PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0217979 PONE-D-18-29429 Research Article Biology and life sciencesPopulation biologyPopulation metricsDeath ratesMedicine and health sciencesMental health and psychiatrySuicidePeople and placesGeographical locationsEuropeEuropean UnionHungaryMedicine and health sciencesEpidemiologyMedical risk factorsTraumatic injury risk factorsRoad traffic collisionsMedicine and health sciencesPublic and occupational healthTraumatic injury risk factorsRoad traffic collisionsMedicine and health sciencesEpidemiologyMedical risk factorsTraumatic injury risk factorsFallsMedicine and health sciencesPublic and occupational healthTraumatic injury risk factorsFallsEarth sciencesSeasonsPeople and placesPopulation groupingsAge groupsEarth sciencesSeasonsSeasonal variations Seasonal variation of mortality from external causes in Hungary between 1995 and 2014 Seasonal variation of mortality from external causes in Hungary between 1995 and 2014 http://orcid.org/0000-0002-5081-7313 Lantos Tamás Conceptualization Data curation Formal analysis Investigation Methodology Software Visualization Writing – original draft Writing – review & editing 1 Nyári Tibor András Conceptualization Data curation Formal analysis Methodology Supervision Writing – original draft Writing – review & editing 1 * McNally Richard J. Q. Formal analysis Methodology Supervision Writing – original draft Writing – review & editing 2 Department of Medical Physics and Informatics, University of Szeged, Szeged, Hungary Institute of Health & Society, Newcastle University, Royal Victoria Infirmary, Newcastle, England, United Kingdom Lanza Queiroz Bernardo Editor Universidade Federal de Minas Gerais, BRAZIL

The authors have declared that no competing interests exist.

 * E-mail: nyari.tibor@med.u-szeged.hu 6 6 2019 2019 14 6 e0217979 10 10 2018 22 5 2019 2019 Lantos et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Objective

To analyze trends in external mortality in Hungary between 1995 and 2014 by sex.

 Methods

Data on the numbers of deaths due to external causes were obtained from the published nationwide population register. Negative binomial regression was applied to investigate the yearly trends in external-cause mortality rates. Cyclic trends were investigated using the Walter-Elwood method.

 Results

Suicide and accidents accounted for approximately 84% of the all-external-cause of deaths in Hungary. Annual suicide, unintentional falls and traffic accidents mortality declined significantly (p-value for annual trend: p < 0.001) from 30.5 (95% CI: 29.5–31.5) to 15.8 (15.1–16.5), from 31.2 (30.2–32.2) to 12.2 (11.7–12.8) and from 17.2 (16.4–18) to 5.4 (5–5.8) per 100 000 persons per year, respectively, during the study period. A significant declining trend in annual mortality was also found for assault, cold/heating-related accidents and accidents caused by electric current. However, the declining trend for drowning-related accidents was significant only for males. Significant winter-peak seasonality was found in the mortality rates from accidental falls, cold/heat-related accidents, other accidents caused by submersion/obstruction and other causes. Seasonal trends with a peak from June to July were observed in death rates from suicide/self-harm, accidental drowning/submersion and accidents caused by electric current. A significant seasonal variation with a peak in September was revealed in the mortality due to traffic accidents.

 Conclusions

This Hungarian study suggests that there was a significant seasonal effect on almost all kinds of deaths from external causes between 1995 and 2014. Environmental effects are involved in the aetiology of suicide and accidents.

 European Social Fund (ESF), European Regional Development Fund (ERDF) EFOP-3.6.1-16-2016-00008 http://orcid.org/0000-0002-5081-7313 Lantos Tamás European Social Fund (ESF), European Regional Development Fund (ERDF) EFOP-3.6.1-16-2016-00008 Nyári Tibor András This research was supported by EFOP-3.6.1-16-2016-00008 (TL, TNy), https://www.palyazat.gov.hu. There was no additional external funding received for this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability 'Dissemination database' published online by the Hungarian Central Statistical Office (KSH in Hungarian): http://statinfo.ksh.hu/Statinfo/themeSelector.jsp?page=2&szst=WNH&lang=en. Introduction

During the period of 20 years between 1995 and 2014, Hungary has recorded (on average) the 4th highest standardised death rates from external causes among the 28 members of the European Union (behind the Baltic states) and the highest one of the Visegrad Group (V4; consisting of the Czech Republic, Hungary, Poland and Slovakia) countries [1]. External causes were one of the most prevalent causes of death in Hungary (annually approximately 7700 deaths on average), ranking fourth with 6.3% of the total mortality among the major causes of death behind circulatory diseases (50.6%), neoplasms (24.9%) and digestive diseases (6.6%)–and ahead of respiratory diseases (4.5%) during this period.

The most common external causes of death in these years have been suicide (nearly 2700 deaths/year on average), followed by unintentional falls (2550) and traffic accidents (1200) which are also the leading external causes of death in many European countries. Additionally, in the last decades, Hungary has registered one of the highest mean mortality rates both from accidental falls and suicide in Europe [1].

Suicide, as one of the external causes of death, has been investigated numerous times and in many ways. Rihmer and colleagues have investigated suicide from clinical-epidemiological perspectives [2]. They reported that completed suicides were decreasing from 1986 to 2006 but this was followed by a stagnant phase. They assumed that the disappearance of the declining trend might be attributable to increasing unemployment and to the cut backs that Hungarian psychiatry faced during those years [3].

Bálint and colleagues have studied socio-epidemiological aspects and found that higher education levels are accompanied by a lower risk of suicide [4]. Marriage was a protective factor against suicide [2].

