Transcript National Research Tomsk State University Research and Education Center «Physics of the ionosphere and electromagnetic environment» TSU SBEI of HPE SSMU of the.

National Research Tomsk State University
Research and Education Center
«Physics of the ionosphere and electromagnetic environment» TSU
SBEI of HPE SSMU of the Ministry of Healthcare and Social Development of Russia
Emergency ward, Tomsk
Phenomenological features of the dynamics of mortality and
morbidity depending on the parameters of heliogeophysical activity
A.S. Borodin, A.G. Kolesnik,
V.V. Kalyuzhin, M.V. Gudina, O.E. Shuba
Tomsk 2012
Goal of the first part of the research
Evaluation of the degree of bio-efficiency of the factors of
heliogeophysical situation by analyzing the contingence of
dynamics of these factors with alterations in the
epidemiological data on morbidity and mortality of
population in Tomsk for the period of time from 1990
through 2008
Objects of the research
1) Medical statistical indicators for the period of time from 1990 through
2008, obtained at Tomsk Regional Analytical Department:
– morbidity of Tomsk population on major disease classes, calculated per
1000 of population for each year of the evaluated period;
– mortality of Tomsk population, calculated per 100 000 of population
considering the structure of death causes.
2) Indicators of heliogeophysical situation gathered from the following
Internet resources http://spidr.ngdc.noaa.gov, http://sosrff.tsu.ru:
– X-ray radiation(X),
– Wolf numbers(S),
– electromagnetic emission flow in spectral window (F),
–Ap-index of geomagnetic storm(А).
Methods of the research
1) In order to eliminate the influence of inhomogenuity of dimensions of the
analyzed variables on the comparison results of their dynamics, a
standardization of the analyzed values was carried out.
2) Maximal (M) and average (M) values as well as standard deviations (S) of
indicators have been calculated during the correspondent years.
3) In order to better visualize time series of the data, the Hemming filter was
used for smoothing the indicators.
4) Analysis of the studied indicators was performed using principal
component analysis to reduce the number of analyzed variables and to
identify common factors and main trends in the change of dynamics of
the analyzed variables.
Conventions for epidemiological indicators
Morbidity on basic nosological
classes
Z1- Infectious and parasitic diseases
Z2- Neoplasms
Z3- Diseases of the endocrine system, eating
disorders, dysmetabolism and dysimmunity
Z5- Diseases of the nervous system and sense
organs
Z6- Diseases of the blood circulatory system
Z7- Diseases of the respiratory organs
Z8- Diseases of the digestive organs
Z9- Diseases of the urogenital system
Z10- Complications of pregnancy, act of
delivery and postnatal period
Z11- Diseases of skin and hypoderm
Z12- Diseases of the musculoskeletal system
and connective tissue
Z14- Traumas and poisonings
Z15- Malignant neoplasms (per 100 000 of
population.)
Mortality depending on the reasons
S1- Mortality caused by infectious and parasitic
diseases
S2- Mortality caused by neoplasms
S3- Mortality caused by the diseases of the endocrine
system, eating disorders, dysmetabolism and
dysimmunity
S8- Mortality caused by the diseases of the blood
circulatory system
S9- Mortality caused by hypertensive disease
S10- Mortality caused by acute myocardial infarction
S11- Mortality caused by the diseases of the
respiratory organs
S12- Mortality caused by the diseases of the
digestive organs
S14- Mortality caused by the diseases of the
urogenital system
S17- Mortality caused by congenital anomalies
S18- Mortality caused by conditions observed during
the perinatal period
S19- Mortality caused by symptoms and inaccurately
defined conditions
S20- Mortality caused by accidents, poisonings and
traumas
Dynamics of some indicators
standardized index
Fig. 1 – Dynamics of solar activity
