Suicide is an important public health problem with multidimensional consequences for societies. One of the under-researched areas of suicide consequences are cross-country analyses of production losses associated with these deaths. The aim of this study was to estimate the production losses (indirect cost) of suicide deaths in 28 European Union states (EU-28) in 2015.
Methods
The study used societal perspective and human capital approach to investigate production losses due to suicide mortality at working age. Eurostat’s data on the number of deaths was used to identify suicide mortality burden in terms of years of potential productive life lost. Labour and economic indicators were applied to proxy the discounted value of potential economic output lost. A one-way deterministic sensitivity analysis was conducted to test the robustness of the estimates.
Results
The production losses attributable to suicide deaths in EU-28 in 2015 were €9.07 billion. The per suicide indirect cost of these deaths was €231,088 for the whole EU-28 population; Luxembourg experienced the highest per suicide burden of €649,148. The per capita production losses of suicides in EU-28 was €17.80 and Ireland experienced the highest per capita burden of €48.57. The losses constituted an economic burden of 0.061% of EU-28’s GDP and this share ranged from 0.018% in Cyprus to 0.161% in Latvia. Most of the losses (71–91%) were due to men’s deaths. The results of the sensitivity analysis exhibit a large variation of losses; the highest (lowest) cost was identified with no adjustment for lower employment rates among those dying by suicide (adjustment for minimum productivity) and was 92.3% higher (59.7% lower) on average than in the base scenario.
Conclusion
Public health actions aimed at prevention of suicides might reduce their health burden but also contribute to the economic welfare of European societies.
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Abkürzungen
BS
Base scenario
CEE
Central and Eastern Europe
DR
Discount rate
EU
European Union
EU-28
28 European Union states
GDP
Gross domestic product
GVA
Gross value added
HCA
Human capital approach
NE
Northern Europe
SE
Southern Europe
WE
Western Europe
YPPLL
Years of potential productive life lost
Background
Suicide, being in the top twenty causes of death worldwide, is considered to be a global health problem. Around 800 thousand people die from suicide each year [1] and over 56,000 of 5.2 million deaths in the European Union (EU) in 2015 were due to intentional self-harm [2]. The consequences of suicide are numerous; for a typical death by suicide, at least six people are directly affected in terms of human suffering [3]. Moreover, major economic consequences, including increased medical utilization, time absent from work, and production output lost are experienced by several groups [3, 4].
Previous contributions aiming at assessing the cost of suicides, include studies from the United States [5‐8], Poland [9], Ireland [10], Australia [4, 11] and Spain [12] among others. However, this topic seems to be understudied; so far no systematic reviews have been published and the only cross-country study on the cost of suicide is concerned with the youth deaths in highly developed countries [13]. International comparisons of suicides’ cost are difficult because particular studies use different methodological approaches, data sources and encompass various cost components. The most comprehensive approaches to the economic burden of suicide include estimation of following cost categories: medical care costs (direct costs), autopsy and investigation costs (medicolegal costs), production losses (indirect costs) and cost of pain and stress (intangible costs). Such studies provide an overall picture of suicides’ cost but are only feasible in a national context and require exhaustive and reliable data sources. This research aims to overcome the low comparability of previous findings from different countries by estimating a single cost category of production losses associated with suicide mortality across the 28 EU countries. With the use of uniform data from all the EU states, the study provides highly comparable estimates of the European states’ economic burden of suicide. Although, suicide mortality is only a single cost category, several studies show that it is a crucial one which constitutes a majority of economic burden of suicide with shares of total economic cost as large as 91–98% in the United States [5‐7], 94% among Australian youth [4] and 84% in New Zealand [14]. Moreover, this single cost category has also been subject to previous studies [9, 12] concerned with the production losses attributable to completed suicides solely.
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Therefore, the aim of this study was to estimate the production losses (indirect cost) associated with suicide mortality among population at working age in the 28 EU countries in the year 2015. Understanding the magnitude of this economic burden is important for assessing potential savings from cost-effective suicide prevention programmes. With introduction of such programmes, the number of suicide deaths could be reduced bringing benefits in terms of not only lives saved and trauma avoided but also economic output gained. Moreover, the estimates provided here might be useful to compare the economic burden of various health problems and, as such, to facilitate prioritization of health policy choices both nationally and at a supranational level.
Methods
The methodological approach used in this research builds on a model designed to estimate the production losses of premature mortality [15] which also has recently been applied for assessment of indirect cost associated with alcohol-attributable mortality [16] in EU countries.