Rihmer and colleagues reported higher suicide rates in south-eastern Hungary compared with the suicide rates in the north-western parts of the country. Furthermore, a greater level of urbanicity is associated with a lower level of suicide rates. Males in all age groups are more at risk than females and the risk of suicide increases with age [2].

Fountalakis and colleagues investigated 29 European countries between 2000 and 2012 [5]. They found that both economic and climatic factors play a role in the development of suicides, but the impact of the latter is stronger. A significantly higher suicide rate was reported in the late spring–early summer peak in Hungary [6].

Although seasonal variation in mortality from suicide was studied in many countries in Europe and, specifically in Hungary [6, 7, 8], the general cyclic pattern and annual trends of mortality rates from external causes has not been investigated yet. These trends are important as they could lead to preventive measures and greater understanding of the underlying causes.

We hypothesize that cyclic trends could be also detected in other categories of deaths from external causes and that annual trends in mortality rates of external causes of deaths are decreasing.

Previous studies showed that the pattern of mortality from external causes is different among males and females [9] and by age-groups [10]. Therefore, we applied a divided age-group structure and calculated mortality trends by gender.

In our study, the annual and seasonal trends in mortality rates from external causes in Hungary were investigated during the 20-year interval between 1 January 1995 and 31 December 2014.

 Materials and methods Study population

Data on the population and numbers of deaths from external causes were obtained from the published nationwide population register of the Hungarian Central Statistical Office (HCSO) [11].

The HCSO provided data on the number of births for each month over the study period but by gender only for each year. The number of births in each month for each gender was estimated assuming no monthly variation in the gender ratio within any year.

The data concerning the external cause of deaths have been published online (Dissemination Database) by the HCSO [12]. These data were classified according to the International Classification of Diseases, 10th Revision (ICD-10) codes. S1 Table summarises the main categories.

Due to similar characteristics, some of the causes of death were grouped together: traffic accidents (in the tables, abbreviated as traffic) include railway accidents (1), motor vehicle accidents (2) and other transport accidents (3); cold/heating-related accidents (abbreviated as cold-heat) include accidents caused by smoke, fire and flames (10) and exposure to excessive cold (11); other causes (abbreviated as other) include other accidents (14) and other external causes of morbidity and mortality (17). Since the numbers of deaths from water transport accidents (4), air transport accidents (5) and lightning (12) were too small (<50), these death causes were added to the category other causes.

Data on both the population and numbers of deaths from external causes were classified by age groups as follows: 0–19 years (‘youth’), 20–34 years (‘young adult’), 35–59 years (‘middle-aged adult’) and over 60 years (‘older adults’).

Thus, age-specific death rates were calculated. Furthermore, the mortality rates of external causes were directly standardised [13] using the European Standard Population (ESP) published in 1976 [14] to make comparisons easier. The population distribution of ESP is the following: 0–19 years 29%, 20–34 years 21%, 35–59 years 34% and over 60 years 16% of the population.

 Statistical methods

Age-standardised mortality rates (ASMRs) in Hungary during the study period were calculated to make the external-cause mortality rates more comparable over time.

There are several methods for analysing cyclic trends [15]. We have used geometrical models for analysing cyclic variation which was introduced by Edwards [16]. However, Edwards’ method used only the number of observations. Walter and Elwood [17] generalised Edwards’ idea by including the population at risk. They described that seasonal fluctuation of an event which occurs on a fixed date every year might be described using cyclic patterns over the time period. The Walter-Elwood seasonality test has greater power for detecting seasonal trends, thus this test is robust for detecting seasonal effects.

Since the monthly population was estimated, Edwards’ method was applied to confirm the findings of the Walter-Elwood method. Stolwijk and colleagues [18] described the application of generalised linear models (GLMs) for investigating seasonality which is an extension of the Walter-Elwood method and based on similar geometrical approaches. We have applied negative binomial regression [19] to investigate both seasonal and annual trends.

The distribution of rare events is usually skewed and can be well approximated by the Poisson distribution. An important property of this distribution is that the mean is equal to the variance. In this case, the so-called ‘dispersion parameter’ (logarithm of its parameter lambda) is 1. However, in the case of population-level data (e.g. for cause-specific mortality), ‘overdispersion’ is a common phenomenon: the variance exceeds the mean; therefore, the assumptions of Poisson regression are not met. In such cases, negative binomial (NB) regression can be applied.

Since the likelihood-ratio test concerning overdispersion was significant, annual trends were investigated using NB regression models in analyses by gender and type of death from external causes (year of death was the only independent factor included in the model). Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were calculated.

Additionally, we have used both Walter-Elwood and Edwards’ methods for analysing cyclic variation to confirm the findings. Note that Edwards’ test is sensitive to occasional extreme values; however, both negative binomial regression and Walter-Elwood test are powerful methods in case of extreme values.

Consequently, data on the month of the death were aggregated over the study period and cyclic trends in these monthly data were investigated using the methods mentioned above. Both NB regression and Walter-Elwood test adjust for the population at risk by grouping the data into months and were used to investigate single or double peaks of seasonality.

Analyses were conducted for all causes and for men and women separately. p-values less than 0.05 were considered statistically significant. All analyses were performed using STATA Software Version 9.0 (Stata Corp LP, College Station, TX, USA).

 Results

Overall, 154 211 deaths from external causes (66.3% males and 33.7% females) were registered in Hungary during the period 1995–2014. Suicide/self-harm, accidental falls and traffic accidents were the most common causes with 53 769 (34.9%), 51 015 (33.1%) and 24 367 (15.8%) deaths, respectively (Table 1).