indicators (XM) and mortality caused by
congenital anomalies (S17)
r =0.60
year
standardized index
Fig. 2 – Dynamics of geomagnetic storm
indicators (ApM) and mortality observed
during the perinatal period (S18)
r = 0.55
year
Distribution by factors of heliogeophysical parameters
Heliogeophysical
parameters
Factor 1
Factor 2
Factor 3
XM
0.751188
0.408861
0.489293
XS
0.426124
0.415981
0.800876
XX
0.431747
0.384971
0.811857
ApM
0.413675
0.859303
0.249933
ApS
0.340177
0.827759
0.437899
ApX
0.464902
0.688882
0.512274
SM
0.902610
0.267525
0.324475
SS
0.886097
0.348986
0.284867
SX
0.889963
0.320121
0.314265
FM
0.867003
0.345070
0.348868
FS
0.835176
0.416574
0.339304
FX
0.813275
0.450380
0.356550
5.937787
3.177844
2.705727
49,4
26,4
22,5
Proper values
Explainable share of
dispersion of
factors (%)
Distribution of morbidity by factors
Morbidity classes
Factor 1Z
Factor 2Z
Factor 3Z
Z1 – infectious diseases
0.147395
0.950640
-0.171662
Z2 - neoplasms
0.895387
0.359937
0.049113
Z3 – endocrine system
0.483074
0.830433
0.086437
Z5 – nervous system
0.477469
0.802803
0.249378
Z6 – blood circulation
0.435403
0.828108
0.271684
Z7 – respiratory organs
-0.244085
0.210945
-0.921257
Z8 – digestive
-0.951662
-0.149408
-0.166466
Z9 - urogenital
0.562165
0.490332
0.627510
Z10 – complications of
pregnancy
0.877522
0.048840
0.331640
Z11 – skin
0.337088
0.899074
-0.188156
Z12 – musculoskeletal
-0.628733
0.709963
-0.203051
Z14 – traumas and
poisonings
-0.354750
0.899685
0.076810
Z15 – malignant
neoplasms
0.791270
0.212897
0.557330
4.786558
5.529917
1.948680
36,8
42,5
14,9
Proper values
Explainable share of
dispersion of factors
(%)
Distribution of mortality indicator by factors
Mortality classes
Factor 1S
Factor 2S
Factor 3S
Factor 4S
Factor 5S
S1 – infectious
-0.146031
0.450613
-0.064101
0.824059
-0.218721
S2 – neoplasms
0.802470
0.515752
-0.136455
0.253716
-0.035020
S3 – endocrine
-0.938247
-0.057622
-0.049817
-0.250011
0.198468
0.397702
0.844115
0.182248
0.220368
0.212061
S9 – hypertensive
disease
-0.955519
0.155320
0.014434
0.221923
0.043484
S10 – miocardial
infarction
0.861264
0.479270
-0.110324
-0.056029
-0.075357
S11 – respiratory
0.164109
0.955793
0.141160
0.129297
-0.051043
S12 – digestive
0.701640
0.571076
0.229042
0.274326
0.160555
S14 – urogenital
0.227290
0.159760
0.135788
0.915696
-0.075371
S17 – congenital
anomalities
0.702011
-0.265806
0.020677
-0.589605
0.281919
S18 – perinatal
-0.169043
0.198374
0.347470
-0.323681
0.836166
S19 – inaccurate
condition
-0.028836
0.198514
0.951199
0.072312
0.218465
S20 – accident
-0.147521
0.912604
0.074857
0.312551
0.128944
4.473430
3.686176
1.193174
2.392695
1.018063
34,4
28,3
9,1
18,4
7,8
S8 – blood circulation
Proper values
Explainable share of
dispersion of factors (%)
Distribution by factors of dynamics of major morbidity and mortality
factors
Factors of morbidity
and mortality classes
Factor 1ZS
Factor 2ZS
Factor 3ZS
Factor 4ZS
Factor 5ZS
Factor 1 by morbidity
classes
0.984886
-0.048738
-0.062311
0.089883
0.057554
Factor 2 by morbidity
classes
0.012755
0.648490
0.074137
-0.264508
0.700656
Factor 3 by morbidity
0.001913
classes
0.093051
-0.911345
-0.311304
-0.090659
Factor 1 by mortality
classes
0.991604
0.024748
-0.045632
-0.027897
Factor 2 by mortality
classes
0.042372
-0.049503
-0.968663
0.116982
0.027370
Factor 3 by mortality
classes
-0.023666
0.995912
-0.047839
0.030638
0.012310
Factor 4 by mortality
classes
0.020320
-0.016485
0.017474
0.034779
0.993477
Factor 5 by mortality
classes
0.034641
-0.022088
0.100701
0.988157
-0.042598
Proper values
1.957413
1.427236
1.791233
1.169322
1.492941
24,4 %
17,8 %
22,3 %
14,6 %
18,6 %
Explainable share of
dispersion of factors
(%)
0.016158
Contingence between the five designated factors of morbidity
and mortality and the three factors of heliogeophysical
parameters
Factors of
heliogeophysical
parameters
Factor
1ZS
Factor
2ZS
Factor
3ZS
Factor
4ZS
Factor
5ZS
Factor 1 of
heliogeophysical
parameters
0.15
0.08
