I.A. Sukhareva, PhD, associate professor of the Department PublicHealth

I.A. Sukhareva, PhD, associate professor of the Department PublicHealth

O.S. Tretiakova, Professor, Head of the Department Public Health.

 

 

Reviewers:

 

V.M. Levanov Professor Department of social medicine and organization of Public Health Nizhny Novgorod State medical academy, Ministry of Public Health.

 

Professor S.E. Shibanov Chief of department Common Hygeinie with Ecology, FSAEIHE «Сrimean Federal University named after V.I. Vernadsky, Medical Academy named after S.I. Georgievsky

 

Recommended and published by the central commission of the Medical Academy named after S.I. Georgievsky since 17 may 2017, protocol No 4.

 

 

Introduction

This book contains the disciplines of the medical statistics for foreign students of the English medical faculty.

It analyses social medical methods and modern methods of the medical statistics. According to modern requirements of studies, each topic is formulated with the main questions of topics and logical structures. Topological tests, control questions for each topic and tables are also contained in this book.

This manual is designed for foreign students and for English-speaking native students studying at the medical university and post-graduate students and lecturers.

 

 

CONTENTS

Introduction

THEME 1        Methodical foundation of statistical research organizations in Public Health.

THEME 2       Methods of calculation and analyses.

THEME 3        Average values and indices of variation.

THEME 4        Parametric methods of estimation. Statistical hypotheses.

Correlation.

THEME 5       Non-Parametric methods.

THEME6         Dynamic row. Standardization.

THEME 7        Demography.

Indices of health of the population.

THEME 8        Sickness rate. Methods of study of sickness rate.

 

 

Main Literatures

1. Общественное здоровье и здравоохранение, экономика здравоохранения под. редакцией члена-корреспондента РАМН професора В.З. Кучеренко, Москва (ГЕЭОТАР-Медиа), 2013

2. Health Economics Edited by Corresponding Member of the Ukrainian Medical Academy of Science, Professor V. Moskalenko. 2010

3. «Соціальна медицина і організація охорони здоров`я» за ред. проф. Москаленка В. Ф. 2009

4. Общественное здоровье и здравоохранение Ю.Г. Лисицин, Г.Е. Улумбекова, Москва (ГЭОТАР-Медиа), 2015

5. Общественное здоровье и здравоохранение руководство к практическим заенятиям, Москва (ГЭОТАР-Медиа), 2013

 

THEME 1

METHODICAL FOUNDATION OF STATISTICAL RESEARCH

ORGANIZATIONS IN PUBLIC HEALTH

 

Features of the theme.

Students are required to study different data about population health and activities of medical institutions; apply statistical data and specific knowledge when choosing a research object, statistical totality, observed units and inherent properties.

Aims of studies.

To understand the basic structural elements of statistical totality.

To study features of general and selective totalities.

Topic contents.

Definition of Biostatistics is a branch of science and section of statistics, which engaged planning and processing of medical results and biological researches.

Definition of statistical totality. Statistical totality is a group of the observed numbers studied with definite temporal and spatial limits.

Definition of observed units. An observed unit is a primary unit which carries statistical data in each taken case.

Definition of specific features that distinguish statistical totality.

Classification of specific features:

By nature – variation and attributive

By role in totality – factual and resultative.

Variation (quantitative) features are expressed digitally.

Attributive (qualitative) features are expressed verbally.

Factual features are dependent and cause alteration of other features (for example, sex, age and occupation).

Resultative features depend on factual features (for example, disease, death, disability and albumin level in the blood).

RISK FACTORS

 

 

ENDOGENOUS                                                       EXOGENOUS

 

                                         

 

Public health

Social medicine and health protection organization: is a medical science studying the role and influence of social conditions and factors on health of the population.

Calculus of Probability: is а mathematical science which studies accuracy of the incidental phenomena.

The law of big Numbers: Theory of Р. L. Chebyshev: as close as possible to "one", is the probability that, regarding quite а large number of observations, the difference between the results of selective and gеnеrаl observations becomes as little as passable."

Statistical totality: is а large group of relatively homogenous observed units studied together wither definite temporal and spatial limits.

 

 Statistics: is а social medical sciences (and main method of social medicine and organization of public health) studying quantitative side of homogenous statistical рhеnоmеn in its indissoluble connection with their qualitative side.

 

The test questions.