10.1371/journal.pone.0217979.t001 Total numbers of deaths by age during the 20 years of study in Hungary.
Traffic Falls Drowning Other drowning Electric current Cold-heat Alcohol Other Suicide Assault
OVERALL
0–19 1362 0 306 62 0 6 0 41 606 48
20–34 5525 130 281 25 30 28 0 421 6135 117
35–59 10329 7076 1386 2176 188 3239 120 3197 27635 1668
60- 7151 43809 540 2685 13 3699 0 3976 19393 808
Total 24367 51015 2513 4948 231 6972 120 7635 53769 2641
Male
0–19 1034 0 275 53 0 6 0 28 559 28
20–34 4947 130 281 25 30 25 0 409 5666 105
35–59 8475 6140 1310 1825 185 2772 114 2775 22232 1175
60- 4839 17310 450 1503 9 2123 0 2017 13074 382
Total 19295 23580 2316 3406 224 4926 114 5229 41531 1690
Female
0–19 328 0 31 9 0 0 0 13 47 20
20–34 578 0 0 0 0 3 0 12 469 12
35–59 1854 936 76 351 3 467 6 422 5403 493
60- 2312 26499 90 1182 4 1576 0 1959 6319 426
Total 5072 27435 197 1542 7 2046 6 2406 12238 951

As was the case with the crude numbers and age-specific mortality rates, the highest age-standardised rate was also detected in the case of suicide and self-harm with an ASMR of 24.06 per 100 000 persons per year (95% CI: 23.86–24.26). The largest number of suicides (more than half of all cases) was found in the age group 35–59 years; however, the highest age-specific mortality rate with 45.07 per 100 000 persons per year was observed in the group aged over 60 years (Table 2).

10.1371/journal.pone.0217979.t002 Age-standardised mortality rates per 100 000 persons in Hungary between 1995 and 2014.
Traffic Falls Drown. Other drown. Electric current Cold-heat Alcohol Other Suicide Assault
OVERALL
0–19 0.879 0.000 0.197 0.040 0.000 0.004 0.000 0.026 0.391 0.031
20–34 2.647 0.062 0.135 0.012 0.014 0.013 0.000 0.202 2.940 0.056
35–59 5.053 3.461 0.678 1.064 0.092 1.584 0.059 1.564 13.518 0.816
60- 2.659 16.292 0.201 0.999 0.005 1.376 0.000 1.479 7.212 0.300
ASMR 11.238 19.816 1.211 2.115 0.111 2.977 0.059 3.271 24.061 1.203
LB (95%CI) 11.097 19.644 1.164 2.056 0.097 2.907 0.048 3.197 23.857 1.158
UB (95%CI) 11.379 19.987 1.258 2.174 0.126 3.047 0.069 3.344 24.264 1.249
Male
0–19 1.303 0.000 0.347 0.067 0.000 0.008 0.000 0.035 0.704 0.035
20–34 4.468 0.122 0.264 0.023 0.028 0.023 0.000 0.384 5.234 0.099
35–59 8.535 6.184 1.319 1.838 0.186 2.792 0.115 2.795 22.390 1.183
60- 4.640 16.598 0.431 1.441 0.009 2.036 0.000 1.934 12.536 0.366
ASMR 19.127 22.904 2.361 3.369 0.223 4.858 0.115 5.148 40.955 1.684
LB (95%CI) 18.857 22.612 2.265 3.256 0.194 4.723 0.094 5.009 40.561 1.603
UB (95%CI) 19.397 23.196 2.458 3.483 0.252 4.994 0.136 5.288 41.349 1.764
Female
0–19 0.434 0.000 0.041 0.012 0.000 0.000 0.000 0.017 0.062 0.026
20–34 0.565 0.000 0.000 0.000 0.000 0.003 0.000 0.012 0.459 0.012
35–59 1.763 0.890 0.072 0.334 0.003 0.444 0.006 0.401 5.139 0.469
60- 1.405 16.098 0.055 0.718 0.002 0.957 0.000 1.190 3.839 0.259
ASMR 4.167 16.988 0.168 1.064 0.005 1.405 0.006 1.620 9.498 0.766
LB (95%CI) 4.052 16.787 0.145 1.011 0.001 1.344 0.001 1.556 9.330 0.717
UB (95%CI) 4.282 17.189 0.191 1.117 0.009 1.465 0.010 1.685 9.667 0.815

Both in view of crude numbers and age-standardised mortality rates, the second most frequent death cause was accidental falls with an ASMR of 19.82 per 100 000 persons per year (95% CI: 19.64–19.99). In the age group below 20 years, there was no death from this cause at all.

This was the only death cause to have had more (even in crude numbers) female victims than male ones (53.8% and 46.2%, respectively). There were many more men among the victims of this external cause in the group aged 35–59 years (the age-specific mortality rates were 18.19 and 2.62 per 100 000 persons per year, respectively). In the age group 20–34 years, there were no female victims of this death cause at all.

However, the excess of deaths from this cause in the above-60 age group was enough for women to ‘surpass’ men even proportionately–even if this difference was not significant (25.12 versus 24.61 per 100 000 persons per year).

The third most prevailing death cause was traffic accidents with an ASMR of 11.24 per 100 000 persons per year (95% CI: 11.10–11.38). Although most cases of death due to traffic accidents were registered in the age group 35–59 years, the highest age-specific mortality rate was found in the group aged over 60 years (Table 2).

Nearly four-fifths (79.2%) of the traffic accident victims were men and there were (both in crude numbers and proportionately) more male victims in each of the age groups. The difference between males and females was most noticeable in the group aged 20–34 years (22.14 versus 2.69 per 100 000 persons per year).

The next most prevalent external cause (cold/heat-related accidents) was already nearly one order of magnitude smaller (in terms of mortality rates) than the leading one (suicide/self-harm). All the results concerning this death cause, together with the other ones not mentioned above were presented only in tabular form.