0.84
0.47
0.05
Factor 2 of
heliogeophysical
parameters
-0.64
0.34
0.11
0.10
-0.45
Factor 3 of
heliogeophysical
parameters
0.46
0.15
0.03
-0.18
-0.78
standardized index
Figure 3 – Dynamics of
variables:
factor 1 (cumulative
solar activity),
factor 3ZS (diseases of
respiratory organs)
r = 0,84
factor 1
factor 3ZS
year
standardized index
Figure 4 – Dynamics of
variables:
factor 1 (cumulative solar
activity),
factor 4ZS (mortality caused
by conditions during the
perinatal period)
r = 0.47
factor 1
factor 4ZS
year
standardized index
factor 3
factor 1ZS
Figure 5 – Dynamics of
variables:
factor 3 (variations of Xray radiation),
factor 1ZS (neoplasms,
mortality caused by
congenital defects,
hypertensive disease,
acute myocardial
infarction)
r = 0.46
year
standardized index
Figure 6 – Dynamics of
variables:
factors 3 (variations of
X-ray radiation) and
factor 5ZS (infectious
diseases, diseases of
endocrine and nervous
systems, skin diseases)
r = - 0.78
factor 3
factor 5ZS
year
Conclusion 1
As result of the study, the impact of parameters of heliogeophysical situation on
indicators of morbidity and mortality of population in Tomsk, general factors were
singled out from the entire aggregation of health indicators of population, which are
accurately correlated with alterations in solar activity indicators as well as the
indicators of geomagnetic storm, and namely:
F 3ZS – diseases of respiratory organs and mortality caused by the diseases of
respiratory organs, blood circulatory system, accidents, F 4ZS – mortality caused by
conditions during the perinatal period correlate with F 1 – cumulative solar activity
(r=0,84; r=0,47).
F 1ZS – neoplasms, complications of pregnancy and act of delivery, diseases of
digestive organs, mortality caused by neoplasms, congenital developmental
anomalities, diseases of digestive organs, endocrine system, hypertensive disease, acute
myocardial infarction correlate with F 2 – geomagnetic storm (r= - 0,64).
F 5ZS - infectious diseases, diseases of the endocrine and nervous systems, skin,
musculoskeletal system, blood circulatory system, traumas and poisonings, mortality
caused by infectious diseases and diseases of urogenital system correlate with F 3 –
variations of X-ray radiation (r= - 0,78).
Goal of the second part of the research
Evaluation of the impact of geomagnetic storms on the
frequency of emergency calls to ambulance during one of the
most powerful geomagnetic storms of October – November,
2003
End of October — beginning of November, 2003 was
rarely “stormy” from the point of view of magnetic
situation: outbursts in the Sun turned out to be the most
powerful for the entire history of the observational
astronomy!
The outburst energy on November 4th,
2003 would be enough to supply
electricity to such city as Moscow for
200 million years!
TECHNOLOGY AND MATERIALS OF THE
RESEARCH
A database was formed containing indicators of solar activity
alterations, local geomagnetic storm and number of calls to the
ambulance, which were all coordinated according to time.
Vadim
METHODS AND MATERIALS OF THE RESEARCH
Heliogeophysical features
(from 01.10.2003 to 25.11.2003)
The power of X-radiation flow
in the range 1-8 Ǻ
(Х, W/m2)
(http://spidr.ngdc.noaa.gov)
Local (Tomsk) geomagnetic
disturbane (К, points)
(http://sosrff.tsu.ru)
METHODS AND MATERIALS OF THE RESEARCH
Data on the number of calls to the ambulance
Table. Format of the original database
Time of reception
Address
Name
Age
Diagnosis
Hospitalization
01:12
7 Govorova str.
Apt 21
Ivanov V.P.
42 years
CHD: myocardial
infraction
Yes
….
….
….
….
….
….
Classes of diseases
Total number of calls
Cl. 1  Chronic coronary heart disease
384
Cl. 2  Acute coronary syndrome
526
Cl. 3 – Acute cerebrovascular diseases
490
Cl. 4 Chronic cerebrovascular diseases
121
Cl. 5  Arterial hypertension
3086
Cl. 6  Heart rhythm disturbance and asequence
692
Cl. 7  Functional disorders of the nervous system
772