1. What questions are studied in social medicine?

2. What is a public health organization?

3. What factors influence population health?

4. Why does the influence of social factors dominate?

5. What methods are used to study population health?

6. What is statistics?

7. Why is а physician necessary to know statistics?

8. What scientists were involved in social medicine in Russia?

9. What is medical ethics and deontology?

10. What is sanitary statistics?

11. What are the criteria to evaluate the population health?

12. What are the factors which influence public health?

13. What is statistical totality?

14. What are types of statistical totality?

15. What are the units of observation and their features?

16. What is representative?

 

 

 

 

 

 

 

 

THEME 2

Features of the theme.

Students are required to assimilate various statistical notions and methods for studying the population health with estimation of quality and efficiency of operations of medical institutions.

Aims of studies.

• Assimilate definition and contents of statistical methods of research as of the main one in social hygiene and health protection, stages of statistical research, kinds of statistical values and graphic images.

• Learn how to work out а plan and program of sanitary statistical research and how to collect statistical data.

• Master methods of summing, grouping, drawing statistical charts and scientific analysis of the data.

• Calculate relative values: indices of extensiveness, intensiveness, clearness, and correlation.

Main part

Aim should be actual for medical science and health protection practice.

Task of the research is a specified and broadened definition of the aim.

Stages of statistical research:

Stage one. Compiling the extensive plan and program of the statistical research.

Stage two. Statistical observation. It includes organizing and collecting required data, stipulated by the research program.

Stage three. Statistical summation. This includes processing the collected data (control, grouping, enciphering, calculating statistical indices, distributing into statistical charts).

Stage four. Scientific analysis (conclusions and suggestions based on the analyses of the research results).

 

II. Statistical observation

By time · Instant(Static) · Routine (dynamic)

By volume · General · Random

By registration · Type · List · Card · Tape

Poll method •Corresponding •Expedition •Self-calculation

 

Table 3

III. Statistical summation

Material control •Arithmetical •Logical

Features •Quantitative •Qualitative

Material processing · Mechanical grouping · Enciphering · Distribution · Shading · Sorting

Calculation •Compiling a chart total •Plan •Group •Combinational

Table 4

IV. Scientific analysis

Absolute data

Relative data · Extensive · Intensive · Presentation · Ratio

Average values · Arithmetical Mean (m) · Average Arithmetical · Deviation (d) · Median (Xe) · Mode (Xo)

Statistical graphics · Graphs · Cartograms · Cartographs

Relative values

Relative values are ratio of one absolute number over another absolute number expressed in percentage, promille or prodecimille.

Index of relative values

1. Intensiveness index — characterizes frequency of the phenomenon occurrence in nature and answers the question "how often?". It is expressed in %, promille - %o, рrodесimille - %oo). 

This index is calculated as one phenomenon divided by another phenomenon which produces the first phenomenon. (Example: birth rate, death rate.)

2.   Extensiveness index — characterizes the proportion of а share to the total and answers the question "what part?". It is expressed in % or in shares (% 2/3, etc. To total).

This index is calculated as part of phenomenon divided by all phenomena multiplied by 100. (Example: leukocyte blood formula.)

3. Presentation index — characterizes the relation of each of comparing values tо initial level, taken in 100. It calculates growth and increase rates.

4. Ratio index — is calculated as intensive index, which characterizes frequency of а phenomenon occurrence in heterogeneous and non-derived totalities.

This index is calculated as one phenomenon divided by another phenomenon which does not produce the first phenomenon. (Examples: doctors and beds per 10 000 inhabitants, etc.)

 

Table 5

Age	Population	Treated	Attended	Number of doctors
15-19	5000	3000	-	-
20-59	25000	30000	-	-
60-69	10000	21000	-	-
70 and more	10000	6000	-	-
Total	50000	600000	500000	50

 

INTENSIVE INDEX

Absolute number of phenomena x    1000

Absolute number of middle

EXTENSIVE INDEX

Size of part of phenomena x 100%

Size of whole phenomena

Portion of treated people (15-19 years old)

Number of treated (15-19 years old) x 100% = 3000 x 100 = 5%

All treated people                                                 60000

Portion of treated people (20-59 years old)

Number of treated (20-59 years old) x 100% = 30000  x 100 = 50%

All treated people                                                  60000

Portion of treated people (60-69 years old)

Number of treated people (60-69 yrs old) x 100% = 21000 x 100=35%

 All treated people                                       60000  

    Portion of treated people (above 70 years old)

Number of treated (above 70 years old) x 100% = 6000 x 100 = 10%

All treated people 60000

RATIO INDEX

PRESENTATION INDEX

Table 6

Indices	15-19 yrs old	20-59 yrs old	60-69 yrs old	Above 70 yrs old
Intensiveness on 1000 population	600	1200	2100	600
Comparison (in % to the group of 15-19 yrs old of age	100	200	350	100

Graphical images serve for a clear representation of statistical values and could be built on absolute and relative values.