General modelling approach
The study used population-based data, the societal perspective [17, 18] and human capital approach (HCA) [19, 20] to estimate the production losses attributable to premature mortality associated with suicides in the 28 EU countries. Premature mortality is defined as those cases of death that occur at working age. The study only accounts for losses borne in formal economy while it does not encompass the value of informal activities foregone due to suicide deaths. With the use of HCA, the indirect cost of mortality was proxied by the discounted value of economic output that would be produced if those who died prematurely were still alive and working until the average age of exiting labour market [21, 22]. Per worker gross domestic product (GDP) was used to measure productivity and the estimates were adjusted for the decreasing marginal productivity of labour by applying a 0.65 adjustment coefficient. Generally, the use of marginal productivity is a preferred choice over average productivity in indirect cost estimation [23, 24] and, therefore, this coefficient was used to account for the law of diminishing marginal productivity. This adjustment results from the fact that the production process relies on several inputs and diminishing one of them (labour) affects only a respective proportion of the production output [25, 26]. Hence, the production lost due to suicide deaths should not be considered to be as high as the average output. The value of 0.65 used reflects the proportion of output attributable to labour by applying a relationship between marginal and average labour productivity; therefore, this adjustment reflects output elasticity of labour in Cobb-Douglas production function as used in the European context [27].
Unless stated otherwise, all the data refer to the year 2015.
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Data sources
The age- and gender-specific data on the number of suicides was taken from Eurostat’s dataset of death causes which reports figures on intentional self-harm mortality [28]. The country-specific data on labour market characteristics used to determine the duration of working life was taken from European Commission’s report on aging [29] (the average effective age of exit from labour market), Eurostat’s database [30] (the employment rates) and provided by Eurostat on the author’s request (the average age of starting first regular job). The population and economic output data was taken from Eurostat [31], while the future country-specific potential per worker GDP growth rates from report [29]. The lower employment rates in the suicide population were proxied by the country-specific data on employment among those with chronic depression [32], which was the only available measure to proxy employment in suicide population for a range of EU countries.
Estimation strategy
In the first stage, the age- and sex-specific number of suicide deaths at 5-years age intervals (0–4; 5–9; …; 60–64; 65–69 years) was extracted from Eurostat database. In order to identify the production loss at a particular age, it was necessary to identify the number of deaths at this certain age. It could be done by assuming that the distribution of deaths in particular intervals was even.1 The average number of suicide deaths throughout the 3-year period of 2014–2016 was used in order to remove the effect of unusual variation in mortality, particularly observable in low populated countries [15]. A half-cycle adjustment was applied meaning that all deaths occurred in the middle of the year [33]. The measure of years of potential productive life lost (YPPLL) [9, 34] was used to weight suicide cases occurring at various ages.
The second stage was to identify the mean time a person at each age would work if had not died from suicide; the following country- and sex-specific labour market measures were used for this purpose: the average age of starting first regular job2; the average age of exiting the labour market (data for the year 2016) [29]; and (5-years) age-specific employment rates3 [30]. These measures were assumed to be unchanged in future, because of the uncertainty of labour market situation in the years to come. Using these labour measures, the average time of work lost due to suicide death was identified separately for men and women at every working age for each of the states.
The average production lost due to suicide was proxied by per worker GDP adjusted for purchasing power parity (PPP)4 and marginal productivity coefficient (0.65). However, to reflect the fact that those dying from suicide are less likely to work, the employment rates of people reporting chronic depression [32] were used and these ranged from 29.5 to 73.0% of employment rates of those without depression in Cyprus and Germany, respectively. The reasoning for using this measure was based on the fact that at least 90% people who die from suicide have suffered from mental disorders with depression being a major one [35]; moreover, previous studies allocate 50–70% of suicides to those who have suffered from depression [36‐38]. However, this choice seems to be conservative comparing to the previous studies which assume much lower employment differences between general and suicide populations, e.g. a 2.25% employment rate gap in the Irish [10] and Scottish [39] studies.
The future losses were discounted using a 5% rate and country-specific potential per worker GDP growth rates for each forthcoming decade [29] were applied to reflect the growth of the EU economies.
The formulas used for calculating the production losses are depicted in Fig. 1.
