Lebedeva and Godina ﻿
Journal of Physiological Anthropology(2024) 43:20
https://doi.org/10.1186/s40101-024-00367-2

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

The impact of hygienic living conditions
on the differentiation of male body heightat the beginning of the twentieth century
in the USSR
Lidia Lebedeva1*   and Elena Godina2   

Abstract
Background The positive changes in hygienic living conditions are commonly believed to explain secular changes
in body height and the age of maturity. However, it is difficult to estimate the separate impacts of these factors dueto the lack of social and economic data and variations in the sources of information.
We hypothesized that final male body height could be associated with various socioeconomic indicators, suchas the development of the medical care system, the quality of nutrition, and the level of sanitary and hygienic condi‑
tions. Moreover, we hypothesized that male body height could be associated with the level of morbidity in the region
during the time of conscript childhood (from 1 to 7 years old).
Materials and methods We used two main sources of information in the analyses. The first is the data from the Sta‑tistical Reference Book published by the Central Statistical Committee in 1929. The second is the annual datafrom the Statistical Reference Book published in the Russian Empire. Since the conscripts were born between 1906
and 1909, we used datasets from 1910 to 1913. To analyze the data, we used a method of analyzing interacting vari‑ables called St. Nicolas House Analysis (SNHA).
Results Our analyses revealed direct associations between the morbidity of some diseases and male body height
and other anthropometric parameters.
Conclusions There are associations between conscript final body height and the morbidity of influenza, dysentery
and some venereal diseases, such as chancroid and syphilis. There were no associations between conscript final bodyheight and the level of morbidity during childhood. However, other final parameters, such as BMI, weight, and chest
circumference, could be associated with the morbidity of malaria, scabies, scurvy, and scarlet fever during childhood.
The prevalence of these diseases could be strongly connected with unfavorable living conditions. The results are
similar for both urban and rural areas.
Keywords Body height, Men, Russia, Twentieth century, Living conditions, Level of morbidity*Correspondence:
Lidia Lebedeva
lidiapakulova@gmail.com
1
N. N. Miklukho‑Maklai Institute of Ethnology and Anthropology, Russian
Academy of Sciences, Lenin Ave., 32A, Moscow 119334, Russia
2
Anuchin Research Institute and Museum of Anthropology, Lomonosov
Moscow State University, Mokhovaya St., 11, Moscow 125009, Russia© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

Background
The most common explanations for secular changes
in body height and age at maturity are improvements
in quality of life, such as advances in hygiene, medical care, and nutrition, as well as decreases in morbidity and infant mortality [1–5]. However, the overall andseparate impacts of these factors are difficult to estimate
due to the lack of social and economic data and differences in sources of information. Moreover, administrative changes in the territory units sometimes made suchstudies impossible due to the relevant changes in the reference books.
In Russia, we have only a few reliable sources of information that could be considered for studying changes inbody height in the nineteenth and twentieth centuries forthe territory of the whole country [6–9]. None of thosesources of information provided initial socioeconomic
data about the various indicators of quality and level oflife to consider in the analyses and to find a proper explanation of the changes in body height. By good fortune, we
found the reference book, the Statistical Reference Book,
which was published by the Central Statistical Committee in 1929 [10]. This reference book contains information about male body height that matches the values ofmale body height from the most reliable source of information (Bunak’s article, 1932) up to two points after thecomma. The reliability of this source of information wasproven by other studies on temporal and spatial secular changes in Russia and neighboring countries [11,12]. Additionally, in the book, a variety of socioeconomic indicators were published that could be includedin the analyses of factors impacting human growth anddevelopment.

Page 2 of 12

Using this source of information in the analyses is an
opportunity to highlight the possible reasons for secularchanges in Russia that took place after the 1920s [11].
The aim of this study was to estimate the factors that
could influence male body height in Russia in the 1920s.
We hypothesized that final male body height could be
associated with various socioeconomic indicators, suchas the development of the medical care system, the quality of nutrition, and the level of sanitary and hygienic
conditions. Moreover, we hypothesized that male bodyheight could also be associated with the level of morbidity of some diseases during the time of conscript’s childhood (from 1 to 7 years old).