The following basic kind of graphic images are distinguished:

1. Graphs (linear, radial, columns, pies, dimensional)

2. Cartograms

3. Cartographs

How to build а graphical image:

1. Clear, precise title

2. Defined scale

3. The legend should be provided (applied coloring or shading)

 

Questions by relevant topic:

1. Stage statistic research

2. Complete plan of research

3. Methods of selection statistical material

4. Scientific analysis statistic research

5. Relative kinds

6. Practical using indices

7. Graphical images in statistics

Calculating statistical indices for the following variants:

VARIANT 1

 Table 7

VARIANT 2

Table 8

VARIANT 3

Table 9

VARIANT 4

Table 10

VARIANT 5

Table 11

VARIANT 6

 Table 12

VARIANT 7

 Table 13

VARIANT 8.

 Table 14

THEME 3

Features of the theme:

The knowledge of average number and skill is necessary for doctors in their practical work for analysis of activity of medical institutions

2. Aims of studies:

To acquaint students with kinds average number and methods of their finding and analysis;

To explain the students the importance of using average numbers in the practical work of doctors and leaders of Public Health;

To teach students how to complete simple and grouped variation sets, mark its parameters, find average number, mode, median, and marked dispersions of variation set.

Topic contents.

The average number is a generalizing numerical characteristic of qualitatively homogeneous numbers describing all statistical set according to some signs by one number.

A variation set is a set of numerical meanings of a certain sign distinguishing from each other in the number and arranged in a decreasing or increasing order.

V – variant – numerical meaning of the sign studied;

f– frequency – repeatability variants;

n– general number of observations (n = Sf);

y– amplitude – difference between the greatest and smallest variants;

i– interval – difference between two adjacent variants;

X_o– mode – variant with the greatest frequency;

X_e – median – variant dividing a variation into halves.

 A variation set can be simple (frequencies equal to 1) or grouped (3-5 variants). The simple one is at small number of observations (n=30); the grouped one is at a greater number of observations (n>30).

Situation tasks.

VARIANT 1.

Table 16

VARIANT 2.

Table 17

VARIANT 3.

Table 18

VARIANT 4.

Table 19

VARIANT 5.

Table 20

VARIANT 6.

 Table 21

VARIANT 7.

Table 22

VARIANT 8.

Table 23

VARIANT 9.

Table 24

VARIANT 10.

 Table 25

THEME 4

Aims of studies.

The student must know the finding of average and relative numbers, their kinds, apply them in practical work of doctors, know the rules of complete general and random total and kind correlation.

Education in studies includes the sense of necessary basic knowledge, sense of professional culture, basic professional outlook, the principal logical mentality and dynamic of thinking.

3. Main part.

Reliability statistic indices - power of their thus conformity reflection reality.

Critera estimation of reliability

1. The error is calculated by:

1.1. For average:

1.2. For relative:

2. Confidence limits between average and relative number.

X_general= X_selective ± tm_x,

P_general= P_selective ± tm_p,

Where tm_p = Δ (delta) confidential interval or limit error indices,

t = confidential coefficient

 

3. T coefficient (by Student)

Reliability difference between average and relative number by criteria t

,

 

1. t > 2: perfect prognosis is 95%, in medicine is enough.

2. t> 3: 99.7% is perfect prognosis, in exactly science. (Physics, Mathematics)

3. t< 2: perfect prognosis is less than 95%, in this case parameter is not reliable.

Statistical hypothesis

Zero hypothesis (Ho) is a statement which does not assume the influence of intervention (treatment) in a population. It is used in Biostatistics to check all hypotheses.

Statistical hypothetical checking in Biostatistics -  consists of using data for the realization of a choice of one of two (or more) various opportunities in making decisions in an ambiguous situation.

The result of checking the hypothesis, is that we can receive conclusion if the hypothesis is present or not.

For example: It is necessary to compare the efficiency of two kinds of treatment towards breast cancer. The effect will consist of decrease of 5-years death rate. With this purpose, zero hypothesis and its alternative are formed.

