Prevalence of Musculoskeletal Diseases Among Children in Saint Petersburg, Russia

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BACKGROUND

Child health is highly dependent on internal and external factors. The formation and consolidation of pathological motor stereotypes and the specific features of physiological responses of a growing body under disease conditions often determine the future risk of developing musculoskeletal disorders in adulthood and old age.

In the 2024 Presidential Address to the Federal Assembly of the Russian Federation,¹ regarding the social agenda, the need for additional measures to improve the country’s demographic situation was emphasized, including the launch of the national project “Long and Active Life” starting in 2025.² The implementation of these strategic goals involves assessing the effectiveness of previous projects and conducting a detailed analysis of public health problems, including the prevalence of specific nosological entities [1, 2].

Musculoskeletal and connective tissue diseases are widespread globally and can lead to severe long-term consequences, such as reduced quality of life, impaired social well-being, disability, and mortality. In 2022, in Russia, the primary incidence of this group of diseases amounted to 12,857.8 per 100,000 population.³ Furthermore, musculoskeletal and connective tissue diseases are the fifth most common cause of overall morbidity (7.4%), second leading cause of total sick-leave days (14.6%), and third leading cause of temporary disability (12.3%) among the working-age population [3, 4].

It is therefore important to highlight the socially significant, severe, and irreversible consequences associated with the musculoskeletal system. Over a 4-year period (2019–2022), the changes in primary disability due to musculoskeletal and connective tissue diseases showed a negative trend; among adults, the indicator increased from 3.0 to 3.2 per 10,000 population, whereas among children, it increased from 6.0 to 7.7 per 10,000 population [3, 5].

Monitoring of morbidity enables, above all at the regional level, differentiation of assessment results related to specific socioeconomic and natural climatic conditions, characteristics of migration flows, medical activity of the population, territorial accessibility, and quality of organizational healthcare resources [5–7].

Over the past 5 years, the health status of the Russian population has significantly deteriorated, largely due to the impact of COVID-19 [8–10]. In Russia, the COVID-19 pandemic officially lasted from January 2020 to mid-2023, severely affecting the country’s healthcare system and demographic indicators. Several studies have reported the direct and indirect effects of SARS-CoV-2 virus on human organs; however, its long-term consequences remain to be fully investigated [11–13]. Studies of the detection rate of the targeted condition during the pandemic revealed unfavorable acute onsets [3]. Moreover, the analysis of follow-up visits, including those for chronic musculoskeletal and connective tissue diseases in children in Saint Petersburg during this period, is of particular interest for in-depth evaluation and analysis.

The work aimed to conduct a comparative analysis of the overall morbidity of musculoskeletal and connective tissue diseases in children in Saint Petersburg during 2017–2023.

METHODS

Data from official statistical compendiums for the years 2018–2023 published by the Central Research Institute of Health Organization and Informatization of the Ministry of Health of the Russian Federation, titled “General Morbidity of the Pediatric Population in Russia,” were analyzed. The data covered children aged 0–14 and 15–17 years in Saint Petersburg (SPb) and the Russian Federation (RF).⁴ The trends of disease prevalence were assessed per 1000 children in the corresponding age group. Analysis of the overall morbidity profile in children was conducted using a quantitative assessment of the odds ratio (OR) for the occurrence of musculoskeletal and connective tissue diseases, both overall and by specific nosological forms presented in the statistical compendiums: arthropathies, juvenile and rheumatoid arthritis, deforming dorsopathies and spondylopathies, and systemic connective tissue disorders. The analysis was performed based on a “one disease = one patient” principle. Two groups were compared: diseased and nondiseased individuals in SPb and other regions of the RF (absolute values, scaled to 1 patient). The number of nondiseased individuals was calculated as the difference between the total pediatric population in the region and number of children with a disease in SPb and the RF (excluding SPb). The OR was defined as the ratio of the number of children with a disease to the number of children without any disease in SPb or the RF region; this ratio was calculated separately for each age group. An OR of >1 for SPb to the RF indicated higher odds of disease occurrence in SPb, showing a direct association between place of residence and musculoskeletal disease diagnosis. An OR of <1 indicated that the prevalence odds of the condition under consideration were higher in other RF regions. The statistical significance of the OR for disease prevalence was determined based on 95% confidence interval (CI); the OR was considered significant if the CI was entirely above or below 1, i.e., did not include 1 (p < 0.05).

To assess the appropriateness of applying different measurement scales for estimating the OR of disease prevalence, a comparative analysis of the OR and its CIs was conducted using three different scales—per 1, 100, and 1000 patients—based on data from adolescents aged 15–17 years. Statistical analysis, data processing, and result visualization were performed using Microsoft Office 2016 (Word and Excel).

