Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Associations between climate variability, unemployment and suicide in Australia: a multicity study

BMC Psychiatry201515:114

https://doi.org/10.1186/s12888-015-0496-8

  • Received: 16 December 2014
  • Accepted: 30 April 2015
  • Published:
Open Peer Review reports

Abstract

Background

A number of studies have examined the associations of suicide with meteorological variables (MVs) and socioeconomic status but the results are inconsistent. This study assessed whether MVs and unemployment were associated with suicide in eight Australian capital cities.

Methods

Data on suicide, population and unemployment rate (UER) between 1985 and 2005 were from the Australian Bureau of Statistics. MVs was provided by Australian Bureau of Meteorology. A generalized linear regression model with Poisson link was applied to explore the association of suicide with MVs and UER.

Results

Temperature difference (ΔT, the difference in mean temperature between current month and previous one month) was positively associated with suicide in Sydney, Melbourne, Brisbane and Hobart. There was also a significant and positive association between UER and suicide in Sydney, Melbourne, Brisbane and Perth. MVs had more significant associations with violent suicide than that of non-violent suicide. There were no consistent associations between other MVs and suicide. A significant interaction between ΔT and UER on suicide was found in Sydney, Melbourne and Brisbane, such that increased temperature amplified the magnitude of the association between UER and suicide.

Conclusions

ΔT and UER appeared to jointly influence the occurrence of suicide in Australian capital cities. This finding may have implications for developing effective suicide prevention strategies.

Keywords

  • Suicide
  • Australia
  • Cities
  • Climate
  • Unemployment

Background

Suicide remains an important public health issue around the world [1]. Since the nineteenth century, the impact of socio-environmental factors on suicide have attracted much public attention, especially in the previous decades with the progress of global climate change and financial crisis [24]. Recently, a number of studies have explored this issue in different countries and found that some meteorological variables (MVs), e.g., temperature [513], rainfall [5, 12, 13], humidity and sunshine [11, 13], were associated with suicide risk. The seasonality of suicide were also examined [1416]. However, all the previous studies were based on local environmental settings and the findings were inconsistent over different environmental settings. For example, a few studies indicated that increased temperature was accompanied with higher suicide risk [6, 7, 9, 13]; however, another study showed a suicide peak in winter with low temperature [5]. Some findings were contradictory even in the same area. For instance, Tsai and Cho [13] indicated that temperature was positively associated with suicide rate in Taiwan using a pure time-series analysis; while another study showed that the association was negative when comparing the regional differences of suicide rate in Taiwan [12]. These discrepancies may be due to the different regional focuses and different statistical methods being used [12].

In Australia, capital cities occupy less than 1 % of the total land area but contain 65 % of total population and account for approximately 60 % of suicides across the whole country [1721]. Capital cities also represent a range of geographical, population size and climatic contexts, from the tropical north climate (e.g. Darwin) to the more temperate southern climate (e.g. Melbourne and Hobart). Thus the association of particular climate variable and suicide may differ across cities, and may affect other more proximal antecedents associated with suicide in populations. Some studies indicated that decreased rainfall and continued drought caused by rainfall deficiency were associated with higher suicide risk in New South Wales (NSW), especially in rural areas [22, 23]. Other studies showed that increased maximum temperature was accompanied with higher suicide rate over time and space in Queensland and Australia [24, 25]. However, each of these studies are based on only one state, thus the results from these studies are difficult to generalize to other areas. A few studies figured out that the associations between environmental factors and health status were modified by socioeconomic differences [26, 27]. Thus it is important to understand how MVs are associated with suicide rates and whether MVs have interactive effects on socioeconomic factors (e.g. unemployment) and suicide in various cities over time, in order to understand how the epidemiology of suicide in Australia differs geographically and to inform suicide prevention programmes locally.

Most suicides occurred in capital cities and climate patterns within each city are relatively homogeneous compared to rural and remote areas with large geographical size, sparse population and suicide cases, and varied climate factors. Thus this study aimed to explore the pattern of suicide in Australian capital cities, and to assess the association of suicide with MVs and unemployment rate in these cities, and extent to which MVs modified associations between unemployment and suicide.

Methods

Data sources

Suicide data (1985 to 2005), including sex, age, International Classification of Disease (ICD) Code relating to suicide or self-inflicted injury (ICD 9:950.0-952.9 for non-violent suicide and 953.0-959.9 for violent suicide; ICD 10: ×60.0- × 69.9 for non-violent suicide and × 70.0- × 84.9 for violent suicide), suicide date, country of birth and statistical local area (SLA) code, were provided by Australian Bureau of Statistics (ABS). As around 5 % of total selected data has only “year” and “month” of suicide but no information of accurate “day” (especially in 2004 due to the quality of original data), and some cities have a relatively small size of population and number of suicide (e.g., Darwin and Hobart), we used monthly data in this study to assure covering relatively recent data (2004 and 2005) and cities with more diverse climate. Suicide data after 2005 were not included because the related procedure for accessing recent ABS data is currently under review. According to the definition from ABS, we used eight Statistical Divisions (SDs) to represent the metropolitan areas of eight Australian capital cities in different climate zones: one in tropical climate zone (Darwin), one in sub-tropical climate zone (Brisbane) and other six in temperate climate zone (Sydney, Melbourne, Adelaide, Perth, Hobart, and Canberra). Each SD was composed of a certain number of SLAs and suicide data at the SLA level were aggregated into SD level. The locations of each city were shown in Fig. 1. The map in Fig. 1 was generated by MapInfo 10.5 using the geographical boundary from ABS [28]. Census population data (SD level, 1986, 1991, 1996, 2000, 2005) by age and sex and monthly unemployment rate (UER, %, total and by sex, seasonally-adjusted), were also obtained from ABS. We interpolated the population size by sex and age groups for each year using census population data. The institutional ethics approval was granted by the Human Research Ethics Committee, Queensland University of Technology.
Fig. 1
Fig. 1

The locations of capital cities in Australia

Monthly meteorological variables (MVs), including rainfall (mm), relative humidity (%), maximum and minimum temperature (Tmax and Tmin, °C), sunshine hours (daily average of each month), were supplied by the Australian Bureau of Meteorology. We examined the monitoring stations within the boundaries of all metropolitan areas of eight capital cities, selected the stations which had valid records covering the whole study period, calculated the mean values of station records in each city, and applied them in the data analyses. The monthly mean temperature (T-mean, °C) was calculated by using the mean value of Tmax and Tmin. As temperature has been found having more significant associations with suicide in published literature than other MVs, we also calculated the temperature difference (ΔT, the difference in mean temperature between current month and previous one month, °C; above 0 °C for increase and below 0 °C for decrease) for following data analyses.

