Journal of Affective Disorders 136 (2012) 862–867

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Seasonal and socio-demographic predictors of suicide in Ireland: A 22year study

Patricia Casey a,⁎, Isla Gemmell b, Urara Hiroeh c, Catherine Fulwood d

a University College Dublin and Consultant Psychiatrist Mater Misericordiae University Hospital, Eccles St., Dublin 7, Irelandb MPH/MRes Programmes in Public Health and Primary Care, G63b Ground Floor Simon Building, University of Manchester, M13 9PL, UKc Northwest Institute for BioHealth Informatics, University of Manchester, 1st Floor Jean McFarlane Building, Oxford Road, Manchester M13 9PL, UKd The University of Manchester, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust,NIHR Manchester Biomedical Research Centre, 29 Grafton Street, Manchester, M13 9WU, UK

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +353 1 8032176; faxE-mail address: apsych@mater.ie (P. Casey).

0165-0327/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.jad.2011.09.020

a b s t r a c t

Article history:Received 10 June 2011Received in revised form 20 September 2011Accepted 20 September 2011Available online 22 October 2011

Background: Seasonal influences on suicide have been studied for many years with inconclu-sive and contradictory findings.Methods: Data on suicide in Ireland from 1980 to 2002 was examined to ascertain the contri-bution of season and demographic variables to suicide. Using Poisson regression modellingand sinusoidal analysis a small seasonal effect (7% from peak to trough) was identified butage, gender, marital status and residence were much larger contributors. The seasonal contri-bution increased in the latter half of the period under study. There was also a small seasonaleffect for method of suicide. The suicide rate was highest in the 40–44 age group after control-ling for confounders.Limitations: Because this was an ecological study, information on other possible contributors,such as mental illness was not available.Conclusions: These findings are discussed in light of international studies. Continuing studiesare required to confirm the trend in increasing seasonality in Ireland. Since suicide is highestin those who are middle aged, preventive strategies should be directed to this group.

© 2011 Elsevier B.V. All rights reserved.

Keywords:SeasonSuicideSocio-demographicMethods

1. Introduction

Seasonal variations in suicide were first recognised by theItalian physician EnricoMorselli (1881)who identified a sum-mer peak, coinciding, he believed, with the time when severemental illness was at its peak. In recent years suicide in coun-tries such as Finland (Partonen et al., 2004), Ireland (Corcoranet al., 2004), Italy (Preti and Miotto, 1998; Rocchi et al., 2007)has continued to show a significant seasonal trend. In some,such as Italy (Rocchi et al., 2007) and Slovenia (Oravecz etal., 2007) the seasonal effect is so marked that changes inthe national suicide rates are reported as corresponding tochanges in the seasonal rates of suicide. However othercountries exhibit a diminishing or absent seasonal pattern, in-

: +353 1 8309323.

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cluding England and Wales (Yip et al., 2000), Australia andNew Zealand (Yip et al., 1998), Switzerland (Ajdacic-Grosset al., 2007) and Singapore (Parker et al., 2001).

The observation of a seasonal pattern in suicide has been re-fined and elaborated upon over the past 20 yearswith evidenceindicating that seasonal patterns also apply to the method ofsuicide. However, the results are confusing and conflicting. InBelgium (Maes et al., 1993) and Israel (Schreiber et al., 1993),researchers have described a clear seasonal pattern to deathsby violent methods, although the December peak, during thecolder months in the Israeli study differed from the summerpeak in the Belgian study. In the Southern hemisphere, Yipet al. (1998) found a seasonal trend for death by hanging inAustralia but none in New Zealand.