Fig. 1
Formulas for calculating production losses associated with suicide deaths in European Union
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Sensitivity analysis
A one-way deterministic sensitivity analysis5 was performed to test the magnitude of changes in the model’s parameters. The following sensitivity scenarios were used: a 3.5 and 0% discount rate (DR); a ± 0.05 variation of the 0.65 correction coefficient; gross value added6 (GVA) instead of GDP and 0 and 2% future economic growth for all the countries. To deal with uncertainty in the future labour market situation two scenarios using homogeneous data for all the states were assumed: average EU values of labour measures; and ages 18 and 67 used as the ages of labour market entry and exit, respectively [15]. Also, scenarios were tested in which all deaths occurred among minimally productive workers7; and with no depression-specific labour employment rate adjustment. Eventually, all the deaths (for those aged 10+ years of age) reported as events of undetermined intent were added to suicide cases to make an attempt to account for under-reporting of suicides. The other possible scenario to be included into sensitivity analysis would be to differentiate male and female productivity. Unfortunately, no sex-specific data on average GDP produced is available and the only figure broken down by sex which could be potentially useful was gender pay gap. This last measure is considered to reflect rather labour market discrimination [40, 41] than real productivity differences; thus, it was not included as a sensitivity scenario.
Results
Suicide mortality at working age and YPPLL
There were 39,265 (estimated) deaths at working age due to suicide in the European Union in 2015 (Table 1). The number of deaths was highest in the Western Europe (WE) countries (16,085 cases) followed by the Central and Eastern Europe (CEE) (9010), Northern Europe (NE) (7315) and Southern Europe (SE) (6854) countries. A majority of the premature deaths was associated with men’s suicides (from 75.3% in WE countries to 86.2% in CEE countries). The suicide rate for the whole EU community was 7.7 cases per 100,000 population and 3.2 (12.4) for women (men). The overall suicide rate was highest in the CEE states (9.9 cases per 100,000 population) and lowest in SE (5.0). Men’s suicide rate was disproportionally high in CEE (17.6) where it was more than twice of the rate in SE (7.9). On the other hand, the rate for women was more even in the sub-regions with the highest values in WE (4.2) and NE (3.3). The countries with the greatest number of suicides at working age were Germany (6438), France (5863) and Poland (4433) while there were less than 40 cases of such deaths in Cyprus, Luxembourg and Malta. However, the relative burden of suicide deaths was highest in Lithuania (23.4 cases per 100,000 pop.), Latvia (13.9) and Belgium (12.4) while the rate of suicides was lowest in Greece (3.3 cases), Cyprus (4.1) and Italy (4.3).
Table 1
Number and incidence of deaths at working age and years of potential productive life lost due to suicide mortality in European Union countries in 2015
Number of suicide deathsa
Suicide deathsa per 100,000 pop.
YPPLL
YPPLL per 10,000 pop.
Men
Women
Total
Men
Women
Total
Men
Women
Total
Men
Women
Total
Central and Eastern Europe
7764
1246
9010
17.6
2.7
9.9
150,126
21,082
171,208
34.1
4.5
18.9
Bulgaria
325
78
403
9.3
2.1
5.6
5968
1322
7290
17.1
3.6
10.2
Czechia
858
176
1034
16.6
3.3
9.8
17,265
3206
20,470
33.3
6.0
19.4
Hungary
941
245
1187
20.1
4.8
12.1
15,558
3559
19,117
33.2
6.9
19.4
Poland
3932
501
4433
21.4
2.6
11.7
79,802
8844
88,645
43.4
4.5
23.3
Romania
1400
194
1595
14.5
1.9
8.1
25,986
3323
29,309
27.0
3.3
14.8
Slovakia
308
52
360
11.6
1.9
6.6
5548
828
6377
20.9
3.0
11.7
Northern Europe
5697
1619
7315