RESULTS

Over the past 7 years, the prevalence of musculoskeletal and connective tissue diseases among children in SPb remained consistently high and exceeded that in the other RF regions. As of 2023, the overall morbidity rate among children aged 0–14 years residing in SPb was 240.5 per 1000 children of the corresponding age group, which was 65.0% higher than the RF average (84.23 per 1000 children) (p ≤ 0.05).

The prevalence of the condition between 2017 and 2022 showed a steady upward trend in SPb and the RF. The increase in morbidity among children in SPb was significantly greater than the national average (growth rate: SPb, 31.9%, RF, 9.5%; p ≤ 0.05). Owing to the differing rates of increase, the difference between the indicators at the beginning and end of the observation period continued to increase; in 2017, it was 2.2-fold, and by 2023, it had reached 2.9-fold.

A slight adjustment in overall morbidity was observed in 2020 against the backdrop of the onset of the COVID-19 pandemic. In this year, the number of visits for musculoskeletal and connective tissue diseases among children decreased by 11.0% in SPb and by 13.0% across the RF. Nevertheless, from 2021, the previously observed upward trend resumed. Notably, during the pandemic period (2020–2023), the morbidity growth rates were more than twice as high as those in the prepandemic period (2017–2019) (30.2% vs 13.1% in SPb and 15.0% vs 7.4% in the RF) (p ≤ 0.05) (Fig. 1).

 

Fig. 1. Overall morbidity rate of musculoskeletal and connective tissue diseases among children aged 0–14 years in Saint Petersburg and across the Russian Federation in 2017–2023 (per 1000 children of the corresponding age group).

 

For specific nosological entities under consideration, among children aged 0–14 years in SPb, an increase in the number of cases was recorded in 2017–2023 for reactive arthropathies (by 21.9%), juvenile arthritis (by 10.6%), overall arthropathies (by 32.8%), deforming dorsopathies (by 29.4%), systemic connective tissue disorders (by 34.1%), and other conditions (by 41.5%). Conversely, the number of visits for rheumatoid arthritis and spondylopathies decreased by 24.7% and 18.1%, respectively (Table 1).

 

Table 1. Changes of musculoskeletal (MS) and connective tissue (CT) disease cases among children aged 0–14 years and odds ratios (OR) with 95% confidence intervals (CI) in Saint Petersburg (SPb) and the Russian Federation (RF), 2017–2023 (absolute numbers, 1-patient scale)

Specific MS and CT nosologies	Number of patients (absolute values) and odds ratio for disease detection in SPb vs the RF with confidence intervals (relative values)								Growth/decline rate, %
	Region, parameter	2017	2018	2019	2020	2021	2022	2023
Reactive arthropathies	SPb	745	824	786	783	621	808	954	21.9
	RF	21,147	21,834	19,949	18,047	16,578	19,019	17, 786	–15.9
	OR SPb/RF	1.20*	1.25*	1.28*	1.38*	1.16*	1.30*	1.71*	29.8
Juvenile arthritis	SPb	1000	1046	955	885	904	1013	1118	10.6
	RF	15,191	16,419	16,679	16,914	17,921	20,392	21,905	30.7
	OR SPb/RF	2.32*	2.17*	1.90*	1.68*	1.59*	1.53*	1.62*	–30.2
Rheumatoid arthritis	SPb	85	109	220	103	116	83	64	–24.7
	RF	2670	2604	3384	3710	2535	3020	2167	–18.8
	OR SPb/RF	1.08	1.39*	2.17*	0.87	1.43*	0.83	0.92	–14.8
All arthropathies	SPb	58,968	62,653	67,521	63,593	72,568	82,084	87,780	32.8
	RF	777,263	842,643	895,226	793,742	870,036	933,449	978,153	20.5
	OR SPb/RF	2.84*	2.70*	2.69*	2.79*	2.88*	3.02*	3.22*	11.8
Deforming dorsopathies	SPb	27,216	27,059	32,319	26,311	31,474	38,287	38,575	29.4
	RF	537,993	545,990	558,057	489,782	514,186	522,476	534,957	–0.6
	OR SPb/RF	1.78*	1.69*	1.96*	1.75*	1.98*	2.38*	2.41*	26.1
Spondylopathies	SPb	404	367	457	281	305	278	331	–18.1
	RF	4602	4312	3386	2898	3663	2915	4017	–12.7
	OR SPb/RF	3.16*	2.97*	4.87*	3.27*	2.71*	3.09*	2.71*	–14.2
Systemic connective tissue disorders	SPb	826	809	1185	995	942	1230	1253	34.1
	RF	6356	7497	9335	4940	10,941	7153	7089	10.3
	OR SPb/RF	4.91*	3.86*	4.54*	7.69*	2.81*	6.10*	6.49*	24.3
Other conditions	SPb	36,934	44,293	50,263	45,356	52,844	57,537	63,121	41.5
	RF	591,662	602,099	629,635	525,789	545,507	573,799	585,300	–1.1
	OR SPb/RF	2.25*	2.63*	2.82*	2.98*	3.35*	3.43*	3.87*	41.9

Note: *p ≤ 0.05 (CI does not include 1).

