwick Hospital, representing all psychiatric admissions in North Cheshire. The mean age for the total sample was 50 years (sd 20), ranging from 18 to 101 (median 43 years); 989 males (47%) and 1081 females (53%). The mean monthly number of admissions for all psychiatric diagnoses, excluding dementia and other organic aetiology, was 150, with a mean age of 40 years (sd 16), ranging from 18 to 90 years. Of the total admissions, 266 (13%) were cases of dementia and other organic aetiology (ICD-9 290, ICD-9, ICD-9 294, ICD-9 293 and ICD-9 310). These were excluded, leaving 1804 admissions, divided into: 508 (28%) affective disorders (ICD-9 296.0-296.9); 263 (15%) schizophrenia, paranoid disorders and related psychosis (ICD-9 295, ICD-9 297 and ICD-9 298); 544 (30%) alcohol- and drug-related (ICD-9 291, ICD-9 292, ICD-9 304 and ICD-9 305); 301 (17%) behavioural, reactive and mixed neurosis (ICD-9 300, ICD9 310, ICD-9 308, ICD-9 309, ICD-9 311 and ICD-9 312); and 188 (10%) cases with other, or no, diagnosis. The reason for the rather high proportion of alcohol- and drug-related cases was the fact that at that time the hospital housed a subregional unit serving a much larger population. Table 1 shows the mean daily values of all available weather parameters for each month and the numbers of psychiatric admissions. Rough inspection of the table may suggest that the proportion of admissions appeared to be slightly lower when there was higher relative humidity, lower diurnal variations in daily temperature and fewer hours of sunshine. Daily hospital admissions provided a rough test as to whether there was inequality between dayof-the-week variations that could confound the study’s positive or negative findings. Table 2 shows the pattern of psychiatric admissions for each day of the week. There was a significant variation in the number of admissions for all diagnoses (P50.05), with 36% of
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admissions being reported during weekends. No information was available as to the immediate causes of the admissions. Table 3 shows monthly admissions for the main diagnostic categories, grouped under five main headings: affective disorders, schizophrenia and related disorders, behavioural and reactive states, alcohol and drugs, and unspecified or other diagnoses. Table 4 shows the Spearman rank correlations for the daily mean values of weather parameters and psychiatric admissions for male and female patients on the same day, and with average values during the 2 weeks preceding admission. A small but significant negative correlation was found for female patients only within the 2 weeks preceding admission, maximum on days 5, 6 and 7 before admission (r70.262, P50.05). Table 5 is a summary of the cross-correlation between daily admissions and weather parameters during the 14 days preceding the admission date. For comparison, correlation with weather parameters on the same admission day was also calculated. A small but significant correlation was found between the number of psychiatric admissions and the relative humidity. The ``cross-correlogram’’ was calculated by computing the correlation between the number of admissions on a given day and the previous days’ mean weather parameters. Table 2 Psychiatric admissions on different days of the week in 1993 Day
No. of admissions
% of total
Mon Tue Wed Thu Fri Sat Sun
165 152 315 241 283 326 322
9 8 17 13 16 18 19
Table 1 Weather conditions and psychiatric admissions (mean values) during 1993
Month
Psychiatric admissions
Max. temp. 8C
Min. temp. 8C
Diurnal variation, 8C temp.
Daily mean humidity, %
Rainfall, mm
Sunshine, h
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
173 163 157 128 137 146 170 148 143 156 153 130
6.95 7.50 10.89 12.74 16.39 18.54 20.03 19.23 17.54 12.01 9.34 7.23
0.70 1.15 4.20 5.16 7.73 9.93 12.6 10.95 9.95 6.13 3.1 1.57
6.25 6.35 6.69 7.57 8.67 8.61 7.44 8.28 7.59 5.93 6.24 5.66
94.1 94.2 91.4 90.5 90.6 92.3 93.9 93.7 92.3 92.9 93.9 95.7
1.81 1.22 1.85 1.87 1.39 1.67 2.56 2.02 2.23 2.93 2.13 2.83
2.13 2.17 2.69 4.2 5.64 6.13 4.84 5.55 4.36 2.62 1.79 1.46
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Table 3 Monthly psychiatric admissions in North Cheshire in 1993, by diagnostic categories.
Table 4 Correlation between weather parameters and number of psychiatric admissions.
