External factors and the incidence of severe trauma: Time, date, season and moon

      Abstract

      Background

      To detect whether external factors (time of day, day of week, month and season, lunar phases) influence incidence and outcome of severely injured trauma patients.

      Patients and methods

      A retrospective cohort analysis of the TraumaRegister DGU® (TR-DGU) was carried out over a period of 10 years (January 2002–December 2011). Data of 35,432 primary admitted patients from Germany with a severe trauma (Injury Severity Score (ISS) >15) were analysed in this study. For the outcome evaluation transferred patients were excluded as well as those who did not have a valid Revised Injury Severity Classification (RISC) prognostic score. The outcome analysis could be performed in 31,596 (89.2%) patients. Incidence, demographics and injury pattern were analysed. For outcome analysis the observed hospital mortality was compared with the expected prognosis.

      Results

      Time of day was the factor that showed the highest variation in trauma incidence due to rush hours. Saturday was the day with the highest accident rate. Most accidents in the night happened on weekends. June and July were the months with the highest trauma rate with a large portion of two-wheel drivers. The days of year with the lowest trauma incidence rate were those between Christmas and New Year, and the highest rate was observed on May 1st. The outcome of the trauma patients was close to the prognosis in all investigated subgroups.

      Conclusion

      There are clear differences in incidence but not in outcome of the patients due to external factors.

      Keywords

      Introduction

      Trauma is the leading cause of death in people younger than 40 years of age world-wide accounting for approximately 10% of deaths [
      • MacKenzie E.J.
      Epidemiology of injuries: current trends and future challenges.
      ]. Therefore, it is essential to evaluate trauma incidence and outcome subject to several factors in order to deliver the highest quality medical care for trauma patients. Obviously, internal factors like experience and organisation of the trauma team or hospital resources influence the quality and outcome of trauma patients. Therefore, guidelines have been implemented to improve these conditions [
      • German Trauma Society (DGU)
      German guideline S3 AWMF registry number 012/019.
      ].
      However, external factors like time of day or day of week have also been made responsible for variation in quality and outcome of care for patients with different diseases [
      • Magid D.J.
      • Wang Y.
      • Herrin J.
      • McNamara R.L.
      • Bradley E.H.
      • Curtis J.P.
      • et al.
      Relationship between time of day, day of week, timeliness of reperfusion, and in-hospital mortality for patients with acute ST-segment elevation myocardial infarction.
      ,
      • Saposnik G.
      • Baibergenova A.
      • Bayer N.
      • Hachinski V.
      Weekends: a dangerous time for having a stroke?.
      ,
      • Gallerani M.
      • Imberti D.
      • Bossone E.
      • Eagle K.A.
      • Manfredini R.
      Higher mortality in patients hospitalized for acute aortic rupture or dissection during weekends.
      ,
      • Peberdy M.A.
      • Ornato J.P.
      • Larkin G.L.
      • Braithwaite R.S.
      • Kashner T.M.
      • Carey S.M.
      • et al.
      Survival from in-hospital cardiac arrest during nights and weekends.
      ]. These studies have demonstrated worse outcomes and generally higher mortality rates at night and/or during weekends, in patient cohorts defined by selected medical conditions, including acute myocardial infarction, stroke, aortic aneurysm and cardiac arrest. For trauma, however, the results are heterogeneous [
      • Carr B.G.
      • Jenkins P.
      • Branas C.C.
      • Wiebe D.J.
      • Kim P.
      • Schwab C.W.
      • Reilly P.M.
      Does the trauma system protect against the weekend effect?.
      ,
      • Egol K.A.
      • Tolisano A.M.
      • Spratt K.F.
      • Koval K.J.
      Mortality rates following trauma: the difference is night and day.
      ,
      • Carr B.G.
      • Reilly P.M.
      • Schwab C.W.
      • Branas C.C.
      • Geiger J.
      • Wiebe D.J.
      Weekend and night outcomes in a statewide trauma system.
      ]. These studies show the whole range of results, from ‘higher mortality’, ‘no change’, or ‘lower mortality’ during weekends and nights. Seasonal effects have been demonstrated to influence the incidence of trauma but not the number of trauma deaths [
      • Bhattacharyya T.
      • Millham F.H.
      Relationship between weather and seasonal factors and trauma admission volume at a Level I trauma center.
      ,
      • Søreide K.
      Temporal patterns of death after trauma: evaluation of circadian, diurnal, periodical and seasonal trends in 260 fatal injuries.
      ]. Studies investigating the lunar effect could not show any effects [
      • Coates W.
      • Jehle D.
      • Cottington E.
      Trauma and the full moon: a waning theory.
      ,
      • Zargar M.
      • Khaji A.
      • Kaviani A.
      • Karbakhsh M.
      • Yunesian M.
      • Abdollahi M.
      The full moon and admission to emergency rooms.
      ].
      Detecting incidence pattern and changes in quality of outcome due to these factors would help to improve trauma care when necessary. Therefore, the aim of this study was to analyse the association of external factors and incidence of severe trauma using a large national dataset, the TraumaRegister DGU® (TR-DGU). The evaluation of a potential influence of external factors on mortality, especially on weekends and during the night, was a further aim of this study.

