Seasonal variation of suicide in South Africa

PSYCHIATRY RESEARCH ELSEVIER

Psychiatry

Research

66 (1997) 13-22

Seasonal variation of suicide in South Africa Alan J. Flisher”“,

Charles

D.H. Parryb, Deborah

Bradshawc,

June M. Juritzd

“Department of Psychiatry, University of Cape Town, J.5 Groote Schuur Hospital, Obsematory 7025. Cape Town, South Apica hNational Urbanisation and Health Research Programme, Medical Research Council, Tygerberg, South Aftica ‘Centre for Epidemiologcal Research in Southern Aftica, Medical Research Council, Tygerberg, South Aftica ‘Depatiment of Statistical Sciences, Uniuersity of Cape Town, Cape Town, South Aftica

Received

1 February

1996; revised 25 July 1996; accepted

9 September

1996

Abstract Seasonal trends in South African suicide incidence were investigated with a view to ascertaining whether they are consistent with those in the northern hemisphere regarding: (1) the existence of the expected pattern; (2) this pattern being more pronounced for less urbanized groups: and (3) the presence of a secondary fall peak for youth and females. Log-linear modelling was performed to investigate the effect of month and relevant demographic variables on the suicide counts. The 16389 nationally registered suicide deaths during 1980-1989 were analysed. The expected pattern, with a peak in the spring (that is, in September/October) or summer and a trough in winter, was present. This pattern was more pronounced for a sub-group that is less urbanized and for another sub-group with a relatively low standard of living. The secondary peak in autumn was not present for youth or females. In the northern hemisphere, this secondary peak has been ascribed to sociodemographic factors associated with the commencement of the academic year and (for females) bioclimatic factors associated with gender-specific biological circannual rhythms. The fact that the academic year commences in summer in South Africa indicates that the present findings 0 1997 Elsevier Science Ireland Ltd. All rights reserved are consistent with the former explanation.

Keywords:

*Corresponding

Age; Sex; Race; Season;

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14

A.J. Flisher et al. / Pqchiatry Research 66 (19971 13-22

1. Introduction Durkheim’s observation that suicide has a seasonal variation, with a peak in the spring or summer and a trough in winter (Durkheim, 1879), was confirmed in a comprehensive review of the published data by Massing and Angermeyer (1985). However, this confirmation was based almost entirely on studies conducted in the northem hemisphere. In the southern hemisphere, the seasons are ‘reversed’ in that spring begins in about September and autumn in about March. The results of studies conducted in the southern hemisphere are not consistent with each other nor, in some cases, with the consensus from studies conducted in the northern hemisphere. Statistical analyses confirmed the existence of the expected pattern for males in Chile (Trucco, 1977) and females in Australia (Parker and Walter, 19821, and failed to do so in Zambia (Rwegellera, 1978), Zimbabwe (Rittey and Castle, 19721, for females in Chile (Trucco, 1977) and for males in Australia (Parker and Walter, 1982). Although seasonal variation in suicide incidence has also been examined in Brazil (Barbosa, 19741, South Africa (Chew and McCleary, 19951, and Uruguay (Chew and McCleary, 19951, tests of statistical significance for monthly trends were not applied. At present, there are thus insufficient grounds for concluding that the seasonal trends in the northern hemisphere are also applicable in the southern hemisphere. In addition to the peak in spring or early summer and the trough in summer, Massing and Angermeyer (1985) reported that in about onefifth of the studies they reviewed there was a second, less clearly defined, peak in the fall. This pattern has been found to be more marked among youth and females (Massing and Angermeyer, 1985; Meares et al., 1981; Micciolo et al., 1989; N%yhti, 1982; Parker and Walter, 1982). None of the southern hemisphere studies included age group in their analyses of seasonal variation in suicide incidence, and only those conducted in Chile (Trucco, 1977) and Australia (Parker and Walter, 1982) examined the influence of gender on seasonal variation. There was no evidence of an autumn peak for females in the former coun-