 

An over-time analysis of ORs was conducted to assess the odds of registered cases of general morbidity from musculoskeletal and connective tissue diseases among children aged 0–14 years in SPb compared with the RF. The prevalence of all considered nosological entities in SPb remained consistently significant (p < 0.05). Starting in 2020, the advantage shifted toward the other RF regions (the OR falling below one) for rheumatoid arthritis. In 2023, in contrast to the RF regions, the prevalence rates in SPb were significantly higher: 1.7 times higher for reactive arthropathies, 1.6 times for juvenile arthritis, 3.2 times for arthropathies, 2.4 times for deforming dorsopathies, 2.7 times for spondylopathies, and 6.5 times for systemic connective tissue diseases (p < 0.05). Over the 7-year period, a negative trend in ORs was observed in SPb, indicating increased morbidity from reactive arthropathies (by 29.8%), overall arthropathies (by 11.8%), deforming dorsopathies (by 26.1%), systemic connective tissue diseases (by 24.3%), and other related conditions (by 41.9%). Moreover, the odds of identifying children with juvenile and rheumatoid arthritis and spondylopathies decreased by 30.2%, 14.8%, and 14.2%, respectively.

The COVID-19 pandemic had a contradictory effect on the changes of musculoskeletal disease prevalence. Compared with the prepandemic period (2017–2019), in 2020–2023, the odds of detecting reactive arthropathies, deforming dorsopathies, and other conditions in children in SPb increased from 8.7% to 23.6%, 9.2% to 27.4%, and 20.2% to 23.0%, respectively, whereas the odds of detecting rheumatoid arthritis, spondylopathies, and systemic connective tissue diseases markedly decreased from 50.2% to 5.4%, 35.1% to 17.1%, and–7.5% to –15.6%, respectively.

A similar pattern was observed in adolescents aged 15–17 years. During the observation period, the overall morbidity rate from musculoskeletal and connective tissue diseases in SPb remained the highest. In 2023, it reached 552.27 per 1000 adolescents, exceeding the RF average by 65.7% (p ≤ 0.05) (Fig. 2).

 

Fig. 2. Prevalence of musculoskeletal and connective tissue diseases among adolescents aged 15–17 years in Saint Petersburg and across the Russian Federation, 2017–2023 (per 1000 children of the corresponding age).

 

The prevalence of musculoskeletal and connective tissue diseases in 2017–2023 demonstrated an upward trend, driven by an increase in registered cases in SPb and the RF by 24.0% and 10.7%, respectively. A slower growth rate in morbidity across the RF resulted in a progressive increase in disparity with SPb; in 2017, the difference was 2.5-fold, and by 2023, it had increased to 2.9-fold (p ≤ 0.05).

Following the onset of the COVID-19 pandemic in 2020, a decrease in adolescent healthcare visits was observed across Russia (by 7.4% in SPb and by 11.8% in the RF), with a subsequent recovery of the upward trend in 2021–2023. Notably, during the prepandemic period (2017–2019), the growth of prevalence rates musculoskeletal disease were significantly lower than those during the pandemic period (2020–2023): 5.1% vs 25.8% in SPb and 2.4% vs 19.0% in the RF (p ≤ 0.05).

The trends in overall morbidity for specific nosological forms among adolescents showed a unidirectional pattern in SPb and the RF. Over the 7-year period (2017–2023), increases were recorded for all considered conditions (reactive arthropathies, by 13.3%; juvenile arthritis, by 19.8%; rheumatoid arthritis, by 56.5%; overall arthropathies, by 45.9%; deforming dorsopathies, by 20.9%; systemic connective tissue diseases, by 54.7%; and other diseases, by 42.5%). The only exception was spondylopathies, for which a decline in visits was observed across the country (decrease rate: 41.1%) (Table 2).

 

Table 2. Changes in the number of adolescents aged 15–17 years diagnosed with musculoskeletal system (MS) and connective tissue (CT) diseases and odds ratios (OR) with 95% confidence intervals in Saint Petersburg (SPb) and the Russian Federation (RF) in 2017–2023 (absolute numbers, 1-patient scale)

