tomsk 2012

Tomsk 2012

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

A.S. Borodin, A.G. Kolesnik,

V.V. Kalyuzhin, M.V. Gudina, O.E. Shuba

Phenomenological features of the dynamics of mortality and morbidity depending on the parameters of heliogeophysical activity

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

Goal of the first part of the research

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

Z1- Infectious and parasitic diseasesZ2- NeoplasmsZ3- 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 systemZ7- Diseases of the respiratory organsZ8- Diseases of the digestive organsZ9- Diseases of the urogenital systemZ10- Complications of pregnancy, act of delivery and postnatal period Z11- Diseases of skin and hypoderm Z12- Diseases of the musculoskeletal systemand connective tissue Z14- Traumas and poisoningsZ15- Malignant neoplasms (per 100 000 of population.)

Morbidity on basic nosological classes

Mortality depending on the reasons

S1- Mortality caused by infectious and parasitic diseasesS2- Mortality caused by neoplasmsS3- Mortality caused by the diseases of the endocrine system, eating disorders, dysmetabolism and dysimmunityS8- Mortality caused by the diseases of the blood circulatory systemS9- Mortality caused by hypertensive diseaseS10- Mortality caused by acute myocardial infarctionS11- Mortality caused by the diseases of the respiratory organsS12- Mortality caused by the diseases of the digestive organsS14- Mortality caused by the diseases of the urogenital systemS17- Mortality caused by congenital anomalies S18- Mortality caused by conditions observed during the perinatal periodS19- Mortality caused by symptoms and inaccurately defined conditionsS20- Mortality caused by accidents, poisonings and traumas

Fig. 1 – Dynamics of solar activity indicators (XM) and mortality caused by congenital anomalies (S17)

r =0.60

Fig. 2 – Dynamics of geomagnetic storm indicators (ApM) and mortality observed during the perinatal period (S18)

r = 0.55

Dynamics of some indicators

year

year

sta

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xst

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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

Proper values5.937787 3.177844 2.705727

Explainable share of dispersion of

factors (%)49,4 26,4 22,5

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

Proper values 4.786558 5.529917 1.948680

Explainable share of dispersion of factors

(%)36,8 42,5 14,9

Distribution of morbidity 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

S8 – blood circulation 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

Proper values4.473430 3.686176 1.193174 2.392695 1.018063

Explainable share of dispersion of factors (%) 34,4 28,3 9,1 18,4 7,8

Distribution of mortality indicator by 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 classes 0.001913 0.093051 -0.911345 -0.311304 -0.090659

Factor 1 by mortality classes 0.991604 0.024748 0.016158 -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

Explainable share of dispersion of factors

(%)24,4 % 17,8 % 22,3 % 14,6 % 18,6 %

Distribution by factors of dynamics of major morbidity and mortality factors

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


-0.64 0.34 0.11 0.10 -0.45


0.46 0.15 0.03 -0.18 -0.78

Contingence between the five designated factors of morbidity and mortality and the three factors of heliogeophysical parameters

Figure 3 – Dynamics of variables: factor 1 (cumulative solar activity), factor 3ZS (diseases of respiratory organs)

r = 0,84

Figure 4 – Dynamics of variables: factor 1 (cumulative solar activity), factor 4ZS (mortality caused by conditions during the perinatal period)

r = 0.47

year

year

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factor 1factor 3ZS

factor 1factor 4ZS

Figure 5 – Dynamics of variables: factor 3 (variations of X-ray radiation), factor 1ZS (neoplasms, mortality caused by congenital defects, hypertensive disease, acute myocardial infarction)

r = 0.46

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

sta

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year

year

factor 3factor 1ZS

factor 3factor 5ZS

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

The outburst energy on November 4th, 2003 would be enough to supply

electricity to such city as Moscow for 200 million years!

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!

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 ambulanceTable. 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

1i ix x x where x- current change in the integral of the function

- Formula used to reveal the total accumulated tendency in changes of epidemiological indicators

Results of the research

Figure 7. Dynamics of X-ray flow (Х) and geomagnetic disturbance (К) in October-November, 2003

Figure 8. Dynamics of the frequency of calling the ambulance (N) in Tomsk in October-November, 2003

Wa

tt/

me

tre2

Number of a three-hour interval Number of a three-hour interval

Nu

mb

er

of

calls

Х (on the left)

K-index (on the right)

Va

lue

of

K-in

de

x

-0.16-0.11

0.220.27

0.17

0.27 0.28

0.14

-0.35-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

кл.1 кл.2 кл.3 кл.4 кл.5 кл.6 кл.7 кл.9 кл.12

нозологические переменные

зн

ач

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оэф

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ор

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коэфиценткорреляции

Results of the research(statistically significant bonds are presented)

Figure 9. Connection between the frequency of calls to the ambulance and the power of X-ray flow (lg(Х))

Figure 10. Connection between the frequency of calls to the ambulance and the value of K-index

Val

ue o

f а c

orre

latio

n co

effic

ient

correlation coefficient

-0,15

0,11

0,58

0,10

0,300,37

0,45 0,430,49

0,08

-0,20

-0,10

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

кл.2 кл.3 кл.4 кл.5 кл.6 кл.7 кл.9 кл.10 кл.11 кл.12

знач

ен

ие к

оэф

иц

иен

та к

ор

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ии

нозологические переменные

коэфициент корреляцииcorrelation coefficient

Val

ue o

f а c

orre

latio

n co

effic

ient

Classes of nosologic units

Classes of nosologic units

Cl .2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 Cl. 7 Cl. 9 Cl. 10 Cl. 11 Cl. 12

Cl .1 Cl. 2 Cl. 3 Cl. 4 Cl. 5 Cl. 6 Cl. 7 Cl. 9 Cl. 12


r = 0. 58

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

Wa

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2

Number of a three-hour interval

Nu

mb

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calls

(st

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ex)

Lg Х (on the left)Cl.4 (on the right)


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

r = 0. 17


Nu

mb

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of

calls

(st

an

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ind

ex)

Poi

nt

K-index (on the left)

Cl.5 (on the right)


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

r = 0.30

Wa

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Nu

mb

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calls

(st

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ex)


Lg Х (on the left)Cl. 6 (on the right)


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

r = 0.27

Nu

mb

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calls

(st

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ex)

Poi

nt



Cl. 6 (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

r = 0.37А Б

r = 0.28

Nu

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(st

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Nu

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(st

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Poi

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Wa

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Number of a three-hour interval Number of a three-hour interval

Lg Х (on the left)




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 X-ray 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!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 cardio-vascular system.

iст

x

x xX

1

n

ii

xx

N

2 2

2 1 1

1

1

n n

i ini i

ii

x

x xx

NN

N 33

n

ix_

x

Standardization of values

- standardized value

- current value

- average value

- mean-square deviation

ordinal number of the row value

total number of values

х

Хст

(1)

( 2 )

( 3 )

(1 )*

0,

nL L COS

NWn

при n N

Hamming filter window:

Wn output value for the original row value

N total number of points used in the filter

n Ordinal number of the row value

0,54L const Hamming window constant

Data conversion

(4)

Method of principle components is expansion of the time series into eigen-functions on orthogonal basis.

Method of principle components

R V = 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.

,where

tomsk 2012

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