      Patients and methods

       Study design

      The study was conducted as a retrospective cohort study by analysing data from the TraumaRegister DGU® (TR-DGU) over a period of 10 years (January 2002–December 2011). This analysis was applied for and approved according to a peer review procedure established by the Sektion NIS of DGU (No. 2012-057).

       Data source

       TraumaRegister DGU®

      The TraumaRegister DGU® (TR-DGU) is a prospective, multicenter, standardised, and anonymous documentation of severely injured patients. Data were collected at four consecutive phases from injury to hospital discharge: (A) prehospital phase; (B) emergency room and initial surgery (until admission to ICU); (C) intensive care unit (ICU); and (D) outcome status at discharge with description of injuries and procedures. The registry contains detailed information on demographics, injury pattern, comorbidities, pre- and in-hospital management, time course, relevant laboratory findings, and outcome of each individual. All injuries are coded with the Abbreviated Injury Scale (AIS, version 2005) which includes an injury grading ranging from 1 (minor) to 6 (actual untreatable) [
      • Greenspan L.
      • McLellan B.A.
      • Greig H.
      Abbreviated injury scale and injury severity score: a scoring chart.
      ]. An injury of grade 3 or more was considered as relevant.
      The TR-DGU has been a voluntary register in the past but now became the obligatory tool for quality assessment in the newly founded regional trauma networks [
      • Ruchholtz S.
      • Mand C.
      • Lewan U.
      • Debus F.
      • Dankowski C.
      • AKUT Steering Committee
      • et al.
      Regionalisation of trauma care in Germany: the ‘TraumaNetwork DGU® – Project’.
      ,

      TraumaNetwork DGU. Available from: http://www.dgu-traumanetzwerk.de.

      ]. It is in compliance with institutional requirements for data protection of the participating hospitals. As part of legally required initiatives for quality assessment in hospitals no patient consent was required for this anonymous data collection. Until December 2011, a total of 93,024 patients from 552 hospitals from 8 countries had been registered in the TR-DGU. About 90% of the registered cases were from Germany.

       Patients

      In the analysed time period from 2002 to 2011, 46,552 patients were registered. Only primary admitted patients from Germany with an Injury Severity Score (ISS) >15 were eligible for this study [
      • Baker S.P.
      • O’Neill B.
      • Haddon W.
      • Long W.B.
      The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care.
      ,
      • Greenspan L.
      • McLellan B.A.
      • Greig H.
      Abbreviated injury scale and injury severity score: a scoring chart.
      ]. Furthermore, it was required that date and time of admission to hospital was documented (available in 97.5% of cases). Finally, 35,432 patients were included in the present study. For the outcome evaluation patients transferred in and out were excluded, as well as those who did not have a valid Revised Injury Severity Classification (RISC) prognostic score [
      • Lefering R.
      Development and validation of the revised injury severity classification score for severely injured patients.
      ]. This score has been developed and validated with data from the TR-DGU, and it allows for comparing the observed hospital mortality with the expected prognosis derived from the RISC score, for groups of patients. The outcome analysis could be performed in 31,596 (89.2%) patients.

       Analysis

      All patients were characterised by typical descriptors like age, sex, ISS, New ISS, type of injury (blunt/penetrating), cause of accident, means of transport (ground/helicopter), the pattern of relevant injuries (AIS ≥ 3), hospital and 24 h mortality, length of stay in the ICU and in hospital, and duration of intubation (see Table 1).
      Table 1Patient characteristics (n = 35,432).
      VariableValue
      Age (years)46.5 ± 21.5
      Males (%)72.2
      Helicopter transport (%)33.1
      ISS28.3 ± 12.1
      New ISS34.5 ± 14.4
      Blunt trauma (%)96.0
      Hospital mortality (%)18.3
      24 h mortality (%)10.5
      Duration of intubation (days)6.11 ± 10.8
      Length of ICU stay (days)12.1 ± 13.2
      Length of stay in hospital (days)22.5 ± 24.8
      Cause of accident (%)
       Car28.7
       Motorbike14.2
       Bicycle7.9
       Pedestrian8.3
       High fall >3 m18.5
       Low fall <3 m13.8
       Others8.6
       Suicide (%)5.9
      Pattern of relevant injuries (AIS ≥ 3, %)
       Head55.3
       Thorax56.9
       Abdomen18.9
       Extremities35.0
      The following external factors were analysed:
      • 1.
        Time of day: hourly and in four periods of 6 h each: night 0:00–5:59 AM, morning 6:00–11:59 AM, afternoon12:00 AM–5:59:PM, evening 6:00–11:59 PM.
      • 2.
        Day of week, for each individual day and also weekdays versus weekend (Saturdays and Sundays).
      • 3.
        Month of year and season: the days were subdivided into four seasons starting on March 21, June 21, September 21 and December 21, respectively.
      • 4.
        Lunar phase: For evaluation of the lunar phases we defined
        • Full moon (FM): day of the full moon plus the previous and the following day.
        • New moon (NM): day of new moon plus the previous and the following day.
        • Waning moon (WNM): all days between full moon and new moon.
        • Waxing moon (WXM): all days between new moon and full moon.
      For each subgroup trauma incidence rates and characteristics of patients were determined. Since the TR-DGU does not cover all German trauma cases it was not possible to calculate population-based incidence rates. However, relative differences between the subgroups and deviations from expected rates could well be calculated. For the outcome analysis we calculated the standardised mortality rate (SMR). SMR is the quotient of the observed hospital mortality and the RISC prognosis. SMR values >1 correspond to an outcome worse than expected, while values <1 show a better outcome than expected. For the observed mortality rates 95% confidence intervals were calculated. The confidence interval for the SMR was then derived by dividing the confidence bounds by the expected mortality rate [
      ].
      The Revised Injury Severity Classification (RISC) score was developed with data from the TR-DGU (1993–2000) and was validated in the subsequent years [
      • Lefering R.
      Development and validation of the revised injury severity classification score for severely injured patients.
      ]. The TR-DGU uses the RISC score since 2003 as the standard tool for outcome adjustment. While observed mortality rates fitted very well the expected ones in the initial years, actual hospital mortality is about 2% lower than the prediction.

