Excess mortality at Christmas due to cardiovascular disease in the HUNT study prospective population-based cohort in Norway

Abstract

Background:
Although it is known that winter inclusive of the Christmas holiday period is associated with an increased risk of dying compared to other times of the year, very few studies have specifically examined this phenomenon within a population cohort subject to baseline profiling and prospective follow-up. In such a cohort, we sought to determine the specific characteristics of mortality occuring during the Christmas holidays.

Methods:
Baseline profiling and outcome data were derived from a prospective population-based cohort with longitudinal follow-up in Central Norway - the Trøndelag Health (HUNT) Study. From 1984 to 1986, 88% of the target population comprising 39,273 men and 40,353 women aged 48 ± 18 and 50 ± 18 years, respectively, were profiled. We examined the long-term pattern of mortality to determine the number of excess (all-cause and cause-specific) deaths that occurred during winter overall and, more specifically, the Christmas holidays.

Results:
During 33.5 (IQR 17.1-34.4) years follow-up, 19,879 (50.7%) men and 19,316 (49.3%) women died at age-adjusted rate of 5.3 and 4.6 deaths per 1000/annum, respectively. Overall, 1540 (95% CI 43-45 deaths/season) more all-cause deaths occurred in winter (December to February) versus summer (June to August), with 735 (95% CI 20-22 deaths per season) of these cardiovascular-related. December 25th-27th was the deadliest 3-day period of the year; being associated with 138 (95% CI 96-147) and 102 (95% CI 72-132) excess all-cause and cardiovascular-related deaths, respectively. Accordingly, compared to 1st-21st December (equivalent winter conditions), the incidence rate ratio of all-cause mortality increased to 1.22 (95% CI 1.16-1.27) and 1.17 (95% 1.11-1.22) in men and women, respectively, during the next 21 days (Christmas/New Year holidays). All observed differences were highly significant (P < 0.001). A less pronounced pattern of mortality due to respiratory illnesses (but not cancer) was also observed.

Conclusion:
Beyond a broader pattern of seasonally-linked mortality characterised by excess winter deaths, the deadliest time of year in Central Norway coincides with the Christmas holidays. During this time, the pattern and frequency of cardiovascular-related mortality changes markedly; contrasting with a more stable pattern of cancer-related mortality. Pending confirmation in other populations and climates, further research to determine if these excess deaths are preventable is warranted.

Full text

R E S E A R C H A R T I C L E Open Access
Excess mortality at Christmas due to
cardiovascular disease in the HUNT study
prospective population-based cohort in
Norway
Trine Moholdt
1,2
, Clifford Afoakwah
3
, Paul Scuffham
3,4
, Christine F. McDonald
5
, Louise M. Burrell
6
and
Simon Stewart
7,8*
Abstract
Background: Although it is known that winter inclusive of the Christmas holiday period is associated with an
increased risk of dying compared to other times of the year, very few studies have specifically examined this
phenomenon within a population cohort subject to baseline profiling and prospective follow-up. In such a cohort,
we sought to determine the specific characteristics of mortality occuring during the Christmas holidays.
Methods: Baseline profiling and outcome data were derived from a prospective population-based cohort with
longitudinal follow-up in Central Norway - the Trøndelag Health (HUNT) Study. From 1984 to 1986,88% of the target
population comprising 39,273 men and 40,353 women aged 48 ± 18 and 50 ± 18 years, respectively, were profiled.
We examined the long-term pattern of mortality to determine the number of excess (all-cause and cause-specific)
deaths that occurred during winter overall and, more specifically, the Christmas holidays.
Results: During 33.5 (IQR 17.1–34.4) years follow-up, 19,879 (50.7%) men and 19,316 (49.3%) women died at age-
adjusted rate of 5.3 and 4.6 deaths per 1000/annum, respectively. Overall, 1540 (95% CI 43–45 deaths/season) more
all-cause deaths occurred in winter (December to February) versus summer (June to August), with 735 (95% CI
20–22 deaths per season) of these cardiovascular-related. December 25th–27th was the deadliest 3-day period of
the year; being associated with 138 (95% CI 96–147) and 102 (95% CI 72–132) excess all-cause and cardiovascular-
related deaths, respectively. Accordingly, compared to 1st–21st December (equivalent winter conditions), the
incidence rate
ratio of all-cause mortality increased to 1.22 (95% CI 1.16–1.27) and 1.17 (95% 1.11–1.22) in men and women,
respectively, during the next 21 days (Christmas/New Year holidays). All observed differences were highly significant
(P< 0.001). A less pronounced pattern of mortality due to respiratory illnesses (but not cancer) was also observed.
(Continued on next page)
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data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: simon.stewart@laureate.edu.au
7
Torrens University Australia, South Australia, Wakefield Campus, Adelaide, SA
5000, Australia
8
University of Glasgow, Glasgow, Scotland, UK
Full list of author information is available at the end of the article
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https://doi.org/10.1186/s12889-021-10503-7
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(Continued from previous page)
Conclusion: Beyond a broader pattern of seasonally-linked mortality characterised by excess winter deaths, the
deadliest time of year in Central Norway coincides with the Christmas holidays. During this time, the pattern and
frequency of cardiovascular-related mortality changes markedly; contrasting with a more stable pattern of cancer-
related mortality. Pending confirmation in other populations and climates, further research to determine if these
excess deaths are preventable is warranted.
Keywords: Population cohort, Longitudinal follow-up, Mortality, Cardiovascular disease, Seasonality
Background
Although age-standardised mortality is typically reported
as the number of deaths per 1000 people at risk per
annum, deaths are rarely evenly distributed throughout
the year. Typically, more deaths due to cardiovascular
disease (CVD) occur in winter compared to summer [1].
Paradoxically, seasonal variations in cardiovascular-
related mortality are not simply explained by exposure
to environmental provocations such as cold tempera-
tures, reduced daylight hours, infections, or increased
pollution [2–5]. Rather, they appear to reflect a more
complex interplay between the environment and an indi-
vidual’s physical and psychological condition, their be-
haviours and the culture/society in which they live [4,6].