Data analysis

A series of statistical methods were applied to investigate associations of MVs and UER with suicide rate. Seasonal differences of suicide in each city were explored using the mean mortality of 12-month cycles. Monthly age-adjusted mortality rate (per 100,000) by sex in each city was calculated. Spearman correlation analysis was applied to assess multicollinearity of independent variables, particularly the temperature indexes. The variables with high multicollinearity (correlation ≥ |0.80|) were included in separated models. A generalized linear model (GLM) with Poisson link was used to investigate whether MVs and the UER were associated with suicide rates across different cities. Then we selected the MVs having the strongest associations with suicide and tested the interaction of MVs on the association between UER and suicide. Monthly rainfall, temperature, ΔT, humidity, sunshine hours and UER were used as independent variables in GLM. UER were added in GLM with different lags (1 to 6 months). Population in each city (total and by sex) with logarithm transfer was used as an offset. Seasonality (Darwin: September to February for wet season; March to August for dry season; other cities: September to November for spring; December to February for summer, March to May for autumn; June to August for winter) was also adjusted as categorical independent variables. All the MVs were seasonally adjusted. The minimum value of Akaike information criterion (AIC) was applied to select the most suitable model. Autocorrelations of residuals for each model were also checked. All the analyses were conducted using the software package SPSS 21.0 [29].

Results

Suicide, climate and unemployment in Australian capital cities

Twenty-eight thousand five hundred one suicide cases (21,999 males and 6,502 females) from eight cities between 1985 and 2005 were included in this study. Sydney and Melbourne had the largest number of suicide case (16,665 for both) and accounted for 58.5 % of total suicides, followed by Brisbane, Perth and Adelaide. Darwin, Hobart and Canberra had the smallest number of suicide cases (1,662 for all three cities and 5.8 % of total suicides). Table 1 shows the distribution of suicide rates, meteorological variables (MVs) and unemployment status based on the mean monthly total. Sydney, Melbourne and Canberra had the lowest suicide rates while Darwin had the highest rates. Darwin also had the highest mean temperature, rainfall and sunshine among all selected cities due to its tropical climate. Hobart had the highest UER, followed by Adelaide and Brisbane; while Canberra had the lowest UER.
Table 1

Summary statistics for meteorological variables, unemployment rate and suicide rates in Australian capital cities (1985–2005, mean of monthly total)

Variables

Cities

Mean

SD

Min

 

Percentiles

Max

25

50

75

Mortality rate (M, F, per 100,000)

Sydney

0.96 (1.51, 0.44)

0.22 (0.38, 0.19)

0.43 (0.71, 0.05)

0.81 (1.22, 0.30)

0.94 (1.47, 0.42)

1.10 (1.74, 0.59)

1.76 (2.94, 1.07)

Melbourne

0.97 (1.50, 0.47)

0.22 (0.38, 0.18)

0.33 (0.47, 0.05)

0.83 (1.23, 0.35)

0.96 (1.50, 0.47)

1.12 (1.75, 0.59)

1.56 (2.41, 0.96)

Brisbane

1.15 (1.84, 0.49)

0.35 (0.58, 0.28)

0.27 (0.41, 0.00)

0.90 (1.38, 0.29)

1.15 (1.78, 0.44)

1.40 (2.28, 0.65)

2.28 (3.83, 1.63)

Adelaide

1.03 (1.63, 0.46)

0.34 (0.59, 0.31)

0.37 (0.39, 0.00)

0.80 (1.21,0.20)

1.00 (1.60, 0.39)

1.24 (2.02, 0.59)

2.00 (3.41, 1.53)

Perth

1.06 (1.69, 0.46)

0.33 (0.57, 0.29)

0.34 (0.41, 0.00)

0.82 (1.25, 0.27)

1.04 (1.60, 0.44)

1.29 (2.13, 0.63)

1.99 (3.28, 1.32)

Hobart

1.27 (2.07, 0.51)

0.82 (1.43, 0.78)

0.00 (0.00, 0.00)

0.53 (1.08, 0.00)

1.08 (2.16, 0.00)

1.64 (3.24, 1.03)

3.95 (7.56, 4.06)

Darwin

1.41 (2.34, 0.40)

1.22 (2.14, 0.92)

0.00 (0.00, 0.00)

0.00 (0.00, 0.00)

1.13 (2.19, 0.00)

2.01 (3.50, 0.00)

5.02 (8.78, 5.73)

Canberra

0.97 (1.52, 0.42)

0.59 (1.03, 0.50)

0.00 (0.00, 0.00)

0.61 (0.72, 0.00)

0.92 (1.36, 0.00)

1.34 (2.03, 0.71)

3.23 (5.34, 1.98)

Rainfall (mm)

Sydney

91.4

80.18

0.7

39.9

70.0

124.5

560.6

Melbourne

62.5

30.98

3.3

40.3

58.5

83.5

166.9

Brisbane

91.4

81.43

0.8

39.9

70.1

122.2

609.8

Adelaide

55.8

40.82

0.0

19.4

50.0

84.5

193.7

Perth

70.1

65.51

0.0

12.8

52.7

115.3

266.8

Hobart

57.2

34.78

2.6

33.0

50.8

74.9

255.2

Darwin

137.8

164.90

0.0

1.7

71.5

234.9

841.5

Canberra

54.6

40.10

0.6

24.0

46.8

73.9

248.2

Humidity ( %)

Sydney

63.5

6.97

41.6

58.8

64.1

68.3

79.4

Melbourne

64.9

7.15

46.7

59.3

64.4

70.5

79.8

Brisbane

63.4

5.57

43.7

59.9

63.5

67.2

78.6

Adelaide

56.6

11.05

35.9

47.5

54.5

65.9

77.5

Perth

59.8

8.93

43.8

51.8

59.6

67.8

77.8

Hobart

61.9

6.59

45.5

57.5

61.8

66.5

77.0

Darwin

62.0

11.21

37

52

61.5

72.5

85.0

Canberra

60.1

9.37

38

53.1

60.3

67.0

79.0

T (ΔT,°C)

Sydney

17.19 (0.01)

3.87 (2.26)

10.36 (−4.82)

13.89 (−1.76)

17.43 (0.20)

20.66 (1.98)

24.41 (4.29)

Melbourne

14.84 (0.01)

3.70 (2.24)

8.58 (−6.09)

11.41 (−1.81)

14.81 (0.23)

18.15 (1.69)

23.31 (5.23)

Brisbane

20.36 (0.01)

3.67 (2.11)

13.79 (−4.45)

17.09 (−1.84)

20.86 (0.05)

23.55 (1.83)

26.50 (3.93)

Adelaide

16.83 (0.00)

4.29 (2.62)

9.49 (−8.21)

12.90 (−1.97)

16.69 (0.38)

20.58 (1.98)

26.83 (7.15)

Perth

18.37 (−0.01)

4.01 (2.40)

11.66 (−5.36)

14.60 (−1.67)