Annual trends in mortality

The NB regression model for annual age-standardised data revealed a declining trend in the yearly ASMRs for all types of death from external causes except for other causes (RR: 1.014, 95% CI: 1.006–1.021; p < 0.001) during the study period (Fig 1A–1H). However, in the case of drowning-related death causes (7–8), the annual trend was significant (p < 0.001) only for males. Furthermore, the annual trend for accidental poisoning and exposure to alcohol (13) was also significant (p = 0.038); however, in the case of males–they accounted for the 95% of the victims–it was no longer significant (p = 0.057).

10.1371/journal.pone.0217979.g001 <p><bold>A-J. Annual trends of deaths from external causes in Hungary between 1995 and 2014.</bold> Annual mortality rates per 100 000 persons: observed (points) and fitted rates (dashed lines) with confidence intervals (grey bands) obtained from negative binomial regression.</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0217979.g001" xlink:type="simple"/> </fig> <p>Referring to the leading external death causes, the annual trend was significant (<italic>p</italic> < 0.001) for each one of them. The annual suicide rate declined by 48.2% from the maximum of 30.5 per 100 000 persons in 1995 to the minimum of 15.8 per 100 000 persons in 2014. There was an RR trend per annum of 0.974 (95% CI: 0.971–0.977). A similar decreasing trend was detected for the annual rate of deaths due to accidental falls (from 31.2 to 12.2, by 60.9%) and traffic accidents (from 17.2 to 5.4, by 68.6%) with an annual RR of 0.957 (95% CI: 0.950–0.964) and 0.951 (95% CI: 0.939–0.963), respectively.</p> <p>Annual trends were displayed by gender (in cases where the results were significant for both sexes) in <bold><xref ref-type="supplementary-material" rid="pone.0217979.s003">S1 Fig</xref></bold>.</p> </sec> <sec id="sec011"> <title>Seasonal trends

The aggregated numbers of deaths are summarised in Table 3. The overall number of the deaths from suicide and self-harm was the highest among all deaths from external causes. Regarding the overall number of external-cause deaths December was the most frequent month of the year.

10.1371/journal.pone.0217979.t003 The monthly numbers of deaths used in seasonal analyses in Hungary during the period 1995–2014.
Traffic Falls Drowning Other drowning Electric current Cold-heat Alcohol Other Suicide Assault Overall
Jan. 1671 4691 75 448 0 1573 10 726 3838 226 13258
Feb. 1392 3999 97 404 3 1141 16 619 3647 188 11506
Mar. 1611 4340 160 484 0 727 8 633 4758 270 12991
Apr. 1736 4120 129 453 4 312 18 561 4875 223 12431
May 1870 4166 170 382 16 133 8 629 5139 211 12724
June 2077 4206 443 354 81 60 3 607 5010 215 13056
July 2230 4163 639 330 73 59 9 614 5182 215 13514
Aug. 2355 3975 462 328 42 86 6 553 4958 224 12989
Sept. 2328 3956 118 375 9 140 6 631 4581 192 12336
Oct. 2639 4612 54 463 0 382 3 698 4261 209 13321
Nov. 2234 4157 89 407 3 805 20 675 3845 230 12456
Dec. 2224 4630 77 520 0 1554 13 689 3675 238 13620
Total 24367 51015 2513 4948 231 6972 120 7635 53769 2641 154211

Using the Walter-Elwood method, a significant cyclic trend was found in the monthly deaths from each kind of external cause except for accidental poisoning/exposure to alcohol (Table 4).

10.1371/journal.pone.0217979.t004 Annual and seasonal trends in Hungary during the period 1995–2014.
Traffic Falls Drowning Other drowning Electric current Cold-heat Alcohol Other Suicide Assault
OVERALL
IRR 0.951 0.957 0.956 0.983 0.916 0.978 0.913 1.014 0.974 0.921
95% CI 0.939 0.950 0.941 0.973 0.894 0.966 0.838 1.006 0.971 0.911
0.963 0.964 0.972 0.993 0.938 0.989 0.995 1.021 0.977 0.931
p for annual trend <0.001 <0.001 <0.001 0.001 <0.001 <0.001 0.038 <0.001 <0.001 <0.001
peak season Sep Dec June/July Jan July Jan Jan/Feb Dec June Feb
p for seasonality NS NS
Male
IRR 0.952 0.965 0.955 0.974 0.921 0.975 0.914 1.007 0.977 0.918
95% CI 0.939 0.959 0.941 0.964 0.899 0.964 0.833 1.000 0.973 0.910
0.964 0.971 0.969 0.984 0.943 0.985 1.003 1.014 0.980 0.926
p for annual trend <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.057 0.039 <0.001 <0.001
peak season Sept Oct/Nov June/July Jan July Jan Jan/Feb Nov June Apr/May
p for seasonality NS NS NS
Female
IRR 0.951 0.950 0.979 1.002 0.985 1.029 0.966 0.928
95% CI 0.941 0.948 0.901 0.986 0.972 1.014 0.962 0.918
0.961 0.952 1.064 1.017 0.999 1.043 0.970 0.939
p for annual trend <0.001 <0.001 0.621 0.837 0.032 <0.001 <0.001 <0.001
peakseason Sept/Oct Jan July Feb Jan Dec/Jan June/July Jan
p for seasonality NS

There was significant seasonality in the mortality rates from accidental falls (Fig 2B) with a winter peak (peak in December). A winter peak was also detected in the mortality rates from other accidents caused by submersion/obstruction (January) (Fig 2D) and cold/heat-related accidents (January/February) (Fig 2F). There was also a significant peak for other causes (in December) (Fig 2G); however, it is a heterogeneous group of external death causes and so needs to be treated with caution.