In Scandinavia, for example, an individual-to-societal
adaptation to extremely cold temperatures undoubtedly
mitigates the cyclic exposure and physiological responses
to seasonally driven provocations to cardiovascular
health [7].
Previous studies have sought to link clusters of in-
creased mortality to large earthquakes [8] and the FIFA
World Cup [9]. Beyond these exceptional events, there is
an event that has strong potential to be detrimental to
an individual’s cardiovascular health on an annual basis
[10,11]. At Christmas, people around the world engage
in potentially stressful social interactions and provoca-
tive behaviours they would not normally expose them-
selves to. In those already at risk of seasonal patterns of
mortality (i.e., where Christmas coincides with winter),
these factors may act as additional, short-term triggers
for a broad range of cardiovascular-related events [12].
A number of studies based on administrative data have
previously demonstrated increased rates of mortality [12,
13], hospitalisation [11] and acute myocardial infarction
(AMI) in Sweden during the Christmas holidays [10].
Beyond these studies, however, this phenomenon re-
mains poorly characterised [1].
We hypothesised that over and beyond long-term sea-
sonal trends within a population periodically exposed to
cold winters, we would find an additional risk of dying
over the Christmas holidays. In effect this would repre-
sent an increased period of increased mortality within an
already high-risk period of the year. We also hypothe-
sised that CVD would be the major contributor to this
phenomenon and that we would find sex-specific differ-
ences in this regard.
Methods
Study context
Norway (population ~ 5.5 million people) has a long
tradition of undertaking insightful, longitudinal popula-
tion cohort studies. This includes the Tromsø Study in
Northern Norway [7,14], and the focus of this report,
the Trøndelag Health (HUNT) Study [15]. Although the
warm currents of the Gulf Stream moderate its weather,
given its northerly latitude, Norway experiences extreme
weather conditions. Central Norway’s Köppen Climate
Classification subtype is Continental Subarctic Climate
[16]. The coldest month is January (mean temperature
of minus 3 °C) and the warmest month is July (around
13 °C) with a mean annual temperature of 4.8 °C overall.
Although Norway enjoys relatively clean air, the winter
solstice and darkest days of the year coincide with
Christmas.
Study design
We examined the long-term pattern of mortality
within the prospective, longitudinal, population-based
HUNT Study cohort living in Central Norway [15,
17]. The present study was approved by the Regional
Committee for Ethics in Medical Research (REK-midt,
no. 2018/1509).
Data collection
The original wave of population screening (HUNT1) was
undertaken during 1984–1986, with 88% of eligible in-
habitants aged ≥20 years in Nord-Trøndelag County re-
cruited. Here, we include the 79,626 men and women
who attended a clinical examination and filled out de-
tailed questionnaires about their health and lifestyle [15].
Specifically, data on socio-economic status, perceived
levels of health and life satisfaction, lifestyle behaviours,
and self-reported cardiovascular health CVD were de-
rived from validated questionnaires [15,17]. We used a
previously developed index of physical activity to cat-
egorise levels of leisure-time physical activity [18].
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Study outcomes
The unique personal identification number of all Norwe-
gian citizens allows linkage of each participant’s record
in the HUNT Study to information from the national
Cause of Death Registry on the timing and primary
cause of death. These are classified according to the
International Classification of Disease (ICD) –with pre-
cise death coding data available until 1st January 2018.
Based on the listed causes of death and the pre-specified
hypotheses, the main codes of interest were - CVD (i.e.,
ICD-9: 390–459 and ICD-10: I00-I99t inclusive of the
specific codes for coronary artery disease [CAD], acute
myocardial infarction [AMI], cerebral infarction, and
sudden cardiac death), as well as cancer/malignancy and
respiratory disease/illness. Regardless of cause of death
coding, data on the occurrence and timing of death were
available for the full follow-up period (1984 to 2020).
Using these data, the specific focus of this study was cal-
culating (if appropriate) the number of excess deaths (on
a crude and adjusted basis) occurring during the key
time-points of interest.
Data analyses
This study conforms to the STROBE guidelines for the
reporting of observational studies [19]. Deaths were ini-
tially grouped according to whether they occurred in
winter (December, January, and February), spring
(March, April, and May), summer (June, July, and
August) or autumn (September, October. and
November). Mortality data were also grouped into 3-day
rolling totals to identify potentially more specific periods
of increased mortality (including Christmas). Subse-
quently, three specific 21-day periods were purposefully
selected for more granular analyses and comparison –1)
the 21 days in which, on a statistical basis, the least num-
ber of all-cause deaths occurred (17th May-6th June); 2)
the 21 days of winter preceding Christmas (1st–21st
December –selected as the reference period for all com-
parisons) in which mortality rates were reflective of the
broader winter period and; 3) the subequent 21 days
inclusive of the Christmas holiday period (22nd
December-11th January) in which mortality rates were
elevated above the winter average. The main outcome
variable is the counts of deaths per day while the main
exposure variable is the time (for example, Christmas or
winter period). We modelled excess mortality by adjust-
ing for baseline characteristic such as sex, age at death,
month, and annual trends. The number of lower/excess
deaths per period was then estimated using the ordinary
least squares (OLS) method. A Poisson approach was
then used to estimate the increased/decreased risk of
mortality (incidence rate ratio [IRR] with 95% CI’s) due
to exposure to the Christmas holiday period. Using the
variables summarised in Table 1, we generated adjusted
hazard ratios (HR) for all-cause mortality for the entire
cohort during the median study period of 33.5 (IQR 17.1
to 34.4) years follow-up using a Cox-Proportional Haz-
ard model (entry model using only those cases with full
profiling data). These same methods (Cox-Proportional
Hazard models) were used to directly compare the
correlates of dying in –a) the first 21 days of winter
(December 1st to 21st) versus the lowest 21-day period
of deaths during the rest of the year (May 17th to June
6th) in 2894 participants who died during this combined
42-day period) and b) December 22nd to January 11th
(21 days inclusive of Christmas/New Year holidays) ver-
sus the preceding 21 days (December 1st to 21st) on a
sex-specific basis. All analyses were performed using
SPSS v26.0 and STATA v13. Statistical significance was
accepted at a 2-sided alpha of P< .05.