18.05 (0.05)

22.10 (1.73)

27.81 (6.03)

Hobart

12.97 (0.01)

3.14 (1.91)

7.28 (−4.28)

10.05 (−1.59)

12.93 (0.30)

15.67 (1.49)

19.63 (3.95)

Darwin

27.52 (0.00)

1.83 (1.39)

22.45 (−4.10)

26.28 (−0.84)

28.03 (−0.10)

28.84 (0.81)

30.23 (3.75)

Canberra

13.39 (0.01)

5.24 (3.00)

4.65 (−6.25)

8.91 (−2.56)

13.45 (0.30)

18.14 (2.70)

22.80 (5.80)

Sunshine hours (daily average)

Sydney

7.1

1.21

3.7

6.4

7.1

7.9

10.4

Melbourne

6.1

1.69

2.7

4.8

6.2

7.4

9.9

Brisbane

8.1

1.22

4.4

7.3

8.2

8.8

10.6

Adelaide

7.6

2.08

3.3

5.8

7.8

9.3

11.9

Perth

8.5

2.22

4.0

6.8

8.2

10.5

12.6

Hobart

6.5

1.52

3.2

5.3

6.4

7.7

10.6

Darwin

8.6

1.89

3.0

7.4

9.2

10.1

11.0

Canberra

7.7

1.69

3.6

6.3

7.8

9.0

11.4

UER (M, F, %)

Sydney

7.4 (7.5,7.3)

1.7 (1.9,1.5)

5.0 (5.0, 4.8)

5.9 (5.9, 5.9)

7.3 (7.4, 7.2)

8.6 (8.5, 8.8)

11.0 (11.9, 9.8)

Melbourne

7.5 (7.4, 7.7)

2.1 (2.4,1.8)

4.7 (4.0, 5.4)

5.8 (5.6, 5.9)

6.7 (6.4, 7.6)

8.9 (9.1, 8.8)

12.5 (12.8, 12.3)

Brisbane

8.4 (8.3, 8.5)

1.6 (1.8,1.4)

4.7 (4.3, 5.1)

7.4 (7.2, 7.7)

8.6 (8.6, 8.6)

9.7 (9.5, 9.8)

10.8 (11.3, 10.7)

Adelaide

8.5 (8.9, 8.0)

1.7 (2.0,1.5)

4.8 (5.2, 4.1)

7.2 (7.7, 6.6)

8.7 (8.7, 8.5)

9.6 (10.3, 9.1)

12.0 (13.2, 10.5)

Perth

7.3 (7.4, 7.3)

1.6 (1.8,1.5)

4.1 (3.8, 4.4)

6.1 (6.5, 6.0)

7.3 (7.3, 7.3)

8.0 (7.9, 8.5)

11.1 (11.6, 10.9)

Hobart

9.4 (10.0, 8.7)

1.6 (1.9,1.5)

5.8 (6.1, 5.5)

8.7 (8.8, 7.7)

9.3 (9.8, 9.1)

10.6 (11.5, 9.9)

13.0 (14.1, 15.4)

Darwin

6.6 (6.7. 6.4)

1.6 (1.8,1.7)

3.7 (3.0, 2.7)

5.3 (5.3, 5.0)

6.6 (6.5, 6.4)

7.5 (7.6, 7.5)

11.2 (11.9, 10.9)

Canberra

5.7 (6.0, 5.5)

1.4 (1.5,1.5)

3.0 (3.0, 2.7)

4.6 (4.9, 4.2)

5.4 (5.6, 5.4)

7.0 (7.5, 6.7)

8.60 (8.8, 9.2)

Note: SD: standard deviation; Min: minimum, Max: maximum; M: male; F: female; T: temperature; ΔT: temperature increase; UER: unemployment rate

Seasonality of suicide

Figure 2 demonstrates various seasonal trend of suicide rates in different cities. There was no obvious seasonal variation of suicide rates in Sydney, while Melbourne had a small peak of suicide (in males) in spring. Brisbane experienced the lowest suicide rates in June (winter), and high but stable suicide rate from spring to summer; which was similar to Perth. In Adelaide, two peaks were observed in May and October (predominantly in males). A sharp peak (predominantly in males) of suicide in October was also found in Darwin and Canberra.
Fig. 2
Fig. 2

Monthly suicide rate by city (total and by sex)

Association of suicide with climate and unemployment

The results of GLM modeling show that ΔT was positively associated with the suicide in Sydney (for males and total rates), Melbourne (in males), Brisbane (in males and total rates), Hobart (in females and total rates) and Canberra (in females) (Table 2). Temperature was only positively (marginally significant) associated with total and male suicide in Darwin. A significant association between humidity and suicide was also found among males in Darwin. Rainfall had a negative association with total and female suicide in Melbourne. No significant associations were discovered between MVs and suicide in Adelaide and Perth. Other MVs were also tested in the models. The associations between maximum temperature and suicide rates were similar as that of mean temperature, while minimum temperature appeared to be less significant. There was a positive association between UER (1 month lag) and suicide in Sydney (total and by sex), Melbourne (total and male), Brisbane (total and by sex) and Perth (total and male). However, this association was not significant in other cities. The results of the pooled data indicate that both ΔT and UER were positively associated with suicide. The associations between adjacent monthly changes of other MVs (besides temperature) and suicide were examined, with less significant associations discovered than that of temperature.
Table 2

The association between meteorological variables, unemployment rate and suicide in Australian capital cities (total and by sex)

City

Parameter Estimates

All

Male

Female

RR

95 % CI

P-value

RR

95 % CI

P-value

RR

95 % CI

P-value

Sydney

Rainfall (100 mm)

1.00

0.97

1.04

0.921

0.99

0.95

1.03

0.700

1.02

0.95

1.08

0.632

Humidity (%)

1.00

1.00

1.01

0.635

1.00

0.99

1.01

0.754

1.00

0.99

1.01

0.858

Temperature (°C)

1.01

0.99

1.02

0.356

1.00

0.99

1.02

0.948

1.02

1.00

1.05

0.097

ΔT (°C)

1.03

1.01

1.04

0.001

1.02

1.01

1.04

0.010

1.03

1.00

1.06

0.089

Sunshine hours

0.98

0.95

1.01

0.229

0.98

0.94

1.02

0.308

0.94

0.87

1.01

0.092

UER (%)

1.05

1.03

1.06

0.001

1.04

1.02

1.05

0.001

1.09

1.06

1.12

0.001

Melbourne

Rainfall (100 mm)

0.91

0.82

1.00

0.034

0.93

0.83

1.03

0.151

0.84

0.67

1.01

0.049

Humidity (%)

1.01

1.00

1.01

0.130

1.01

1.00

1.01

0.121

1.00

0.99

1.01

0.892

Temperature (°C)