       Statistical analysis

      Results are presented as mean with standard deviation (SD) for continuous measurements, and as percentage with number of cases for categorical data. Mortality rates and standardised mortality rates (SMR) were presented together with their respective 95% confidence intervals (CI). Formal statistical testing was avoided here since the large number of cases would indicate even minor differences as ‘significant’. Furthermore, repeated subgroup analyses for several external factors, each with multiple variables, would inflate the number of statistical tests, which also is not desired. With 3000 cases per group, the detectable difference between two rates would be about ±2%.

      Results

       Patient data

      The mean age of the trauma patients was 46.5 years (Table 1). 72.2% were male and the average ISS was 28.3 points. Nearly all patients (96%) sustained a blunt trauma and one third of the patients (33.1%) were transported to the hospital by helicopter. 10.5% of patients died within the first 24 h and 18.3% died in hospital. Table 1 show that 8.6% of cases were suspected or definite suicides. Traffic accidents were the most frequent cause of injury (59.1%), and car accidents the most prevalent amongst those. The documentation of the localisation of the life-threatening injuries (AIS ≥ 3) shows head (55.3%) and thorax (56.9%) to be the most commonly affected body regions.

       Time of day

      Fig. 1 shows the hourly distribution of trauma during a day. Irrespective of the weekday there is a peak of accidents in the early evening (5–6 PM). On weekends more accidents occur during the night-time than on weekdays, especially on early Saturday and Sunday morning (2–6 AM) the incidence rate is about three times higher than on weekday nights.
      Figure thumbnail gr1
      Fig. 1Hour of day: percent of cases per day according to hour of hospital admission.
      Table 2 shows patient and injury characteristics in four subgroups according to the time of admission. During the night the least number of accidents happen (10.5%, n = 3707) and in the afternoon the most ones happen (39.0%, n = 13,798). The percentage of head injuries (61.6%) as well as suicide (8.0%) has their peaks during the night. Patients having their accidents at night were considerably younger (mean age 38.6 years). They were 6–11 years younger than those in other time periods. The percentage of men is 6–8% higher at night. There is also an obvious time dependency of the different causes of accidents, e.g. the relative portion of car accidents is highest at night. More details are given in Fig. 2.
      Table 2Time of the day.
      NightMorningAfternoonEvening
      Number3,707 (10.5%)8023 (22.7%)13,798 (39.0%)9892 (27.9%)
      Age38.6 ± 19.647.3 ± 21.449.4 ± 21.344.7 ± 21.7
      Gender (male%)78.270.471.971.9
      ISS28.7 ± 12.328.6 ± 12.427.9 ± 11.928.4 ± 12.3
      New ISS35.1 ± 14.434.6 ± 14.534.1 ± 14.334.8 ± 14.6
      Head injury (%)61.654.553.056.9
      Blunt trauma (%)94.396.096.795.7
      Cause of accident
       Car37.133.625.628.1
       Motorbike6.49.216.718.5
       Bicycle4.07.38.98.3
       Pedestrian8.38.57.210.3
       High fall >3 m20.420.920.114.5
       Low fall <3 m13.812.212.812.0
       Others10.18.28.78.3
       Suicide8.07.44.85.4
      Outcome evaluation
      Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).
       Number3054681211,7738333
       RISC prognosis20.021.921.421.6
       Mortality (%) with 95% CI17.6

      16.2–18.9
      19.2

      18.3–20.1
      18.8

      18.1–19.5
      19.5

      18.7–20.4
       SMR with 95% CI0.88

      0.81–0.95
      0.88

      0.84–0.92
      0.88

      0.85–0.91
      0.91

      0.87–0.95
       Days in hospital22.2 ± 22.324.5 ± 25.624.6 ± 25.623.7 ± 23.8
      a Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).
      Figure thumbnail gr2
      Fig. 2Cause of injury: percent of each category according to hour of hospital admission.
      The observed mortality ranged from 17.6% in the night to 19.5% in the evening. The observed mortality was lower than the predicted mortality (RISC) in all four subgroups. The standardised mortality ratio (SMR) was around 0.9 in all four subgroups, 0.88 during night, morning and afternoon and 0.91 in the evening.