10.1371/journal.pone.0217979.g002 <p><bold>A–H. Seasonal variation in month of deaths from external causes in Hungary during the period 1995–2014.</bold> Monthly mortality rates per 100 000 persons: observed (points) and fitted rates (dashed lines) with confidence intervals (grey bands) obtained from negative binomial regression.</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0217979.g002" xlink:type="simple"/> </fig> <p>The Water-Elwood method revealed significant seasonality in the mortality rates from <italic>suicide/self-harm</italic> (<bold><xref ref-type="fig" rid="pone.0217979.g002">Fig 2H</xref></bold>) with a summer peak (June). A summer peak was also observed in the mortality rates from <italic>accidental drowning/submersion</italic> (June/July) (<bold><xref ref-type="fig" rid="pone.0217979.g002">Fig 2C</xref></bold>) and <italic>accidents caused by electric current</italic> (July) (<bold><xref ref-type="fig" rid="pone.0217979.g002">Fig 2E</xref></bold>). The mortality rates from traffic accidents peaked significantly in September (<bold><xref ref-type="fig" rid="pone.0217979.g002">Fig 2A</xref></bold>). However, no more cyclical variation was discovered in the mortality rates either from any other kind of external-cause deaths or from all external causes combined.</p> <p>It is worth noting that, in the case of accidental falls, the seasonality was significant only for females; for the other types of external death causes, there was no such difference between men and women(<bold><xref ref-type="table" rid="pone.0217979.t004">Table 4</xref></bold>).</p> <p>Seasonal trends were displayed by gender (in cases where the results were significant for both sexes) in <bold><xref ref-type="supplementary-material" rid="pone.0217979.s004">S2 Fig</xref></bold>.</p> <p>NB regression models confirmed the findings obtained by the Walter-Elwood method. There was no significant double peak model of seasonality.</p> </sec> </sec> <sec id="sec012" sec-type="conclusions"> <title>Discussion Main findings

The leading external causes of death were suicide, unintentional falls and traffic accidents, accounting for more than five-sixths (83.8%) of the mortality from all external causes combined. The number of deaths due to external causes was higher (both in crude numbers and proportionately) for males in all four age groups; the smallest difference (in crude numbers) was found in the above-60 age group, which can be explained by the characteristics of the age pyramid (more females than males in this age group). Overall, this age group accounted for more than half (53.2%) of the external-cause mortality, which was attributable to the large number of fall-related (female) victims aged over 60. However, for males only, the leading age group was the one between 35 and 59 years (due to the large number of deaths from suicide and traffic accidents). Nonetheless, the highest age-specific rate for males was also observed in the group aged over 60.

Either significant seasonal variation peaking in winter or significant summer-peak seasonality was observed in most of the external-cause mortality rates. However, a significant cyclical variation with a peak in September was discovered in the death rates from traffic accidents. Nevertheless, no seasonal trend was found in the combined mortality from external causes.

Decreasing annual trends in the ASMRs for almost all kinds of death from external causes were found during the study period. Consequently, an improving tendency can be observed in Hungary relating to the mortality due to external causes. The annual rate declined by 55.9% from the maximum of 94.4 per 100 000 persons in 1995 to the minimum of 41.6 per 100 000 persons in 2014. This decrease is particularly important since the mortality from external causes is traditionally high in the Central and Eastern European Countries (CEECs).

However, Hungary is still far from the EU-15 average in respect to mortality rates due to external causes (nearly twice higher than the ones in EU-15). Furthermore, the decrease in deaths due to external causes is primarily attributable to the spectacular fall observed in accidental mortality. This is linked to the EU accession in 2004 and the directive that was introduced at this time, which makes it compulsory to install safety belt systems in all types of vehicles that are placed on the market. Nevertheless, we cannot fail to mention that Hungary had switched from manual coding of death causes to machine coding in the same year.

 Strengths and limitations

The data were obtained from vital registers, which could have been influenced by a certain simplification of categorisation during the 20-year interval of the study. Additionally, the official Hungarian population estimates did not account for international migration in the period 1995–2001 (S1 Appendix), which caused a sudden increase in population between 1999 and 2000. Although the vital statistics performance index of Hungary is one of the best in the world [20] and the percentage of garbage coded deaths is also quite low in Hungary [21], there can be cause-of-death biases (e. g. youth suicides registered as fatal traffic accidents or fatal falls/accidents due to circulatory diseases). However, we are confident that our results do reflect real trends.

Over-dispersion did not influence our results as the NB regression method was employed in the investigation of annual mortality trend. One of the additional advantages of the present study is the analysis by gender.

The description of incidence rates in terms of seasonal variation or cyclic trends is important in many epidemiological studies since the population-level investigation can be utilised in the prevention. As far as we are aware, this is the first epidemiological study reporting the effect of seasonality for deaths from external causes in Hungary.

Environmental effects are involved in the aetiology of mortality. The available Hungarian monthly mean temperature [22] and precipitation [23] data generally supported these hypotheses for external-cause mortality. Unfortunately, there were no monthly (detailed) environmental data available to investigate causality more precisely. Despite daily temperature data being unavailable, heat-related seasonal effects were found in mortality from suicide, accidental drowning/submersion and traffic accidents.