While season is one variable influencing suicide ratesothers have also been identified including age (Bertolote andFleischmann, 2002), gender (Canetto and Sakinofsky, 1998),

863P. Casey et al. / Journal of Affective Disorders 136 (2012) 862–867

residence (Middleton et al., 2003) and marital status (Yip andThorburn, 2004). It is notable that many of the studies whichhave examined seasonality have considered it as a single vari-able rather than as one of many that might influence thesuicide rate (Ho et al., 1997; Yip et al., 1998) although somestudies have controlled for age and gender in the analysis(Corcoran et al., 2004) or have stratified the data by age andgender for analysis (Preti and Miotto, 1998).

This study has a number of aims, the first aimwas to exam-ine the magnitude of the contribution of season, along withother variables, such as age, gender, marital status, year andresidence (urban/rural), to suicide. The secondwas to ascertainif the suicide rate in Ireland amongst those aged 15 years andover exhibited a seasonal pattern. A third aimwas to determineif the seasonal pattern changed over time and the fourthwas toascertain if a seasonal pattern existed for any particularmethodof suicide.

2. Method

Information on suicide occurring in Ireland between 1980and 2002, along with the Irish census data, was obtainedfrom the Central Statistics Office, Dublin. This data containedinformation on the dates and methods (coded using Interna-tional Classification of Diseases version 9), age group (15 andover, grouped in 5 year intervals), gender, marital status andresidence. With regard to residence rates were available forthe five cities in Ireland, for the large conurbation outsideDublin (the capital) and for the remaining counties. A pragmat-ic decision was take to combine the cities and conurbations toform the urban variable while data from the countieswas com-bined to form the rural variable., Census data was available in5 year intervals so that the 1981 census provided the data foryears 1980–1984, 1986 for 1985–1989, etc. This was used tocalculate suicide rates for each of the demographic variables.Data for those under the age of 15was excluded from the anal-ysis due to the small number of suicides.

Multivariate Poisson regressionmodelling was used to esti-mate which variables were independently associated with sui-cide. Poisson regression modelling enables estimation of theindependent effect of a set of predictor variableswhen the out-come variable is count data (Cameron and Trivedi, 1998) and isparticularly applicable when modelling aggregated ecologicaldata. In this case the outcome variable is number of suicides ag-gregated by the predictor variables (gender, age group, marital

Table 1Count and crude rates of suicides by sex and 5 year age groups.

Age group Male count % Rate/1000000 Female count

15–19 464 7.49 9.61 9620–24 873 14.10 21.05 15725–29 788 12.72 21.15 13530–34 685 11.06 18.80 14735–39 595 9.61 17.50 15840–44 571 9.22 18.52 17445–49 446 7.20 16.27 16150–54 392 6.33 16.12 16455–59 374 6.04 17.21 14760–64 355 5.73 18.27 12965+ 650 10.50 13.26 238Total 6193 100.00 16.73 1706

status, residence, year and month). The method estimates theincidence rate ratio (IRR) for each category of the predictor var-iables compared with a baseline category (IRR 1.00).

Sinusoidal analysis involves fitting a cosine curve to dataand can be used to assess the degree of seasonal variationin mortality and morbidity (Gemmell et al., 2000). Here weused sinusoidal analysis to estimate the percentage changein IRR from the trough to the peak of the seasonal curve inthe Poisson model after adjustment for gender, year, agegroup, marital status and residence. This technique was alsoused to estimate whether there was a difference in seasonal-ity according to method of suicide.

Data analysis was carried out using SPSS 14.0 and STATAversion 9.

3. Results

There were 7899 suicides in people aged 15 years andover between 1980 and 2002 in the Republic of Ireland andthe numbers and crude rates are shown in Table 1.

Fig. 1 presents the age standardised suicide rates for menand for women over the period studied. This shows an overallincrease in suicide, especially for males, up to 1998 with asuggestion of a plateau thereafter.

Multivariate Poisson regression modelling was used to es-timate which variables were significant predictors of the sui-cide rates over the study period. All variables in the modelcontributed significantly, however the smallest effect wasfor month of the year after the others had been controlledfor. The results of the analyses are given in Table 2.