       Day of week

      Fig. 3 shows the distribution of trauma patients on different weekdays. Given an equal distribution throughout the week one would expect 14.7% of trauma cases every day. In our data we found a minor peak on Saturdays (16.4%). Less men get injured during the week than on weekends (71.4% vs. 74.6% on Saturday and 73.4% on Sunday). There are no differences in ISS and head injuries, whereas suicides were slightly more frequent on Sundays (6.5%) than on work days (5.9%) and Saturdays (5.2%). The quantity of car accidents is almost the same during the whole week whereas motorbike accidents mostly happen on Sundays (workdays 12.1%, Saturdays 16.1%, Sundays 20.4%). Pedestrians get injured mostly on workdays (9.1% vs. 7.3% on Saturdays and 6.0% on Sundays) and high fall accidents also (19.6% vs. 18.3% on Saturdays and 14.5% Sundays).
      Figure thumbnail gr3
      Fig. 3Day of week: percent of cases per day of week (each day sums up to 100%).
      Mortality is higher on workdays (19.5%) than on Saturdays (17.8%) and Sundays (17.8%). There are no relevant deviances of the SMR between weekdays (0.89), Saturday (0.86) and Sunday (0.88) (Table 3). Fig. 4 shows the relation of time and weekday. There is a slight difference in the pattern of trauma admission between weekday and weekend with more cases in the night between 0 and 6 AM on Saturday and Sunday and a shift of the peak admission time of 5–6 PM towards the earlier afternoon (3–4 PM) on Saturdays.
      Table 3Outcome evaluation of lunar phases and days of the week.
      Full moonWaning moonNew moonWaxing moonWorkdaysSaturdaysSundays
      Outcome evaluation
      Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).
      RISC prognosis21.521.521.321.221.820.820.2
      Mortality (%) with 95% CI19.2

      17.8–20.6
      18.8

      18.2–19.5
      18.9

      17.5–20.3
      19.0

      18.3–19.7
      19.5

      19.0–20.1
      17.8

      16.8–18.9
      17.8

      16.7–18.9
      SMR with 95% CI0.89

      0.83–0.96
      0.88

      0.84–0.91
      0.89

      0.82–0.95
      0.90

      0.86–0.93
      0.89

      0.87–0.92
      0.86

      0.81–0.91
      0.88

      0.83–0.94
      a Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).
      Figure thumbnail gr4
      Fig. 4Day of week: percent of cases per day according to week day of hospital admission.

       Time of year: month and season

      Fig. 5 shows the distribution of the trauma patients according to the month of year. January is the month with the lowest incidence rate (61%). There is a continuous increase of rates up to July, the month with the highest trauma incidence rate (10.1%). Thus trauma incidence rate in summer is nearly twice as high as in winter time. Assuming an equal distribution over the year one would expect 8.3% of cases in each month. Regarding the four seasons (expected portion: 25%) winter had the lowest (19%, n = 6725) and summer had the highest (29%, n = 10,398) trauma rate. There is a seasonal variation in the causes of accidents as shown in Table 4. Pedestrian accidents happen mostly in fall and winter whereas motorbike and bicycle accidents mostly happen in spring and summer. There is an obvious peak of low fall accidents in winter, and suicide rate is highest in winter, too. The observed mortality is highest in winter (20.7%). Nevertheless the SMR is close to 0.9 in all four seasons. The day of year with the most trauma admissions was the first of May (Labour Day, n = 149). The 28th of December was the day with the least trauma admissions (n = 39), followed by Christmas day (n = 43) and Boxing Day (n = 51).
      Figure thumbnail gr5
      Fig. 5Month of year: percent of cases per month of year. The horizontal dashed line shows the percentage of cases if there was an equal number of cases each month (8.3%).
      Table 4Subgroup analysis according to season of year.
      SpringSummerFallWinter
      Number9819

      27.7%
      10,398

      29.3%
      8490

      24.0%
      6725

      19.0%
      Age45.8 ± 21.345.4 ± 21.147.95 ± 21.947.4 ± 21.8
      Gender (male%)73.673.670.670.2
      ISS28.4 ± 12.328.3 ± 12.228.4 ± 12.427.9 ± 11.8
      New ISS34.5 ± 14.434.5 ± 14.334.7 ± 14.734.3 ± 14.3
      Head injury (%)54.353.755.858.7
      Blunt trauma (%)96.396.196.195.3
      Cause of accident
       Car26.325.931.333.0
       Motorbike19.019.49.64.6
       Bicycle9.410.06.44.5
       Pedestrian6.15.711.411.7
       High fall >3 m18.119.018.718.3
       Low fall <3 m12.411.914.817.3
       Others8.67.97.810.8
       Suicide (%)5.75.45.77.3
      Outcome evaluation
      Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).
       Number8385879971365661
       RISC prognosis20.020.622.322.4
       Mortality (%) with 95% CI18.3

      17.5–19.1
      18.0

      17.2–18.8
      19.6

      18.7–20.5
      20.7

      19.6–21.7
       SMR with 95% CI0.88

      0.84–0.92
      0.87

      0.83–0.91
      0.88

      0.84–0.92
      0.92

      0.88–0.97
       Days in hospital25.1 ± 27.324.2 ± 24.522.9 ± 22.223.9 ± 24.4
      a Refers only to the subgroup for outcome evaluation (missing RISC and transfers excluded).