try, while in the latter a statistically significant peak for females in May (i.e. late fall/early winter) was observed. Although a second peak for females in the fall is observed only in a minority of northern hemisphere countries, there is even less clarity in this regard in the southern hemisphere countries. Contrasts between the trends in South Africa and previous studies could elucidate some of the mechanisms for seasonal trends and hence the etiology of suicide (Ngyhti, 1982). Explanations for seasonal trends fall into two broad classes: bioclimatic and sociodemographic (Chew and McCleary, 1995). Bioclimatic factors may affect circannual rhythms in specific biochemical processes that are associated with vulnerability or resistance to stressors (Maes et al., 1993). There is evidence that these processes may involve dopamine, serotonin, or melatonin metabolism (Brewerton, 1991; Parker and Walter, 1982; Pine et al., 1995; Schreiber et al., 1993; Skutsch, 1981; Sou&tre et al., 1990). Sociodemographic explanations are based on seasonal changes in social activity (Chew and McCleary, 1995). When social activity in a community increases, those who are isolated are most likely to be brought face to face with their predicament resulting in suicidal feelings (Durkheim, 1879). In a recent analysis of time series data for 28 countries, Chew and McCleary (1995) showed that both the extent of seasonal fluctuation and the ratio of the number of suicides in spring in relation to winter are positively correlated with the proportion of the workforce engaged in agriculture. The agriculture-seasonality correlation explained more than two-thirds of the total variance in suicide seasonality (Chew and McCleary, 1995). This is consistent with both bioclimatic and sociodemographic explanations for suicide seasonal trends. Those in less industrialised and more rural societies are less likely to be buffered from the effects of bioclimatic factors through insulated buildings, electrical heaters, and air conditioning apparatuses (Ntiyh%, 1982). Also, they are more likely to be exposed to seasonal changes in social activity. How could data from South Africa address this issue? Partly because of the Apartheid policies of the previous government,

A.J. Flisher et al. /Psychiatry

67.2% of black South Africans live in rural areas compared to 8.7% or less for the other racial/ethnic groups (South African Labour and Development Research Unit, 1994). One would thus expect that blacks would display a relatively great seasonal variation in suicide mortality. The fall peaks that were observed for youth and females in a subset of the studies conducted in the northern hemisphere have been explained by sociodemographic factors related to the commencement of the academic year. When transferring to an academic routine, a sense of isolation may be experienced both by youth (who are confronted by an intensification of social life by their contemporaries) and women (whose sense of isolation may be brought into focus by their children commencing their studies) (Nayha, 1982). In addition, it has been argued that the fall peak for females could be determined by gender-specific circannual biological rhythms related to bioclimatic variables (Meares et al., 1981). Again, how could data from South Africa address this issue? In South Africa, the commencement of the academic year is in January or February, that is in summer, and not in the fall as in countries in the northern hemisphere. If the fall peak is indeed caused by sociodemographic factors related to the commencement of the academic year, one would not expect to find a peak in the fall. Conversely, if (in the case of females) bioclimatic factors are responsible, one would expect this peak to be preserved. South Africa was included in Chew and McCleary’s (1995) review of the spring peak in suicide in 28 countries. However, they did not address differences due to urbanization, gender, or age group, nor the existence of the fall peak, in South Africa specifically. The present report comprises the first analysis focusing on seasonal variation in suicide mortality in South Africa, and one of the few in the southern hemisphere. The following specific questions were addressed: (1) is the pattern of seasonal variation that has been observed in countries in the northern hemisphere, with a peak in the spring or early summer and a trough in winter, also present in South Africa? (2) If the expected pattern is present, is it more pronounced for less urbanized subgroups? (3) Is there a fall peak for youth and females?