Results
Cohort characteristics
The study cohort comprised 40,353 women (50.1%) and
39,273 men aged 50 ± 18 and 48 ± 18 years, respectively.
Two-thirds were married and just over half had < 10
years of formal education. Most participants reported
generally positive health and life-satisfaction levels. Al-
ternatively, many had relatively high levels of risk for
CVD and other chronic diseases, including elevated
baseline levels of blood pressure (BP) and smoking com-
bined with relatively high levels of sedentary behaviours
and overweight status (Table 1).
All-cause mortality
During the 35-year study period, there were 39,195
deaths (49.2%) comprising 19,879 (50.7%) men and 19,
316 (49.3%) women. As shown in Fig. 1, these deaths
were not evenly distributed over time. Age-adjusted
mortality was slightly higher in men compared to
women (5.3 and 4.6 deaths per 1000/annum, respect-
ively); rising from 1.6 to 224 deaths and from 1.1 to 183
deaths per 1000/annum in men and women initially
aged < 30 years and > 80 years, respectively. An increased
risk of all-cause mortality (P< .001 for all comparisons
unless indicated) was correlated with advancing age (ad-
justed HR 1.11, 95% CI 1.11–1.12 per year), male sex
(1.59, 1.55–1.64 versus women), lower education (1.15,
1.11–1.18 for ≤9 years education versus rest), greater
unhappiness (1.30, 1.21–1.39 for any degree of life
dissatisfacton versus rest), being divorced/separated
(1.15, 1.06–1.20 versus unmarried), obesity (1.13, 1.09–
1.18), being a current smoker (1.89, 1.79–1.91 versus
rest), excessive alcohol intake (1.09, 1.02–1.16 for > 10
drinks in 14-days versus abstinence; P= .017), an ele-
vated heart rate (1.03, 1.02–1.03 per 5 beats/min), higher
systolic (1.02, 1.02–1.03 per 5 mmHg) and diastolic BP
(1.01, 1.00–1.02 per 5 mmHg), as well as a self-reported
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Table 1 Baseline characteristics according to survival status
Total
(n= 79,626)
Alive
(n= 40,431)
Dead
(n= 39,195)
Demographic Profile
Women, % 40,353 (50.7) 21,037 (52.1) 19,316 (47.9)
Age Groups, %
Aged < 35 years 22, 208 (27.9) 20, 739 (50.4) 1469 (3.7%)
Aged 35–44 years 15,556 (19.5) 12,602 (31.2) 2954 (7.5%)
Aged 45–64 years 24,874 (31.2) 7063 (17.5) 17,811 (45.4%)
Aged 65+ years 16,988 (21.3) 27 (0.001) 16,961 (43.3%)
Mean age at baseline (years) 49.1 ± 18.0 35.7 ± 9.6 62.9 ± 13.6
Mean age at end follow-up (years) 74.9 ± 11.6 70.1 ± 11.4 79.9 ± 9.6
Married, % (n= 76,775) 52,709 (66.6) 26,532 (69.5) 26,177 (67.8)
≤9 years education, % (n= 61,240) 32,928 (53.9) 9082 (29.9) 23,886 (77.4)
Employment status, % (n= 76,870)
Full-time employment 32,333 (42.1) 21,585 (66.8) 10,748 (28.0)
Part-time employment/housework 21,381 (27.8) 13,219 (34.4) 8162 (21.2%)
Non-employed/ retired 23,156 (30.1) 3623 (15.6) 19,533 (50.8)
Health Status
Life Satisfaction, % (n= 75,815)
Dissatisfied (Quite to Extremely) 2005 (2.6) 630 (1.7) 1375 (3.6)
Satisfied (Quite to Extremely) 62,342 (82.2) 32,967 (86.6) 29,375 (77.9)
General Health Status, % (n= 76,863)
Bad 2023 (2.6) 202 (0.5) 1821 (4.7)
Poor 18,752 (24.4) 4615 (12.0) 14,317 (36.7)
Good 44,215 (57.5) 24,411 (63.6) 19,804 (51.5)
Very Good 11,873 (15.4) 9165 (23.9) 2708 (7.0)
Physical Activity Status, % (n=57,212)
Inactive 27,145 (47.4) 13,157 (45.1) 13,988 (49.9)
Low 18,730 (32.7%) 9728 (33.3) 9002 (32.1)
Moderate 8283 (14.5) 4951 (17.0) 3332 (11.9)
High 3054 (5.3) 1362 (4.7) 1692 (6.0)
Alcohol intake, % (n= 61,520)
4 or less drinks in 14 days 50,376 (81.9) 27,143 (88.3) 23,233 (76.6)
5 or more drinks in 14 days 3608 (5.9) 1685 (5.5) 1923 (5.1)
Abstains 7536 (12.2) 1899 (6.2) 5637 (18.3)
Current smoker, % (n= 60,421) 20,667 (34.2) 10,885 (36.7) 9782 (32.9)
Mean BMI kg/m
2
(n= 74,330) 25. ±3.9 24.2 ± 3.4 26.2 ± 4.1
Mean heart rate, bpm (n= 74,906) 74.9 ± 12.6 73.8 ± 11.9 76.0 ± 13.1
Mean BP, mmHg (n= 74,832)
Systolic BP/ 139 ± 23.5 / 127 ± 15.2 / 150 ± 25.0 /
Diastolic BP 84.6 ± 15.2 80.9 ± 15.2 88.5 ± 11.7
Angina pectoris (%) (n= 76,742) 3450 (4.5) 113 (0.3) 3337 (8.7)
AMI, % (n= 76, 723) 1986 (2.6) 39 (0.1) 1947 (5.1)
Stroke, % (n= 76,794) 1412 (1.8) 59 (0.2) 1353 (3.5)
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history of AMI (1.65, 1.55–1.76), angina pectoris (1.26,
1.20–1.33) and stroke/cerebral event (1.48, 1.36–1.60).
Alternatively, being married (adjusted HR 0.80, 95% CI
0.77–0.83 versus unmarried), better self-reported general
health (0.76, 0.74–0.79 for good/very good versus rest),
mild alcohol intake (0.94, 0.91–0.97 1–4 drinks in 14-
days versus abstinence) and greater levels of exercise
(0.89, 0.86–0.92 for moderate to high adherence to rec-
ommended exercise versus rest) were associated with a
reduced risk of all-cause mortality.