       Lunar phases

      10.1% of all accidents happen during the 3 days of full moon (FM) and 10.2% during the 3 days of new moon (NM), which is very close to the expected rate of 9.8% (3 of 30.5 days). The rates for waxing moon (WXM, 39.2%) and waning moon (WNM, 40.5%) were also very similar, respectively. There were slightly less pedestrian accidents during FM periods (FM 7.6%, WNM 8.2%, NM 7.9%, WXM 8.8%), however, this difference was not significant (p = 0.056). There was no further relevant difference in any of the parameters due to the different lunar phases (Fig. 6). The outcome evaluation did also show no differences (Table 3).
      Figure thumbnail gr6
      Fig. 6Day of moon month: percent of cases per day of moon month. The horizontal line shows the number of cases if there was an equal distribution for each day. One lunar cycle lasts 29–30 days. Therefore there is only half the number of cases on day 30.

      Discussion

      This study investigated the relationship of time of day, day of week, time of year (month and season) and severe trauma and the influence of these factors on the outcome of the trauma patients.

       Time of day and day of week

      Time of day is the factor that shows the highest variation in trauma incidence. There are three peaks on weekdays that can be explained by the traffic volume: most accidents happen between 5 and 6 PM. This is the typical “rush-hour” on workdays due to commuter traffic [
      ADAC: traffic and accident statistics (ADAC).
      ]. On the way to work in the morning only half as many accidents happen. The middle peak is between 11 AM and 1 PM, the so called school-traffic, when children go home from school or are picked up from school [
      ADAC: traffic and accident statistics (ADAC).
      ]. This might be the reason why the pattern differs on weekends. Saturday is the day with the highest accident rate although this peak is only moderate. There is a shift of the main peak of accidents from 5 to 6 PM towards 3 to 4 PM on weekends due to the change of people's activity on weekends. Most accidents in the night time happen on weekends, especially on early Saturday and Sunday morning between 2 and 6 AM. The reasons for this are the so called disco-journeys [
      ADAC: traffic and accident statistics (ADAC).
      ], and we also assume violence to be a major cause. This is emphasised by the mean age being almost 10 years younger than in other time periods, the percentage of men is being considerably higher and head injury and penetrating trauma having their peak at night. Nocturnal violence seems to be an even more relevant topic in US trauma centres: Watkins et al. found that even more patients were admitted in the night than during the day, with the highest number of admissions between midnight and 6 AM on weekends. The latter is comparable to our findings as well as the mean age being 10 years younger in the night than in the day and the increased nocturnal penetrating trauma rate [
      • Watkins C.J.
      • Feingold P.L.
      • Hashimoto B.
      • Johnson L.S.
      • Dente C.J.
      Nocturnal violence: implications for resident trauma operative experiences.
      ]. This age pattern is approved by other studies, too [
      • Egol K.A.
      • Tolisano A.M.
      • Spratt K.F.
      • Koval K.J.
      Mortality rates following trauma: the difference is night and day.
      ].
      We assume that the two peaks of car accidents and pedestrians in the morning and the evening can be explained by the people travelling to work by car or public transportation. Even more pedestrians get injured in dawn, maybe because they are not well identified during the dark times. Motorbikes and bicycles are not only means of transportation but also used for leisure activities, which could be the reason why most motorbike and bicycle accidents happen on weekends. High fall accidents have their peak around 11 AM and between 3 and 5 PM. They also happen less frequently on Sundays, which proves our assumption of high fall accidents being mostly work related.

       Time of year: month and season

      There are obvious differences in quantity and pattern of trauma during the year. There is a clear peak in summer with June and July being the months with the highest trauma rate. The high number of two-wheel drivers, especially motorbikes, is responsible for a relevant portion of the summer peak in trauma incidence. There are four times more motorbike accidents in spring and summer than in winter and more than twice as many bicycle accidents. Not surprisingly the number of injured pedestrians is highest in the dark seasons (fall and winter). Low fall accidents are highest in winter, resembling the number of pedestrians slipping on slippery and icy streets and boardwalks. Nevertheless they only contribute little to the whole number of accidents. In total the bad weather conditions in wintertime do not lead to an increased number of accidents. The summertime with its vacation season and increased outdoor activities is clearly more dangerous than the winter.
      The assumption that leisure accidents take a relevant part in the total number of accidents is emphasised by the fact that the first of May is the day of year with the highest trauma rate. Usually at that time of year there are the first warm and sunny days after the winter period and everybody enjoys the nice weather outside taking part in leisure activities, maybe sometimes even exhilarated by high spirits. In contrast the days with the lowest trauma rate are days between Christmas and New Year, when people are at home with their families.
      Our findings are quite comparable to other studies from the U.S. and Norway showing higher trauma incidences in the summer months, too [
      • Søreide K.
      Temporal patterns of death after trauma: evaluation of circadian, diurnal, periodical and seasonal trends in 260 fatal injuries.
      ,
      • Ovadia P.
      • Szewczyk D.
      • Walker K.
      • Abdullah F.
      • Schmidt-Gillespie S.
      • Rabinovici R.
      Admission patterns of an urban level I trauma center.
      ]. This might be a typical pattern of temperate climate zones as a significant relationship between maximum daily temperature and trauma admission had been shown by Bhattacharyya et al. [
      • Bhattacharyya T.
      • Millham F.H.
      Relationship between weather and seasonal factors and trauma admission volume at a Level I trauma center.
      ].