Materials
We used two main sources of information in the analyses.
The first is the data from the Statistical Reference Book
published by the Central Statistical Committee in 1929
[10]. It was used to study the impact of various socioeconomic factors on male body height. From this dataset,
we used data on the body height and weight of the conscripts. Twenty-nine territories and more than 20 socioeconomic indicators were considered in the analyses. We
chose socioeconomic indicators that are important forevaluating quality of life. In Table 1, we provide the titleof the indicators and factors that explain which aspects oflife conditions could be accounted for.
The second is the annual data from the Statistical Reference Book published in the Russian Empire. As theconscripts were born in 1906–1909, we used the datasets for the years from 1910 to 1913. These datasets wereused to assess the impact of different illnesses on morbidity during the conscript’s childhood. We calculated

Table 1 The indicators of quality and level of life in the USSR in the 1920s [10]No

The title of the indicatorYear, thatthe indicatoraccounted for

Factors of quality of life

1.

Population density, pers. per sq. km.

1928

Impact of economic activity

2.

Migration activity (the difference between those who came to the region
and left it)

1928

Impact of economic activity

3.

Infant mortality under 1 year of age (per 1000 births, died before 1 yearof age)

1927

The development of the healthcare system

4–5.

Distribution of potatoes for personal consumption in the city/in the village,
thousand tons

1926/1927

Quality of nutrition

6–7.

Distribution of meat and lard for personal consumption in the village/inthe city, thousand tons

1926/1927

Quality of nutrition

8–9.

Male literacy in villages and towns, %1926

Level of education

10.

The number of crimes committed against a person (murders, fights etc.)

1928

Security of the territory

11.

The number of crimes committed against property (robbery etc.)

1928

Security of the territory

12–24. Morbidity of some illnesses:
1927
dysentery, diphtheria, pulmonary tuberculosis, influenza, syphilis, chancroid,gonorrhea, scurvy, trachoma, malaria, smallpox, scarlet fever, measles

Level of sanitary and hygienic conditions

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

the average morbidity rate for 4 years. Forty-four regions
were included in the analyses [13] (Table 2).
It is worth mentioning that in the analyses, we used themen’s body height for rural and urban areas according to
the exact territory. We considered that the inner bordersof the country changed greatly from 1913 to 1929 andremoved those territories that had completely changed
their borders from the dataset.

Methods
Spearman correlation ratios and controlled p values
were used to estimate groups of socioeconomic factors.
St. Nicolas House Analysis (SNHA) was used for further
analysis of the associations between interacting variables[14].
The key advantage of the SNHA package is that it
explores interacting variables by searching association
chains where correlation coefficients between variables
drop in a regular order between a set of variables.
While more typical methods in this area are visualization of pairwise correlations, principal component analysis, or multidimensional scaling, the SNHA package
provides an alternative approach by uncovering orderedsequences of correlation coefficients, which can also bereversed. Thus, the basic assumption of this method isthat the correlation coefficients between two variables,
where one variable directly influences the other, arelarger than those of secondary associations. Therefore, if
we suggest that a variable A influences a variable B and
B influences C, it can be assumed that r(AB) > r(AC) andthat in the opposite direction, r(CB) > r(CA). The existence of such association chains with the correct ordering of three or more nodes is much less likely to exist byaccident than significant pairwise correlations. Therefore,
the SNHA method has very limited requirements forPage 3 of 12choosing thresholds. Basically, they are the meanings ofthe p value and the correlation ratio [14–16].
The correlation chains, existing in the dataset, aretranslated into edges between the variables. The variables
become nodes of a graph. The graph can then be visualized, and the major relations between the variables arevisible.
Moreover, we can use bootstrapping to evaluate andillustrate the quality of the chains. The solid line between
the nodes indicates that this edge (association) was foundin more than 75% of all resamplings of our data. A broken
line indicates that the edge was found in more than 50%
of all resamplings, and a dotted line indicates that theedge was found in more than 25% of all resamplings [14,16]. Also, the SNHA package allows the calculation of theR-squared values for a given dataset, graphs, and nodes
based on the linear regression model. All figures in thismanuscript and statistical analyses were performed using
R version 4.3.1 (released on 16.06.2023) [17].