Specific MS and CT nosologies	Region, parameter	Number of patients (absolute values) and odds ratio for disease detection in SPb vs the RF with confidence intervals (relative values)							Growth/decline rate, %
		2017	2018	2019	2020	2021	2022	2023
Reactive arthropathies	SPb	157	251	231	229	222	124	181	13.26
	RF	6158	6305	5563	6025	6795	6216	6623	7.02
	OR SPb/RF	0.94	1.47*	1.52*	1.39*	1.19*	0.72	1.00	6.0
Juvenile arthritis	SPb	392	406	394	369	398	497	489	19.84
	RF	5725	6128	6528	6580	7189	7569	8157	29.81
	OR SPb/RF	2.66*	2.52*	2.25*	2.09*	2.07*	2.52*	2.27*	–14.7
Rheumatoid arthritis	SPb	20	35	62	48	52	48	46	56.52
	RF	843	892	1387	1095	1072	1159	1153	26.89
	OR SPb/RF	0.88	1.45*	1.64*	1.61*	1.80*	1.55*	1.48*	40.5
All arthropathies	SPb	14,096	15,282	16,904	16,952	19,532	23,763	26,056	45.9
	RF	200,725	219,851	237,321	218,577	248,308	275,216	313,900	36.1
	OR SPb/RF	2.98*	2.90*	2.96*	3.27*	3.39*	3.95*	3.77*	21.0
Deforming dorsopathies	SPb	21,038	20,118	21,326	18,928	22,692	26,023	26,583	20.9
	RF	281,531	286,202	295,395	266,236	291,531	309,559	337,842	16.7
	OR SPb/RF	3.37*	3.04*	3.10*	3.00*	3.42*	3.90*	3.55*	5.1
Spondylopathies	SPb	163	132	228	140	148	105	96	–41.1
	RF	2408	2523	2752	2763	2406	2310	2128	–11.6
	OR SPb/RF	2.62*	1.96*	3.16*	1.88*	2.31*	1.70*	1.68*	–35.9
Systemic connective tissue disorders	SPb	53	58	104	114	133	142	117	54.7
	RF	1310	1581	1711	1960	1819	1595	1943	32.6
	OR SPb/RF	1.52*	1.35*	2.26*	2.17*	2.78*	3.50*	2.28*	33.3
Other conditions	SPb	10,870	11,651	14,128	13,414	15,899	15,808	18,906	42.5
	RF	195,682	196,279	205,879	176,505	190,555	195,727	205,265	4.7
	OR SPb/RF	2.24*	2.38*	2.79*	3.13*	3.54*	3.46*	4.05*	44.7

Note: *p ≤ 0.05 (CI does not include 1).

 

Analysis of the ORs for identifying specific nosological forms among adolescents in SPb compared with other RF regions revealed a generally stable and significant advantage. As of 2023, adolescents in SPb had 2.3-, 1.5-, 3.8-, 3.6-, 1.7-, 2.3-, and 4.1-fold higher odds of experiencing juvenile arthritis, rheumatoid arthritis, overall arthropathies, deforming dorsopathies, spondylopathies, systemic connective tissue disorders, and other musculoskeletal diseases, respectively, than those in the RF (p ≤ 0.05). The OR for reactive arthropathies in SPb relative to the RF was nonsignificant (OR = 1.0).

Over the 7-year period, the odds of identifying specific musculoskeletal diseases among adolescents in SPb increased as follows: +6.0%, reactive arthropathies; +40.5%, rheumatoid arthritis; +21.0%, overall arthropathies; +5.1%, deforming dorsopathies; +33.3%, systemic connective tissue diseases; and +44.7%, other musculoskeletal diseases. Conversely, the odds decreased for juvenile arthritis and spondylopathies (by 14.7% and 35.9%, respectively).

The trends in odds of disease detection among adolescents during the COVID-19 pandemic exhibited certain features. During the pandemic period (2020–2023), the odds of detecting juvenile arthritis, overall arthropathies, deforming dorsopathies, and systemic connective tissue disorders in SPb steadily increased by 7.9%, 13.3%, 15.5%, and 22.7%, respectively, compared with those during the prepandemic period (2017–2019). However, the odds of detecting reactive arthropathies, rheumatoid arthritis, and spondylopathies among adolescents decreased by 16.0%, 8.1%, and 10.6%, respectively, during 2020–2023.

The OR is a reliable mathematical statistics tool. However, notably, the data used—sourced from official reports of the Central Research Institute for Health Organization and Informatics of the Ministry of Health of Russia on the morbidity of children and adolescents—reflect the number of cases in which children of the relevant age group sought medical care for the respective condition. These data are presented in absolute (per individual patient) and relative terms (per 100,000 population of the corresponding age group in a given region). This scale for relative frequency is well established. Furthermore, such smoothing aims to mitigate random error. Population size in the regions is derived from official statistical reports compiled based on census results, adjusted for birth and mortality rates and migration. These datasets are collected using principles that significantly differ from those applied to morbidity data collection, and they are subject to random errors of a different nature. Moreover, the population size in any region undergoes continuous changes, making researchers’ estimates unanchored to any specific moment or a concrete time period. Thus, when analyzing ORs for disease detection within a patient group and establishing CIs using a per-patient scale, a narrower interval estimate is obtained with inherently higher statistical reliability than when using aggregated scales (per 100 or 1000 patients).