       Outcome analysis

      Several studies compared mortality of patients inside and outside normal working hours, the so called “weekend” or “off-hour” effect in various diseases with inconsistent results. A recent publication showed a higher mortality of all patients being admitted to emergency departments in the U.S. throughout the year 2008 on weekends. Irrespective of the diagnosis [
      • Sharp A.L.
      • Choi H.
      • Hayward R.A.
      Don’t get sick on the weekend: an evaluation of the weekend effect on mortality for patients visiting US EDs.
      ], Egol et al. showed higher mortality rates during the night in all trauma patients from the National Trauma Data Bank in the U.S. 2002–2006 [
      • Egol K.A.
      • Tolisano A.M.
      • Spratt K.F.
      • Koval K.J.
      Mortality rates following trauma: the difference is night and day.
      ]. Bell and Redelmeier showed the weekend effect for certain diseases even if the outcome is adjusted to the diagnosis [
      • Bell C.M.
      • Redelmeier D.A.
      Mortality among patients admitted to hospitals on weekends as compared with weekdays.
      ]. However, their investigation only included diagnosis, that need a relevant part of medical care outside the emergency department and the ICU. The authors discussed the reduced staffing as the relevant factor for the worse outcome. Carr et al. did not found any difference in mortality of trauma patients in a single centre study [
      • Carr B.G.
      • Jenkins P.
      • Branas C.C.
      • Wiebe D.J.
      • Kim P.
      • Schwab C.W.
      • Reilly P.M.
      Does the trauma system protect against the weekend effect?.
      ], but lower mortality on weekends in a state-wide trauma system [
      • Carr B.G.
      • Reilly P.M.
      • Schwab C.W.
      • Branas C.C.
      • Geiger J.
      • Wiebe D.J.
      Weekend and night outcomes in a statewide trauma system.
      ]. Di Bartolomeo described the “weekend” or “off-hour” effect as a possible quality indicator for trauma care [
      • Di Bartolomeo S.
      The ‘off-hour’ effect in trauma care: a possible quality indicator with appealing characteristics.
      ].
      In our data, mortality is lower in the night (17.6%) and on weekends (weekdays 18.5%; Saturdays 17.0%, Sundays 16.8%), while the ISS is similar. However, the ISS alone is not sensitive enough for mortality adjustment. Therefore, the Revised Injury Severity Classification (RISC) was used for outcome adjustment but no relevant differences could be detected. The observed and the expected mortality rates were quite well balanced in all subgroups. A ‘weekend’ or ‘off-hour’ effect could therefore not be detected in our data. Irrespective of time of day, or day of week, the severity adjusted outcome (SMR) of patients admitted on off-hours was approximately the same. This could be interpreted as a kind of evidence for a high standard of trauma care in the participating hospitals in Germany.

       Lunar phases

      A possible influence of the lunar phases on the frequency of accidents is often smiled at, but it is still examined repeatedly in serious publications. It is a common myth that at full moon (FM) people's behaviour change and more accidents happen. Some underlying theories consider people's higher aggressiveness during FM due to ‘human tidal waves’ [
      • Thakur C.P.
      • Sharma D.
      Full moon and crime.
      ] and/or less sleep during FM [
      • Röösli M.
      • Jüni P.
      • Braun-Fahrländer C.
      • Brinkhof M.W.
      • Low N.
      • Egger M.
      Sleepless night, the moon is bright: longitudinal study of lunar phase and sleep.
      ]. Thakur saw the ‘human tidal waves’ caused by gravitational pull of the moon responsible for a higher incidence of crime rate on FM days compared to non-FM days in a 5-year investigation period in India [
      • Thakur C.P.
      • Sharma D.
      Full moon and crime.
      ]. Lieber et al. demonstrated higher numbers of homicides and aggravated assaults with a statistically significant clustering around FM [
      • Lieber A.L.
      Human aggression and the lunar synodic cycle.
      ]. Considering Röösli's findings with less sleep on FM nights, also the fatigue on days following a full moon night might be considered for a higher accident rate around FM due to less concentration [
      • Röösli M.
      • Jüni P.
      • Braun-Fahrländer C.
      • Brinkhof M.W.
      • Low N.
      • Egger M.
      Sleepless night, the moon is bright: longitudinal study of lunar phase and sleep.
      ]. This could explain the slightly higher incidence of car accidents during FM in our data (29.5% vs. 28.1 at WXM).
      However in our data no relevant effect on overall trauma incidence could be found in any of the lunar phases. This is in line with data from other studies: Coates et al. did not find any relation between the 3 days of FM and non-FM days in 1444 trauma patients in the USA. There was no increase of trauma during FM in 58,000 patients within 13 months in Tehran [
      • Zargar M.
      • Khaji A.
      • Kaviani A.
      • Karbakhsh M.
      • Yunesian M.
      • Abdollahi M.
      The full moon and admission to emergency rooms.
      ].
      In contrast to the hypothesis that more accidents would happen during FM, some publications even show the opposite effect as a tendency without statistically significance [
      • Coates W.
      • Jehle D.
      • Cottington E.
      Trauma and the full moon: a waning theory.
      ,
      • Stomp W.
      • Fidler V.
      • Ten Duis H.-J.
      • Nijsten M.W.
      Relation of the weather and the lunar cycle with the incidence of trauma in the Groningen region over a 36-year period.
      ]. Coates speculates that a brighter night might deter criminal acts and better illuminate highways [
      • Coates W.
      • Jehle D.
      • Cottington E.
      Trauma and the full moon: a waning theory.
      ]. According to our data this would explain the slightly less incidence of pedestrian accidents during FM periods (7.6%).