Month
Affective disorders
Jan Feb Mar April May June July Aug Sept Oct Nov Dec
37 38 48 60 55 45 42 36 35 40 34 34
Numbers of admissions Behavioural Schizophrenia and reactive
Weather
26 22 21 17 29 21 22 20 21 24 20 20
42 24 20 25 21 27 30 16 25 28 21 22
Male
P
Correlation coefficent Min. temp. Same day Preceding 2 weeks Max. temp. Same day Preceding 2 weeks Diurnal variation in temp. Same day Preceding 2 weeks Humidity Same day Preceding 2 weeks Rainfall, mm Same day Preceding 2 weeks Sunshine, h Same day Preceding 2 weeks
Alcohol and drugs
No diagnosis
51 44 55 33 42 48 42 50 48 47 51 33
17 13 19 17 10 10 21 14 11 23 16 17
Female
P
Correlation coefficent
0.062 0.051
40.05 40.05
70.022 70.031
40.05 40.05
0.082 0.073
40.05 40.05
70.015 70.014
40.05 40.05
0.031 0.333
40.05 50.05*
0.016 0.034
40.05 50.05*
70.013 70.103
40.05 50.05*
70.063 70.262
40.05 50.05*
70.012 70.025
40.05 40.05
70.086 70.055
40.05 40.05
0.044 0.043
40.05 40.05
0.023 0.032
40.05 40.05
*Significant
Table 5 Correlation between weather parameters and numbers of daily psychiatric adult admissions in North Cheshire in 1993.
Weather parameter Max. temp. Min. temp. Dirunal variation in temp. Humidity Rainfall, mm Sunshine, h
*Significant
Same day Preceding Same day Preceding Same day Preceding Same day Preceding Same day Preceding Same day Preceding
2 weeks 2 weeks 2 weeks 2 weeks 2 weeks 2 weeks
Correlation coefficient
P
0.035 0.044 0.063 0.062 0.011 70.124 70.037 70.198 70.091 0.087 70.024 70.122
40.05 40.05 40.05 40.05 40.05 40.05 40.05 50.05* 40.05 40.05 40.05 40.05
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Cross correlation function (CCF)
0.1 0.05 0 ± 0.05 ± 0.1 ± 0.15 ± 0.2
1
2
3
4
5
6
8
9
10
11
12
13
14
Figure 1 C h a rt o f c r o s s-c o r re la tio n s b e tw e en p s y c h ia tric a d m iss io n s a nd r ela tiv e h u m id ity ( m a x im u m n eg a tiv e c o r re la tio n b e tw e e n p s y c h ia tr ic a d m is s io n s a nd re la tiv e h u m id ity 3 d a y s p re v io u s ly ) 96 95
40
94 30 93 20 92 10
91
2
3
4
5
6
DISCUSSION
7
8
9
10
11
12
ICD-9 296 admissions
50
90 1
0 13
Period relative humidity + % ICD-9 296 admissions
METHODOLOGICAL LIMITATIONS
Dotted lines: % affective disorders admissions
Figure 2 M e a n d a ily v a ria tio n s in r ela tiv e h u m id ity a n d a d m iss io n s c a te g o r iz e d a s IC D -9 29 6 a s a p e rc e n ta g e o f to ta l a d m iss io n s d ur in g th e 1 3 2 8 -d a y p e rio d s 100
96 95
80
94 60 93 40 92 20
91 90
1
2
3
4
5
6
7
8
9
10
11
12
13
ICD-9 296 admissions
1. Methodological problems encountered in this study are mainly related to the reliance on static weather codes to describe the weather changes in relation to psychiatric admissions. 2. The catchment area of the hospital is geographically distant from the point at which weather data was collected. The nearest meteorological office where weather data are recorded is about 30 miles away from the study population, which could have reduced the degree of accuracy of weather data in relation to this population. Also, residents of North Cheshire may perhaps be less exposed to weather changes than some other populations living under different conditions. 3. The ICD-9 diagnosis used in 1993 was included regardless of whether it was recorded as a primary or secondary diagnosis, to ensure that no admissions were missed. All subtypes of affective disorders (ICD-9 296.0-296.9) were re-coded into a single diagnostic category (ICD-9 296). This may have affected the accuracy of the data in some cases, and may have detracted from more subtle possible associations. 4. ICD coding was only included on the termination of an inpatient episode; some patients (albeit a very small number), who remained in hospital, were not included in the data collection. 5. The accuracy of ICD-9 coding by various doctors, and also the re-coding of other diagnoses into fewer but
7
Lag (days)
Relative humidity
A separate analysis was carried out for the different diagnoses. Figure 1 shows that the maximum negative correlation between psychiatric admissions and relative humidity occurred with a lag of 3 days, gradually increasing till admission day. A negative correlation was also found between psychiatric admissions and diurnal variation in temperature and hours of sunshine, but a positive correlation with the amount of rainfall; however, this was not significant (Table 5). Weather conditions on the same admission day did not appear to have any association with the number of psychiatric admissions. Because of the day-of-the-week variation, the patterns of relative humidity and number of psychiatric admissions for all affective disorders (ICD-9 296), and all psychiatric diagnoses other than affective disorders, were plotted on 3 the basis of 13 28-day periods. Figure 2 shows the negative relationship between relative humidity and number of admissions for affective disorders, particularly between periods 3 and 6 (spring). Figure 3 shows no such association between relative humidity and daily admissions for other than affective disorders. Figures 2 and 3 suggest that the small but significant correlation between the number of psychiatric admissions and the relative humidity is mainly confined to the affective disorders.