Specific causes of death
The three most common causes of death in men and
women were CVD (8355 [43.6%] and 7969 deaths
[43.0%], respectively), cancer (5051 [26.4%] and 4150
deaths [22.4%]), and respiratory disease/illness (1599
[8.3%] and 1606 [8.7%] deaths). Collectively, these
accounted for 78 and 74% of all deaths in men and
women, respectively. Other causes of death included
endocrine disorders (1343 [3.4%]), psychiatric disorders
(1118 [2.9%]) and external factors including motor ve-
hicle accidents and violence (951 [2.5%]). Consistent
with all-cause mortality, there were marked fluctuations
(with clear peaks and troughs) in those deaths attribut-
able to CVD and respiratory disease.
Seasonal patterns of mortality
On an absolute basis, 1707 more deaths occurred in
winter (10,790 [27.5%]) compared to summer (9083
[23.2%]) during the 35-year study period. The differential
between cardiovascular- and respiratory-related mortal-
ity occurring in winter (4446 [27.4%] and 1037 [32.4%]
deaths) versus summer (3832 [23.5%] and 661 [20.6%]
deaths) contributed to 59% (1010 deaths) of the ob-
served variance between winter and summer. Although a
more stable pattern of mortality was observed in spring
(9900 [25.3%] deaths) and autumn (9442 [24.0%] deaths),
a seasonal pattern was still evident. On adjusted basis,
each winter there were 44 (95% CI 43–45/annum) more
deaths when compared to the equivalent 3 months of
summer. The main contributors to the excess deaths oc-
curring in winter were CVD (21, 95% CI 20–22 deaths/
annum), respiratory disease (13, 95% CI 13–14 deaths/
annum) and other miscellaneous conditions (14, 95% CI
13–14 deaths/annum). Alternatively, as indicated by
Fig. 2, over the entire 35-year study period, cancer-
related deaths occurred at a far more stable, seasonal
rate (the absolute difference between winter versus
summer-being 10 deaths).
The Christmas holiday effect
Regardless of the season, accumulative 3-day mortality
consistently fluctuated between 90 and 110 deaths, apart
from a clear increase in mortality commencing on the
22nd of December. The subsequent 3-day period over
Christmas was the deadliest of the year (Fig. 3) with 439
all-cause deaths occurring on 25th –27th December.
This was not a random phenomenon and was largely
driven by an increase in cardiovascular-related and, to a
lesser extent, cancer-related deaths (Fig. 4). On an ad-
justed basis, over the entire 35-year study period, an
additional 138 (95% CI, 114–159) more all-cause deaths
occurred during this specific 3-day period compared to
those same calendar days during the rest of the year.
CVD (an extra 105 [95% CI 75–138] deaths per day) was
the main contributor to this phenomenon. This elevated
mortality rate persisted until early January. During the
21 days from the 22nd of December, there were 2679
deaths (51.1% women) compared to 2351 deaths (49%
women) during the preceding 21 days versus 2016 deaths
Fig. 1 Fluctuating Patterns of Mortality. Total all-cause and cause-specific death counts were plotted in 3-monthly intervals (synchronised to each
distinctive season) over the entire 35-year study period
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(49.6% women) during the lowest 21 days of mortality
May 17th through June 6th.
Compared to the already elevated levels of mortality
observed during the first 21 days of December/winter,
over the 35-years study period, on an adjusted basis,
there were 28 (95% CI 21–35) more deaths per day dur-
ing the subsequent Christmas/New Year period. The
major contributors to this phenomenon were CVD and
to lesser extent, cancer, and other causes –Supplemen-
tary Figure S1. When compared to the preceding 21
days, the Christmas period was also notable in respect to
within and between differences among men and women
in respect to fatal AMI (78 versus 16 more deaths, re-
spectively), strokes (13 fewer versus 32 more deaths) and
heart failure (1 more versus 12 more deaths). Similarly,
in men and women, the number of cancer- (18 and 29
more deaths, respectively) and respiratory-related (19
and 33 more deaths, respectively) deaths also increased.
Winter and Christmas vulnerability
Overall, except for cancer-related mortality (both sexes)
and respiratory disease in men, compared to the first 21
days of December/winter, the risk of dying in the late
spring/early summer period of 17th May to 6th June was
significantly lower - Supplementary Figure S2. Alterna-
tively, except for an increased risk of dying from respira-
tory illnesses/disease among women, men had a higher
risk of dying over the equivalent 21-day Christmas
Fig. 2 Seasonal Comparisons of Mortality. The adjusted, annual number of deaths (error bars show 95% CI) occurring in spring, autumn, and
winter are plotted above and below the reference (low mortality) season of summer for - all-cause mortality (blue symbols) and those related to
cancer (orange), CVD (red), respiratory disease (green) and other causes (brown). The total difference in deaths for all-causes (with 95% CI) and
specific causes over the entire 35-years are also shown adjacent to each symbol
Fig. 3 3-Day Mortality Across the Calendar Year. This graph plots the 3-day, rolling average of all-cause deaths occurring during the entire 35-year
study period, starting with the calendar days of 1st –3rd July and ending in the 28th –30th June. The Christmas period of increased mortality is
highlighted in red
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period; the major contributor to this increased mortality
risk (from 6 to 22% higher overall) being CVD - Supple-
mentary Figure S3.
Beyond advancing age, a combination of baseline
demographic, health perceptions and clinical factors
were independently correlated with dying during –1)
late spring/early summer (May 17th to June 6th) versus
early winter (Dec 1st –21st), and then 2) early winter
(Dec 1st –21st) versus the Christmas holiday period
(Dec 22nd –Jan 11th). Whilst these factors were broadly
similar for both sexes, including a 30% reduced risk dur-
ing the Christmas holidays associated with being married
at baseline, there were some notable differences. For ex-
ample, consistent with an excess number of strokes
among women, but not men, during the Christmas holi-
days, a pre-existing history of stroke conferred a 2-fold
risk of dying during this period among women. Educa-
tional status among women also appeared to modulate
the additional risk of dying during this period –see
Table 2.