       Limitations of the study

      The data from the TR-DGU allow only limited statements about the absolute incidence of trauma, because not all hospitals have participated in the registry in the past. The nation-wide introduction of trauma networks will change this in near future. Furthermore, severe injuries where the victims died at the scene are not included, as well as patients with minor and moderate injuries. Thus it was possible to only provide relative distributions of severe cases from several hundred trauma centres, but not population-based figures for Germany.
      A general problem of registry data is data quality and the completeness of the reported cases. The process of data collection includes several plausibility and consistency checks. However, correctness and completeness of data could only be verified in selected samples. Such data verification is implemented as part of the regular audits performed in all hospitals organised in local trauma networks.
      The tool for adjustment of injury severity, the Revised Injury Severity Classification (RISC) had been developed by the TR-DGU. Therefore it is perfectly adapted to the given data. Validation studies have proven the prognostic properties as well as the superiority to other trauma scoring systems like TRISS [
      • Lefering R.
      Development and validation of the revised injury severity classification score for severely injured patients.
      ]. However, the RISC prognosis refers to the 1990s in Germany, and therefore most recent data show mortality rates, which lie about 1–2% below the prediction. Nevertheless, the score could well be used to identify subgroups where observed and expected mortality rates differ from the overall results.

      Conclusion

      In summary we found that the incidence of trauma is depending on several external factors. The late afternoon and the summer season show clear peaks in trauma incidence. The days of week only showed a minor variation. The moon phases did not show any influence. The outcome of trauma patients was not affected by the factors analysed here, specifically during the nights and on weekends. This could be considered as an indirect evidence of adequate trauma care 24/7/365 in Germany.

      Conflict of interest statement

      Dr Rolf Lefering has a consulting and cooperation contract with the AUC which is owner of the TraumaRegister DGU. There are no further financial interests or personal relationships that could have created a conflict of interest.

      Acknowledgements

      The authors would like to acknowledge Gunnar Pietzner (Private University Witten/Herdecke; Faculty of Business) who was involved in the first analysis of external factors in 2005 where also weather data had been considered. We most gratefully acknowledge the immense effort from hundreds of participating hospitals who supported to collect and register data from thousands of severely injured patients. Their effort has made the TR-DGU to an outstanding tool for health services research in acute care. There was no financial support for this study. The TR-DGU is sponsored by fees of the participating hospitals. The AUC company, a 100% subsidiary of the German Trauma Society (DGU, Deutsche Gesellschaft für Unfallchirurgie), is holder and owner of the data. Rolf Lefering receives funding from AUC for scientific consult and statistical support.