Results
First, we estimated that the correlation ratios between
various socioeconomic factors and body height inurban and rural areas were nonsignificant for all variables except for the level of morbidity of some diseases(Table 3).
Body height in urban areas was significantly but
negatively correlated with the morbidity of dysentery (r = − 0.46, p value = 0.01), influenza (r = − 0.44, pvalue = 0.02), whooping cough (r = − 0.43, p value = 0.02),
and trachoma (r = − 0.38, p value = 0.05). In addition,
there was a positive correlation between the morbidity of
the chancroid (r = 0.43, p value = 0.02) and the incidenceof syphilis (r = 0.4, p value = 0.04).

Table 2 The title and type of the illnesses used in the analyses, 1910–1913, 1929 [9, 11]
The type of diseases
Children’s

The Statistical Reference Books for Russian Empire, 1910–
1913

The Statistical ReferenceBook for the USSR, 1929

Diphtheria

Diphtheria

Measles

Measles

Whooping _cough–Scarlet fever

Scarlet fever

Respiratory

Influenza

Influenza

Pulmonary tuberculosis

Pulmonary tuberculosis

Social-hygienic

ScurvyScurvyTrachoma

Trachoma

Scabies–

Sexually transmitted

Chancroid

Chancroid

Syphilis

Syphilis

OthersMalaria

Malaria

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

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Table 3 The correlation ratios between various socioeconomic factors and body height in urban and rural areas
No

The title of the indicator

Body height in urban
areas, 1928

Body height inrural areas, 1928r

p valuer

p value

1.

Population density, pers. per sq. km.

− 0.310.1048− 0.300.12622.

Migration activity (the difference between those who came to the region and left it)

0.26

0.20290.26

0.2105

3.

Infant mortality under 1 year of age (per 1000 births, died before 1 year of age)

− 0.370.1784− 0.320.24834.

Distribution of potatoes for personal consumption in the city, thousand tons

0.22

0.33250.16

0.4941

5.

Distribution of potatoes for personal consumption in the village, thousand tons

− 0.030.8953− 0.080.7432

6.

Distribution of meat and lard for personal consumption in the city, thousand tons

0.13

0.59480.16

0.5038

7.

Distribution of meat and lard for personal consumption in the village, thousand tons

0.26

0.27260.26

0.27348.

Male literacy in towns, %− 0.190.3344− 0.150.45109.

Male literacy in villages, %− 0.260.1780− 0.280.1547

10.

The number of crimes committed against a person (murders, fights etc.)− 0.180.3662− 0.250.2164

11.

The number of crimes committed against property (robbery etc.)− 0.200.3134− 0.270.1654

12–24.

Morbidity of some diseases

See Figs. 1 and 2.

Fig. 1 The correlation ratio: morbidity levels of certain illnesses vs. male body height in urban areas

Body height in rural areas was significantly but negatively correlated with dysentery morbidity (r = − 0.43,
p value = 0.02) and whooping cough (r = − 0.37,
p value = 0.02), as well as with venereal diseases
such as chancroid disease (r = 0.48, p value = 0.01),

syphilis (r = 0.5, p value = 0.01) and gonorrhea (r = 0.41, pvalue = 0.03).
It is necessary to explain the possible reasons forthe different directions of the correlation ratio. It
seems to be obvious when we speak about the negative

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

Page 5 of 12

Fig. 2 The correlation ratio: morbidity levels of certain illnesses vs. male body height in rural areas

correlation ratio—the greater the body height is, thelower the morbidity of particular illnesses. Thus, theseillnesses are easily transmitted when living conditions
are more unfavorable.
In what ways could we explain the positive correlation ratio? We should take a deep dive into the original
dataset. We will see that the leaders of venereal diseases are the southern territories of the Soviet Union– Uzbekistan, Kazakhstan, Turkmenistan, and others.
The illness was easily transmitted among the local ethnic population [18–27], but the values of body heightused in the analyses and provided in the reference book[10] were about Russian conscripts, as Russian conscripts are usually greater than those of people of different ethnicities. According to the analyzed dataset,the difference could reach 2–3 cm [12].
Furthermore, SNHA graphs were constructed. Figure 3 shows that associations between the value ofbody height in urban areas and indicators of morbidity of influenza and dysentery were found in more than
75% of all dataset resamplings (solid lines); associations
between body height values in urban areas and indicators of morbidity of whooping cough, syphilis, and trachoma were found in more than 50% of all resamplings(broken lines); and associations between body heightvalues in urban areas and morbidity of chancroid were