Sensitivity analyses
We conducted sensitivity analyses by estimating four dif-
ferent models to test if the phenomenon of Christmas-
related excess mortality is a reliable and consistent
observation. All four models supported the findings of a
significant increase in mortality over the Christmas
period –Supplementary Table S1.
Discussion
We investigated the seasonal pattern of mortality within
the HUNT Study cohort living in Central Norway. This
population cohort is regarded as representative for the
Norwegian population as a whole, except for a lower
proportion of non-whites and the absence of large cities.
Our analyses revealed a striking long-term difference in
mortality occurring in winter compared to summer.
CVD accounted for half of this seasonality. Although not
the coldest, December proved to be the deadliest month,
with 22 more people dying each year compared to June.
Overall, the 3-day period of 25th–27th December was
revealed to be the deadliest time of the year with CVD
the major contributor. Critically, both the frequency and
cause of death in men and women appeared to change
over the Christmas period. Compared to the same pre-
Christmas/wintery period, men were 22 and 17% more
likely to die from all-causes and CVD (particularly
AMI), respectively. In women, the equivalent risk in-
creases were 17 and 15%, with the contribution of CVD
(particularly stroke) even more prominent. Although
previous studies have also identified a specific Christmas
effect on mortality [10–13,20], we are unaware of any
studies and findings equivalent to those reported here.
There is pre-existing evidence to support the hypoth-
esis that Christmas can be harmful to some individuals.
A study of the overall pattern of mortality in the US dur-
ing 1973–2001 revealed a “holiday effect”during Christ-
mas, with ~ 5% excess deaths, after adjustment for the
winter season [12]. Similarly, data from a nationwide
coronary care unit registry in Sweden revealed a 15% in-
crease in AMI cases during the Christmas holidays [10].
A higher risk of 30-day mortality or readmission among
those hospitalised at Christmas in Ontario, Canada has
also been found [11]. From a Southern Hemisphere
Fig. 4 Excess Christmas Mortality. The adjusted, annual number of deaths (error bars show 95% CI) occurring during the 3-day period 25th–27th
December are plotted against the reference period (deaths occurring during 25th–27th day of every other calendar month) for - all-cause
mortality (blue symbols) and those related to cancer (orange), CVD (red), respiratory disease (green) and other causes (brown). The total
difference in deaths (with 95% CI) for all-causes and specific causes over the entire 35-years are also shown above each symbol
Moholdt et al. BMC Public Health (2021) 21:549 Page 7 of 11
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perspective there is both supportive [13] and contrary
evidence [21] of an equivalent phenomenon occurring in
summer conditions. Overall, our population-based data,
suggest that like the US [12], there is an increased risk
of dying at Christmas in Norway. This likely applies to
similar regions across Europe. To put this phenomenon
into perspective, if the same pattern of excess deaths at
Christmas had occurred within the entire population of
Norway (a minimum of 3 million adults alive in 1980)
on an age- and sex-specific basis, there would have been
more than 11,000 excess deaths (around 350 more per
annum) over the Christmas holidays alone in the past
30 years.
In the (understandable) absence of prospective studies,
it is challenging to delineate between the overall impact
of winter and a Christmas-specific effect. As shown by
the Tromsø Study [14], there is evidence of winter peaks
in blood pressure, heart rate, body weight, total choles-
terol, and overall CVD risk. Seasonal variation in phys-
ical activity may also be an important consideration for
cardiovascular-related mortality [22]. Aerobic exercise,
especially with high intensity, can acutely lower systolic
BP in the hours following exercise [23].
As in many parts of the world, life in Central Norway
during the Christmas holiday period is characterised by
festive celebrations, travel away from home/central ser-
vices, and reduced health services. This typically begins
in early December and peaks (regardless of public holi-
days and weekends) during the week of December 23rd
to 31st (New Year’s Eve) with concurrent public holidays
on December 25th and 26th. Reduced access to follow-
up health care was noted to contribute to 26 excess
deaths (and 188 hospital readmissions) per 100,000 pa-
tients in Canada during the Christmas holidays [11].
However, this phenomenon does not fully explain the
size of the phenomenon we observed within our cohort
and the contributory reasons are likely to be multifactor-
ial. Consuming a high-fat diet for only 3 days exacer-
bates insulin resistance and glycolipid metabolism
disorders in men with obesity [24]. Even among healthy
men, decreasing physical activity for 1–3 weeks de-
creases insulin sensitivity and attenuates postprandial
lipid metabolism [25]. Vascular stiffness, due to impaired
endothelial function of the conduit vessels, is an import-
ant factor in the development of hypertension and an
independent risk factor for a fatal cardiovascular event
[25]. After a high-fat meal, which is typically consumed
during Christmas in Norway, endothelial function de-
creases substantially postprandially [26]. The potential
negative impact of increased emotional stress associated
with dealing with loneliness and family tensions [27]
with the potential for seasonally triggered depression
[28], also cannot be ignored. As suggested by our sex-
specific findings, any, or all of these “stressors”may
Table 2 Correlates of All-Cause Mortality at Key Periods of the Year
Dec 1st –21st (Winter) versus
May 17th –June 6th (Summer)
Dec 22nd –Jan 11th (Christmas/New Year)
versus Dec 1st –21st (Winter/Pre-Christmas)
Men
(n= 1543)
PWomen
(n= 1351)
PMen
(n= 1636)
PWomen
(n= 1424)
P
Demographic profile, adjusted HR (95% CI)
Age at baseline (per year) 1.06 (1.05–1.07) .001 1.05 (1.04–1.06) .001 1.06 (1.05–1.07) .001 1.05 (1.04–1.06) .001
≤9 years education vs. rest 0.92 (0.72–1.78) .362 0.97 (0.78–1.21) .804 1.05 (0.90–1.15) .519 1.25 (1.03–1.52) .026