 Zero hypothesis (Ho) shows that the differences between two totalities are appreciated on their average meanings, that is 5-years death rate is identical with different treatment.

The opposite (alternative) hypothesis (Нi) shows, that difference of effects not equal to zero. Acceptance of zero hypothesis means that the obtained difference has only incidental character, the deviation of zero hypothesis - is a non-incidental difference.  

Whether the criteria of checking a hypothesis allow determining enough arguments for rejecting the zero hypothesis. The alternative hypothesis in the latter case is accepted.

When we wrongly reject a zero hypothesis and when we find out differences actually are not present, we will receive a so-called mistake of the first sort (α) also named a significant value. The level of the statistical importance of the found out differences designated as P.

This level the significance value of checking criteria: a hypothesis is rejected, if we received value Р below significant value (α). If Р exceeds the chosen significance value, the hypothesis is accepted.

This probability more often also is resulting in the publications by authors, designating it as «Р <0,05» or «Р <0,01». In medicine and biology, as much as possible admitted probability of such mistake usually is equal to 5 % (0,05) or 1 % (0,01).

If we accept a zero hypothesis, and when the alternative hypothesis shows the presence of differences, we committed a so-called mistake of the second sort (ß).

 In this case probability to find out such difference is equal (1-ß). This also can be named as sensitive or capacity of the statistical criterion.

Capacity is a probability of a deviation of a zero hypothesis when it is false, and it is possible to solve. For example, an analysis as chance (in %) to find out the real clinical effect in the given totality with statistically significant volume. Capacity is fixed at a level of 70 %, 90 % or 95 % and in medico-biological researches should be not less than 80 %.

CORRELATION

Functional, alternative and correlation (statistical) connections are distinguished.

 Functional connection – when the cause is followed by an effect. For example, connection between area of a circle and length of its radius. The more the radius is, the more the area of the circle is.

Alternative or qualitative connection. It is characterized by the presence or the absence of a qualitative feature. For example, a patient is vaccinated – not vaccinated, sick – not sick (other things being equal). Qualitative connection is determined by coefficient of association.

Correlation or statistical connection – is revealed in a mass of big number of observations. For instance, there is a correlation connection between height and weight of a human body. The more is the height, the more, as a rule, is the weight. Correlation connection, or simply correlation, can be direct (with positive sign+) or inverse (with negative sign-). In case of direct correlation, when one phenomenon either increases or decreases the other one increases or decreases respectively. In case of inverse correlation – vice versa, when one phenomenon increases, the other one decreases. For instance, when frequency of preventive examinations increases, number of calls to paramedics decreases.

As a measure of connection closeness serves correlation coefficient. Its value may vary from 0 to ±1. The connection closeness is estimated by the special table (see below).

Table 26

ESTIMATION OF RELIABILITY

 

VARIANT 1

At study of progress of the students of medical institute – not working and combination of study with job – were the following data at not working given a number X₁=14,(m₁=±0,09) and combining study with job X₂=3,65 (m₂=±0,05) are received.

VARIANT 2

At study of work capacity at the patients suffering from myocardial infarction at presence hypertensive disease and without it(her),the following data were received date come back to work which has transferred a myocardial infarction hypertensive disease by illness P₁=61,0% (mp₁=±4,0%), without hypertensive disease is equal to P₂=75,0% (mp₂=±3,0%).

VARIANT 3

At study of frequency of suppuration after appendectomy in 2groups patients of which in one penicillin is applied and in the other not applied, the following data were received, in the first group P₁ suppuration had 30,0% of the patients (mp₁=±5,1%), in 2^nd group 40,0% (mp₂=±5,4%)

VARIANT 4

At the medical student investigated pulse rate (one minute) before passing an examination. Often of pulse on the average before examination (X₁) has made 94,2 impacts one minute (m₁=±3,9 of impact one minute), after examination X₂=82,0 of impact one minute (m₂=±4,1 impact one minute).

VARIANT 5

In groups patients with coronary arteriosclerosis research on the contents of cholesterol in blood. The contents of cholesterol in blood before application of choline on average

(X₁) has made 231,0 mg% (m₁=±4,0mg%), after application of choline X₂=204,0mg% (m₂=±3,0mg%).

VARIANT 6

At medical student investigated maximal arterial pressure before and after passing examinations. Before examinations on the average (X₁) has made 127,2mmHg (m₁=±3,0mmHg), after delivery X₂=117,0mmHg (m₂=±4,0mmHg).