1.00

0.98

1.02

0.998

0.99

0.97

1.01

0.585

1.01

0.98

1.05

0.479

ΔT (°C)

1.02

1.00

1.03

0.097

1.02

1.00

1.04

0.033

0.99

0.95

1.02

0.536

Sunshine hours

1.02

0.99

1.06

0.202

1.04

1.00

1.08

0.043

0.96

0.89

1.03

0.286

UER (%)

1.02

1.01

1.03

0.001

1.02

1.01

1.03

0.001

1.01

0.98

1.03

0.580

Brisbane

Rainfall (100 mm)

0.97

0.92

1.03

0.319

0.97

0.91

1.03

0.279

0.97

0.86

1.08

0.589

Humidity (%)

1.00

0.99

1.01

0.716

1.00

0.99

1.01

0.893

0.99

0.97

1.02

0.560

Temperature (°C)

1.01

0.99

1.03

0.458

1.01

0.99

1.04

0.364

0.99

0.95

1.04

0.798

ΔT (°C)

1.03

1.01

1.06

0.010

1.04

1.01

1.07

0.015

1.03

0.97

1.08

0.315

Sunshine hours

0.97

0.92

1.01

0.144

0.96

0.91

1.01

0.152

0.98

0.88

1.08

0.627

UER (%)

1.05

1.03

1.07

0.001

1.04

1.02

1.06

0.001

1.09

1.04

1.13

0.001

Adelaide

Rainfall (100 mm)

1.06

0.90

1.22

0.482

1.09

0.90

1.27

0.385

1.00

0.66

1.33

0.976

Humidity (%)

1.00

0.99

1.01

0.712

1.00

0.98

1.01

0.932

1.01

0.99

1.04

0.340

Temperature (°C)

0.99

0.96

1.02

0.391

0.98

0.95

1.01

0.158

1.03

0.97

1.08

0.401

ΔT (°C)

0.98

0.96

1.01

0.171

0.99

0.96

1.02

0.490

0.96

0.91

1.01

0.126

Sunshine hours

1.01

0.96

1.07

0.634

1.02

0.96

1.08

0.484

0.99

0.87

1.10

0.830

UER (%)

0.99

0.96

1.01

0.223

0.98

0.96

1.01

0.165

1.00

0.95

1.05

0.951

Perth

Rainfall (100 mm)

1.10

0.97

1.22

0.152

1.09

0.95

1.23

0.227

1.05

0.78

1.32

0.722

Humidity (%)

0.99

0.98

1.00

0.158

0.99

0.98

1.00

0.224

0.99

0.96

1.02

0.422

Temperature (°C)

1.00

0.97

1.03

0.979

1.00

0.97

1.03

0.943

1.00

0.94

1.05

0.941

ΔT (°C)

1.02

0.99

1.04

0.259

1.01

0.98

1.04

0.358

1.02

0.96

1.08

0.516

Sunshine hours

1.02

0.97

1.07

0.442

1.00

0.95

1.05

0.988

1.07

0.96

1.17

0.247

UER (%)

1.05

1.02

1.07

0.001

1.04

1.02

1.06

0.001

1.04

0.99

1.09

0.131

Hobart

Rainfall (100 mm)

1.09

0.85

1.34

0.471

1.04

0.76

1.32

0.785

1.30

0.80

1.79

0.303

Humidity (%)

1.01

0.99

1.04

0.321

1.01

0.98

1.03

0.625

1.05

0.99

1.11

0.091

Temperature (°C)

0.96

0.89

1.03

0.229

0.97

0.90

1.05

0.462

0.91

0.76

1.07

0.247

ΔT (°C)

1.08

1.01

1.15

0.032

1.04

0.97

1.12

0.301

1.25

1.09

1.40

0.005

Sunshine hours

1.06

0.92

1.20

0.403

1.05

0.90

1.21

0.506

1.15

0.84

1.46

0.381

UER (%)

1.00

0.95

1.06

0.923

1.02

0.97

1.07

0.375

0.91

0.78

1.03

0.129

Darwin

Rainfall (100 mm)

1.06

0.90

1.22

0.458

1.09

0.92

1.26

0.326

0.94

0.41

1.47

0.812

Humidity (%)

0.98

0.95

1.01

0.223

0.97

0.93

1.00

0.043

1.11

1.00

1.21

0.052

Temperature (°C)

1.16

1.02

1.29

0.031

1.18

1.03

1.32

0.025

0.93

0.55

1.31

0.701

ΔT (°C)

1.07

0.95

1.18

0.282

1.08

0.96

1.21

0.194

0.91

0.59

1.24

0.586

Sunshine hours

0.99

0.80

1.17

0.876

0.94

0.74

1.13

0.517

1.51

0.95

2.07

0.152

UER (%)

0.95

0.88

1.03

0.194

0.96

0.89

1.03

0.235

0.63

0.42

0.84

0.001

Canberra

Rainfall (100 mm)

1.12

0.88

1.36

0.359

1.21

0.94

1.48

0.173

0.84

0.29

1.39

0.532

Humidity (%)

1.00

0.98

1.02

0.955

1.00

0.98

1.02

0.986

1.01

0.96

1.05

0.727

Temperature (°C)

0.97

0.94

1.01

0.181

0.97

0.93

1.02

0.212

0.98

0.90

1.06

0.630

ΔT (°C)

1.02

0.98

1.07

0.319

1.01

0.95

1.06

0.839

1.09

0.99

1.19

0.074

Sunshine hours

1.07

0.95

1.19

0.259

1.08

0.95

1.22

0.246

1.05

0.80

1.29

0.709

UER (%)

1.03

0.98

1.08

0.277

1.04

0.98

1.10

0.180

1.03

0.93

1.14

0.552

All eight cities

Rainfall (100 mm)

0.99

0.97

1.02

0.597

0.99

0.96

1.02

0.507

1.00

0.95

1.05

0.926

Humidity (%)

1.00

1.00

1.01

0.196

1.00

1.00

1.01

0.334

1.00

0.99

1.01

0.670

Temperature (°C)

1.00

0.99

1.01

0.644

1.00

0.99

1.01

0.882

1.00

0.99

1.02

0.694

ΔT (°C)

1.02

1.01

1.03

0.001

1.02

1.01

1.03

0.001

1.01

1.00

1.03

0.135

Sunshine hours

1.00

0.99

1.02

0.822

1.00

0.98

1.02

0.975

1.00

0.96

1.03

0.936

UER (%)

1.03

1.02

1.03

0.001

1.02

1.02

1.03

0.001

1.03

1.02

1.05

0.001

Note: ΔT (temperature increase), UER (unemployment rate); Seasonality was adjusted