Married vs. rest 0.93 (0.56–1.49) .769 0.98 (0.55–1.69) .903 0.71 (0.59–0.86) .001 0.70 (0.54–91) .001
Well-being, adjusted HR (95% CI)
Good/V. good physical health vs. rest 0.75 (0.64–0.87) .001 0.82 (0.70–0.95) .008 0.79 (0.68–0.92) .002 0.82 (0.70–0.95) .009
Life dissatisfaction vs. rest 1.65 (1.23–3.23) .004 1.52 (1.03–2.25) .036 0.76 (0.50–1.15) .194 1.13 (0.77–1.66) .530
Medical History, adjusted HR (95% CI)
Angina pectoris vs. rest 1.26 (0.98–1.63) .074 1.57 (1.19–2.08) .002 1.39 (1.06–1.80) .016 1.99 (1.05–1.84) .021
Acute myocardial infarction vs. rest 1.52 (1.11–2.07) .008 1.64 (0.92–2.91) .092 1.45 (1.06–1.99) .021 2.41 (1.46–3.80) .001
Stroke vs. rest 1.23 (0.78–1.94) .371 1.50 (0.87–2.58) .142 1.25 (0.78–1.97) .326 2.01 (1.28–3.17) .002
Lifestyle, adjusted HR (95% CI)
Current smoker vs. rest 1.28 (1.06–1.53) .009 1.24 (1.04–1.49) .018 1.39 (1.16–1.66) .001 1.51 (1.26–1.82) .001
Vital Signs, adjusted HR (95% CI)
Heart rate (per 5 beats/minute) 1.03 (1.01–1.06) .011 1.02 (0.98–1.05) .360 1.03 (1.00–06) .045 1.02 (0.99–1.95) .152
Systolic BP (per 5 mm/Hg) 1.04 (1.02–1.07) .001 1.04 (1.02–06) .001 1.03 (1.01–05) .002 1.03 (1.00–03) .019
Diastolic BP (per 5 mm/Hg) 0.98 (0.94–1.03) .445 1.07 (1.03–09) .001 0.99 (0.96–1.04) .822 0.96 (0.92–1.01) 0.087
Moholdt et al. BMC Public Health (2021) 21:549 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

affect men and women differently. For example, it has
been demonstrated that diabetes, high-density lipopro-
tein levels and triglyceride levels have more impact on
cardiovascular health of women compared to men [29].
The emerging literature around Tako-tsubo cardiomy-
opathy with a predominance of women affected [30]is
notable when considering the small, but intriguing, in-
crease in deaths due to heart failure in women, but not
men, at Christmas.
Unfortunately, in the absence of specific interventions,
expert clinical guidelines rarely mention or address sea-
sonality. We are currently conducting a randomized trial
to address seasonal patterns of hospitalization in 300
vulnerable individuals with chronic heart disease in Mel-
bourne, Australia. Beyond ensuring appropriate vaccin-
ation against influenza [31], there is a strong justification
for more proactive screening and management of high-
risk patients by general practitioners leading up to
Christmas. The identification of educational levels in
women and marriage status as modifying mortality risk
in both sexes, reinforces the importance of considering
health literacy and the emotional well-being of individ-
uals leading up to provocative times of the year. Promo-
tion of a healthy lifestyle should occur all year round
[32], but should perhaps be highlighted and re-
emphasized in the lead-up to Christmas: a time of exces-
sive indulgence of all kinds with potentially tragic conse-
quences. The current COVID-19 pandemic both directly
(via residual cardio-pulmonary impairment post-
infection [33]) and indirectly (via its negative effects on
emotional and psychological well-being, patterns of so-
cial interaction, seeking care for pre-existing chronic
conditions and reduced exercise levels), has further po-
tential to exacerbate Christmas mortality [34].
Study limitations
To robustly test our primary hypothesis, we examined
patterns of long-term mortality within the HUNT cohort
[15,17] in Central Norway. Although this is a well-
characterised population, the pattern of risk and subse-
quent health outcomes in this semi-rural population
may not be reflective of the broader Norwegian popula-
tion or that of Western Europe. Nor was the study spe-
cifically designed to examine the issue of seasonal
patterns of disease. As previously noted, Norway has a
distinctive climate and culture, and these specific condi-
tions may have contributed to our specific findings.
Hence, there is a need to validate these findings in other
population cohorts with equivalent data. To maintain
the size of outcome data for analyses, we relied upon
baseline profiling of the original cohort and mortality
outcomes. For many individuals there may be multiple
contributing causes of death, so any findings from
cause-specific mortality data should be interpreted with
some caution. The administrative timing of reported
deaths (particularly over the Christmas period) may also
be disrupted during holiday periods. To date, we have
yet to examine the association between observed
changes in risk profiles over time with seasonal patterns
of mortality. Nor have we confirmed if the same pattern
of seasonality and increased risk of death at Christmas is
reflected in the pattern of hospital admissions. We have
plans to address these limitations. However, we will not
be able to ascertain the quality of care and extent of out-
patient follow-up at key times such as Christmas and the
New Year period. However, the timing of death (unless a
sudden cardiac death) is not indicative of exactly when a
person becomes unwell and/or is admitted to hospital
[11]. Moreover, we do not have specific data on seasonal
changes in risk behaviours (e.g. increased alcohol and
food intake) to correlate with the subsequent timing and
trajectory of illness and death. Finally, we examined the
pattern of mortality on a historical basis, during which
time, significant changes in the pattern of life-style be-
haviours and public health measures have occurred.
Conclusions
During long-term follow-up of the HUNT population
cohort, there was a distinctive pattern of a seasonal in-
crease in mortality during winter when compared to
summer months. Over and above this broad pattern, a
distinctive pattern of excess mortality predominantly,
but not exclusively linked to CVD, was evident over the
Christmas holiday period. The number of excess deaths
over Christmas was substantial.
Abbreviations
AMI: Acute myocardial infarction; BP: Blood pressure; BMI: Body mass index;
CAD: Coronary artery disease; CVD: Cardiovascular disease; HUNT: Trøndelag
Health Study; OLS: Ordinary least squares
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12889-021-10503-7.
Additional file 1: Figure S1. 21-day Pattern of Mortality –Pre-
Christmas versus Summer Low and Christmas Holiday Period. Figure S2.
Sex-Specific Risk of Mortality –Pre-Christmas versus Summer Low Period.
Figure S3. Sex-Specific Risk of Mortality –Pre-Christmas versus Christmas
Holiday Period. Table S1. Sensitivity analyses.