11.9
3.3
7.5
125,570
34,393
159,963
26.1
7.0
16.4
Denmark
329
107
435
11.6
3.7
7.7
6322
1954
8276
22.4
6.8
14.6
Estonia
131
25
156
21.2
3.6
11.9
2943
534
3476
47.7
7.6
26.4
Finland
467
135
602
17.2
4.9
11.0
10,307
2988
13,294
38.1
10.7
24.2
Ireland
327
80
407
13.9
3.4
8.6
8005
1929
9934
34.0
8.1
21.0
Latvia
229
44
273
25.5
4.1
13.9
4584
815
5400
50.9
7.6
27.4
Lithuania
588
87
675
44.5
5.6
23.4
12,083
1575
13,658
91.5
10.1
47.3
Sweden
613
259
873
12.3
5.3
8.8
13,589
5723
19,312
27.3
11.7
19.6
United Kingdom
3012
881
3893
9.3
2.7
6.0
67,736
18,875
86,612
20.9
5.7
13.2
Southern Europe
5251
1603
6854
7.9
2.3
5.0
96,498
28,558
125,057
14.6
4.1
9.2
Croatia
335
94
429
16.6
4.3
10.2
5678
1387
7065
28.2
6.4
16.9
Cyprus
27
8
35
6.6
1.8
4.1
648
172
820
15.6
3.9
9.6
Greece
284
72
357
5.4
1.3
3.3
4937
1240
6178
9.5
2.2
5.7
Italy
2039
580
2619
6.9
1.9
4.3
38,189
10,227
48,416
13.0
3.3
8.0
Malta
23
6
29
10.1
2.9
6.5
469
132
601
20.5
5.9
13.3
Portugal
489
163
653
10.0
3.0
6.3
8690
2701
11,391
17.8
5.0
11.0
Slovenia
204
38
242
19.9
3.7
11.7
4034
677
4711
39.4
6.5
22.8
Spain
1849
641
2490
8.1
2.7
5.4
33,853
12,022
45,876
14.8
5.1
9.9
Western Europe
12,110
3975
16,085
13.3
4.2
8.7
221,667
67,125
288,792
24.3
7.1
15.5
Austria
624
184
807
14.5
4.2
9.3
12,286
3181
15,467
28.6
7.2
17.8
Belgium
1003
391
1395
18.1
6.8
12.4
18,353
6136
24,489
33.1
10.7
21.7
France
4506
1358
5863
13.9
4.0
8.8
77,555
21,826
99,382
24.0
6.4
14.9
Germany
4863
1574
6438
12.1
3.8
7.9
91,642
27,562
119,203
22.8
6.6
14.6
Luxembourg
31
8
39
10.6
2.9
6.8
520
131
651
17.8
4.6
11.3
Netherlands
1083
460
1543
12.8
5.4
9.1
21,310
8290
29,600
25.2
9.7
17.4
European Union
30,821
8444
39,265
12.4
3.2
7.7
593,862
151,158
745,020
23.8
5.8
14.6
Notes: The mortality figures refer to average 2014–2016 values for each country. a – deaths at country- and sex-specific working age; YPPLL years of potential productive life lost
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Suicide mortality led to 745,020 YPPLL in the EU and this number was highest in the WE states (288,792) followed by the CEE (171,208), NE (159,963) and SE countries (125,057). The number of YPPLL was three to seven times higher for men’s suicides compared to women’s and the difference between the sexes in this respect was most pronounced in CEE. The YPPLL rate was 14.6 per 10,000 population in the whole EU (5.8 for women and 23.8 for men) and ranged from 9.2 in SE to 18.9 in CEE. The rates of YPPLL associated with men’s suicides were much higher than those of women’s in all the sub-regions. The YPPLL rate (per 10,000 pop.) was highest in three Baltic states – Lithuania (47.3), Latvia (27.4) and Estonia (26.4) – while it was lower than ten in four SE countries, namely Greece (5.7), Italy (8.0), Cyprus (9.6) and Spain (9.9).
Production losses associated with suicide mortality
The total production losses (indirect cost) associated with suicide mortality at working age in 2015 were €9.07 billion in the EU and almost half of this burden was observed in the WE countries (€4.50 billion). The cost in other sub-regions was €1.16 billion in SE, €1.43 billion in CEE and €1.98 billion in NE (Table 2). A vast majority of this cost was due to male mortality (from 78.8% in WE to 89.0% in CEE). The indirect cost was highest in Germany (€2.04 billion), France (€1.60 billion) and the United Kingdom (€1.00 billion) while it was less than €10 million in Cyprus (€3.6 million) and Malta (€5.5 million). In relative terms, the economic burden of suicide mortality in the whole EU economy was 0.061% of GDP and ranged from 0.033% in SE countries to 0.081% in CEE. The highest share of GDP lost was estimated for three Baltic states – Latvia (0.161%), Lithuania (0.143%) and Estonia (0.124%) – while this burden was lowest in Cyprus (0.018%), Greece (0.022%) and Italy (0.030%).