found only in more than 25% of all resamplings (dottedlines).
Then, we calculated the R-squared values by a linear
model for all nodes that were associated with the indicator of body height in urban areas. We used the function “SNHA_rsquare” [16]. The linear model predicts thenode by an additive mode using its neighboring nodes in
the graph. The mean R-square value for all variables associated with body height was 0.37, but the R-square value
for body height was 0.47. Thus, the morbidity of diseases
such as influenza, dysentery, and chancroid could explain
up to 0.52 of the variation in body height in urban areas
during the observed period (Fig. 4).
The same steps were performed for the dataset for rural
areas. Figures 5 and 6 show that body height in rural
areas was associated mostly with the morbidity of venereal diseases (chancroid, syphilis) and was less strongly
associated with gonorrhea and with the diseases transmitted due to low quality of life and low sanitation (dysentery and whooping cough). All of these associations
could explain up to 0.43 variations in all the provided
variables. Three variables (chancroid, syphilis, and dysentery) could explain up to 0.66 variations in body height inrural areas (see Fig. 6).
For this dataset, we also checked the associations
between the morbidity of various illnesses and body mass

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

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Fig. 3 Associations between the values of body height in urban areas and the morbidity of various illnesses

Fig. 4 R-square values for the urban areas

index in the same territories; however, no significant correlations or associations were found for either urban orrural areas.
Finally, we investigated whether the childhood epidemiological situation of conscripts in the regions where

they were growing up could influence their final bodyheight and other physical parameters, such as bodyweight, BMI, and chest circumference.
For every abovementioned trait, we followed the same
steps as were previously described. Figures 7 and 8

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

Page 7 of 12

Fig. 5 Associations between the values of body height in rural areas and morbidity due to various illnesses

Fig. 6 R-square values for the rural areas

illustrate the results of the calculation of four R-squaredgraphs for urban and rural territories. Thus, the values of conscripts’ body height could be up to 0.56 forthe indicators strongly associated with body weight andchest circumference in urban areas and up to 0.49 for theindicators strongly associated with body weight in rural
areas. No association between the level of morbidity inthe regions during childhood and conscripts’ body height
was found. Moreover, the conscripts’ body weight, chest
circumstance, and BMI were significantly correlated withthe morbidity of various illnesses, such as influenza, scabies, scurvy, scarlet fever, and malaria. These associations could explain up to 0.33 and 0.27 of the variationin conscripts’ final body weight in rural and urban areas,

respectively. The final chest circumference of the conscripts reached 0.27 and 0.43, and the BMIs of the conscripts reached 0.31 and 0.49.
Therefore, we suggest that the morbidity of influenza,
scabies, and other illnesses during childhood might beassociated with the physical characteristics of the conscripts, such as BMI, weight, and chest circumference.

Discussion
The role of diseases has changed dramatically over the
centuries. Some illnesses that we have considered in
the analyses are currently forgotten or extremely rare in
medical practice, such as malaria, scabies, or trachoma.
Therefore, to understand what those illnesses indicatedLebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

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Fig. 7 R-square values for the urban areas for the dataset that illustrates the level of morbidity during conscripts’ childhood. Abbreviations: CC—
chest_circumference, PT—pulmonary_tuberculosis, WC—whooping_cough, SF—scarlet_feverin the first decades of the twentieth century, we useddescriptions from the Great Medical Encyclopedia by
N. Semashko published in 1928–1936 in 35 volumes[18–27]. Every article about diseases contains the historical background of the disease, the statistical informationon the prevalence of illnesses worldwide and in someparticular territories, and its clinical description andepidemiology that was relevant to the period when the
Encyclopedia was published. Moreover, to understandthe prevalence of the diseases, we aggregated statisticalinformation from the data sources used in the analyses[10, 13]. As shown in Fig. 9, there were three key diseases
at the beginning of the century—malaria, scabies, andinfluenza. They are markers of unfavorable living conditions and a low level of sanitation culture.
Malaria is a mosquito-transferring infectious disease
that affects humans and other animals. Children areespecially prone to malaria. Malnutrition, migration, andlow socioeconomic development of the territory could
lead to severe epidemics, especially among immigrants

arriving in malarial areas. The areas of high morbidity ofmalaria are traditionally located in the southern regionsof the Russian Empire, namely, the Caucasus and SouthAsia, due to a lack of medical care and high population
density. However, the morbidity of malaria was also highin the regions near the Volga River and Central Russia
after the Civil War. The key reasons are as follows: a significant number of people migrated from the southernparts of the country to the North, and there was a sharp
increase in the air temperature in 1921–1922, which
led to the spread of wild soils and swamping due to theabsence of normal agricultural activity. There was a dramatic decrease in in-livestock numbers and an overall
decline in living conditions [18].
Another disease strongly associated with the lack ofhygienic conditions was scabies. It is a contagious skindisease caused by the scabies mite. The level of morbidityof scabies increases during wars and among populations
with low socioeconomic status and low sanitary conditions, with a lack or absence of health care. After the Civil