This leads to selecting an appropriate scale of measurement when comparing two distinct datasets. Using the example of ORs among adolescents aged 15–17 years, the authors demonstrated differences in the reliability of results depending on the different scale. Three different groupings were applied to compare the OR and CI values: per 1, per 100, and per 1000 patients (Table 3).

 

Table 3. Trends in the odds ratios for overall musculoskeletal morbidity among children aged 15–17 years in Saint Petersburg and the Russian Federation, with corresponding 95% confidence intervals (CIs), across three different measurement scales: per 1 patient, per 100 patients, and per 1000 patients

Measurement scale	Years	Odds ratios with 95% confidence intervals for various nosological forms over time (lower CI limit; upper CI limit)
		Reactive arthropathies	Juvenile arthritis	Rheumatoid arthritis	Arthropathies	Deforming dorsopathies	Spondylopathies	Systemic connective tissue disorders	Other conditions
Per 1 patient	2017	0.943 [0.805; 1.105]	2.655* [2.396; 2.942]	0.876 [0.562; 1.365]	2.979* [2.925; 3.034]	3.369* [3.317; 3.422]	2.619* [2.234; 3.071]	1.520* [1.155; 2.001]	2.244* [2.199; 2.290]
	2018	1.469* [1.295; 1.667]	2.518* [2.277; 2.785]	1.446* [1.032; 2.028]	2.900* [2.849; 2.951]	3.036* [2.989; 3.084]	1.956* [1.642; 2.331]	1.349* [1.038; 1.753]	2.375* [2.329; 2.422]
	2019	1.516* [1.328; 1.729]	2.250* [2.032; 2.491]	1.636* [1.268; 2.111]	2.960* [2.911; 3.010]	3.097* [3.050; 3.144]	3.162* [2.761; 3.622]	2.264* [1.856; 2.760]	2.789* [2.738; 2.840]
	2020	1.390* [1.218; 1.587]	2.093* [1.884; 2.325]	1.613* [1.207; 2.154]	3.271* [3.217; 3.327]	3.001* [2.953; 3.049]	1.878* [1.584; 2.226]	2.173* [1.798; 2.626]	3.125* [3.068; 3.184]
	2021	1.192* [1.042; 1.362]	2.071* [1.871; 2.291]	1.799* [1.361; 2.377]	3.388 *[3.334; 3.442]	3.420* [3.370; 3.472]	2.313* [1.959; 2.732]	2.784* [2.334; 3.322]	3.536* [3.475; 3.598]
	2022	0.728 [0.609; 0.869]	2.520* [2.300; 2.760]	1.545* [1.158; 2.063]	3.952* [3.894; 4.011]	3.897* [3.842; 3.953]	1.704* [1.401; 2.072]	3.498* [2.944; 4.157]	3.460* [3.400; 3.520]
	2023	1.0 [0.863; 1.159]	2.2758 [2.075; 2.493]	1.479* [1.101; 1.987]	3.767* [3.715; 3.821]	3.553* [3.504; 3.603]	1.682* [1.371; 2.064]	2.282* [1.893; 2.751]	4.047* [3.982; 4.112]
Per 100 patients	2017	0.943 [0.193; 4.604]	2.655 [0.950; 7.418]	0.876 [0.010; 73.951]	2.979* [2.481; 3.577]	3.369* [2.884; 3.937]	2.619 [0.534; 12.858]	1.520 [0.097; 23.753]	2.244* [1.831; 2.751]
	2018	1.469 [0.415; 5.200]	2.518 [0.919; 6.903]	1.446 [0.049; 42.506]	2.900* [2.431; 3.458]	3.036* [2.594; 3.553]	1.956 [0.339; 11.296]	1.349 [0.098; 18.580]	2.375* [1.950; 2.893]
	2019	1.516 [0.405; 5.665]	2.250 [0.811; 6.241]	1.636 [0.128; 20.901]	2.960* [2.501; 3.503]	3.097* [2.657; 3.609]	3.162 [0.814; 12.279]	2.264 [0.311; 16.461]	2.789* [2.326; 3.344]
	2020	1.390 [0.371; 5.214]	2.093 [0.731; 5.991]	1.613 [0.089; 29.124]	3.271* [2.765; 3.870]	3.001* [2.557; 3.522]	1.878 [0.343; 10.292]	2.173 [0.328; 14.419]	3.125* [2.595; 3.764]
	2021	1.192 [0.312; 4.545]	2.071 [0.752; 5.699]	1.799 [0.111; 29.195]	3.388* [2.891; 3.970]	3.420* [2.946; 3.971]	2.313 [0.438; 12.216]	2.784 [0.476; 16.285]	3.536* [2.974; 4.205]
	2022	0.728 [0.123; 4.310]	2.520* [1.013; 6.268]	1.545 [0.086; 27.809]	3.952* [3.411; 4.579]	3.897* [3.381; 4.493]	1.704 [0.240; 12.079]	3.498 [0.624; 19.619]	3.460* [2.908; 4.115]
	2023	1.0 [0.228; 4.385]	2.275 [0.91; 5.684]	1.479 [0.077; 28.255]	3.767* [3.273; 4.337]	3.553* [3.091; 4.085]	1.682 [0.217; 13.038]	2.282 [0.352; 14.804]	4.047* [3.446; 4.753]
Per 1000 patients	2017	0.943 [0.006; 141.970]	2.655 [0.103; 68.415]	0.876 [0.000; 1083113.414]	2.979* [1.670; 5.313]	3.369* [2.060; 5.512]	2.619 [0.017; 401.139]	1.520 [0.000; 9063.564]	2.244* [1.179; 4.272]
	2018	1.469 [0.027; 79.967]	2.518 [0.104; 61.088]	1.446 [0.000; 63525.487]	2.900* [1.662; 5.060]	3.036* [1.847; 4.991]	1.956 [0.008; 500.595]	1.349 [0.000; 5395.600]	2.375* [1.273; 4.431]
	2019	1.516 [0.023; 98.027]	2.250 [0.089; 56.670]	1.636 [0.001; 5156.765]	2.960* [1.738; 5.040]	3.097* [1.908; 5.026]	3.162 [0.043; 230.747]	2.264 [0.004; 1201.004]	2.789* [1.570; 4.951]
	2020	1.390 [0.021; 90.860]	2.093 [0.075; 58.230]	1.613 [0.000; 15192.224]	3.271 *[1.922; 5.568]	3.001 *[1.808; 4.980]	1.878 [0.009; 407.323]	2.173 [0.005; 862.991]	3.125* [1.735; 5.628]
	2021	1.192 [0.017; 82.158]	2.071 [0.084; 50.877]	1.799 [0.000; 12092.225]	3.388* [2.052; 5.592]	3.420* [2.134; 5.482]	2.313 [0.012; 446.252]	2.784 [0.010; 741.918]	3.536* [2.045; 6.116]
	2022	0.728 [0.003; 201.831]	2.520 [0.141; 44.971]	1.545 [0.000; 14392.196]	3.952* [2.481; 6.295]	3.897* [2.487; 6.108]	1.704 [0.003; 834.201]	3.498 [0.015; 816.289]	3.460* [1.998; 5.989]