Acknowledgements
The Trøndelag Health Study is a collaboration between the HUNT Research
Centre (Faculty of Medicine and Health Sciences, Norwegian University of
Science and Technology), Trøndelag County Council, Central Norway
Regional Health Authority, and the Norwegian Institute of Public Health. We
thank all the individuals who contributed to the data collection in HUNT.
Authors’contributions
TM and SS contributed to the conception of the work. CA contributed to the
analysis of study data. PS, CM and LB contributed to the interpretation of
study data. TM and SS drafted the manuscript. CA, PS, CM and LB critically
Moholdt et al. BMC Public Health (2021) 21:549 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

revised the manuscript. All gave their approval and agree to be accountable
for all aspects of the work, ensuring its integrity and accuracy.
Funding
SS is supported by the National Health and Medical Research Council of
Australia (GNT1135894).
Availability of data and materials
The Trøndelag Health Study has invited persons aged 13–100 years to four
surveys between 1984 and 2019. Comprehensive data from more than
140,000 persons having participated at least once and biological material
from 78,000 persons are collected. The data are stored in HUNT databank
and biological material in HUNT biobank. HUNT Research Centre has
permission from the Norwegian Data Inspectorate to store and handle these
data. The key identification in the data base is the personal identification
number given to all Norwegians at birth or immigration, whilst de-identified
data are sent to researchers upon approval of a research protocol by the Re-
gional Ethical Committee and HUNT Research Centre. To protect participants’
privacy, HUNT Research Centre aims to limit storage of data outside HUNT
databank, and cannot deposit data in open repositories. HUNT databank has
precise information on all data exported to different projects and are able to
reproduce these on request. There are no restrictions regarding data export
given approval of applications to HUNT Research Centre. For more informa-
tion see: http://www.ntnu.edu/hunt/data
Declarations
Ethics approval and consent to participate
The Trøndelag Health Study conforms to the Declaration of Helsinki and was
originally approved by the relevant ethics committee [15,17]. All study
participants provided written informed consent to be studied and followed-
up. The present study was approved by the Regional Committee for Ethics
in Medical Research (REK-midt, no. 2018/1509).
Consent for publication
Not applicable.
Competing interests
The authors have no conflicts of interest to declare.
Author details
1
Department of Circulation and Medical Imaging, Norwegian University of
Science and Technology, Trondheim, Norway.
2
The Women’s Clinic, St.Olav
Hospital, Trondheim, Norway.
3
Centre for Applied Health Economics, Griffith
University, Nathan, Queensland, Australia.
4
Menzies Health Institute
Queensland, Griffith University, Southport, Queensland, Australia.
5
Department of Respiratory and Sleep Medicine, Austin Health, Institute for
Breathing and Sleep, University of Melbourne, Melbourne, Australia.
6
Department of Medicine, Austin Health, University of Melbourne,
Melbourne, Australia.
7
Torrens University Australia, South Australia, Wakefield
Campus, Adelaide, SA 5000, Australia.
8
University of Glasgow, Glasgow,
Scotland, UK.
Received: 7 December 2020 Accepted: 12 February 2021
References
1. Stewart S, Keates AK, Redfern A, McMurray JJV. Seasonal variations in
cardiovascular disease. Nat Rev Cardiol. 2017;14(11):654–64.
2. Barnett AG, Dobson AJ, McElduff P, Salomaa V, Kuulasmaa K, Sans S, Project
WM. Cold periods and coronary events: an analysis of populations
worldwide. J Epidemiol Community Health. 2005;59(7):551–7.
3. Marti-Soler H, Gonseth S, Gubelmann C, Stringhini S, Bovet P, Chen PC,
Wojtyniak B, Paccaud F, Tsai DH, Zdrojewski T, et al. Seasonal variation of
overall and cardiovascular mortality: a study in 19 countries from different
geographic locations. PLoS One. 2014;9(11):e113500.
4. StewartS,MoholdtTT,BurrellLM,SliwaK,MocumbiAO,McMurrayJJ,
Keates AK, Hawley JA. Winter peaks in heart failure: an inevitable or
preventable consequence of seasonal vulnerability? Card Fail Rev. 2019;
5(2):83–5.
5. Cold exposure and winter mortality from ischaemic heart disease,
cerebrovascular disease, respiratory disease, and all causes in warm and
cold regions of Europe. The Eurowinter group. Lancet. 1997;349(9062):
1341–6.
6. Donaldson GC, Ermakov SP, Komarov YM, McDonald CP, Keatinge WR. Cold
related mortalities and protection against cold in Yakutsk, eastern Siberia:
observation and interview study. BMJ. 1998;317(7164):978–82.
7. Hopstock LA, Wilsgaard T, Njolstad I, Mannsverk J, Mathiesen EB, Lochen ML,
Bonaa KH. Seasonal variation in incidence of acute myocardial infarction in
a sub-Arctic population: the Tromso study 1974-2004. Eur J Cardiovasc Prev
Rehabil. 2011;18(2):320–5.
8. Kloner RA, Leor J, Poole WK, Perritt R. Population-based analysis of the effect
of the Northridge earthquake on cardiac death in Los Angeles County,
California. J Am Coll Cardiol. 1997;30(5):1174–80.
9. Wilbert-Lampen U, Leistner D, Greven S, Pohl T, Sper S, Volker C, Guthlin D,
Plasse A, Knez A, Kuchenhoff H, et al. Cardiovascular events during world
cup soccer. N Engl J Med. 2008;358(5):475–83.
10. Mohammad MA, Karlsson S, Haddad J, Cederberg B, Jernberg T, Lindahl B,
Frobert O, Koul S, Erlinge D. Christmas, national holidays, sport events, and
time factors as triggers of acute myocardial infarction: SWEDEHEART
observational study 1998-2013. BMJ. 2018;363:k4811.
11. Lapointe-Shaw L, Austin PC, Ivers NM, Luo J, Redelmeier DA, Bell CM. Death
and readmissions after hospital discharge during the December holiday
period: cohort study. BMJ. 2018;363:k4481.