VARIANT 7

At study of parameters of lethality in 2 urban hospitals were received the following data. Hospital and parameter of lethality in (P₁) was equal 2,7%(m₁=±0,07%), in hospital P₂=3,2% (m₂=±0,04%). The structure of the patients on branches was approximately identical.

VARIANT 8

At study of influence anabolic hormones at a myocardial infarction on albumin exchange the following data. General fiber before treatment (P₁) were received has made 7,14% (mp₁=±0,17%), after treatment (P₂) =8,04% (mp₂=±0,12%).

ESTIMATION CORRELATION

VARIANT 1

Table 28

VARIANT 2

 Table 29

VARIANT 3

 Table 30

Level of systolic diastolic pressure (in cm. Hg) in 12 healthy young men at the age of 18 years:

Systolic pressure	105	115	115	110	110	120	20	120	125	110	125	120
Diastolic pressure	65	70	65	65	70	75	75	70	75	70	80	80

VARIANT 4

Table 31

Results of measurement of growth and weight of the students at the age of 20 years:

 

Height(cm)	157	158	160	165	167	162	171	174	168	176	170	180
Weight(kg)	56	57	57	57	58	60	63	65	67	72	79	82

VARIANT 5

 Table 32

Probability of death due to cranial vascular defect on 10000 women depending on the age:

Age	Probability of death
15-14	5
20-24	5,6
25-29	5,7
30-34	5,7
35-39	5,6
40-44	7,6
45-49	7,7
50-54	9,3
55-59	10,7
60-64	10,5
70-74	14,1
75-79	15
80 and above	23,2

VARIANT 6

Table 33

VARIANT 7

Table 34

VARIANT 8

Table 35

Death rate due to breast and cervical cancer in 5 districts (in 100000 women):

District	Breast cancer	Cervical cancer
A	28,6	14,9
B	23,5	13,4
C	21,1	16,3
D	5,8	15,3
E	3,3	19,1

Main questions:

1. What is a main method of calculating reliability of results of statistic research?

2. What is error of average and relative number?

3. Calculate reliability difference from average and relative number.

4. Calculate reliability limit.

5. Method of calculation of coefficient of correlation and its evaluation.

THEME 5

NON-PARAMETRIC METHODS

Change in radioactivity of the blood of irradiated animals that received (A) treatment and did not receive (V) treatment (arbitrary units)

Variations on a number of X and Y	Frequency options for groups		Cumulative frequencies for groups		Accumulated part by groups of		Difference │(Sx / nx)- -(Sy/ ny)│
	Px	Py	Sx	Sigh	Sx / nx	Sy/ ny
24	2	0	2	0	0,22	0	0,22
26	3	0	5	0	0,56	0	0,56
28	1	2	6	2	0,67	0,25	0,42
30	1	1	7	3	0,78	0,38	0,40
32	1	0	8	3	0,89	0,38	0,51
34	1	1	9	4	1	0,50	0,50
36	0	1	9	5	1	0,63	0,37
38	0	1	9	6	1	0,75	0,25
40	0	2	9	8	1	1	0
	Nx=9	Ny=8

 

1. The numerical values of two variation series combined into a single variation series, versions of which are placed in ascending order.

2. Determine the frequencies of variants for both groups of observations.

3. Determine the frequencies obtained for both groups.

4. Determine the received parts, for which the accumulated frequency divided by the number of observations for each group.

5. Calculated as the difference obtained by the frequency of groups X and Y without signs.

6. Determine the maximum difference - D = 0.51

7. Determine the criteria (λ²) by the formula

8. Comparative result with the limit values of the Kolmogorov-Smirnov test.

If λ² more limiting value, the difference between the two groups is significant.

For this task, λ² = 1,10. Comparing this result with borderline value λ^²_0.05 = 1.84 and λ²_{0, 01} = 2.65, we conclude that no significant difference between the comparison groups.

 

CRITERIA PEARSON:

 

F-is reality frequency;                                                                            

F₁- is theoretical frequency

THEME 6

Features of the theme

In practical work doctors must analyze some processes in time. If the analysis is difficult we use standardized coefficients that help to eliminate the difference.

Aims of studies

To teach students the method of finding indices dynamic row and carrying out the analysis of direct method of index standardization and practical using.

Topic contents

Dynamic row is a row of heterogeneous statistical values showing alteration of a definite phenomenon within temporal and spatial limits. Figures constituting dynamic row are its levels (absolute, relative or average values).