Note: *p ≤ 0.05 (CI does not include 1).

 

In the obtained results, the OR values remained unchanged, whereas the CIs for most parameters markedly increased. With small sample sizes and the use of larger aggregation scales (per 100 or 1000 patients), the significance of the differences tended to diminish. From a mathematical standpoint, researchers lose precision in their estimates; however, from a clinical perspective, the accuracy may increase owing to the reduction of sampling bias in the mean. In histogram analysis, the choice of measurement scale is analogous to the window width or bin size adjustment [14, 15]. Moreover, a relevant comparison may be drawn with the antithesis proposed by Liubishchev, namely, “accuracy vs precision,” wherein accuracy reflects conformity with reality and precision represents the degree of abstraction [16].

Therefore, the continuous increase in the prevalence of musculoskeletal diseases among children in SPb indicates an unfavorable forecast for public health indicators. The level and changes of ORs for the occurrence of these disorders are crucial for assessing the current disease burden and its trends. However, the issue of the significance of such assessments remains relevant and is addressed by decreasing the aggregation scale of the sample.

DISCUSSION

Data on the prevalence of diseases among children based on outpatient clinic visits have several limitations and are highly susceptible to external influences. Some studies have reported the influence of population healthcare-seeking behavior, varying medical infrastructure levels, and the quality of diagnostics and statistical record-keeping of diagnoses [17–19]. This view is difficult to dispute when reviewing summary reports by region that include healthcare institution data. Nevertheless, specialized primary health care remains the gold standard for patients with musculoskeletal diseases, and morbidity data in children are the basis for decision-making regarding patient routing within the region, staffing and material support of medical institutions, quality assessment of medical services, and development of preventive programs [5].

The findings of the present study revealed that musculoskeletal morbidity among children in SPb significantly exceeded the RF average, including during the pandemic. Notably, this trend is largely attributable to the specific characteristics of SPb. Being a federal-level city, it includes a large number of specialized medical institutions with advanced diagnostic, therapeutic, and rehabilitative equipments and highly qualified personnel. These advantages lead to high demand for medical care to children from neighboring and remote regions of Russia, resulting in artificially increased rates and a discrepancy with other regions [3, 20].

As noted in earlier studies, the human and diagnostic resources of SPb are critical for recording the primary morbidity of musculoskeletal condition in children (with the highest rates across the RF), whereas the city’s therapeutic and rehabilitative capacities allow for long-term follow-up of such patients [3, 20]. These features underpin the inter-regional patient referral system and domestic medical tourism [21, 22].