12. Phillips DP, Jarvinen JR, Abramson IS, Phillips RR. Cardiac mortality is higher
around Christmas and new Year's than at any other time: the holidays as a
risk factor for death. Circulation. 2004;110(25):3781–8.
13. Knight J, Schilling C, Barnett A, Jackson R, Clarke P. Revisiting the “Christmas
holiday effect”in the southern hemisphere. J Am Heart Assoc. 2016;5(12):
e005098.
14. Hopstock LA, Barnett AG, Bonaa KH, Mannsverk J, Njolstad I, Wilsgaard T.
Seasonal variation in cardiovascular disease risk factors in a subarctic
population: the Tromso study 1979-2008. J Epidemiol Community Health.
2013;67(2):113–8.
15. Krokstad S, Langhammer A, Hveem K, Holmen TL, Midthjell K, Stene TR,
Bratberg G, Heggland J, Holmen J. Cohort profile: the HUNT study, Norway.
Int J Epidemiol. 2013;42(4):968–77.
16. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF.
Present and future Koppen-Geiger climate classification maps at 1-km
resolution. Sci Data. 2018;5:180214.
17. Midthjell K, Lee CM, Langhammer A, Krokstad S, Holmen TL, Hveem K,
Colagiuri S, Holmen J. Trends in overweight and obesity over 22 years
in a large adult population: the HUNT study, Norway. Clin Obes. 2013;
3(1–2):12–20.
18. Moholdt T, Wisloff U, Lydersen S, Nauman J. Current physical activity
guidelines for health are insufficient to mitigate long-term weight gain:
more data in the fitness versus fatness debate (the HUNT study, Norway). Br
J Sports Med. 2014;48(20):1489–96.
19. Sharp MK, Tokalic R, Gomez G, Wager E, Altman DG, Hren D. A cross-
sectional bibliometric study showed suboptimal journal endorsement rates
of STROBE and its extensions. J Clin Epidemiol. 2019;107:42–50.
20. Huang HK, Chang WC, Hsu JY, Wang JH, Liu PS, Lin SM, Loh CH. Holiday
season and weekend effects on stroke mortality: a Nationwide cohort study
controlling for stroke severity. J Am Heart Assoc. 2019;8(8):e011888.
21. Lennon MJ, Neuen DR, Godwin MR. No ‘Christmas holiday effect’in
Australia for cardiovascular and stroke mortality. Public Health. 2020;182:
190–2.
22. Matthews CE, Freedson PS, Hebert JR, Stanek EJ 3rd, Merriam PA, Rosal MC,
Ebbeling CB, Ockene IS. Seasonal variation in household, occupational, and
leisure time physical activity: longitudinal analyses from the seasonal
variation of blood cholesterol study. Am J Epidemiol. 2001;153(2):172–83.
23. Olsen RH, Krogh-Madsen R, Thomsen C, Booth FW, Pedersen BK. Metabolic
responses to reduced daily steps in healthy nonexercising men. JAMA.
2008;299(11):1261–3.
24. Feng R, Sun G, Zhang Y, Sun Q, Ju L, Sun C, Wang C. Short-term high-fat
diet exacerbates insulin resistance and glycolipid metabolism disorders in
young obese men with hyperlipidemia, as determined by metabolomics
analysis using ultra-HPLC-quadrupole time-of-flight mass spectrometry. J
Diabetes. 2019;11(2):148–60.
25. Loader J, Khouri C, Taylor F, Stewart S, Lorenzen C, Cracowski JL, Walther G,
Roustit M. The continuums of impairment in vascular reactivity across the
Moholdt et al. BMC Public Health (2021) 21:549 Page 10 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

spectrum of cardiometabolic health: a systematic review and network meta-
analysis. Obes Rev. 2019;20(6):906–20.
26. Gaenzer H, Sturm W, Neumayr G, Kirchmair R, Ebenbichler C, Ritsch A, Foger
B, Weiss G, Patsch JR. Pronounced postprandial lipemia impairs
endothelium-dependent dilation of the brachial artery in men. Cardiovasc
Res. 2001;52(3):509–16.
27. Wallert J, Held C, Madison G, Olsson EM. Temporal changes in myocardial
infarction incidence rates are associated with periods of perceived
psychosocial stress: a SWEDEHEART national registry study. Am Heart J.
2017;191:12–20.
28. Meesters Y, Gordijn MC. Seasonal affective disorder, winter type: current
insights and treatment options. Psychol Res Behav Manag. 2016;9:317–27.
29. Roeters van Lennep JE, Westerveld HT, Erkelens DW, van der Wall EE. Risk
factors for coronary heart disease: implications of gender. Cardiovasc Res.
2002;53(3):538–49.
30. Qiu Q, Abdelghany M, Subedi R, Scalzetti E, Feiglin D, Wang J, Liu K. Discrepant
myocardial microvascular perfusion and mechanics after acute myocardial
infarction: characterization of the “Tako-tsubo effect”with real-time myocardial
perfusion contrast echocardiograph. Int J Cardiol. 2019;276:1–7.
31. Aidoud A, Marlet J, Angoulvant D, Debacq C, Gavazzi G, Fougere B.
Influenza vaccination as a novel means of preventing coronary heart
disease: effectiveness in older adults. Vaccine. 2020;38(32):4944–55.
32. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, Cooney
MT, Corra U, Cosyns B, Deaton C, et al. European guidelines on
cardiovascular disease prevention in clinical practice: the sixth joint task
force of the European Society of Cardiology and Other Societies on
cardiovascular disease prevention in clinical practice (constituted by
representatives of 10 societies and by invited experts) developed with the
special contribution of the European Association for Cardiovascular
Prevention & rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.
33. Baldi E, Sechi GM, Mare C, Canevari F, Brancaglione A, Primi R, Klersy C, Palo
A, Contri E, Ronchi V, et al. COVID-19 kills at home: the close relationship
between the epidemic and the increase of out-of-hospital cardiac arrests.
Eur Heart J. 2020;41(32):3045–54.
34. Piccininni M, Rohmann JL, Foresti L, Lurani C, Kurth T. Use of all cause
mortality to quantify the consequences of covid-19 in Nembro, Lombardy:
descriptive study. BMJ. 2020;369:m1835.
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