In the seasonality, monthly cyclic trend analyses, we applied the Walter-Elwood method which is a generalisation of Edwards' test, specifically designed to investigate the seasonality of events with a variable population at risk. The Walter-Elwood's test avoids difficulties relating to the former method: Edwards' test ignored any possibility of variation in the population at risk which might be responsible for the cyclic variation of the disease incidence, the test was sensitive to occasional extreme values [24], it suffered from lack of power for small sample sizes [25] and its assumption of equally spaced time intervals might not be fulfilled in practice. Additionally, the Walter-Elwood seasonality test has greater power for detecting seasonal trends following a sinusoidal pattern than the Pearson's χ2 test [26].

 Comparison with other studies

Several studies investigated annual trends in suicide [27, 28, 29]. In concordance with the results of Scandinavian studies, there was a significant decrease in the annual trend during the 20 years of investigation. In Hungary, despite the higher rate of decline, age-standardised suicide rate is still about 1.5 times higher than in the EU overall. Nevertheless, although Eastern Europe (Hungary, Lithuania) has become one of the leading geographical regions based on suicide mortality rates, there is now evidence of an increasing trend in Asia (particularly South Korea) [30].

Rihmer and colleagues investigated suicides from epidemiological and clinical aspects in Hungary, giving recommendations for prevention [2, 31]. Their studies highlighted the role of general practitioners (and health care workers, in general) in prevention: since most of the suicides associated with depression can be prevented, then further training in this area would be important.

In 2012, Hungary had started a major restructuring of its healthcare system; in the next two years, there was a decrease in suicide rates (Fig 1I). On the other hand, raising the standard of living, reducing unemployment, improving the quality of health and social care and the proper media communication on depression and suicide go beyond health care. Prevention programs focusing specifically on men would also be needed as they are generally more exposed to and susceptible to social and psychological stress [9]. As far as suicide prevention (especially) for the elderly is concerned, the education for the grieving process would be of high importance.

In a study published in 2015 [32], Majdan and his colleagues conducted research that studied the mortality from accidental falls among the elderly (>65 years) in two countries with a similar population pyramid to Hungary (Slovakia, Austria) between 2003 and 2010. A growing trend was found in Slovakia; however, a declining standardised annual trend was detected in Austria, as we did in Hungary. The difference was explained (at least partially) by the better health system.

In Hungary, a sharp drop in the number of fatal falls was observed in a certain year: from 2004 to 2005 (Fig 1B). We might assume that some regulations had a role in this decrease: a new decree on the professional rules for managing local public roads was adopted in 2004, which obliges municipalities to develop winter road management plans [Decree 5/2004 (I. 28.) of the Ministry of Economy and Transport (GKM) on the Professional Rules for the Management of Local Roads].

Hungary used to be the leading one among the countries with highest fall-induced mortality rates in the EU; in 2012, its place was taken by Slovakia in this respect. Consequently, the creation of appropriate regulatory background and its enforcement are important in the prevention of accidental falls.

Both death rates from suicide and falls were higher among the elderly; therefore, the development of a social and health care system that supports older people can be an important element of prevention.

Consistent with the results of other studies [33, 34], there was also a declining trend (especially since 2007; Fig 1A) in the mortality from traffic accidents in Hungary. This is likely to be the result of a restriction of the traffic law, with substantial changes in May 2008. One of the main elements of this regulation is that drunk driving is (even in international terms) strictly punishable (“zero tolerance”), and a high fine on the spot can be imposed even in the case of low alcohol consumption.

It is important to note that the 2007 amendment to the law had earned impressive media coverage; thereby, citizens were properly informed about the changes, which could also have contributed to a large decrease in accidents. We speculate that further limitations on speed in the Highway Code would have similar (positive) consequences.

The seasonality of suicide attempts (parasuicide) and completed suicide has been investigated in many countries earlier [27, 29, 35, 36, 37, 38, 39]. We focused on the latter, but the attempts can be also authoritative for suicide in this respect (approximately every tenth attempt is “successful”). The results of the studies mentioned above were the same as ours: late spring–early summer seasonality with a peak in May-June. The previous Hungarian studies [6, 8] came to similar conclusions. This result can be explained by the temperature and mood fluctuations (typical for the elderly) and by the phenomenon “relative unhappiness” caused by the increasing intensity of social activity [40, 41]. Christodoulou and his colleagues stated in their overview that “the seasonal variation is evident in Eastern European countries”; however, they also mentioned that the seasonal association was weakening in Hungary [7, 37].

It would be important for general practitioners to know when suicide peaks: although the summer climax is no longer surprising for researchers in this area, it is much less known among GPs. Of course, not only health professionals are responsible for prevention; raising public awareness on suicide is also essential: since it is still a general misconception that winter holidays are the most depressive period of the year, depressed people get less attention in the summer when they would really need it.

Having investigated the seasonal variation in fall-induced fractures [42] and deaths [43] among the elderly (>65 years), previous studies identified cooler seasons and colder states as risk factors, respectively. This is consistent with the winter peak seasonality (peak in December) we obtained, which can also be explained by the slippery sidewalks and roads due to snow and ice [44].

Several studies on seasonality of injuries and deaths from traffic accidents reported late summer and early autumn seasonality [33, 45, 46]. The September peak obtained in our study is consistent with their results, which can be attributable to the drowsiness (dozing at the wheel, driving mistakes) due to the scorching sun and humidity.

 Conclusions

Our ecological study presented the seasonality pattern of some external-cause mortality which might be related to environmental factors. We found seasonal effects related to–among others–suicide, accidental falls and traffic accidents with peaks in June, December and September, respectively. These death causes account for overwhelming majority of the all-external-cause mortality. Environmental effects are involved in the aetiology of these external causes of death in Hungary.

To reduce the overall mortality rate from external causes and to prolong life expectancy, measures must be taken (e.g. cultural education, paying special attention to the elderly in summer, increased police presence on the roads at the opening of the academic year) to reduce those mortality rates associated with seasonal differences; however, further cohort studies should be carried out to investigate this hypothesis using detailed individual data.