Research 66 (1997) 13-22

2. Materials

15

and methods

Mortality data for 1980-1989, which is the most recent decade for which data are available, were extracted from tapes supplied by the South African Central Statistical Service (CSS). The total number of suicides in a particular month in all 10 years was analysed (i.e. January 1980 + January 1981 + _.. + January 1989). This was done to minimise the influence of bias caused by extreme counts in any particular year. The ‘homelands’ of Transkei, Bophuthatswana, Venda, and Ciskei (‘TBVC states’) were excluded from the analysis since data for these territories were not available. An autopsy performed by a district surgeon is required for every death with an unnatural cause. An inquest is then held, the aim of which is to establish the cause of death. This cause is then forwarded to the CSS (Kielkowsb et al., 1989). Causes of death are classified according to a system based on the International Classification of Diseases (ICD-9) (World Health Organisation, 1978). The term ‘suicide’ is used to denote all deaths reported as due to suicide or self-inflicted injury (ICD categories E950-959 and E979). Age was grouped in the categories: 15-24; 25-34; 35-44; 45-54; 55-64; and 65 or greater. Race/ethnicity was defined according to the Population Registration Act of 1950, which has been repealed. According to this act, the population of South Africa was divided into four groups: black (approximately 76% of the population); white (13%); Coloured (derived from Asian, European, Khoisan, and African ancestry) (9%); and Asian (3%) (South African Institute of Race Relations, 1992). The proportions of these groups that lived in rural (as opposed to urban or metropolitan) areas are as follows: black 67.2%; white 8.7%; Coloured 6.6%; and Asian - 0.0% (South African Iabour and Development Research Unit, 1994). The CSS data for 1980-1989 were cross-classified into a multi-dimensional contingency table (2 X 6 X 4 X 12), with the first dimension being sex, the second age, the third race/ethnicity, and the fourth month. The four dimensions were denoted by S (sex), A (age), R (race/ethnicity), and

A.J. Flisher et al. /Psychiatry Research 66 (1997) 13-22

16

M (month), where the single letters S, A, etc. denote the main effects of the factors; and RS, etc. denote both the main effects and their interactions. Log-linear modelling was performed to investigate the effects of the four variables on the suicide counts. Log-linear modelling is well suited to studying the relationships between categorical variables which are treated alike as ‘response variables’ (Bishop et al., 1975). To use alternative methods such as logistic regression or discriminant analysis, with suicide as a dichotomous outcome variable or grouping variable, respectively, requires information on those who do not commit suicide. Given that the focus of this study is on the effect of month of suicide, such techniques would be inappropriate. It was assumed that the natural log of the expected counts in the table can be explained by the main effects of S, R, A and M and some of their two- and three-way interactions (Bishop et al., 1975). Hierarchical models were fitted, imply-

Table 1 Demographic

features

Sex

Population

Age in years

Month

group

for deaths

due to suicide (1980-I

Male Female White Coloured Asian Black 15-24 25-34 15-44 45-54 55-64 65 + January February March April May June July August September October November December

ing that if an interaction was included in the model all the associated lower order interactions and the corresponding main effects were also included. Goodness of fit of the model was measured by the Pearson chi-squared statistic. Further details regarding the statistical analysis are provided in the Appendix. 3. Results There were 16 389 deaths ascribed to suicide in the period 1980-1989. The relevant demographic features are presented in Table 1, and the numbers of suicides in each month are depicted in Fig. 1. Investigating the statistical significance of partial and marginal effects (Table 2) showed that a provisional model is S*A* R + R* M. After the preliminary selection of the model S*A* R + R* M, some models which differed from this one by only a few terms were investigated further. The final model adopted was: S*A* R + M + junbl + decbl + aprcol + deccol

989) (N = 16 389)

13240 3149 8298 1440 694 5957 3222 4606 3414 2370 1505 I272 1492 1346 1410 1258 1361 1212 1320 1337 1400 1439 1378 1436

80.8

19.2 50.6 8.8 4.2 36.3 19.7 28.1 20.8 14.5 9.2 7.8 9.1 8.2 8.6 7.7 8.3 7.4 8.1 8.2 8.5 8.8 8.4 8.8

17

A.J. Flisher et al. /Psychiatry Research 66 (1997) 13-22 Table 2 ANOVA

table for sex by age by race/ethnicity

by month

with suicide

count

Effect

DF

Partial assoc chi-square

PROB

Sex (S)