Relative humidity
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Relative humidity and affective disorders
0
Period relative humidity + all other diagnoses Dotted lines: all other diagnoses excluding ICD-9 296
Figure 3 M e a n d aily relativ e h u m id ity a nd nu m be r o f a dm issio ns fo r a ll o th e r IC D -9 d ia g n o se s, e xc lu ding a ffec tive d iso rd e rs ( IC D -9 2 96 ) , d u rin g th e 1 3 28 -da y p er io d s
larger groups, may have influenced some of the negative (no association) findings, leading to statistical Type II error. 6. Some significant correlations may have been the product of multiple testing, which could limit the findings and the inferential value of the study.
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INTERPRETATION OF THE FINDINGS The study found a statistically significant time-lagged inverse relationship between the admissions for affective disorders and the relative humidity in the week preceding admission. The inverse relationship between relative humidity and admissions for affective disorders was consistent when the data were expressed in terms of monthly variation, or as 13 28-day periods (removing the day-of-the-week effect), or when admissions for affective disorders were shown as a percentage of the total hospital admissions. Many social factors influence the availability of hospital beds, or accessibility of persons who can arrange admission, although in some instances the condition would have been severe enough to over-ride such factors. In virtually all cases, a time lapse between the onset of a psychiatric disorder and admission would be expected. This time lapse is likely to be least for psychotic disorders (including affective disorders) because of their relative severity. In many cases admission will correspond with a worsening of symptoms, although some other factors may be important in determining whether or not the patient is admitted, including social factors such as availability of support at home, provision of community mental health services and the number of beds available in the admitting hospital. When a hospital fills most or all of its psychiatric beds, a negative feedback effect will occur, i.e. admission rates will fall and the threshold for admission will rise. Any changes in mental state in response to variations in the weather may vary with different populations, predictability of the climate, occupation, the amount of exposure of the population to the weather, etc. Social factors may also vary for individuals with the time of year, e.g. variation in workload of doctors and rate of referrals. In psychiatry, it is not uncommon that a patient may be admitted because of the mental health of a distressed carer, not because of the worsening of an already existing psychiatric condition. In other instances, sudden changes in behaviour, aggression, mood swings and other affective features may determine the timing of admission. The inverse relationship between admissions for some 3 affective (manic) disorders and relative humidity is not dissimilar to the results of this study. A large twin study has shown seasonal variations in mood and behaviour to have a genetic contribution,1 7 which would apply both to psychiatric patients and their carers. The inverse relationship between dementia admissions and relative humidity was attributed to possible affective changes in either patients or their carers which might lead to admissions.1 5
CONCLUSION The effect of weather parameters on mental health is likely to be influenced by seasonal as well as non-climatic factors
(predominantly social), which may have contributed to the study findings. Psychiatric admissions reflect the behaviour of patients, carers and medical professionals. The complexity of this behaviour and the day-of-the-week periodicity may have confounded variations associated with the weather. Although the study provides somewhat interesting but largely inconclusive findings, we hope that future studies of this intriguing possibility, using longer time series to control seasonal effects, may overcome the various methodological limitations we encountered. One approach would be to examine the patterns over several years, thus excluding the effects of using data for a single year only, which might have been the result of some particular idiosyncracy. It may also be interesting for future studies to look at a part of the world where daily weather variations are much more pronounced than in the climatically equable, Atlantic weather zone of Cheshire, e.g. the eastern seaboard of the USA, or a mid-continental location. Annual seasonality of admissions and day-of-theweek effects should be adjusted for, while attempting to interpret subtle daily weather variations.