Cl. 8  Thromboembolism of the main pulmonary artery
10
Cl. 9  Traumas
67
Cl. 10  Suicides
80
Cl. 11  Pregnancy pathologies
154
Cl. 12  Biological death
444
x  xi xi1
where x- current change in the integral of the function
- Formula used to reveal the total accumulated tendency in
changes of epidemiological indicators
Number of calls
Watt/ metre2
Value of K-index
Results of the research
Х (on the left)
K-index (on
Number of a three-hour interval
the right)
Figure 7. Dynamics of X-ray flow (Х) and
geomagnetic disturbance (К) in October-November,
2003
Number of a three-hour interval
Figure 8. Dynamics of the frequency of calling
the ambulance (N)
in Tomsk in October-November, 2003
Results of the research
(statistically significant bonds are presented)
coefficient
а correlationкорреляции
Value ofкоэфициента
значение
0,70
0,58
0,60
0,50
0,45
0,49
0,43
0,37
0,40
0,30
0,30
0,20
0,11
0,10
коэфициент
correlation
корреляции
0,08
0,10
coefficient
0,00
-0,10
кл.2
Cl .2
-0,20
-0,15
кл.3
кл.4
кл.5
кл.6
кл.7
Cl.
3 Cl.
4 Cl.
5 Cl.
6 Cl.
7
кл.9
Cl.
9 кл.10
Cl. 10 кл.11
Cl. 11 кл.12
Cl. 12
нозологические
переменные
Classes
of nosologic
units
Figure 9. Connection between the frequency of calls to the ambulance and the power of X-ray
flow (lg(Х))
значение коэфициента корреляции
Value of а correlation coefficient
0.4
0.27
0.3
0.27
0.28
0.22
0.17
0.2
0.14
0.1
коэфице нт
correlation
coefficient
корре ляции
0
-0.1
кл.1
Cl
.1
-0.2
-0.16
кл.2
Cl. 2
кл.3
Cl. 3
кл.4
Cl.
4
кл.5
Cl. 5
кл.6
Cl. 6
кл.7
Cl.
7
кл.9
Cl. 9
кл.12
Cl. 12
-0.11
-0.3
-0.35
-0.4
нозологические
переменные
Classes of nosologic
units
Figure 10. Connection between the frequency of calls to the ambulance and the value of Kindex
Watt/ metre2
Number of calls (standardized index)
Results of the research
r = 0. 58
Lg Х (on the left)
Cl.4 (on the right)
Number of a three-hour interval
Figure11. Dynamics of the frequency of calls to the ambulance to patients with chronic
cerebrovascular disease (cl.4) in Tomsk and the power of X-ray flow (lg(Х)) over the
analyzed period of time
Point
Number of calls (standardized index)
Results of the research
r = 0. 17
K-index (on the left)
Cl.5 (on the right)
Number of a three-hour interval
Figure 12. Dynamics of the number of calls to the ambulance to patients with arterial
hypertension (cl.5) and the value of K-index in Tomsk over the analyzed period of time
Watt/ metre2
Number of calls (standardized index)
Results of the research
r = 0.30
Lg Х (on the left)
Cl. 6 (on the right)
Number of a three-hour interval
Figure 13. Dynamics of the number of calls to the ambulance to patients with heart
rhythm disturbances (cl.6) in Tomsk and the power of X-ray flow (lg(Х)) over the
analyzed period of time
Point
Number of calls (standardized index)
Results of the research
r = 0.27
K-index (on the left)
Cl. 6 (on the right)
Number of a three-hour interval
Figure 14. Dynamics of the number of calls to the ambulance to patients with heart
rhythm disturbances (Cl.6) and the value of K-index in Tomsk over the analyzed
period of time
Results of the research
А
Б
Point
Watt/ metre2
Number of calls (standardized index)
r = 0.28
Number of calls (standardized index)
r = 0.37
K-index
(on the left)
Lg Х (on the left)
Cl. 7 (on the right)
Number of a three-hour interval
Cl. 7
Number of a three-hour interval
(on the right)
Figure 15 (А, B) . Dynamics of the number of calls to the ambulance to patients with
functional nervous sytem disorders (cl.7), on the one hand, and the power of X-ray flow (A) as
well as the value of K-index in Tomsk (B) over the analyzed period of time, on the other hand
Conclusion 2
The carried out research allowed to reveal statistically and
clinically significant correlation bonds between the number of
calls to the ambulance in Tomsk to patients with the most
widespread socially significant diseases, on the one hand,
and local geomagnetic disturbance as well as the power of Xray flow, on the other hand.
SUMMARY