Simple dynamic row and its indices consist of absolute values.

Derived dynamic row - consist of relative or average values.

Instant dynamic row – values are given in a definite time instant, for example, the number of physicians by the end of the year.

Interval dynamic row – values are given for a definite time period (month, quarter).

Basic indices characterizing dynamic row:

1. Absolute increase (decrease) – difference between succeeding and preceding levels.

2. Rate of increase (decrease) – proportion of absolute increase in preceding level.

3. Value of 1% (decrease) – proportion of absolute increase to increase rates.

4. Rate of growth – proportion of succeeding level to preceding one.

I. Dynamic row

II. Method to smooth out dynamic row:

a) Consolidation interval

b) Smooth out row: - find grouping average

- find sliding average

Standardization

Comparison of parameters in sets which differ in structure, demands their standardization, it means the amendment, provided that the structure of sets will be shown to the uniform standard. Doctors and scientific workers often compare the statistical indices according to groups. In these cases, methods of standard indices are applied to eliminate differences in different groups.

Methods of standardization:

 1.Direct

 2. Indirect

 3. Approximate.

Here we apply the direct method of standardization.

Direct method of index standardization is conducted in five stages:

Stage I. Calculation of intensive indices in compared groups. (Sickness rate, for example, city and village population).

Stage II. Choice and calculation of standard

Stage III. The calculation of “expected” figures in every group of standard.

Stage IV. Calculation of standardized indices.

Stage V. Comparative of simple intensive and standardized indices, conclusion.

Table 39

DYNAMIC ROW

Example:

Number of the population of Russian from 1991-2011.

1991	216.3
1996	232.2
2001	243.9
2006	255.6
2011	266.6

 

1. Absolute growth of difference between the past and following years:

1996 – 232.2 2001 – 243.9 2006 – 255.6 2011 – 266.6

 

1991= 216.3 1996 = 232.2 2001 = 243.9       2006 = 255.6

15.9              11.7              11.7                     11.0

For the past 20 years: 2011-266.6

                   1991= 216.3

50.3

2. Rate of growth = Absolute growth x 100

Previous level

15.9 x 100 =7.3% 11.7 x 100 =5% 11.7 x 100 = 4.8% 11.0 x 100 =4.3%

26.3                           232.2               243.9                 255.6                  

 

For the past 20 years: 50.3 x 100 =23.2%

3. Value of 1% = Absolute growth x 100

                           Rate of growth

15.9 = 2.2  11.7= 2.3    11.7= 2.4  11.0 = 2.6

7.3               5.0               4.8          4.3

For the past 20 years: 50.3 = 2.2

                                23.2

4. Rate of growth= Previous level x 100

                              Past level

232.2 x 100 = 107.3% 243.9 x 100 = 105% 255.6 x 100 = 104.8%

216.3                          232.2                     243.9

266.6 x 100 = 104.3%

255.6

For the past 20 years: 266.6 x 100 = 123.2%

                               216.3

Calculate the indices of dynamic row for the following variants:

Variant 1

Number of the urban population

.

1991 – 107.9

1996 – 123.7

2001 – 138.8

2006 – 155.1

2011 – 168.9

 

Variant 2

Number of the village population

 

1991 – 108.4

1996 – 108.5

2001 – 105.1

2006 – 100.5

2011 – 97.7

Variant 3

Number of the man’s population

 

1991 – 97.9

1996 – 106.3

2001 – 112.6

2006 – 118.7

2011 – 124.5

Variant 4

Number of the female population

 

1991 – 118.4

1996 – 125.9

2001 – 131.3

2006 – 136.9

 

Variant 5

Number of the doctors of all specialties

 

1990 – 554.2

1995 – 668.2

2005 – 834.1

2010 – 995.6

Variant 6

Number of the average medical personnel

 

1990 – 1692

1995 – 2123

2000 – 2515

2005 – 2790

Variant 7

Number of  hospital beds

 

1990 – 2226

1995 – 2663

2000 – 3009

2005 – 3324

Variant 8

Natural growth of the population

 

1990 – 17.8

1995 – 11.1

2000 – 9.2

2005 – 8.8

2010 – 8.0

2011 – 142.1

STANDARDIZATION

 

VARIANT 1

Table 42

VARIANT 2

 Table 43

VARIANT 3

Table 44

VARIANT 4

Table 45

VARIANT 5

Table 46

VARIANT 6

 Table 47

VARIANT 7

Table 48

Distribution of population by age and number of births in persons corresponding to age on territories A and B.