Table 3 explores the associations of MVs and UER with suicide by methods. There was no significant association of rainfall and humidity with either violent or non-violent suicide in all cities. No significant association of suicide with MVs and UER was discovered in Adelaide, Hobart, Darwin and Canberra. MVs (e.g., ΔT) were only associated with violent suicide. However, non-violent suicide had higher relative risk (RR) in associating with UER than that of violent suicide in Sydney, Melbourne, Brisbane, Perth and all cities together.
Table 3

The association between meteorological variables, unemployment rate and suicide in Australian capital cities (by suicide methods)

City

Parameter Estimates

Violent

Non-violent

RR

95 % CI

P-value

RR

95 % CI

P-value

Sydney

Temperature (°C)

1.01

0.99

1.02

0.368

1.01

0.98

1.03

0.580

ΔT (°C)

1.03

1.01

1.06

0.002

1.01

0.98

1.04

0.541

Sunshine hours

0.99

0.95

1.03

0.642

0.96

0.90

1.02

0.160

UER (%)

1.03

1.02

1.05

0.001

1.08

1.06

1.10

0.001

Melbourne

Temperature (°C)

0.99

0.96

1.01

0.185

1.00

0.98

1.03

0.769

ΔT (°C)

1.02

1.00

1.04

0.105

1.00

0.97

1.03

0.860

Sunshine hours

1.06

1.02

1.11

0.010

1.02

0.96

1.08

0.517

UER (%)

1.01

1.00

1.02

0.124

1.03

1.01

1.05

0.002

Brisbane

Temperature (°C)

1.03

1.01

1.06

0.021

0.97

0.93

1.01

0.100

ΔT (°C)

1.04

1.01

1.07

0.019

1.02

0.98

1.06

0.301

Sunshine hours

0.95

0.90

1.01

0.106

0.99

0.91

1.06

0.710

UER (%)

1.03

1.00

1.06

0.022

1.08

1.05

1.11

0.001

Perth

Temperature (°C)

1.01

0.97

1.04

0.670

0.99

0.95

1.03

0.621

ΔT (°C)

1.00

0.97

1.04

0.924

1.03

0.99

1.07

0.123

Sunshine hours

1.02

0.96

1.09

0.491

1.02

0.95

1.10

0.586

UER (%)

1.01

0.98

1.04

0.684

1.10

1.06

1.13

0.001

All eight cities

Temperature (°C)

1.01

1.00

1.01

0.284

1.00

0.99

1.01

0.678

ΔT (°C)

1.02

1.01

1.03

0.001

1.01

0.99

1.02

0.322

Sunshine hours

1.00

0.97

1.02

0.647

1.01

0.99

1.04

0.378

UER (%)

1.01

1.00

1.02

0.011

1.05

1.04

1.06

0.001

Note: ΔT (temperature increase), UER (unemployment rate); Seasonality was adjusted

The interaction of ΔT and unemployment on suicide

As ΔT and unemployment were two key predictors of suicide, the interactive effects of these two variables on suicide were also examined by dividing both of UER (1 month lag) and ΔT into two levels (lower or higher than the median value in each city in the whole study period), respectively (Tables 4 and 5), using months with low ΔT and low UER as reference group. The Chi-square test indicated that most cities had significant overall effects except for Hobart (Table 4). In Table 5, months with high ΔT and high UER and months with low ΔT but high UER had higher suicide in Sydney, Melbourne, Brisbane and all cities together. Months with low ΔT but high UER had higher suicide risk only in Brisbane. In Canberra, suicide risk was only high in months with both high ΔT and UER. Only Adelaide had lower suicide risk in months of high ΔT and low UER (female) and months of low ΔT and high UER (all). There are no significant association in Perth, Hobart and Darwin.
Table 4

Chi square test of the interaction of temperature on unemployment rate

Cities

Chi-square

P-value

Sydney

9.815

0.020

Melbourne

31.649

<0.001

Brisbane

35.984

<0.001

Adelaide

42.074

<0.001

Perth

10.007

0.019

Hobart

1.358

0.715

Darwin

15.747

0.001

Canberra

10.221

0.017

All eight cities

17.402

0.001

Table 5

The interaction of temperature on unemployment rate on suicide

City

Parameter Estimates

All

Male

Female

RR

95 % CI

P-value

RR

95 % CI

P-value

RR

95 % CI

P-value

Sydney

High ΔT*High UER

1.22

1.14

1.29

0.001

1.20

1.12

1.29

0.001

1.27

1.11

1.43

0.003

Low ΔT* High UER

1.13

1.07

1.19

0.001

1.11

1.04

1.18

0.005

1.26

1.14

1.39

0.001

High ΔT*Low UER

1.01

0.93

1.09

0.855

0.99

0.90

1.18

0.891

1.00

0.83

1.16

0.982

Low ΔT* Low UER

1

   

1

   

1

   

Melbourne

High ΔT*High UER

1.14

1.06

1.22

0.001

1.15

1.05

1.24

0.005

1.18

1.02

1.35

0.044

Low ΔT* High UER

1.06

1.00

1.13

0.061

1.08

1.00

1.15

0.049

1.14

1.00

1.27

0.051

High ΔT*Low UER

0.97

0.88

1.05

0.413

0.99

0.89

1.08

0.790

1.02

0.86

1.19

0.781

Low ΔT* Low UER

1

   

1

   

1

   

Brisbane

High ΔT*High UER

1.32

1.21

1.43

0.001

1.34

1.22

1.46

0.001

1.33

1.08

1.58

0.023

Low ΔT* High UER

1.09

1.00

1.18

0.056

1.05

0.94

1.15

0.374

1.29

1.10

1.48

0.010

High ΔT*Low UER

1.18

1.07

1.28

0.003

1.17

1.04

1.29

0.014

1.21

0.97

1.44

0.120

Low ΔT* Low UER

1

   

1

   

1

   

Adelaide

High ΔT*High UER

0.92

0.79

1.06

0.257

0.92

0.77

1.08

0.340

0.86

0.59

1.13

0.291

Low ΔT* High UER

0.88

0.77

0.99

0.023

0.91

0.78

1.03

0.135

0.82

0.60

1.05

0.085

High ΔT*Low UER

0.89

0.76

1.03

0.110

0.97

0.82

1.13

0.729

0.73

0.44

1.02

0.030

Low ΔT* Low UER

1

   

1

   

1

   

Perth

High ΔT*High UER

1.11

0.97

1.25

0.129

1.08

0.93

1.24

0.315

1.13

0.84

1.42

0.423

Low ΔT* High UER

1.04

0.94

1.15

0.385

1.06

0.95

1.17

0.294

1.01

0.79

1.23

0.903

High ΔT*Low UER

0.94

0.81

1.08

0.405

0.94

0.79

1.10

0.475

0.98

0.68

1.28

0.909

Low ΔT* Low UER

1

   

1

   

1

   