ACKNOWLEDGEMENTS The authors are grateful to Miss Bernadet Hayes, Chief Librarian, Hollis Park and Miss Kate Spencer (Beckett Unit) for their help with this paper. The authors would also like to thank Mrs Janet Davies (Pfizer) for her support.
KEY POINTS . The study reports a time-lagged inverse relationship between hospital admissions for affective disorders and relative humidity in the week preceding admission. The undetermined study power may limit its inferential value . It is possible that the ``no association’’ between weather conditions and other psychiatric diagnoses may have been the result of a Type II error, due to the small number of admissions . The reliance on static weather codes and the distance of the meteorological office from the study population could have influenced the study findings . The study highlights that for affective disorders, hospital admissions may reflect the behaviour of patients, carers and medical professionals . The complexity of this behaviour and the day-ofthe-week periodicity may have confounded variations associated with the weather. Longer time series are needed in order to control seasonal effects
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REFERENCES 1. Krueger AP, Reed EJ (1976) Biological impact of small air ions. Science 193: 1209 ± 13. 2. Sluman FG, Levy D, Peeifer Y et al (1974) Air ionometry of hot, dry desert winds (Sharav) and treatment with air ions of weather-sensitive subjects. Int J Biometeorol 18: 313 ± 8. 3. Mawson D, Smith A (1981) Relative humidity and manic admissions in the London area. Br J Psychiatry 138: 134 ± 8. 4. Brewerton TD (1989) Seasonal variation of serotonin function in humans. Research and clinical implications. Ann Clin Psychiatry 1: 153 ± 64. 5. Maes M, Meltzer HY, Suy E, De Meyer F (1993) Seasonality in severity of depression: relationships to suicide and homicide occurrence. Acta Psychiatr Scand 88: 156 ± 61. 6. Maes M, De Meyer F, Thompson P et al (1994) Synchronised annual rhythm in violent suicide rate, ambient temperature and light-dark span. Acta Psychiatr Scand 90: 191 ± 6. 7. Salib E, Gray N (1997) Weather conditions and fatal self harm in North Cheshire 1989-1993. Br J Psychiatry 170: 473 ± 7. 8. Carney PA, Fitzgerald CT, Monaghan CE (1988) Influence of climate on the prevalence of mania. Br J Psychiatry 152: 820 ± 3. 9. Myers DH, Davies P (1978) The seasonal incidence of mania and its relationship to climatic variables. Psychol Med 8: 433 ± 40. 10. Sayer HK, Marshall S, Melsopp GW (1991) Mania and seasonality in the southern hemisphere. Affect Disorders 23: 151 ± 6.
11. Parker G, Walter S (1982) Seasonal variation in depressive disorders and suicidal deaths in New South Wales. Br J Psychiatry 140: 626 ± 63. 12. Eastwood MR, Stiasny S (1978) Psychiatric disorder, hospital admission and season. Arch Gen Psychiatry 35: 769 ± 71. 13. Maes M, De Meyer F, Meltzer HY (1993) The periodicities in and biometeorological relationships with bed occupancy of an acute psychiatric ward in Antwerp, Belgium. Int J Biometeorol 37: 78 ± 82. 14. Maes M, De Meyer F, Meltzer HY (1992) Seasonal variations and meteotropism in various self-rated psychological and physiological features of normal people. Int J Biometeorol 36: 195 ± 200. 15. Salib E, Sharp N (1999) Does the weather influence dementia admissions? Int J Geriatric Psychiatry 14: 925 ± 935. 16. Modai I, Kikinzon L, Valevski A (1994) Environmental factors and admission rates in patients with major psychiatric disorders. Chronobiol Internat 11: 196 ± 9. 17. Madden PAF, Heath AC, Rosenthal NE, Martin MG (1996) Seasonal changes in mood and behaviour. The role of genetic factors. Arch Gen Psychiatry 53: 47 ± 55.
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