Age in years	Territory A		Territory B
	Number of population	Number of births for one year	Number of population	Number of births for one year
15-20	2.000	40	2.000	20
21-30	3.000	120	6.000	180
31-49	5.000	50	2.000	20
Altogether	10.000	210	10.000	200

 

For standardization take half sum of population by age on territories A and B.

VARIANT 8

 Table 49

THEME 7

DEMOGRAPHY

Features of the theme

For a plan and organization of Public Health it is necessary to use information about population of the country, population of the region, must analyses parameters population, demographic indices, characterize quantitative and qualitative change in the process of reproduction of the population

 

Aim of studies

Students acquire knowledge of demography statistical methods, calculate demographic indices and appraise them.

Aims of task

The student must know the aims and tasks of demographics. Statistics are necessary in the practical work of doctors, for analysis of parameters of the population, by plan of national economy, by plan of Public Health, by the plan of prophylaxis.

The student must know how to count demographic indices; birth rate, mortality, natural growth natal mortality, and other age indices.

 

Main part.

For the planning and organization of public health services it is necessary to have the information on the country, to be able to analyze parameters of population, demographic parameters describing qualitative and quantitative changes of the process of reproduction of the population.

Accouterment of demographic statistic and of knowledge on methods of account of demographic parameters, ability estimates them by the students.

Demography is the science about the population in its public development.

The basic directions of statistical study of the population:

Static - the number of population at a certain moment of time.

Dynamic -themovement of the population is the change of parameters of the population for a centain period of time.

a) Mechanical movement (migration);

b) Natural movement (birth rate - mortality).

Demographic process

· Mechanical internal and external migration (emigration and immigration):the number of population and structure.

· Natural: the birth rate, mortality and natural increase.

Parameters of natural movement of the population.

1. General demographic parameters.

2. Special demographic parameters.

 

General demographic parameters are estimated for 100 populations.

 

1.

 

2.

 

3.

 

4.   

 

5.

 

Special demographic parameters are parameters of mortality and birth rate for separateage sexual groups:

1.

 

 

2.

 

 

3.

 

4.

 

5. Reproduction of the population:

· Brutt ocoefficient – average number of the girls, delivered by one woman, in the fertile period (15-49 year old);

· Netto coefficient - average number of the girls, born by one woman, the fertile period and lived up to the period reproduction.

· Total-coefficient – average of children, born by one woman at  the fertile period.

 

Control  question s:

 

1. What is demography?

2. What are the main branches studiedin demography?

3. Kinds of movement of population (natural, mechanical).

4. Estimation of population. Principles of the population estimation.

5. Parameter of population.

6. Migration of population. Kind of migration.

7. Reproduction of the population.

8. Indices of natural movement.

9. The coefficient of birth rate, its characteristics.

10. The coefficient of mortality, its characteristics.

11. Factors which influence the birth rate, the mortality.

12. Evaluation of level of the birth rate and the mortality.

13. Special demographic indices.

14. Kinds of children`s mortality.

Variant 1

In city A in 2013:
Number of population	100 000
Was born	2000
Was died	660
Number of deaths:
Till 1 year	50
Till 1 month	25
In maternity hospital:
Born alive	2000
Born dead	15
Died before 1 week	15
Among the children who died before 1 year(50):
Deaths from pneumonia	25
Deaths from gastro-enteric diseases	5
Deaths from other reasons	5
Demographic parameters in city A in 2012:
Birth rate	25%o
Death rate	8 %o
Natural growth	13 %o
Infant death rate	27 %o
Early neonatal death-rate	12 %o
Perinatal death rate	20 %o

                                                                                                   Table 51

Variant 2

In city A in 2013:
Number of population	80 000
Was born	1600
Was died	800
Number of deaths:
Till 1 yr	48
Till 1 month	24
In maternity hospital:
Born alive	1600
Born dead	20
Died before 1 week	20
Among the children who died before 1 yr (50):
Deaths from pneumonia	20
Deaths from newborn’s disease	15
Deaths from gastro-enteric diseases	10
Deaths from other reasons	3
Demographic parameters in city A in 2012:
Birth rate	22%o
Death rate	8,3%o
Natural growth	13,7%o
Infant death rate	27%o
Early neonatal death-rate	13%o
Perinatal death rate	21%o