Additionally, it is important to consider the potential influence of climatic conditions on overall childhood morbidity. The humid and cold climate contributes to a high incidence of viral infections and inflammatory responses, and low solar insolation causes chronic vitamin D deficiency and disturbances in calcium–phosphorus metabolism among children [23–25].

The results of the present study confirm the relevance of inflammatory and degenerative diseases of the bones and joints in the region. Over the 7-year period, children in SPb showed increased odds of overall morbidity from reactive arthropathies, overall arthropathies, deforming dorsopathies, systemic connective tissue disorders, and other musculoskeletal diseases. In contrast, the odds of juvenile arthropathies and spondylopathies decreased, likely due to their more prolonged and chronic clinical course.

Moreover, age-specific patterns were observed. Children aged 0–14 years demonstrated higher odds of reactive arthritis, spondylopathies, and systemic connective tissue disorders. Higher odds of juvenile arthritis, rheumatoid arthritis, deforming dorsopathies, and other musculoskeletal diseases were noted among adolescents. The overall indicator for arthropathies was approximately equal in both age groups. It may be concluded that children aged 0–14 years more often experienced acutely manifesting nosological entities, whereas adolescents aged 15–17 years more commonly had conditions characterized by indolent or chronic progression [3, 26].

Notably, the study period included the COVID-19 pandemic (2020–2023), which resulted in widespread infections with immune suppression and multisystem involvement and introduced significant restrictions in the delivery of medical care [27–29]. In addition to prolonged periods of population isolation, a decrease in the volume of routine medical care and health screenings was found, which adversely affected the detection and further diagnosis of musculoskeletal disorders [30].

The findings of the present study revealed that during the pandemic period, there was either an increase or a slowing of the previously decreasing ORs for juvenile arthritis, overall arthropathies, deforming dorsopathies, and other disorders. In contrast, spondylopathies showed a steady decrease in odds in the RF regions.

To change the negative trend in the prevalence of musculoskeletal and connective tissue diseases in children in SPb and the other RF regions, the resource base in the field of traumatology and orthopedics should be expanded. Key measures include nationwide digitalization, standardized statistical reporting systems, education and training of young specialists and related healthcare professionals, and promotion of strategic scientific directions.

The primary reason for inter-regional patient routing and domestic medical tourism is the insufficient availability of material and human resources in many regions. The acute shortage of orthopedic trauma surgeons in regional healthcare systems leads to increased workload among specialists in related fields, primarily pediatric surgeons and pediatricians, resulting in low-quality statistical reporting and challenges in early diagnosis and timely treatment.

Furthermore, internal migration is driven by high competition among medical institutions for funding [8, 21, 22]. Under market economy conditions, medical marketing is an effective strategic tool, enabling large clinics to promote their services and individual doctors’ brands to attract target audiences. Due to this, the patients become tied to major diagnostic centers in metropolitan areas for initial diagnostic procedures and subsequent stages of treatment and rehabilitation.

The ongoing advancement of medical data digitalization (e.g., the Unified State Health Information System and Telemedicine) is expected to support the decentralization of pediatric care in this specialty. It may be beneficial for mitigating the limited availability and quality of healthcare services in resource-constrained regions, particularly in rural areas. Furthermore, the development of a dedicated pediatric Traumatology and Orthopedics subsystem within the Unified State Health Information System—with capabilities for tracking patient routing, providing consultations, facilitating diagnostics, and managing postoperative follow-up and rehabilitation—would improve the monitoring and quality of care in Traumatology and Orthopedics at the place of residence [31].

The organization of care and satisfaction of the population’s need for orthopedic trauma services across the Russian regions depends on the qualifications of healthcare professionals. Educational activities conducted by research institutes, such as training and advancing professional development of physicians in related specialties and specific fields, along with scientific conferences and off-site seminars involving leading experts in the treatment of particular nosological forms in in-person and remote formats can help expand the knowledge and competencies of attending physicians regarding advanced, resource-intensive diagnostic, therapeutic, and rehabilitative methods. Furthermore, developing effective strategies to motivate healthcare professionals to participate in educational activities will improve the quality of human resources.

CONCLUSION

The study indicates an unfavorable situation regarding the prevalence of musculoskeletal and connective tissue diseases among children in SPb compared with other regions. The negative trend highlights the need to improve current approaches for the prevention and treatment of these diseases in the region under study, expand the resource base, optimize the system of traumatology and orthopedic care, and enhance physician education across Russia. Modern digital technologies provide wide-ranging opportunities for addressing this issue.

ADDITIONAL INFORMATION

Author contributions: D.N. Kokushin: conceptualization, methodology; V.V. Sokolova, L.L. Sharafutdinova: data curation; V.V. Sokolova, V.V. Kirilenko: formal analysis, validation; L.L. Sharafutdinova, N.A. Guryeva: writing—review & editing; D.N. Kokushin, V.V. Sokolova, A.V. Zaletina, V.V. Kirilenko: writing—original draft. All authors approved the version of the manuscript to be published and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding sources: The authors declare no external funding was received for conducting the study or publishing the article.