Table 2
Production losses (indirect cost) attributable to suicide mortality in European Union countries in 2015
Total cost (€ 1000 PPP)
Total cost as % GDP
Cost per capita
Cost per suicide
Men
Women
Total
Men
Women
Total
Men
Women
Total
Central and Eastern Europe
1,272,742
157,807
1,430,549
0.081
14.03
1.74
15.77
163,930
126,602
158,766
Bulgaria
30,311
6396
36,707
0.037
4.22
0.89
5.11
93,347
82,053
91,160
Czechia
210,792
35,234
246,026
0.092
19.99
3.34
23.33
245,631
200,526
237,966
Hungary
104,058
22,179
126,237
0.065
10.58
2.26
12.84
110,555
90,402
106,388
Poland
736,863
73,173
810,036
0.106
19.40
1.93
21.33
187,417
146,061
182,743
Romania
143,331
14,449
157,780
0.049
7.25
0.73
7.98
102,355
74,410
98,952
Slovakia
47,386
6376
53,762
0.044
8.73
1.17
9.90
153,933
121,844
149,271
Northern Europe
1,579,419
402,398
1,981,816
0.063
16.23
4.13
20.36
277,256
248,618
270,920
Denmark
89,916
24,941
114,857
0.055
15.82
4.39
20.21
273,578
233,470
263,739
Estonia
30,140
5325
35,465
0.124
22.89
4.04
26.93
230,357
210,760
227,185
Finland
132,008
37,488
169,496
0.097
24.05
6.83
30.88
282,815
276,950
281,496
Ireland
191,894
37,805
229,699
0.094
40.58
7.99
48.57
586,116
474,104
564,178
Latvia
50,566
8420
58,986
0.161
25.67
4.27
29.95
220,356
191,512
215,718
Lithuania
79,697
10,674
90,372
0.143
27.60
3.70
31.29
135,519
122,562
133,848
Sweden
202,644
82,750
285,394
0.081
20.53
8.38
28.92
330,376
319,245
327,070
United Kingdom
802,553
194,994
997,546
0.049
12.27
2.98
15.26
266,452
221,312
256,236
Southern Europe
947,177
215,449
1,162,626
0.033
6.97
1.59
8.56
180,388
134,367
169,622
Croatia
27,748
6244
33,991
0.047
6.62
1.49
8.11
82,911
66,242
79,248
Cyprus
2957
672
3628
0.018
3.48
0.79
4.27
108,568
87,620
103,966
Greece
39,934
7142
47,076
0.022
3.70
0.66
4.36
140,502
98,683
132,015
Italy
418,556
80,014
498,571
0.030
6.90
1.32
8.21
205,246
137,924
190,336
Malta
4698
779
5477
0.046
10.43
1.73
12.15
203,093
122,953
185,868
Portugal
80,495
21,919
102,414
0.044
7.79
2.12
9.91
164,450
134,355
156,927
Slovenia
38,999
6068
45,067
0.092
18.89
2.94
21.82
191,142
158,952
186,069
Spain
333,790
92,612
426,402
0.035
7.19
1.99
9.18
180,552
144,398
171,240
Western Europe
3,547,092
951,627
4,498,719
0.071
19.08
5.12
24.19
292,907
239,377
279,677
Austria
151,293
36,919
188,211
0.058
17.39
4.24
21.64
242,586
200,863
233,089
Belgium
228,860
69,342
298,202
0.077
20.30
6.15
26.45
228,085
177,194
213,806
France
1,278,819
323,312
1,602,131
0.079
19.17
4.85
24.01
283,817
238,159
273,246
Germany
1,606,692
432,078
2,038,771
0.069
19.67
5.29
24.96
330,361
274,452
316,689
Luxembourg
20,963
4431
25,395
0.058
36.38
7.69
44.07
677,983
540,423
649,148
Netherlands
260,465
85,545
346,010
0.055
15.33
5.03
20.36
240,563
185,876
224,251
European Union
7,346,429
1,727,281
9,073,710
0.061
14.41
3.39
17.80
238,356
204,560
231,088
The indirect cost of suicide mortality per capita in 2015 was €17.80 in the EU (€14.41 for male and €3.39 for female suicides). This average cost was highest in WE (€24.19) and NE (€20.36), followed by the CEE (€15.77) and SE (€8.56) states. Ireland experienced the greatest per capita economic burden in absolute values with the indirect cost of €48.57 per person and other countries with large average losses were Luxembourg (€44.07) and Finland (€30.88). Finally, the average cost per suicide was €231,088 in the EU and varied sub-regionally from €158,766 in CEE to €279,677 in WE. This per suicide cost was highest in Luxembourg (€649,148), Ireland (€564,178) and Sweden (€327,070) while the lowest values were observed in Croatia (€79,248), Bulgaria (€91,160) and Romania (€98,952). For the whole EU community, the indirect cost of a single male suicide was 16.5% higher than a female suicide.