We carried out the epidemiological research on the effect of heliogeophysical
activity in various timeframes on the basis of the regional data.

We evaluated the degree of bioeffectiveness of the factors of heliogeophysical
setting over one-year periods, taken on the basis of Karhunen-Loeve method and
epidemiological data of mortality and morbidity of Tomsk population from 1990 to
2008. The analysis of the effect of changes in solar activity and geomagnetic
disturbances on the indicators of mortality and morbidity has shown, that among all
the indicators in various nosological classes we can reveal general factors which
credibly correlate with major components of variances of characteristic indicators of
solar activity and geomagnetic disturbance.

We determined the features of the degree of effect of heliogeophysical activity over
the frequency of emergency calls to the ambulance in Tomsk, with 3-hour intervals for
data averaging, during one of the most powerful disturbances of 2003. It was
discovered that X-ray flow and geomagnetic disturbance are positively correlated with
such classes of diseases as cerebrovascular diseases, arterial hypertension, heart
rhythm disturbance and asequence as well as functional nervous system disorders.
Herewith, variations of epidemiological indicators are connected both with
independent effect of X-ray flow and geomagnetic disturbance and with joint effect of
these factors.
Thank you for your attention!
Conclusion
R
Conclusion
Alfven Hannes
Otto Schumann

Evaluation of the effect of variations of the
environmental complex of physical fields
on functioning of the human cardiovascular system.
Data conversion
Hamming filter window:
Standardization of values
Xст 
x
i x
(1)
x
n
x 
x
i
i 1
(2)

n




L

(
1
L
)
*
C
O
S


 
(4)
W
n

N





0
,п
р
и
n

N


Wn output value for the original row value
N
n
2
2
i
i
i
1
i
1
N
n
x
x

n
2
x

i 
N
x  i1
N
1
Хст
- standardized value
xi
- current value
(3)
total number of points used in the filter
n  Ordinal number of the row value
L0
,5
4c
o
n
st
Hamming window constant
_
x
х
n
N
- average value
- mean-square deviation
ordinal number of the row value
total number of values
33
Method of principle components
Method of principle components is expansion of the time series into
eigen-functions on orthogonal basis.
RV=
where

V
,
R – mattix array for which the solution is
sought;
V – desired eigen-vector,

- eigen-value
The number of revealed factors is usually determined by the
number of eigen-values which are more or equal to 1.