Table 2 provides amore detailed analysis of themultivariatePoisson regression results and shows the IRR for each variablethus allowing more meaningful comparisons between them.

Month of the year was a small but statistically significantpredictor of suicide after controlling for other variables(p=0.038). Overall, themost significant predictor was gender,with female rates over 3 times lower thanmale rates (IRR 0.29,pb0.001). Age was also a significant factor in suicide, with apeak in those aged 40–44 years (IRR 4.63, pb0.001). The inci-dence rate of suicide was higher in those aged between 35and 64 compared with those aged less than 35 and over 65after controlling for other variables. Marital status was also astrong predictor; the suicide rates amongst those who weremarried, separated/divorced and widowed were significantlylower than those for who were single, when age and other

% Rate/1000000 Total Count % Rate/1000000

5.63 2.08 560 7.09 5.949.20 3.89 1030 13.04 12.597.91 3.60 923 11.69 12.348.62 4.03 832 10.53 11.419.26 4.07 753 9.53 10.34

10.20 5.76 745 9.43 12.219.44 6.06 607 7.68 11.249.61 6.94 556 7.04 11.598.62 6.76 521 6.60 11.987.56 6.39 484 6.13 12.21

13.95 3.76 888 11.24 7.91100.00 4.49 7899 100.00 10.53

Fig. 1. Age standardised rates of suicide/100 000 (using 1980 as the base year) for men, women and the total population 1980–2002.

864 P. Casey et al. / Journal of Affective Disorders 136 (2012) 862–867

variables were taken into account. Those who were separatedor divorced had the lowest rates. Urban dwellerswere also pro-tected from suicide, with rates reduced by around 30% com-pared with those living in rural areas (IRR 0.76, pb0.001).Year of death was also significant, with indications of a generalincrease over the study period (Table 2). While month was asmall contributor rates tended to be higher in late spring andearly summer and lower in autumn (see Fig. 2).

Table 2Multivariate Poisson regression analysis controlling for year and month of death, g

Predictor IRRa Std Err

Gender Male 1.00Female 0.29 0.008

Year 1.03 0.002Age group b20 1.00

20–24 2.29 0.12025–29 2.93 0.15730–34 3.62 0.20235–39 4.06 0.23540–44 4.63 0.27045–49 4.24 0.25850–54 4.28 0.26655–59 4.31 0.27160–64 4.24 0.27165+ 2.55 0.149

Marital status Single 1.00Married 0.32 0.009Sep/div 0.16 0.021Widowed 0.62 0.037

Residence Rural 1.00Urban 0.76 0.019

Month Jan 1.00Feb 0.95 0.053Mar 1.04 0.057Apr 1.08 0.059May 1.06 0.058Jun 1.02 0.057July 1.05 0.058Aug 1.06 0.058Sep 0.95 0.053Oct 1.06 0.058Nov 1.01 0.056Dec 0.92 0.052

a Incidence rate ratio.

Sinusoidal analysis showed that the seasonal percentageincrease in IRR for suicide from 1980 to 2002, adjusted foryear, age, gender, marital status and residence, was 7.2%(95% CI 7.1–7.3) from an autumn trough to a spring peak.Amongst males and females the seasonal variations were7.1% (95% CI 7.0–7.3) and 7.3% (95% CI 7.1–7.6) respectively.Amongst those who were single and were married the equiv-alent figures were 6.7% (95% CI 6.6–6.9) and 8.3% (95% CI 8.1–

ender, age group, marital status and residence.