      References

        • MacKenzie E.J.
        Epidemiology of injuries: current trends and future challenges.
        Epidemiol Rev. 2000; 22: 112-119
        • German Trauma Society (DGU)
        German guideline S3 AWMF registry number 012/019.
        2011 (Available from: http://www.awmf.org/leitlinien/detail/ll/012-019.html)
        • Magid D.J.
        • Wang Y.
        • Herrin J.
        • McNamara R.L.
        • Bradley E.H.
        • Curtis J.P.
        • et al.
        Relationship between time of day, day of week, timeliness of reperfusion, and in-hospital mortality for patients with acute ST-segment elevation myocardial infarction.
        JAMA. 2005; 294: 803-812
        • Saposnik G.
        • Baibergenova A.
        • Bayer N.
        • Hachinski V.
        Weekends: a dangerous time for having a stroke?.
        Stroke. 2007; 38: 1211-1215
        • Gallerani M.
        • Imberti D.
        • Bossone E.
        • Eagle K.A.
        • Manfredini R.
        Higher mortality in patients hospitalized for acute aortic rupture or dissection during weekends.
        J Vasc Surg. 2012; 55: 1247-1254
        • Peberdy M.A.
        • Ornato J.P.
        • Larkin G.L.
        • Braithwaite R.S.
        • Kashner T.M.
        • Carey S.M.
        • et al.
        Survival from in-hospital cardiac arrest during nights and weekends.
        JAMA. 2008; 299: 785-792
        • Carr B.G.
        • Jenkins P.
        • Branas C.C.
        • Wiebe D.J.
        • Kim P.
        • Schwab C.W.
        • Reilly P.M.
        Does the trauma system protect against the weekend effect?.
        J Trauma. 2010; 69: 1042-1047
        • Egol K.A.
        • Tolisano A.M.
        • Spratt K.F.
        • Koval K.J.
        Mortality rates following trauma: the difference is night and day.
        J Emerg Trauma Shock. 2011; 4: 178-183
        • Carr B.G.
        • Reilly P.M.
        • Schwab C.W.
        • Branas C.C.
        • Geiger J.
        • Wiebe D.J.
        Weekend and night outcomes in a statewide trauma system.
        Arch Surg. 2011; 146: 810-817
        • Bhattacharyya T.
        • Millham F.H.
        Relationship between weather and seasonal factors and trauma admission volume at a Level I trauma center.
        J Trauma. 2001; 51: 118-122
        • Søreide K.
        Temporal patterns of death after trauma: evaluation of circadian, diurnal, periodical and seasonal trends in 260 fatal injuries.
        Scand J Surg. 2010; 99: 235-239
        • Coates W.
        • Jehle D.
        • Cottington E.
        Trauma and the full moon: a waning theory.
        Ann Emerg Med. 1989; 18: 763-765
        • Zargar M.
        • Khaji A.
        • Kaviani A.
        • Karbakhsh M.
        • Yunesian M.
        • Abdollahi M.
        The full moon and admission to emergency rooms.
        Indian J Med Sci. 2004; 58: 191-195
        • Ruchholtz S.
        • Mand C.
        • Lewan U.
        • Debus F.
        • Dankowski C.
        • AKUT Steering Committee
        • et al.
        Regionalisation of trauma care in Germany: the ‘TraumaNetwork DGU® – Project’.
        Eur J Trauma Energ Med. 2012; 38: 11-17
      1. TraumaNetwork DGU. Available from: http://www.dgu-traumanetzwerk.de.

        • Baker S.P.
        • O’Neill B.
        • Haddon W.
        • Long W.B.
        The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care.
        J Trauma. 1974; 14: 187-196
        • Greenspan L.
        • McLellan B.A.
        • Greig H.
        Abbreviated injury scale and injury severity score: a scoring chart.
        J Trauma. 1985; 25: 60-64
        • Lefering R.
        Development and validation of the revised injury severity classification score for severely injured patients.
        Europ J Trauma Emerg Surg. 2009; 35: 437-447
      2. Gardner M.J. Altman D. Statistics with confidence: Confidence intervals and statistical guidelines. 1989 (British Medical Journal, London)
      3. ADAC: traffic and accident statistics (ADAC).
        2012 (Available from: http://www.adac.de/infotestrat/ratgeber-verkehr/statistiken/default.aspx)
        • Watkins C.J.
        • Feingold P.L.
        • Hashimoto B.
        • Johnson L.S.
        • Dente C.J.
        Nocturnal violence: implications for resident trauma operative experiences.
        Am Surg. 2012; 78: 657-662
        • Ovadia P.
        • Szewczyk D.
        • Walker K.
        • Abdullah F.
        • Schmidt-Gillespie S.
        • Rabinovici R.
        Admission patterns of an urban level I trauma center.
        Am J Med Qual. 2000; 15: 9-15
        • Sharp A.L.
        • Choi H.
        • Hayward R.A.
        Don’t get sick on the weekend: an evaluation of the weekend effect on mortality for patients visiting US EDs.
        Am J Emerg Med. 2013; 31: 835-837
        • Bell C.M.
        • Redelmeier D.A.
        Mortality among patients admitted to hospitals on weekends as compared with weekdays.
        New Engl J Med. 2001; 345: 663-668
        • Di Bartolomeo S.
        The ‘off-hour’ effect in trauma care: a possible quality indicator with appealing characteristics.
        Scand J Trauma Resusc Emerg Med. 2011; 19: 33
        • Thakur C.P.
        • Sharma D.
        Full moon and crime.
        Br Med J. 1984; 289: 1789-1791
        • Röösli M.
        • Jüni P.
        • Braun-Fahrländer C.
        • Brinkhof M.W.
        • Low N.
        • Egger M.
        Sleepless night, the moon is bright: longitudinal study of lunar phase and sleep.
        J Sleep Res. 2006; 15: 149-153
        • Lieber A.L.
        Human aggression and the lunar synodic cycle.
        J Clin Psychiatry. 1978; 39: 385-392
        • Stomp W.
        • Fidler V.
        • Ten Duis H.-J.
        • Nijsten M.W.
        Relation of the weather and the lunar cycle with the incidence of trauma in the Groningen region over a 36-year period.
        J Trauma. 2009; 67: 1103-1108