1 3 5 II 3 5 11 15 33 55 I5 33 55 167

6681.22 10 693.89 2989.13 50.25 114.66 21.51 5.43 847.22 53.43 65.75 66.44 43.60 71.37 185.99

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9084 o.ooOQ 0.0137 0.1520 0.0000 0.1026 0.0680 0.1496

Race (RI Age (A) Month (M) SR SA SM RA RM AM SAR SRM SAM RAM

Feb

Mar

Apr

WY

JUtl

JUI

as dependent

AUS

variable Marginal assoc chi-square

PROB

113.96 20.45 5.77 847.77 55.17 66.75 63.00 38.43 68.77 181.46

0.0000 0.0010 0.8880 o.OQoo 0.0091 0.1331 0.0000 0.2371 0.1003 0.2101

SOP

Months t Fig.

White -a- Coloured +-Asian

-m- Black

1, Number of suicides per month for each race/ethnic

group.

083

NOV

De.2

A.J. Flisher et al. /Psychiatry Research 66 (1997) 13-22

18

where the last four terms account for the only significant race by month interactions. Here junbl and decbl refer to suicides in the black population in June and December, respectively, and aprcol and deccol refer to suicides in the Coloured population in April and December, respectively. Overall the fit of the model was good ( xz = 491.6, df = 513, P = 0.2495). Interpreting this model, it appears that there were no significant differences in the monthly trends according to sex and age group. The monthly effect is, however, significant and independent of any sex, race or age classification apart from the differential effects of Coloureds in April and December and blacks in June and December. There were no differences between the monthly trends for whites and Asians. Using each

race/ethnic group’s January suicide count as the baseline (see Appendix), it appears that for all race/ethnic counts for March, September, and October are not significantly different from January; for example, the count for July is 88.5% that of January. Also, for all race/ethnic groups, the suicide counts for February, April to August, and November are significantly less than January. For Coloureds and blacks in April and June, respectively, the counts were also significantly lower than those for whites and Asians in these months. For Coloureds in April, for example, the count is 66.5% that of whites and Asians in this month; furthermore, given that the count for whites and Asians in April was 86.0% that of January, the count for Coloureds was 66.5% x 86.0% = 56.3% that of January (see Appendix). Similarly, for December, the counts for whites

130 n 120

110 A 100 I

Percentages

ww t A

I Feb

Mar

JUn

APT

JUI

Aug

*P

OCi

NW

Dee

Months t Fig. 2. Result of log-linear to a monthly effect, percentages

Expected

modeling. Percentages

relative

to the expected

that are significantly different

n

A Blacks

Coloureds

by which the expected number

number

of suicides in January,

of suicidesin

for each month.

each race/ethnic

group changes due

For blacks and Coloureds

from Asians and whites are given. (See text for details.)

only the

A.J. Flisher et al. /Psychiatry Research 66 (1997) 13-22

and Asians were significantly lower than the counts for January while for blacks there was no significant difference and for Coloureds the count was significantly higher than for January. In summary, these results indicate that for both blacks and Coloureds the winter trough and the summer peak are more pronounced than for whites and Asians (Fig. 2). The overall monthly pattern is preserved when correcting for the different number of days in the months. This is not relevant in the analysis comparing the seasonal effect between different levels of gender, age, and race/ethnic@ since each level is equally affected. 4. Discussion As mentioned above, in southern hemisphere countries the seasons are ‘reversed’ compared to the northern hemisphere. Thus, in South Africa, spring occurs from September through November, summer from December through February; fall from March through May; and winter from June through August. The pattern of seasonal variation that has been observed in countries in the northern hemisphere, with a peak in the spring or summer, is also present in South Africa for all race/ethnic and age groups and both genders. This is consistent with the results for Uruguay (Chew and McCleary, 19951, males in Chile (Trucco, 1977) and females in Australia (Parker and Walter, 1982). For females in Chile (Trucco, 1977) and the other countries in the southern hemisphere for which statistical analyses of seasonal suicide trends are available (Rittey and Castle, 1972; Rwegellera, 1978), it is possible that the small sample sizes resulted in a failure to detect seasonal trends. Although the seasonal variation for males in Australia was not significant, there was a peak in December (Parker and Walter, 1982). The expected pattern was more pronounced for black and Coloured South Africans. The former finding is consistent with the expectation that less urbanized sectors of the population would display greater seasonal variation, and provides further evidence for the importance of urbanization in accounting for differences in the seasonal trends