Hobart

High ΔT*High UER

1.12

0.86

1.38

0.390

0.96

0.66

1.26

0.790

1.26

0.71

1.81

0.407

Low ΔT* High UER

0.93

0.70

1.16

0.527

1.01

0.76

1.26

0.950

0.55

0.02

1.08

0.026

High ΔT*Low UER

0.99

0.72

1.26

0.938

1.06

0.76

1.35

0.700

1.10

0.51

1.70

0.753

Low ΔT* Low UER

1

   

1

   

1

   

Darwin

High ΔT*High UER

0.85

0.45

1.24

0.414

1.11

0.72

1.50

0.603

0.90

−0.05

1.84

0.822

Low ΔT* High UER

0.90

0.57

1.23

0.533

1.27

0.93

1.60

0.166

0.71

−0.22

1.65

0.479

High ΔT*Low UER

1.13

0.81

1.45

0.463

1.44

1.06

1.82

0.062

0.93

0.06

1.80

0.874

Low ΔT* Low UER

1

   

1

   

1

   

Canberra

High ΔT*High UER

1.34

1.05

1.62

0.049

1.32

0.99

1.65

0.104

1.42

0.82

2.01

0.253

Low ΔT* High UER

1.09

0.87

1.31

0.444

1.11

0.86

1.36

0.418

1.06

0.60

1.53

0.793

High ΔT*Low UER

1.18

0.88

1.48

0.279

1.25

0.91

1.59

0.198

1.05

0.45

1.65

0.875

Low ΔT* Low UER

1

   

1

   

1

   

All eight cities

High ΔT*High UER

1.15

1.11

1.19

0.001

1.15

1.10

1.19

0.001

1.16

1.07

1.24

0.001

Low ΔT* High UER

1.05

1.02

1.09

0.002

1.06

1.02

1.10

0.003

1.10

1.03

1.17

0.005

High ΔT*Low UER

1.00

0.96

1.04

0.962

1.02

0.97

1.07

0.477

0.99

0.90

1.08

0.883

Low ΔT* Low UER

1

   

1

   

1

   

Note: UER (unemployment rate); ΔT (temperature increase). Seasonality, rainfall, temperature, humidity and sunshine were adjusted. The median ΔT and UER of the whole study period in each city were applied to identify high or low ΔT and UER in each city

Discussion

This study examined the association of suicide with meteorological variables (MVs) and unemployment rate (UER) in eight Australian capital cities. There was a relatively more significant association across all cities between ΔT and suicide than for other MVs. The associations between ΔT and suicide were more significant in Sydney, Melbourne, Brisbane and Hobart than other cities. A higher UER was also associated with a higher suicide rate in Sydney, Melbourne, Brisbane and Perth. There was some interaction of ΔT on the association between UER and suicide in various cities.

Temperature and suicide

In general, ΔT (in particular, temperature increases from last one month) had more significant impacts on suicide rates compared to other MVs included in analyses (e.g., temperature, rainfall and sunshine) in this study. These was a more significant association between ΔT and suicide among males than that in females in Sydney, Melbourne, Brisbane and all cities together. For ΔT, a higher RR was observed in violent suicide than that in non-violent suicide. This is consistent with previous studies [8, 13, 30]. The proposed mechanism for this association relates to lower levels in the action of serotonin 5-HT2A receptors in human body in winter, and increases with higher daily sunshine hours and temperature, especially in spring [31]. The receptors in human brain may have rapid responsiveness in the circumstance with increased air temperature [32]. Human mood may be influenced by the variation of serotonin 5-HT2A receptor activity. Thus suicidal behavior may be triggered during extreme weather, e.g., heatwave [33, 34]. In this study, there were marked increases in the suicide rate from winter to spring, particularly in Melbourne and Brisbane. In above cities (all in temperate climate zones), the temperature change was greater in relative magnitude from winter to spring than that from spring to summer. Thus higher ΔT may lead to higher suicide risks. However, Darwin has the highest mean temperature and lowest changes of temperature between months among the Australian capital cities. This may explain that ΔT in Darwin has a weaker association with suicide rate than in other cities. In general, the seasonality of suicide was associated with temperature change over months, and seasonality was demonstrated through different variables significantly associated with suicide (e.g., ΔT in Sydney and mean temperature in Darwin).

Rainfall and suicide

There was no significant association between rainfall and suicide in Sydney in this study, which differs from previous NSW studies [22, 23]. Each of the studies covered both urban (including Sydney area) and rural areas. Usually prolonged droughts in rural and remote areas lead to decreased agricultural production and the income of the local population, especially farmers [3538]. Thus mental health problems, such as anxiety, despair, may emerge in drought affected areas, where the availability of mental health care and other social services is more limited compared to urban areas and a potential driver of suicidal behavior in these areas [39]. However, the socioeconomic impact of drought in urban areas may be not as dramatic as that in rural areas. So it is difficult to find the association between rainfall and suicide rate in most urban areas, including Sydney. There was an increased suicide risk in months with less rainfall in Melbourne, when temperature was also high in these months. No significant effects of rainfall were found in other cities.

Sunshine and suicide

The significant association between sunshine and suicide rate was only found among males in Melbourne in this study, which is consistent with the previous studies in Victoria (including Melbourne area) and other places [10, 4043]. The study by Vyssoki et al. in Austria also indicated a positive short term (lag of ten days and less) effect but negative mid-long term (lag of 14–60 days) effect of sunshine on daily suicide [40]. Our study used monthly data and examined the association of sunshine and suicide in the same month, thus the lag effect could not be detected. Some studies measured more specific parameters of serotonin metabolism, suggesting that extracellular serotonin is low during winter, especially with less sunshine, which may have a protective effect on human mood [31, 41]. A higher level of serotonin in summer can trigger impulsiveness and aggression, which may lead to some violent self-harm behaviours [31, 34, 36, 41]. Melbourne lies on high latitude location in Australia with less rainfall in summer than that in winter. Thus sunshine peaks in summer (December and January) and drops to bottom in July (winter), and sunshine change is positively correlated with ΔT. However, Darwin, a tropical city, has plenty of rainfall in wet season (summer in other cities) and rare in dry season (winter in other cities); then there is less sunshine in wet season than in dry season and sunshine is negatively correlated with temperature. This may explain the opposite trends of the associations of suicide rate with rainfall and sunshine and in Darwin (although not significant) and in Melbourne.

Unemployment and suicide

In this study, higher UER were significantly associated with suicide in most of the Australian capital cities, even after adjusting for seasonality and for lagged periods of 1 to 6 months). This is consistent with other Australian and international studies [4446]. Usually, unemployment can lead to the reduction of personal and family income, add the stress, tension between family members and despair, especially for long term unemployment with limited financial aid from government and civil organizations [4446]. Thus suicidal behaviors may be resulted from deteriorating economic conditions. This study also tested other lag effect of 2 to 6 months between unemployment and suicide and found that the trend was as similar as 1 month lag.