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

Fig. 8 R-square values for the rural areas for the dataset that illustrates the level of morbidity during conscript childhood. CC—chest_
circumference, PT—pulmonary_tuberculosis, WC—whooping_cough, SF—scarlet_fever

Fig. 9 Levels of morbidity of different illnesses at the beginning of the twentieth century, per 10,000 people in Russia

Page 9 of 12

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

War, the morbidity of scabies declined rapidly due to thedevelopment of the medical care system throughout thewhole territory. Unfortunately, no statistical information was found to support this statement in our reference
books or even in the Encyclopedia, except for one note
about the level of morbidity in the city of Magnitagorsk
in 1931. This figure was 27.6 per 10,000 people, approximately ten times less than that of the Russian Empire[19].
Influenza is a contagious disease that can cause bothgeneral symptoms (fever, headache, soreness in the muscles and back) and complications of organs in the upper
respiratory tract. These complications usually become
the causes of death. Influenza can spread pandemically.
The most well-known influenza pandemic occurred in
1918–1919 just after the 1st World War and is known as
the Spanish flu. Some influenza outbreaks were statistically recorded in 1926 and 1927 in Russia [20].
Before the First World War, the mortality rate frominfluenza in European countries averaged 10–20 people per 100,000 people. In 1918, this figure was 293 inGermany, − 336 in England, and − 471 in Sweden. At thesame time, the mortality rate in these cities is two to
three times greater [20].
During the period from 1908 to 1915, approximately
200 sick people per 10,000 people were registered annually in the Russian Empire. In 1927, there were 326
registered cases (in the USSR). Due to differences inadministrative boundaries, it is problematic to compare
the results for different territories. However, a comparison of the cities revealed the same trend as that inEurope—a significant increase in the number of casesin 1918–1919 and in 1926. Moreover, the morbidity in
the Moscow region in 1906–1910 and 1926 was greater
among children under 1 year of age and among adults
aged 20 to 29 years, both among men and women. Usually, people of that age have more social contacts and agreater risk of being infected; thus, they can transmit illnesses more easily [20].
Other diseases that indicate low quality of life and lackof hygienic culture are trachoma, pulmonary tuberculosis, dysentery, and scurvy. According to our analyses, theincidence of these diseases during conscripts’ childhood
was associated with BMI, body weight, and chest circumference. Trachoma is a chronic eye infection that leads
to blindness. The spread of trachoma is related to thepoor health status of the healthcare system, poor living
conditions, and poor hygienic culture of the population(e.g., using common towels) [21]. Scurvy is a form of vitamin deficiency in which long-term vitamin C deficiency
occurs. Scurvy occurs more frequently in malnourishedindividuals [22]. Dysentery is an infectious disease thatspreads through household contact (through unwashed