In the whole group of countries, the highest share of mortality cost was associated with suicides of those at ages 30–39 and 40–49 years (Fig. 2; country-specific results in Additional file 1). A slightly lower share of total losses was due to young adults’ suicides (20–29 years) and this share was higher for men than women. On the other hand, the relative magnitude of losses attributable to mortality among those being between 0 and 19 years of age was higher for women. Additionaly, the between-country variation in all these shares was much higher for women than men.
Fig. 2
Age groups distribution of production losses attributable to suicide mortality in European Union countries in 2015. Notes: Average shares are calculated as unweighted means
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Sensitivity analysis
The one-way deterministic sensitivity analysis shows that the cost estimates are subject to notable variation as a result of changes in some of the model’s parameters (Table 3). The high changes in the cost result from the scenario which does not account for decreased employment rate in suicide population; the production losses would be 92.3% higher on average if general population employment rates were applied (from 36.9% in Germany to 238.9% in Cyprus). On the other hand, the cost would be 59.7% lower on average if minimum productivity adjustment was used. Using the 3.5% discount rate raised the cost by 17.4% on average, with relatively low variation among countries (14.9–20.5%). With no discounting, the indirect cost would be 86.5% higher on average. Using GVA instead of GDP as a productivity measure decreased the estimates by 11.6% on average. The assumption of 0% (2%) future economic growth changed the results by −13.4% (3.8%) on average. The use of alternative data on labour market characteristics had minor effect on the losses estimated; with the entry age of 18 and exit age of 67 for both sexes the results changed by 5.5% on average with the highest (lowest) change of 14.9% (1.8%) in Luxembourg (Sweden). When the average EU values for these labour measures were used for all the countries, the cost variation was even lower. Classification of all undetermined deaths as suicides had varying effect on estimates and this depended on the country; for Slovakia, the cost was as much as 77.4% elevated while for Italy it was only 0.1% higher. A detailed, country-specific sensitivity analysis is available online in Additional file 2.
Table 3
Summary of sensitivity analysis for estimates of production losses attributable to suicide deaths in European Union countries in 2015
Averagea change
Minimum change from base scenario
Maximum change from base scenario
Discount rate (BS: 5%)
3.5%
17.4%
14.9% (Greece)
20.5% (Latvia)
0%
86.5%
71.1% (Greece)
105.7% (Estonia)
Coefficient to adjust for decreasing marginal labour productivity (BS: 0.65)
0.6
−7.7%
−7.7% (all countries)
0.7
7.7%
7.7% (all countries)
Productivity measure (BS: gross domestic product)
Gross value added
−11.6%
−7.4% (Ireland)
−16.0% (Croatia)
Minimum productivity adjustmentb (BS: average productivity)
−59.7%
−47.6% (Slovenia)
−65.7% (Spain)
No lower employment rate adjustment (BS: employment rate for those reporting chronic depression)
92.3%
36.9% (Germany)
238.9% (Cyprus)
Future economic growth (BS: country-specific)
0% for all the countries
−13.4%
−2.0% (Italy)
−33.5% (Latvia)
2% for all the countries
3.8%
0.1% (Hungary)
−19.9% (Latvia)
Labour market entry and retirement age (BS: country-specific)
Sex–specific, average EU values
−0.4%
0.2% (Czechia)
8.1% (Luxembourg)
Both sexes: 18 years and 67 years
5.5%
1.8% (Sweden)
14.9% (Luxembourg)
Events of undetermined deaths included
19.2%
0.1% (Italy)
77.4% (Slovakia)
Notes: a – unweighted average; b – minimal productivity was obtained by dividing the minimum wage by the average wage in particular economies. There was no minimum wage legislation in six EU countries in 2015 (Denmark, Italy, Cyprus, Austria, Finland, Sweden); therefore, the weighted (by population) average value for the remaining countries was used for the states with no minimum wage; this mean was 39.3% of the average wage. Data for undetermined deaths in Cyprus was not available for none of the years; for some other countries and age intervals only some data was obtainable
Discussion
This is the first study which aims to identify the cross-country economic burden of suicide in general population by estimating the production losses associated with suicide deaths in 28 EU countries. By applying a HCA model and Eurostat data, I obtained highly comparable estimates which reflect the specificity of economic burden experienced in particular countries.