 Supporting information External causes of death.

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 Deviance goodness of fit (GoF) for annual and seasonal trends in Hungary 1995–2014.

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 <p>A-F. Annual trends of deaths from external causes by gender in Hungary between 1995 and 2014. Annual mortality rates per 100 000 persons for males (red) and females (green): observed (dots/triangles) and fitted rates (dashed lines) with confidence intervals (shaded bands) obtained from negative binomial regression.</p> <p>(TIF)</p> </caption> </supplementary-material> <supplementary-material id="pone.0217979.s004" mimetype="image/tiff" position="float" xlink:href="info:doi/10.1371/journal.pone.0217979.s004" xlink:type="simple"> <label>S2 Fig</label> <caption> <title/> <p>A-F. Seasonal variation in month of deaths from external causes by gender in Hungary 1995–2014. Monthly mortality rates per 100 000 persons for males (red) and females (green): observed (dots/triangles) and fitted rates (dashed lines) with confidence intervals (shaded bands) obtained from negative binomial regression.</p> <p>(TIF)</p> </caption> </supplementary-material> <supplementary-material id="pone.0217979.s005" mimetype="application/pdf" position="float" xlink:href="info:doi/10.1371/journal.pone.0217979.s005" xlink:type="simple"> <label>S1 Appendix</label> <caption> <title>Methodology section of “Demographic yearbook 2005”.