Z p-value 95% CI

44.65 b0.001 0.27 0.3016.86 b0.001 1.03 1.03

15.74 b0.001 2.06 2.5319.97 b0.001 2.64 3.2623.06 b0.001 3.24 4.0424.22 b0.001 3.62 4.5526.27 b0.001 4.13 5.1923.71 b0.001 3.76 4.7823.46 b0.001 3.79 4.8423.26 b0.001 3.81 4.8822.60 b0.001 3.74 4.8116.31 b0.001 2.28 2.85

41.57 b0.001 0.31 0.3413.98 b0.001 0.13 0.21−8.10 b0.001 0.55 0.69

10.83 b0.001 0.72 0.80

−0.99 0.323 0.85 1.050.72 0.475 0.93 1.161.39 0.165 0.97 1.201.12 0.262 0.95 1.180.44 0.659 0.92 1.140.93 0.351 0.94 1.171.07 0.286 0.95 1.18

−0.93 0.353 0.85 1.061.12 0.262 0.96 1.180.25 0.804 0.91 1.13

−1.53 0.125 0.82 1.02

Incidence rate ratio

0.8

0.9

1

1.1

1.2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 2. Incidence rate ratio by month.

865P. Casey et al. / Journal of Affective Disorders 136 (2012) 862–867

8.5) demonstrating that the seasonal variation in suicide wassignificantly greater in those who were married as comparedto the single category. In the under 30 age group the figurewas 6.9% (95% CI 6.7–7.1), for those aged 30–49 the variationwas 7.5% (95% CI 7.3–7.7) and amongst those over 50, 7.1%(95% CI 6.9–7.4). For those living in rural and urban areasthe figures were 7.1% (95% CI 6.8–7.4) and 7.4% (95% CI 7.2–7.6) respectively. The results therefore show that all of theabove variables are significantly associated with some sea-sonal variation and that this is significantly greater in thosewho are married compared with single individuals.

3.1. Subgroup analysis

In order to examine our third hypothesis, the study periodwas divided into two parts. The sinusoidal analysis showedthat the seasonal increase in suicide adjusted for year, age,gender, marital status and residence for the earlier years1980–1991 was 6.8% (95% CI 6.7–7.0) while from 1992 to2002 the seasonal increase was 7.5% (95% CI 7.3–7.6); thusthe seasonal pattern was significantly greater in the lateryears although the overall contribution was still small.

3.2. Method of suicide

Examining the methods of suicide Table 3 shows thenumber of suicides in people aged 15 or over between 1980and 2002 recorded by method according to ICD-9.

Adjusted for year, age, gender, marital status and resi-dence, the seasonal percentage increase in IRR shown in thesinusoidal analysis was 6.81% (95% CI 6.5%–7.1%) for poison-ing, 6.95% (95% CI 6.8%–7.2%) for hanging and 6.74% (95% CI6.5%–6.9%) for drowning. Thus there is a small but significantcontribution of season but this does not differ significantlybetween the three methods.

Table 3Method of suicide classified by ICD 9 codes.

ICD-9 code Method Number of cases %

950 Poisoning 1290 16.3953 Hanging 3056 38.7954 Drowning 1959 24.8951–952, 955–959 Other causes 1594 20.2Total 7899 100

4. Discussion

The study of seasonal variations in suicide is important fora number of reasons. If a clear-cut seasonal pattern wereidentified this might guide service providers to enhance in-terventions during the periods of highest risk. In addition,understanding the role of season in determining suicidemight assist in understanding its possible biological and so-ciological aetiology.

Despite its potential usefulness, the evidence for suicideseasonality has not been conclusive and many of the findingsare conflicting and confusing. Possible reasons includes theshort time span examined in some studies (Corcoran et al.,2004; Parker et al., 2001) raising the possibility that the find-ings are transient. Other studies have examined differenttime periods from a single country, during which the season-al variation may have altered. For example, in Britain a dis-tinct seasonal pattern was found in a study of suicide datafrom 1958 to 1974 (Meares et al., 1981), while a study (Yipet al., 2000) of data from 1982 to 1996 showed a greatly di-minished seasonal effect. In Ireland a study by Reid et al.(1980) analysed suicide data from 1971 to 75 and found noseasonal trend for either sex while Corcoran et al. (2004),using data from 1990 to 1998, found a seasonal variationfor men. Thus some of the contradictory findings may relateto cohort effects in a particular jurisdiction.