Page 10 of 12

hands), water (through an infected water source), or food.
Additionally, the role of flies as carriers of this disease issometimes noted. The low socioeconomic status of the
population and its lack of hygienic habits contribute to
the spread of dysentery. The level of morbidity during
the 1st WW in European countries and some cities of theRussian Empire supports this idea. Dysentery can also be
associated with malnutrition and starvation. Due to thesehardships, people become more susceptible to illnesses.
Pulmonary tuberculosis is also an infectious disease
that is associated with poor nutrition and low quality oflife. People from the working class were exposed to thisdisease both in European countries and in the Russian
Empire [23].
Other types of diseases associated with conscripts’
final height, such as syphilis and chancroid, are venereal
diseases. Infection occurs mainly through sexual contact and less often through kisses, bites, and household
items. These diseases spread due to the migration of people caused by unemployment or poor standards of living. Urban territories face these diseases more often thanrural territories. The disease most often spreads among
unmarried men aged 20–39 years with a low level of education and social position. They live in dormitories withlow social and hygienic culture [24].
In rural territories of the Russian Empire and the
USSR, syphilis was brought by soldiers and seasonal
workers who were employed during the winter in thesecities. This type was called social household syphilis andbecame widespread through the use of common household items. The low level of health education in the 1920s
prevented the detection of disease in the early stages. In
the USSR, the health care system started to organize a
network of medical care services and to improve hygienic
literacy. These methods help to decrease the level of morbidity and improve disease spread [24].
The last group of diseases included in the analyses were
diphtheria, scarlet fever, and whooping cough. As shownin Fig. 9, the average morbidity was approximately 20–30
cases per 10,000 people. These diseases are usually spread
between people by direct contact or through the air. They
may also be spread by contaminated objects. Children who
lived in houses with many family members and poor socioeconomic conditions were more likely to be infected. Thus,
at the beginning of the twentieth century in St. Petersburg,the morbidity of scarlet fever was 18.5 cases per 10,000
people for wealthy people and 26.1 cases for poor people.
Diphtheria most often affects children under 5 years of age,those with scarlet fever, and those with whooping coughunder 9 years (Table 4). In the 1910s, there was an epidemicof diphtheria in Russia. The vaccine was invented only inthe 1920s and helped to overcome this illness [25–27].

Lebedeva and Godina ﻿Journal of Physiological Anthropology(2024) 43:20

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Table 4 The morbidity of scarlet fever and whooping cough among various age groups in Moscow city and the Moscow region in1926 [26, 27]
1926

1906–1908

Scarlet fever

Whooping coughMoscow region

Moscow City

Moscow region

Moscow City

Moscow region

Male

Male

Male

Male

Male

FemaleFemaleFemaleFemaleFemale1 year and less

50

46

50

84

199

231

249

289

301.1

318.6

1–4 years

114

111

295

298

145

177

317

360

249

297.1

5–9 years

77

88

237

255

64

76

140

145

115.4

142.4

10–14 years

27

35

78

89

10

13

12

18

21.3

29.4

15–19 years

9

9

24

23

1

1

1

1

1.7

3.8

20–29 years

4

3

7

9

1

3

1

1

0.6

2

Thus, our hypothesis regarding the influence of theepidemiological situation in childhood on final bodyheight and other physical parameters is as follows: bodyweight, BMI, and chest circumference were partially confirmed. Moreover, body weight, BMI, and chest circumference was associated with the prevalence of diseases
spread among children.

Conclusion
Our analyses revealed direct associations between themorbidity of some diseases and male body height andother anthropometric parameters. Our results revealed
the morbidity of such illnesses as influenza, dysentery
and chancroid could explain up to 0.52 of the variationin body height in adults in urban areas. Three variables(chancroid, syphilis and dysentery) could explain up to
0.67 variations in body height in rural areas. There are no
associations between conscripts’ final body height andthe level of morbidity during childhood. However, otherfinal morphological characteristics such as BMI, weightand chest circumference could be associated with thelevel of morbidity of malaria, scabies, scurvy and scarlet
fever during conscripts’ childhoods in urban and rural
areas. The prevalence of these diseases could be strongly
connected with poor living conditions.
Limitations
Our analysis has several limitations. The first limitation isconnected with the absence of statistical information on
final body height for women. Thus, we could not confirm
our conclusions for a significant part of the population.
The statistical data about the morbidity rate for the whole
population were used. The second limitation concerns
the period of our analyses. The collapse of the Russian
Empire led to significant changes in the administrative
system, government boundaries and the system of collecting vital statistics. All these factors greatly influence

the ability to organize the data for analysis. However, we
extracted and considered as much information as possible from the statistical reports and other resources.
Acknowledgements
The authors are grateful to Detlef Groth for his help with statistical analysis.
Authors’ contributions
LL and EG contributed to the research conceptualization. LL contributed to thedata analysis and wrote the initial manuscript version. EG contributed to editing
and revising the manuscript. All authors read and approved the final manuscript.
Funding
This study was performed under Research Project No. AAAAA19-119013090163–2 “Anthropology of Eurasian populations (biologicalaspects)”.
Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding author upon reasonable request.

Declarations
Competing interests
The authors declare that they have no competing interests.
Received: 27 March 2024 Accepted: 28 July 2024

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