The total production losses attributable to suicides at working age in the EU in 2015 were €9.07 billion, being a result of 39,265 suicides and 745 thousand years of potential productive life lost. A notable geographical difference was observed among the member states both in terms of the suicide rates and cost itself. All in all, the countries experiencing the highest relative burden of suicides were three Baltic states (Estonia, Lithuania, Latvia), Poland and Finland. On the other hand, the ones with the lowest detrimental economic effect of suicides were Cyprus, Greece, Italy, Spain and Bulgaria. Therefore, the countries located in the southern regions of Europe experienced minor health and economic burden of suicide compared to those situated in the north. This clearly reflects the geographical distribution of suicide mortality, particularly low death rates in the Mediterranean states and higher rates in Scandinavia and the Baltic post-Soviet republics. However, this mortality pattern is strengthened in terms of production losses because the NE states generally have higher employment rates (Denmark, Sweden, Latvia) and/or longer time of economic activity (Estonia, Ireland, Sweden) and/or higher prospects of future economic growth (Lithuania, Latvia, Estonia) than the SE countries. On the other hand, the greatest relative economic burden, measured by the share of GDP lost, in the CEE countries arises mainly from high suicide rates among men and dynamic projected economic growth which make future losses greater.
The results show that the cost of suicide is mainly driven by males’ death and this obviously reflects the sex disparities in suicide incidence [42, 43]. As much as 89.0% of production losses were due to suicides of men in CEE and this share was 71.0% in Sweden, a country with the most even gender distribution of cost. Considering the age distribution of suicide cost, the highest burden was identified for those at the ages 30–39 and 40–49 years. The age distribution of losses was similar for both sexes with a slightly higher magnitude of cost shares among women aged 0–19 years and young men (20–29 years).
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The stability of results was tested using a one-way deterministic sensitivity analysis. The results changed notably with no discounting applied and this can be explained by the fact that suicide mortality is disproportionally high among relatively young people. Another scenario which varied estimates markedly was the use of general population employment rates instead of rates for depression population. It should be emphasized that this choice regarding employment rate is crucial for the indirect cost estimates. Yet, I decided to opt for the conservative scenario of a heavily reduced employment rate because the previous adjustment in this respect might be understated. For most other parameter changes used in the sensitivity analysis, the results’ variation was lower.
Because this is the first cross-country study on the cost of suicide mortality in general population, the findings of this research could only be compared to previous estimates from single states. The recent results from Spain [12] identified the production losses of €380.5 million in 2013 (in 5% DR scenario) which is a close value to my estimate of €426.4 million in 2015. This similarity is irrespective of some important differences in the methods and data sources used; e.g. for the productivity measure, the Spanish study applied gross salary while mine–GDP adjusted with 0.65 coefficient. The study from Poland [9] reported the indirect cost of completed suicides as equivalent to €694.5 million in 2012 (using 5% DR) and this value is, again, comparable to my estimate of €810.0 million in 2015 for this country. This similarity might be surprising regarding the fact that my estimates are adjusted for lower employment rates among those who killed themselves. According to the conservative adjustment used here, the employment rates in the populations under study are 33.2 percentage points lower in Spain and 31.7 percentage points lower in Poland as compared to general employment rates. Therefore, this similarity of results seems to be coincidental. However, I argue that using employment rates for general population in a country bias the results upward because those dying by suicide are possibly less likely to work. Finally, the Irish study identified the cost of lost market output attributable to suicides in 2001 and 2002 to be €205.9 million and €192.8 million, respectively [10]. In my estimates, the value of market production lost in Ireland was €229.7 million in 2015 and the difference between the results of the two studies is low regarding a 13–14 years divergence of the time settings. The methodological discrepancies seem to explain this difference as the Irish estimates are based on employment rates revised downward by only 2.25 percentage points, while here this adjustment is of greater magnitude (27.1 percentage points). When it comes to estimates from non-EU developed countries, the most recent American study reported indirect cost of suicide deaths as being $US 53.0 billion in 2013 [7], an equivalent of 0.32% of GDP. The share of GDP lost for this US study is more than double than the according share for Latvia–the country with the highest losses estimated here. However, the referred US estimates differ notably from mine in methodological terms, i.a. by including fringe benefits and household productivity losses while not accounting for lower productivity of those dying by suicide.