The interaction of ΔT and unemployment with suicide

Both of socioeconomic variables and physical response to climate factors can influence human mood and were associated with suicide seasonality [47]. Thus the interaction of ΔT and UER with suicide may significantly associated with suicide, especially during a period with both of high UER and dramatic temperature change over time (ΔT) among a relatively large size of population and suicide cases in particular area [48]. This finding was consistent for Sydney, Melbourne and Brisbane. The three cities represent approximately 7 % of all capital city populations and 73 % of suicide cases. Thus it can explain the similarity of the findings in each of the three cities as those in capital cities altogether, compared to other capital cities having much smaller population and suicide number. In general, most of variables were more significantly associated with male suicide than female suicide. However, the interaction analysis indicated that female suicide had higher RR than male suicide (e.g., Sydney, Brisbane and all eight cities together), suggesting that females were more vulnerable in some circumstances, particularly in months when both UER and ΔT were high during the study period.

Strengths and limitations

This study has three key strengths. Firstly, this is the first study exploring the association between MVs and suicide in major Australian cities over time. Secondly, both MVs and UER were taken into account, and the key risk factors at a macro level were investigated in different cities. This study also examined the interactive effect of MVs and a key socio-economic determinant of suicide, and investigated the extent to which the epidemiology of suicide in Australia differs by geographically and meteorologically distinct areas. Finally, the findings in this study may help public health decision-makers and practitioners to improve current suicide control and prevention strategies.

However, some limitations should also be acknowledged. Firstly, more detailed personal information of each suicide case, e.g., health status and mental disorder before suicide, consumption of alcohol at the individual level, economic condition, intake of omega-3 fatty-acid, were not available in the dataset. Secondly, using monthly meteorological data may mask some extreme weather conditions, e.g., short term heat waves, which may have potential impact on mental health among population. Finally, the impact of climate and unemployment on suicide after 2005 was not examined due to availability of dataset.

Conclusion

In general, the associations between MVs and suicide rates differed across Australian capital cities. As some non-capital urban areas, rural and remote areas also have a high risk of suicide, it is necessary to explore the potential socio-environmental determinants of suicide using comprehensive national data, especially from spatiotemporal aspects. Suicide control and prevention strategies can be more targeted and specific on the basis of better understanding of the geographical and meteorological patterning of key socio-environmental risk factors associated with suicide.

Declarations

Acknowledgements

X.Q. was funded by the QUT Postgraduate Research Award Scholarship and Xi’an Jiaotong University Early Career Researcher Start-up Fund (DW080038K0000001), and S.T. was supported by a National Health and Medical Research Council Research Fellowship (#553043).

Authors’ Affiliations

(1)
School of Public Health, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
(2)
School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
(3)
School of Science and Health, University of Western Sydney, Building 24, Room 4.53D, Campbelltown Campus, Locked Bag, 1797, Penrith, NSW, 2517, Australia