                                                                                                  Table 52

Variant 3

In city A in 2013:
Number of population	200 000
Was born	4500
Was died	1500
Number of deaths:
Till 1 yr	120
Till 1 month	58
In maternity hospital:
Born alive	4500
Born dead	42
Died before 1 week	45
Among the children who died before 1 yr (50):
Deaths from pneumonia	62
Deaths from newborn’s disease	28
Deaths from gastro-enteric diseases	18
Deaths from other reasons	12
Demographic parameters in city A in 2012:
Birth rate	21,5%o
Death rate	7,2%o
Natural growth	14,3%o
Infant death rate	24%o
Early neonatal death-rate	13%o
Perinatal death rate	20%o

Table 53

Variant 4

In city A in 2013:
Number of population	100 000
Was born	1700
Was died	600
Number of deaths:
Till 1 yr	45
Till 1 month	24
In maternity hospital:
Born alive	1700
Born dead	30
Died before 1 week	20
Among the children who died before 1 yr (50):
Deaths from pneumonia	20
Deaths from newborn’s disease	5
Deaths from gastro-enteric diseases	15
Deaths from other reasons	5
Demographic parameters in city A in 2012:
Birth rate	18,5%o
Death rate	7,2%o
Natural growth	11,3%o
Infant death rate	25,1%o
Early neonatal death-rate	12,7%o
Perinatal death rate	24%o

Table 54

Variant 5

In city A in 2013:
Number of population	150 000
Was born	3200
Was died	1100
Number of deaths:
Till 1 yr	83
Till 1 month	40
In maternity hospital:
Born alive	3200
Born dead	72
Died before 1 week	28
Among the children who died before 1 yr (50):
Deaths from pneumonia	43
Deaths from newborn’s disease	20
Deaths from gastro-enteric diseases	11
Deaths from other reasons	9
Demographic parameters in city A in 2012:
Birth rate	21%o
Death rate	7,1%o
Natural growth	13,9%o
Infant death rate	22%o
Early neonatal death-rate	12%o
Perinatal death rate	25%o

Table 55

Variant 6

In city A in 2013:
Number of population	135 000
Was born	2500
Was died	900
Number of deaths:
Till 1 yr	60
Till 1 month	28
In maternity hospital:
Born alive	2500
Born dead	35
Died before 1 week	20
Among the children who died before 1 yr (50):
Deaths from pneumonia	32
Deaths from newborn’s disease	15
Deaths from gastro-enteric diseases	7
Deaths from other reasons	6
Demographic parameters in city A in 2012:
Birth rate	18,5%o
Death rate	6,9%o
Natural growth	11,6%o
Infant death rate	22%o
Early neonatal death-rate	12%o
Perinatal death rate	23%o

Table 56

Variant 7

In city A in 2013:
Number of population	200 00
Was born	4000
Was died	1900
Number of deaths:
Till 1 yr	100
Till 1 month	60
In maternity hospital:
Born alive	4000
Born dead	39
Died before 1 week	41
Among the children who died before 1 yr (50):
Deaths from pneumonia	55
Deaths from newborn’s disease	30
Deaths from gastro-enteric diseases	10
Deaths from other reasons	5
Demographic parameters in city A in 2012:
Birth rate	18%o
Death rate	8,5%o
Natural growth	9,5%o
Infant death rate	26%o
Early neonatal death-rate	12,5%
Perinatal death rate	24%o

THEME 8

SICKNESS RATE

Features of the theme

The achievement of a high level of condition of good health of the population, the perfection of the form and methods of medical help, the control of the work of the establishment of public health is impossible without detailed study of sickness rate.

Aims of studies

To teach students to differentiate kinds of sickness, count its indices, acquire a habit of working with primary medical documents, registration forms (out-patient’s card, history of disease, statistic coupon, control card of dispensary patient’s, card left of the hospital) which is basic for work up results.

Aims of task

The student must know necessary information of sickness of population, its finds, methods and springs of standing, the main indices of sickness, its analysis for working-out optimal ways of improvement of quality of medical help, for solving prophylactic questions.

The student must master the differentiation of kinds of disease, analysis and count of indices of different kinds of sickness.

3. Main part.

Common sickness of the population shows the level of frequency of occurrence of all diseases together and each individual among the population in common and separately by age, sex, socially, professionally and other groups.

For studying and characterizing common sickness of population there are 3 rules: primary sickness, illness and pathological affection.

Primary sickness - totally new, never happened before, first time in this year among the population this disease occurs.