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 References World Health Organization. European Mortality Database [cited 20 May 2019]. Available from: https://gateway.euro.who.int/en/datasets/european-mortality-database Rihmer Z, Gonda X, Kapitany B and Dome P. Suicide in Hungary-epidemiological and clinical perspectives. Ann Gen Psychiatry. 2013; 12: 21. doi: 10.1186/1744-859X-12-21 23803500 Bitter I and Kurimay T. State of psychiatry in Hungary. Int Rev Psychiatry. 2012; 24: 307–13. doi: 10.3109/09540261.2012.694856 22950769 Balint L, Osvath P, Rihmer Z and Dome P. Associations between marital and educational status and risk of completed suicide in Hungary. J Affect Disord. 2016; 190: 777–83. doi: 10.1016/j.jad.2015.11.011 26625089 Fountoulakis KN, Chatzikosta I, Pastiadis K, et al. Relationship of suicide rates with climate and economic variables in Europe during 2000–2012. Ann Gen Psychiatry. 2016; 15: 19. doi: 10.1186/s12991-016-0106-2 27508001 Sebestyen B, Rihmer Z, Balint L, Szokontor N, Gonda X, Gyarmati B, et al. Gender differences in antidepressant use-related seasonality change in suicide mortality in Hungary, 1998–2006. World J Biol Psychiatry. 2010; 11: 579–85. doi: 10.3109/15622970903397722 20218927 Lester D, Moksony F. Seasonality of suicide in eastern Europe: a comment on "Evidence for lack of change in seasonality of & suicide from Timis County, Romania". Percept Mot Skills. 2003; 96: 421–2. doi: 10.2466/pms.2003.96.2.421 12776823 Zonda T, Bozsonyi K, Veres E. Depression and suicidal behavior. Crisis. 2005; 26: 34–5. doi: 10.1027/0227-5910.26.1.34 15762082 Luy M. Causes of Male Excess Mortality: Insights from Cloistered Populations. Population and Development Review. 2003;29(4):647–676. DOI: 10.1111/j.1728-4457.2003.00647.x Remund A, Camarda CG and Riffe T. A Cause-of-Death Decomposition of Young Adult Excess Mortality. Demography. 2018; 55: 957–78. doi: 10.1007/s13524-018-0680-9 29869068 Hungarian Central Statistical Office. Demographic yearbook 1995–2014. Budapest: KSH, 1996–2015. Hungarian Central Statistical Office. Dissemination database [cited 20 May 2019]. Available from: http://statinfo.ksh.hu/Statinfo/themeSelector.jsp Association of Public Health Epidemiologists in Ontario. Standardization of rates [cited 20 May 2019]. Available from: http://core.apheo.ca/resources/indicators/Standardization%20report_NamBains_FINALMarch16.pdf French Institute for Demographic Studies and Max Planck Institute for Demographic Research. European Standard Population. The Human Cause-of-Death Database [cited 20 May 2019]. Available from: https://www.causesofdeath.org/docs/standard.pdf Rau R. Seasonality in human mortality: a demographic approach. Berlin, New York: Springer; 2007. Edwards JH. The recognition and estimation of cyclic trends. Ann Hum Genet. 1961; 25: 83–7. 13725808 Walter SD, Elwood JM. A test for seasonality of events with a variable population at risk. Br J Prev Soc Med. 1975; 29: 18–21. doi: 10.1136/jech.29.1.18 1137765 Stolwijk AM, Straatman H and Zielhuis GA. Studying seasonality by using sine and cosine functions in regression analysis. J Epidemiol Community Health. 1999; 53: 235–8. doi: 10.1136/jech.53.4.235 10396550 Agresti A. Categorical Data Analysis. New York: Wiley; 2003. Phillips DE, Lozano R, Naghavi M, Atkinson C, Gonzalez-Medina D, Mikkelsen L et al. A composite metric for assessing data on mortality and causes of death: the vital statistics performance index. Popul Health Metr. 2014; 12: 14. doi: 10.1186/1478-7954-12-14 24982595 GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017; 390: 1151–210. doi: 10.1016/S0140-6736(17)32152-9 28919116 Monthly mean temperature of Hungary between 1971 and 2000 [cited 20 May 2019]. Available from: https://www.met.hu/en/eghajlat/magyarorszag_eghajlata/altalanos_eghajlati_jellemzes/homerseklet/ Monthly mean precipitation of Hungary between 1971 and 2000 [cited 20 May 2019]. Available from: https://www.met.hu/en/eghajlat/magyarorszag_eghajlata/altalanos_eghajlati_jellemzes/csapadek/ Wehrung DA, Hay S. A study of seasonal incidence of congenital malformations in the United States. Br J Prev Soc Med. 1970; 24: 24–32. doi: 10.1136/jech.24.1.24 5435085 Roger JH. A significance test for cyclic trends in incidence data. Biometrika. 1977; 64: 152–5. DOI: 10.1093/biomet/64.1.152 Walter SD. The power of a test for seasonality. Br J Prev Soc Med. 1977; 31: 137–40. doi: 10.1136/jech.31.2.137 884398 Bridges FS, Yip PS, Yang KC. Seasonal changes in suicide in the United States, 1971 to 2000. Percept Mot Skills. 2005; 100: 920–4. doi: 10.2466/pms.100.3c.920-924 16158678 Holopainen J, Helama S, Bjorkenstam C, Partonen T. Variation and seasonal patterns of suicide mortality in Finland and Sweden since the 1750s. Environ Health Prev Med. 2013; 18: 494–501. doi: 10.1007/s12199-013-0348-4 23835646 Bramness JG, Walby FA, Morken G, Roislien J. Analyzing Seasonal Variations in Suicide With Fourier Poisson Time-Series Regression: A Registry-Based Study From Norway, 1969–2007. Am J Epidemiol. 2015; 182: 244–54. doi: 10.1093/aje/kwv064 26081677 Varnik P. Suicide in the world. Int J Environ Res Public Health. 2012; 9: 760–71. doi: 10.3390/ijerph9030760 22690161 Rihmer Z, Dome P and Gonda X. The role of general practitioners in prevention of depression-related suicides. Neuropsychopharmacol Hung. 2012; 14: 245–51. DOI: 10.5706/nph201212005 23269211 Majdan M, Mauritz W. Unintentional fall-related mortality in the elderly: comparing patterns in two countries with different demographic structure. BMJ Open. 2015; 5: e008672. doi: 10.1136/bmjopen-2015-008672 26270950 Trudeau R. Monthly and daily patterns of death. HEALTH REPORTS-STATISTICS CANADA. 1997; 9: 43–52. Korhonen N, Niemi S, Parkkari J, Palvanen M, Kannus P. Unintentional injury deaths among adult Finns in 1971–2008. Injury. 2011; 42: 885–8. doi: 10.1016/j.injury.2010.08.033 20851395 Partonen T, Haukka J, Nevanlinna H, Lonnqvist J. Analysis of the seasonal pattern in suicide. J Affect Disord. 2004; 81: 133–9. doi: 10.1016/S0165-0327(03)00137-X 15306138 Rocchi MB, Sisti D, Cascio MT, Preti A. Seasonality and suicide in Italy: amplitude is positively related to suicide rates. J Affect Disord. 2007; 100: 129–36. doi: 10.1016/j.jad.2006.10.003 17126407 Voracek M, Tran US, Sonneck G. Facts and myths about seasonal variation in suicide. Psychol Rep. 2007; 100: 810–4. doi: 10.2466/pr0.100.3.810-814 17688099 Casey P, Gemmell I, Hiroeh U, Fulwood C. Seasonal and socio-demographic predictors of suicide in Ireland: a 22 year study. J Affect Disord. 2012; 136: 862–7. doi: 10.1016/j.jad.2011.09.020 22018639 Makris GD, Reutfors J, Osby U, Isacsson G, Frangakis C, Ekbom A, et al. Suicide seasonality and antidepressants: a register-based study in Sweden. Acta Psychiatr Scand. 2013; 127: 117–25. doi: 10.1111/j.1600-0447.2012.01891.x 22676408 Preti A. Seasonal variation and meteotropism in suicide: clinical relevance of findings and implications for research. Acta Neuropsychiatr. 2002; 14: 17–28. doi: 10.1034/j.1601-5215.2002.140103.x 26983863 Christodoulou C, Douzenis A, Papadopoulos FC, Papadopoulo A, Bouras G, Gournellis R, et al. Suicide and seasonality. Acta Psychiatr Scand. 2012; 125: 127–46. doi: 10.1111/j.1600-0447.2011.01750.x 21838741 Bulajic-Kopjar M. Seasonal variations in incidence of fractures among elderly people. Inj Prev. 2000; 6: 16–9. doi: 10.1136/ip.6.1.16 10728535 Stevens JA, Thomas KE and Sogolow ED. Seasonal patterns of fatal and nonfatal falls among older adults in the U.S. Accid Anal Prev. 2007; 39: 1239–44. doi: 10.1016/j.aap.2007.03.011 17920848 Flinkkila T, Sirnio K, Hippi M, et al. Epidemiology and seasonal variation of distal radius fractures in Oulu, Finland. Osteoporos Int. 2011; 22: 2307–12. doi: 10.1007/s00198-010-1463-3 20972668 Radun I and Radun JE. Seasonal variation of falling asleep while driving: An examination of fatal road accidents. Chronobiol Int. 2006; 23: 1053–64. doi: 10.1080/07420520600921096 17043037 Gjerde H, Normann PT, Christophersen AS, Samuelsen SO and Morland J. Alcohol, psychoactive drugs and fatal road traffic accidents in Norway: a case-control study. Accid Anal Prev. 2011; 43: 1197–203. doi: 10.1016/j.aap.2010.12.034 21376919