A further concern it that studies have examined the con-tribution of season as a single variable (Ho et al., 1997; Yipet al., 1998) and not taken account of the relative contribu-tion of other variables such as age, gender, marital statusetc. The study reported here is unique in that it examinedthe role of season as a possible determinant of seasonal pat-terns along with other variables such as gender, age, maritalstatus and residence to the suicide rate over time. By control-ling for these confounders the relative magnitude of seasonas an independent predictor could be evaluated

A further strength of this study is that it spans a longer pe-riod (22 years) than many other studies of seasonality, whichhave ranged from 8 to 20 years (Corcoran et al., 2004; Ho etal., 1997; Meares et al., 1981; Parker et al., 2001; Preti andMiotto, 1998; Yip et al., 1998, 2000), although some have cov-ered longer periods of between 20 and 42 years (Bridges et al.,2005; Fruehwald et al., 2004; Oravecz et al., 2007; Partonen etal., 2004; Rocchi et al., 2007). This study has reduced the pos-sibility that the effects were temporary or had changed over

866 P. Casey et al. / Journal of Affective Disorders 136 (2012) 862–867

time since it covered a relatively long period and it was alsopossible to compare recent with older time periods.

There are a number of limitations that should also be consid-ered when interpreting the results. Because this is an ecologicalstudy the data available for analysis was limited. Thereforemeasures of psychiatric illness, social isolation or alcohol con-sumption were absent although these have been shown to beassociated with suicide and with a seasonal pattern (Goodwinand Jamison, 1990; Durkheim 1951; Uitenbroek, 1996); Unem-ployment is also associated with suicide (Kposowa, 2001) andexhibits a seasonal pattern but was excluded because of logisticproblems with the data set for this variable.

Notwithstanding these considerations we believe that thisstudy makes an important contribution to the internationalliterature on seasonality and suicide.

4.1. Variables influencing suicide

In accordance with our first aim, we identified the magni-tude of the contribution of a number of variables other thanseason to the suicide rate. The finding that the most importantpredictor was gender, followed by marital status, year, agegroup, residence (rurality) then month, was not surprisinggiven what is already known about suicide in Ireland (NationalOffice for Suicide Prevention 2009). With regard tomarital sta-tus, two aspects of our findings are worthy of comment. Firstly,our finding that rates of suicide were lowest in those who aredivorced, rather than amongst those are married as describedin other studies (Corcoran and Nagar, 2010), was surprisingas the divorced have generally been shown to have the highestsuicide rates inmost studies (Kposowa, 2000). One possible ex-planation is that divorcewas only introduced in Ireland in 1994and so the beneficial effects of marriage may be continuing asthe data collection covered a period before and shortly afterthis change in Irish law. Another possible explanation is thatthe trend in Ireland is following the trend in Britain in whichthe suicide rate in the divorced has been reducing for severaldecades (Yip and Thorburn, 2004) possibly as divorce becomesless stigmatised and less associated with psychopathology. Ourresults were consistent with studies finding that being singleincreases the risk (Masocco et al., 2008).

The finding that the risk of suicide was highest in those inthe middle years of life was surprising since National datashows that it is those under 25who are at highest risk (NationalOffice for Suicide Prevention Report, 2009). However, the cur-rent study controlled for a number of possible confounder vari-ables that national reports do not control for and so this effectmay not be apparent. Finally the possible impact of the nationalsuicide prevention programme needs to be considered al-though this was not introduced until 1998 (Department ofHealth and Children, 1998) and any effect would be unlikelyto be evident between that date and 2002, the final year forwhich data was analysed.