Disclosure of interests: The authors have no relationships, activities, or interests for the last three years related to for-profit or not-for-profit third parties whose interests may be affected by the contents of the article.

Statement of originality: Some portions of the present manuscript incorporate previously published material authored by the same researcher ([Kokushin D.N. Specific Features of Morbidity from Musculo-skeletal and Connective Tissue Diseases Among Children Under 14 in the Context of the Coronavirus Infection. Scientific Review. Medical Sciences. 2023;(6):76–80. DOI: 10.17513/srms.1375]; [Kokushin D.N. Prevalence of Musculoskeletal Pathologies in Adolescents During the Coronavirus Pandemic. Modern Problems of Science and Education. 2023;(6):157. DOI: 10.17513/spno.33153]. The content is distributed under the terms of the CC BY 4.0 license).

Data availability statement: All data generated during this study are available in this article.

Generative AI: No generative artificial intelligence technologies were used to prepare this article.

Provenance and peer-review: This paper was submitted unsolicited and reviewed following the standard procedure. The peer review process involved an external reviewer and an in-house reviewer.

¹ Putin V.V. Presidential Address to the Federal Assembly. Presidential Address of the Russian Federation to the Federal Assembly, February 29, 2024. Available at: https://www.consultant.ru/document/cons_doc_LAW_471111/

² Prolonged and Active Life. National Projects of the Russian Federation. Available at: https://xn--80aapampemcchfmo7a3c9ehj.xn--p1ai/new-projects/prodolzhitelnaya-i-aktivnaya-zhizn/

³ Okladnikov S.M., Nikitina S.Yu., Alexandrova G.A., et al. Healthcare in Russia. 2023: Statistical Compendium. Rosstat. Moscow; 2023. 179 p.

⁴ Aleksandrov G.A., Golubev N.A., Tyurina E.M., et al. General Morbidity of the Child Population in Russia Aged 0–14 Years in 2018: Statistical Report. Part V. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2019. 144 p. Aleksandrov G.A., Golubev N.A., Tyurina E.M., et al. General Morbidity of the Child Population in Russia Aged 15–17 Years in 2018: Statistical Report. Part X. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2019. 148 p. Kotova E.G., Kobyakova O.S., Starodubov V.I., et al. General Morbidity of the Child Population in Russia Aged 0–14 Years in 2020: Statistical Materials. Part VI. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2021. 147 p. Kotova E.G., Kobyakova O.S., Starodubov V.I., et al. General Morbidity of the Child Population in Russia Aged 15–17 Years in 2020: Statistical Materials. Part X. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2021. 147 p. Deev I.A., Kobyakova O.S., Starodubov V.I., et al. General Morbidity of the Child Population in Russia Aged 0–14 Years in 2023: Statistical Materials. Part VI. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2024. 156 p. Deev I.A., Kobyakova O.S., Starodubov V.I., et al. General Morbidity of the Child Population in Russia Aged 15–17 Years in 2023: Statistical Materials. Part X. Moscow: Federal Research Institute for Health Organization and Informatics of the Ministry of Health of the Russian Federation; 2024. 158 p.

About the authors

Dmitriy N. Kokushin

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: partgerm@yandex.ru
ORCID iD: 0000-0002-2510-7213
SPIN-code: 9071-4853

MD, PhD, Cand. Sci. (Medicine)

Russian Federation, Saint Petersburg

Vera V. Sokolova

Saint Petersburg State Pediatric Medical University

Email: vera-Sokol@inbox.ru
ORCID iD: 0000-0001-7034-9281
SPIN-code: 9708-3639

MD, PhD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Saint Petersburg

Anna V. Zaletina

H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery

Email: omoturner@mail.ru
ORCID iD: 0000-0002-9838-2777
SPIN-code: 4955-1830

MD, PhD, Cand. Sci. (Medicine)

Russian Federation, Saint Petersburg

Vadim V. Kirilenko

Saint Petersburg State Pediatric Medical University

Email: vadimvlkir@bk.ru
ORCID iD: 0000-0001-7642-4561
SPIN-code: 4718-9184

PhD, Cand. Sci. (Economics), Assistant Professor

Russian Federation, Saint Petersburg

Lyubov L. Sharafutdinova

Saint Petersburg State Pediatric Medical University

Email: socp_ozz@mail.ru
ORCID iD: 0000-0002-3478-6043
SPIN-code: 2230-8341

MD, PhD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Saint Petersburg

Natalya A. Guryevа

Saint Petersburg State Pediatric Medical University

Author for correspondence.
Email: socp_ozz@mail.ru
ORCID iD: 0000-0001-8827-3537
SPIN-code: 8111-3775

МD, PhD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Saint Petersburg

References

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