19

for suicide mortality (Chew and McCleary, 199.5; Durkheim, 1879; Massing and Angermeyer, 1985). Unfortunately, the data required to provide further evidence of this by examining the patterns of seasonal variation for blacks of differing urbanization status are not available. It was not expected that Coloureds would also display greater seasonal variation than whites and Asians since they are not less urbanized. An explanation for this anomalous finding is suggested by an incidental finding of Chew and McCleary (1995), who included standard of living as indexed by per capita gross national product (GNP) as a control variable in their analysis. They found that the magnitude of the spring-winter ratio was signifi cantly negatively correlated with GNP (P < 0.001). This variable could be contributing to the greater seasonal variation observed for Coloureds since their standard of Iiving is tower than that of Asians and whites (South African Institute of Race Relations, 1992). Other confounding factors may also be contributory, such as the method of suicide. Maes et al. (1993) reported that there was seasonal variation for Belgians dying from violent suicide but not non-violent suicide. Although the proportion of Coloured suicide victims using violent methods is not higher than that of whites and Asians for either gender, this is not the case for blacks (Flisher and Parry, 1994). Among black males committing suicide, 87% used violent methods compared to 69% for white males and 82% for Asian males (Flisher and Parry, 1994). Despite the absence of similar trends for black females, the effect of method of suicide on the seasonal variation of suicide in South Africa requires further investigation. There were no significant differences in the monthly trends according to sex and age group. The fall peak that was differentially present for youth and females in a subset of studies conducted in the northern hemisphere was thus not detected in South Africa. This is consistent with the sociodemographic explanation for these peaks in the northern hemisphere that accounts for their existence by changes in social activity caused by the commencement of the academic year (which occurs in summer in South Africa). Con-

20

A.J. Flishrr et cd. /Psychiatry Krsearch 66 (1997) 13-22

versely, if bioclimatic factors were responsible for the fall peak in females as was suggested by Meares et al. (19811, one would expect a peak to be differentially present for females in the fall (about March in the southern hemisphere). Although a peak was not found to be differentially present for youth or females in the months corresponding to the commencement of the academic year in South Africa, it is possible that such a peak was overshadowed by the summer peak. In addition to the above findings that relate to specific research questions, it was found that the proportions of suicides committed in December is higher for Coloureds and blacks than for the other population groups. This may be due to the influence of Christmas (Nayha, 1982; Masterton, 1991; Phillips and Wills, 1987; Central Statistical Service, 1991). These groups are economically disadvantaged relative to whites and (to a lesser extent) Asians (South African Institute of Race Relations, 1992). It is possible that the hardship resulting from this is experienced particularly intensely during the Christmas period; not only are people unable to afford to celebrate Christmas as they would have chosen to, but the expenses of Christmas could increase financial adversity. An additional factor protecting Asians from a relatively high suicide rate in December in relation to whites is that only 12% are Christians as compared to 93% and 89% of whites and Coloureds, respectively (Brewerton, 1991; Central Statistical Service, 1991). In conclusion, this analysis has: (1) confirmed that the pattern of seasonal variation in suicide that has been observed in countries in the northern hemisphere is also present in South Africa; (2) provided data that are consistent with the relevance of urbanization in explaining seasonal trends in suicide; and (3) provided data that are consistent with a sociodemographic explanation for the autumn peak in suicide incidence found for youth and females in some studies conducted in the northern hemisphere. Acknowledgements The authors would like to thank Mr. David Yamey for assisting with the processing of the