The major advantage of this research is the fact that it is the first study providing internationally comparable data on the production losses of suicide deaths, a major cost category of the total economic burden of suicides. Moreover, country-specific measures regarding labour market characteristics (real-life data on market entry and exit) and future economic growth are used and this reflects real differences among the countries investigated. With the use of homogenous data for these indicators, the peculiarities among the 28 states could not be unravelled.
On the other hand, the caveats of this analysis should also be pointed to. First, it only provides estimates on the burden of completed suicides in terms of potential production lost, while it does not investigate health care, medicolegal and intangible costs. However, the cost of mortality is a major cost driver of the suicide-attributable economic burden; therefore, my estimates provide useful insight into the topic. Second, although the model uses homogenous data from a set of countries, some of the measures employed do not represent the reality in a perfect manner; e.g. the employment rates of those suffering from depression possibly deviate from the real employment among suicide population. Yet, it seemed to be the best choice available to account for different employment engagement of suicide population in particular countries. Moreover, such potential inaccuracies resulting from the use of proxy input data have been addressed by the sensitivity analysis. Third, the use of the HCA for the assessment of production losses is criticized for inflating the real economic burden [22, 23]; still, it is the most common method for assessment of indirect cost and the alternative of the friction cost method has its own weaknesses [44]. The detailed discussion on pros and cons of the two methods is beyond the scope of this paper (and can be found elsewhere [24]) but the choice of HCA can be justified by the fact that also the previous studies used it and this allows for direct comparison between the cost estimates. Fourth, the estimates from this research might be biased downward because of under-reporting suicides [7, 45]; however, it is not clear how serious this issue is and under-reporting data is critically missing in international, comparable context [46]. Moreover, this is just one of the problems concerning validity and reliability of the suicide statistics. The cross-country study using WHO European mortality data shows that the quality of suicide statistics varies notably between countries while the reasons for this variability are numerous [47] making the magnitude of the figures’ uncertainty difficult to assess. All and all, until now the ambiguous quality of suicides statistics appears to be an unsolved problem [48] and the only reasonable way to account for this issue was to classify all the undetermined deaths as suicides in sensitivity analysis. However, the results appear not to be satisfactory in this scenario, because there is enormous by-country variation in this death cause category; e.g. there were only 2 deaths cases among men aged 10–69 years in Italy in 2015 while in Poland–which has lower population than Italy–the according figure was 1240. Finally, the analysis only accounts for the cost borne in formal economies and does not include the burden experienced in terms of household activities undone or unregistered production lost because of suicides. Unfortunately, with the data presently at hand, this shortcoming could not have been addressed.
Conclusion
Because suicide is an avoidable mortality risk factor, public health actions aimed at prevention of suicides might not only reduce its health burden but also contribute to societies’ welfare. Therefore, preventive actions towards reduction of suicide incidence should be judged not only in terms of their cost but also benefits for production losses’ reduction resulting from lower mortality rates achieved.
Acknowledgements
The author thanks Agnieszka Matysiak for her language assistance and two anonymous reviewers for their valuable comments on the earlier versions of the manuscript.
Declarations
Ethics approval and consent to participate
The study did not involve any human participants; it relied solely on publicly available data collected for other purposes. Because this study was not an experiment I did not seek the ethics approval.
Consent for publication
Not applicable.
Competing interests
The author declares no competing interests.
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For example, supposing that there were 85 suicide deaths among males aged 30–34, it was assumed that there were 17 deaths at each age in this interval.
For 15–19 years of age employment rates I used 20–24 years employment rates because of low reliability and missing values in the former age interval for several countries (data for 4th quarter of 2015). This might bias the estimates upwards because employment rates are higher in the older age interval. Yet, this bias does not seem to have large impact on results as the number of suicides in the 15–19 age group is generally low. Moreover, in a majority of the countries the age of starting a first job is close to the upper bound of the age interval (19 years) and the employment rates at this age are plausibly closer to the rates for 20–24 years age interval.
Purchasing power parity of a particular country indicates how many units of national currency is needed in that country in order to maintain the purchasing power of one euro in the EU.
A ‘one-way’ sensitivity analysis refers to a single change of input parameter (e.g. discount rate) at a time with other parameters held constant. ‘Deterministic’ character of sensitivity analysis means that the isolated effects of changes in individual parameter is assessed, while ‘probabilistic’ sensitivity analysis assumes joint parameter uncertainty.
Gross value added is an alternative (to GDP) measure of productivity; the relationship between GDP and GVA is: GVA = GDP – Taxes on products + Subsidies on products. GVA is sometimes considered to be a superior to GDP in assessing economic welfare of population.