4.2. Season and suicide

Our second goal, of examining the contribution of seasonto suicide resulted in the identification of a small but statisti-cally significant seasonal variation in suicide rates in multi-variate analysis, with a 7% increase in the rate of suicidefrom an autumn trough to a spring peak in the sinusoidal

analysis. Unlike Corcoran et al. (2004) who only found a sea-sonal contribution to male suicide, this study identified a sea-sonal effect for both genders. The results of this study aresimilar to those in a number of other studies which haveconfirmed a seasonal effect in several countries includingHong Kong and Taiwan, Italy, Finland and Slovenia (Ho etal., 1997; Oravecz et al., 2007; Partonen et al., 2004; Pretiand Miotto, 1998; Rocchi et al., 2007) although studies fromothers, such as Australia and New Zealand and England andWales have shown a diminishing effect or absence of seasonalinfluences (Yip et al., 1998, 2000).

4.3. Subgroup analysis

It is now accepted that the reduction in the seasonal effectbegan in the early part of the 20th century (Ajdacic-Grosset al., 2005; Dreyer, 1959) and that in some countries this iscontinuing (Ho et al., 1997; Parker et al., 2001; Yip et al.,1998, 2000).

Thus our third aim was explored in the current studyagainst the background of a previous Irish study (Reid et al.,1980) which found no seasonal variation. Dividing the studyperiod in two parts covering the earlier and later years respec-tively, we failed to find any statistically significant loss of sea-sonal effect and on the contrary there was a slight butsignificant increase in the percentage difference betweenpeak and trough from 6.8% during 1980–1992 to 7.5% during1992–2000. One possible explanation for the seeming emer-gence of an increasing seasonal pattern is that the reliabilityof the data has improved in recent years in comparison tothe earlier years before suicide was decriminalised (Kelleheret al., 1997)making it now possible to capture a slightly largerseasonal effect. Combined with the findings of Corcoran et al.(2004) of a seasonal effect for men only, the results presentedin the study reported here of a seasonal effect for both menand women and of an increase over time since the earlierstudy (Reid et al., 1980) suggest that it may be a true findingand that the role of season is increasing in both sexes albeitstill only making a small contribution. Such an evolving pat-tern was identified in the United States also (Bridges et al.,2005). Clearly further studies over time are warranted to ex-plore this changing pattern in Ireland and to examine possiblecauses, should it continue.

4.4. Method of suicide

In exploring our fourth aim concerning the contribution ofseason to method of suicide we found a small seasonalincrease from peak to trough for the three major methodsi.e. poisoning, hanging and drowning after controlling for allthe available variables. Unlike other studies (Lester andFrank, 1988; Linkowski et al., 1992; Maes et al., 1993; Pretiand Miotto, 1998; Yip et al., 1998) the seasonal effect in ourstudy was not limited to violent methods. As in those studiesthe peak was identified in the warmer months and differingfrom a study of an Israeli sample which identified a peakduring the colder months (Schreiber et al., 1993).

What are the implications of these findings? The finding of asmall but statistically significant effect from season might sug-gest that, for certain months of the year, services for those atrisk of suicide should be increased. However, since season

867P. Casey et al. / Journal of Affective Disorders 136 (2012) 862–867

was a much smaller contributor to the overall suicide rate thanother variables, these, rather than season, should be prioritisedfor a targeted approach to prevention. In particular our findingsthat suicide risk is highest in the middle years of life lendweight to a view that the preventive focus should specificallytarget those in this age category of life (Reach Out, 2006).Such an approach would include psychological autopsy studiesto better understand the aetiology of suicide in this age group.

There should also be continuing research into the seasonalinfluences on suicide in Ireland, as this may be increasingrather than diminishing as in other countries. It should alsoexplore possible reasons for the seasonal effect should itscontribution continue to grow.

Role of funding sourceThis was not funded by any external sources.

Conflict of interestNone for any of the authors.

AcknowledgementsWe wish to thank the Central Statistics Office for providing us with sui-

cide and census data. We also wish to thank Ms. Sandra Tobin, Statistician,Vital Statistics Department, Central Statistics Office for calculating the stan-dardised suicide rates.

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