mortality data, Professor Brian Robertson and Drs. Stephan Maritz, Daniel Pine, John Seager, and Derek Yach for their comments on the manuscript, and Dr Madelyn Gould for facilitating access to key references. The first author was partially supported by a Postdoctoral Overseas Scholarship from the South African Medical Research Council and Grant MH-46091 (Principal Investigator: Dr Christina Hoven) from the National Institute of Mental Health (USA). Appendix Investigating the statistical significance of partial and marginal effects showed that a provisional model is S*A* R + R* M. If yijk, is the number of suicides recorded for sex i, age j, race k, in month I (i.e. the number of suicides in the ijkl-th cell of the table), and pijk, is the expected number that occurs in cell ijkl, then the log-linear model is: log( /,Q,)

= /_l + A; -t. A; + hk” + h;M + A;‘;” + *;S/ +

/\4,.R

+

/k

hR.M kl

+

hS.f.R lik

for i = 1,2; j = 1...6; k = 1...4; I = 1...12, where a A with any subscript equal to 1 is zero. Taking the anti-log, we obtain a multiplicative model which factors the counts in the cell into a number of terms, each of which can be associated with sex, age, race, or month and their interactions. The factored count can be written as: &,k/ = exp/l.exp(Af) Xexp( h:).exp(hp).exp(h:“)exp(

A;‘;“)

X exp( hfiR >.exp( ha-“) X exp( h:jl’).exp(

Afj:f.R)

After the preliminary selection of the model S*A* R + R* M, the R.M interactions were examined further, and it was found that only a few were significantly different from zero. The final model adopted was:

A.J. Flisher et al. / Pgchiatry

21

Research 66 (1997) 13-22

Table 3 Monthly effects Race

White/ Asian

Month

Jan Feb Mar

A Pr May Jun JUI

A us

ColouredC BlackC

Sep Ott Nov Dee Apr Dee Jun Dee

Factor“

Significance

95% limit lower

upper

0.538 0.879 0.797 0.848 0.794 0.822 0.832 0.872 0.897 0.858 0.783 0.523 1.009 0.617 0.912

0.971 1.016 0.929 0.982 0.944 0.953 0.965 1.009 1.037 0.994 0.939 0.846 1.516 0.836 1.224

I .ooo 0.902 0.945 0.860 0.912 0.866 0.885 0.896 0.938 0.964 0.924 0.857 0.665 1.237 0.718 1.056

Sig” NS Sig Sig Sig Sig Sig NS NS Sig Sig Sig Sig Sig NS

“The proportion of suicides in a given month against the number of suicides in January for a particular race group. hSig, significant (P < 0.05); NS, non-significant. ‘For Coloureds and blacks only the factors that are significantly different from those of Asians and whites are given. The factors for the remaining groups are the same as for Asians and whites.

S*P R + M + junbl + decbl + aprcol + deccol where the last four terms account for the only significant race by month interactions. Here junbl and decbl refer to other significant additional effects on the suicide counts in the black population in June and December, respectively, and aprcol and deccol refer to other significant additional effects on the suicide counts in the Coloured population in April and December, respectively. For any given sex, race and age group the monthly effect can be interpreted as a factor that modifies the basic count of the group. The monthly factors are given by:

The estimates of the monthly effects are given in Table 3 together with an approximate 95% confidence interval. These can be interpreted as the fraction, or if multiplied by 100, the percentage by which the expected number of suicides in any group changes due to a monthly effect. So for instance, the May suicide counts are expected to be only 91% of those in January. In April the expected number of suicides for all groups except Coloureds is 86% of the expected January count. For Coloureds in April the expected count is only 56% of the January count (100 x 0.860 x 0.655). If the approximate confidence interval includes the value 1, then the factor for that month is not significantly different from January. Table 3 is interpreted in the Results section.

exp(hy).exp(A,f,) References where the last term accounts for the differential effects in the black and Coloured groups. The month of January is used as the baseline here, so exp(h;W = 1)

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