References

  1. WHO. Preventing suicide, a global imperative. Geneva, Switzerland: World Health Organization; 2014.Google Scholar
  2. Berry HL, Bowen K, Kjellstrom T. Climate change and mental health: a causal pathways framework. Int J Public Health. 2010;55:123–32.View ArticlePubMedGoogle Scholar
  3. Durkheim E. Suicide: a study in sociology [1897]. Glencoe, Illinois: The Free Press; 1951. Translated by Spaulding JA and Simpson G.Google Scholar
  4. Deisenhammer EA. Weather and suicide: the present state of knowledge on the association of meteorological factors with suicidal behaviour. Acta Psychiatr Scand. 2003;108:402–9.View ArticlePubMedGoogle Scholar
  5. Ajdacic-Gross V, Lauber C, Sansossio R, Bopp M, Eich D, Gostynski M, et al. Seasonal associations between weather conditions and suicide-evidence against a classic hypothesis. Am J Epidemiol. 2007;165:561–9.View ArticlePubMedGoogle Scholar
  6. Kim Y, Kim H, Kim DS. Association between daily environmental temperature and suicide mortality in Korea (2001–2005). Psychiatry Res. 2011;186:390–6.View ArticlePubMedGoogle Scholar
  7. Lee HC, Lin HC, Tsai SY, Li CY, Chen CC, Huang CC. Suicide rates and the association with climate: a population-based study. J Affect Disord. 2006;92:221–6.View ArticlePubMedGoogle Scholar
  8. Likhvar V, Honda Y, Ono M. Relation between temperature and suicide mortality in Japan in the presence of other confounding factors using time-series analysis with a semiparametric approach. Environ Health Prev Med. 2011;16:36–43.View ArticlePubMedGoogle Scholar
  9. Page LA, Hajat S, Kovats RS. Relationship between daily suicide counts and temperature in England and Wales. Br J Psychiatry. 2007;191:106–12.View ArticlePubMedGoogle Scholar
  10. Preti A, Miotto P. Seasonality in suicides: the influence of suicide method, gender and age on suicide distribution in Italy. Psychiatry Res. 1998;81:219–31.View ArticlePubMedGoogle Scholar
  11. Ruuhela R, Hiltunen L, Venäläinen A, Pirinen P, Partonen T. Climate impact on suicide rates in Finland from 1971 to 2003. Int J Biometeorol. 2009;53:167–75.View ArticlePubMedGoogle Scholar
  12. Tsai JF. Socioeconomic factors outweigh climate in the regional difference of suicide death rate in Taiwan. Psychiatry Res. 2010;179:212–6.View ArticlePubMedGoogle Scholar
  13. Tsai JF, Cho W. Temperature change dominates the suicidal seasonality in Taiwan: a time-series analysis. J Affect Disord. 2012;136:412–8.View ArticlePubMedGoogle Scholar
  14. Tsai JF, Cho W. The secular trend of suicide rate and the socio-economic, media, and climatic factors in Taiwan, 1976–2009: a population-based study. J Affect Disord. 2011;129:270–4.View ArticlePubMedGoogle Scholar
  15. Dixon PG, McDonald AN, Scheitlin KN, Stapleton JE, Allen JS, Carter WM, et al. Effects of temperature variation on suicide in five U.S. counties, 1991–2001. Int J Biometeorol. 2007;51:395–403.View ArticlePubMedGoogle Scholar
  16. Yang AC, Tsai SJ, Huang NE. Decomposing the association of completed suicide with air pollution, weather, and unemployment data at different time scales. J Affect Disord. 2011;129:275–81.View ArticlePubMedGoogle Scholar
  17. ABS. 3309.0. Suicides, Australia, 1921 to 1998. Canberra, Australia: Australian Bureau of Statistics; 2000.Google Scholar
  18. ABS. 3309.0. Suicides, Australia, 2005. Canberra, Australia: Australian Bureau of Statistics; 2007.Google Scholar
  19. ABS. 3309.0. Suicides, Australia, 2010. Canberra, Australia: Australian Bureau of Statistics; 2012.Google Scholar
  20. Cheung YT, Spittal MJ, Pirkis J, Yip PS. Spatial analysis of suicide mortality in Australia: investigation of metropolitan-rural-remote differentials of suicide risk across states/territories. Soc Sci Med. 2012;75:1460–8.View ArticlePubMedGoogle Scholar
  21. Qi X, Hu W, Page A, Tong S. Spatial clusters of suicide in Australia. BMC Psychiatry. 2012;12:86.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Hanigan IC, Butler CD, Kokic PN, Hutchinson MF. Suicide and drought in New South Wales, Australia, 1970–2007. Proc Natl Acad Sci U S A. 2012;109:13950–5.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Nicholls N, Butler CD, Hanigan I. Inter-annual rainfall variations and suicide in New South Wales, Australia, 1964–2001. Int J Biometeorol. 2006;50:139–43.View ArticlePubMedGoogle Scholar
  24. Qi X, Tong S, Hu W. Preliminary spatiotemporal analysis of the association between socio-environmental factors and suicide. Environ Health. 2009;8:46.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Qi X, Hu W, Mengersen K, Tong S. Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis. BMC Public Health. 2014;14:681.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Tong S, McMichael AJ, Baghurst PA. Interactions between environmental lead exposure and sociodemographic factors on cognitive development. Arch Environ Health. 2000;55:330–5.View ArticlePubMedGoogle Scholar
  27. Yu W, Vaneckova P, Mengersen K, Pan X, Tong S. Is the association between temperature and mortality modified by age, gender and socio-economic status? Sci Total Environ. 2010;408:3513–8.View ArticlePubMedGoogle Scholar
  28. MapInfo Corporation. MapInfo Professional 10.5 User Guide. NY, U.S.A: MapInfo Corporation; 2010.Google Scholar
  29. Arbuckle JL. IBM SPSS Amos 21 User’s Guide. NY, U.S.A: IBM Corporation; 2012.Google Scholar
  30. Lin HC, Chen CS, Xirasagar S, Lee HC. Seasonality and climatic associations with violent and nonviolent suicide: a population-based study. Neuropsychobiology. 2008;57:32–7.View ArticlePubMedGoogle Scholar
  31. Lambert GW, Reid C, Kaye DM, Jennings GL, Esler MD. Effect of sunlight and season on serotonin turnover in the brain. Lancet. 2002;360:1840–2.View ArticlePubMedGoogle Scholar
  32. Zhang G, Tao R. Enhanced responsivity of 5-HT(2A) receptors at warm ambient temperatures is responsible for the augmentation of the 1-(2,5-dimethoxy-4-iodophenyl)-2-aminopropane (DOI)-induced hyperthermia. Neurosci Lett. 2011;490:68–71.View ArticlePubMedGoogle Scholar
  33. Turecki G, Brière R, Dewar K, Antonetti T, Lesage AD, Phil M, et al. Prediction of level of serotonin 2A receptor binding by serotonin receptor 2A genetic variation in postmortem brain samples from subjects who did or did not commit suicide. Am J Psychiatry. 1999;156:1456–8.PubMedGoogle Scholar
  34. Pandey GN, Dwivedi Y, Rizavi HS, Ren X, Pandey SC, Pesold C, et al. Higher expression of serotonin 5-HT2A receptors in the postmortem brains of teenage suicide victims. Am J Psychiatry. 2002;159:419–29.View ArticlePubMedGoogle Scholar
  35. Alston M. Rural male suicide in Australia. Soc Sci Med. 2012;74:515–22.View ArticlePubMedGoogle Scholar
  36. Gunn KM, Kettler LJ, Skaczkowski GL, Turnbull DA. Farmers’ stress and coping in a time of drought. Rural Remote Health. 2012;12:2071.PubMedGoogle Scholar
  37. Kelly BJ, Lewin TJ, Stain HJ, Coleman C, Fitzgerald M, Perkins D, et al. Determinants of mental health and well-being within rural and remote communities. Soc Psychiatry Psychiatr Epidemiol. 2011;46:1331–42.View ArticlePubMedGoogle Scholar
  38. Sartore GM, Kelly B, Stain HJ. Drought and its effect on mental health–how GPs can help. Aust Fam Physician. 2007;36:990–3.PubMedGoogle Scholar
  39. Linkowski P, Martin F, De Maertelaer V. Effect of some climatic factors on violent and non-violent suicides in Belgium. J Affect Disord. 1992;25:161–6.View ArticlePubMedGoogle Scholar
  40. Vyssoki B, Kapusta ND, Praschak-Rieder N, Dorffner G, Willeit M. Direct effect of sunshine on suicide. JAMA Psychiatry. 2014;71(11):1231–7.View ArticlePubMedGoogle Scholar
  41. Maes M, De Meyer F, Thompson P, Peeters D, Cosyns P. Synchronized annual rhythms in violent suicide rate, ambient temperature and the light–dark span. Acta Psychiatr Scand. 1994;90:391–6.View ArticlePubMedGoogle Scholar
  42. Lambert G, Reid C, Kaye D, Jennings G, Esler M. Increased suicide rate in the middle-aged and its association with hours of sunlight. Am J Psychiatry. 2003;160:793–5.View ArticlePubMedGoogle Scholar
  43. Praschak-Rieder N, Willeit M, Wilson AA, Houle S, Meyer JH. Seasonal variation in human brain serotonin transporter binding. Arch Gen Psychiatry. 2008;65:1072–8.View ArticlePubMedGoogle Scholar
  44. Chang SS, Sterne JA, Huang WC, Chuang HL, Gunnell D. Association of secular trends in unemployment with suicide in Taiwan, 1959–2007: a time-series analysis. Public Health. 2010;124:49–54.View ArticlePubMedGoogle Scholar
  45. Milner A, Page A, Lamontagne AD. Duration of unemployment and suicide in Australia over the period 1985–2006: an ecological investigation by sex and age during rising versus declining national unemployment rates. J Epidemiol Community Health. 2013;67:237–44.View ArticlePubMedGoogle Scholar
  46. Yamasaki A, Araki S, Sakai R, Yokoyama K, Voorhees AS. Suicide mortality of young, middle-aged and elderly males and females in Japan for the years 1953–96: time series analysis for the effects of unemployment, female labour force, young and aged population, primary industry and population density. Ind Health. 2008;46:541–9.View ArticlePubMedGoogle Scholar
  47. Preti A. The influence of seasonal change on suicidal behaviour in Italy. J Affect Disord. 1997;44:123–30.View ArticlePubMedGoogle Scholar
  48. Preti A. The influence of climate on suicidal behaviour in Italy. Psychiatry Res. 1998;78:9–19.View ArticlePubMedGoogle Scholar

Copyright

© Qi et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement