Population models for the general and primary incidence of borderline mental disorders in the Russian Federation in 1992—2022

Mitikhin V.G.; Solokhina T.A.

doi:https://doi.org/10.17116/jnevro2025125021113

Mitikhin V.G.

Mental Health Research Centre

ORCID: 0000-0002-9502-5395

Solokhina T.A.

Mental Health Research Centre

ORCID: 0000-0003-3235-2476

Population models for the general and primary incidence of borderline mental disorders in the Russian Federation in 1992—2022

Authors:

Mitikhin V.G., Solokhina T.A.

More about the authors

Journal: S.S. Korsakov Journal of Neurology and Psychiatry. 2025;125(2): 113‑121

DOI: 10.17116/jnevro2025125021113

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To cite this article:

Mitikhin VG, Solokhina TA. Population models for the general and primary incidence of borderline mental disorders in the Russian Federation in 1992—2022. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;125(2):113‑121. (In Russ.)
https://doi.org/10.17116/jnevro2025125021113

Подписка 2026 Журнал неврологии и психиатрии им. С.С. Корсакова (S.S. Korsakov Journal of Neurology and Psychiatry)

Использование инфографики авторами научных работ

OBJECTIVE

To develop mathematical models at the population level to assess the relationship of incidence and prevalence of borderline mental disorders (BMD) of the Russian population with demographic, socio-economic factors and the human resources of mental health care in 1992—2022.

MATERIAL AND METHODS

We used official demographic and socio-economic data at the population level, as well as analytical data from specialized medical and research institutions. Methods of systemic data analysis and statistical analysis were used to develop linear models of the relationship between the factors and BMD incidence rates. The least squares method was used to build a nonlinear logistic model of the BMD prevalence in the population using the MS Excel Solver add-in.

RESULTS

Socio-economic and demographic factors that significantly affect the BMD prevalence and incidence in the period under review have been identified. Regression linear models with a high level of significance for the BMD incidence and prevalence in the Russian population in the period 1992—2022 were developed.

A logistic model has been developed to predict the prevalence of BMD with changes in the human resources of mental health care and the population size. The logistic model was used to estimate the marginal level of BMD prevalence for the Russian population. Examples of the use of the obtained models to predict incidence rates are given.

CONCLUSION

Operational (using linear models) and long-term (based on the logistic model) estimates of BMD incidence and prevalence in the Russian population make it possible to consider the influence of demographic and socio-economic factors on the population’s life and the human resources of the mental care system.

Keywords:

mental health

borderline mental disorders

population

prevalence

incidence

mathematical model

prediction

Authors:

Mitikhin V.G.

Mental Health Research Centre

ORCID: 0000-0002-9502-5395

Solokhina T.A.

Mental Health Research Centre

ORCID: 0000-0003-3235-2476

Received:

21.08.2024

Accepted:

16.10.2024

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References:

Aleksandrovsky Ju.A. Pogranichnaja psihiatrija i sovremennye social’nye problemy. Rostov-na-Donu: Feniks. 1996. (In Russ.).
Aleksandrovsky Ju.A. Pogranichnye psihicheskie rasstrojstva. M.: Medicina. 2000. (In Russ.).
Gurovich IYa, Golland VB, Son IM, et al. Psychiatric institutions In Russia: performance indicators (1999—2006). M.: Medpraktika-M. 2007. (In Russ.).
Gurovich IYa, Shmukler AB, Golland VB, Zaitchenko NM. Psychiatric service In Russia in 2006-2011. M.: Medpraktika-M. 2012. (In Russ.).
Demcheva NK. Tendencies of prevalence of boundary mental frustration in the Russian Federation in 1999—2010. Vestnik nevrologii, psikhiatrii i neirokhirurgii. 2012;9:22-29. (In Russ.).
Kekelidze ZI, Bukreeva ND, Demcheva NK, Makushkin EV. Epidemiological indicators of mental disorders in the Russian Federation in 2009–2018. Measures to improve mental health care. Psikhicheskoe zdorovie. 2019;(10):3-10 (In Russ.).
Socially significant diseases of the Russian population in 2022: Statistical materials. Kotova E.G., Kobyakova O.S., Starodubov V.I. M.: FGBU «CNIIOIZ» Minzdrava Rossii. 2023. (In Russ.). https://doi.org/10.21045/978-5-94116-121-8-2023
WHO. World Mental Health Surveys, 2008. Global Perspectives on the Epidemiology of Mental Disorders. Edited by: Kessler RC, Bedirhan Ustun T. World Health Organization, Geneva.
Pogranichnaja psihicheskaja patologija v obshhemedicinskoj praktike: red. A.B. Smulevich. M.: Russkij vrach. 2000. (In Russ.).
Kirkbride JB, Anglin DM, Colman I, et al. The social determinants of mental health and disorder: evidence, prevention and recommendations. World Psychiatry. 2024;23:58-90. https://doi.org/10.1002/wps.21160
Kuzminova MV, Solokhina TA. The Role of Social Factors in the Genesis of Mental Disorders: Development of Research in the 21st Century. Psikhiatriya. 2023;21(7):31-45. (In Russ.). https://doi.org/10.30629/2618-6667-2023-21-7-31-45
Healthcare In Russia. M: Rosstat. 2023. (In Russ.).
Resources and activities of medical healthcare organizations. Part 1. Medical personnel: Statistical materials. E.G. Kotova, O.S. Kobyakova, V.I. Starodubov, et al. M.: FGBU «CNIIOIZ» Minzdrava Rossii. 2023. (In Russ.). https://doi.org/10.21045/978-5-94116-124-9-2023
Epidemiological indicators and performance indicators of psychiatric services in the Russian Federation (2005-2013). Stat. spravochnik. Pod red. Kekelidze Z.I. i Kazakovtseva B.A. M. FGBU «NMICPN im. V.P. Serbskogo» Minzdrava Rossii. 2015. (In Russ.).
Yastrebov VS, Mitikhina IA, Mitikhin VG. K metodologii postroenija mediko-demograficheskih modelej dlja dinamicheskoj ocenki psihicheskogo zdorov’ja naselenija. Psikhiatriya. 2008;2(32):38-47. (In Russ.).
Mitikhin V, Yastrebov V, Solokhina T. Models of the influence of socio-economic factors on the indicators of mental health among the population of the Russian Federation. Journal of Culture and Mental Health. 2018;11(1):10-16. https://doi.org/10.1080/17542863.2017.1394655
Mitikhin VG, Yastrebov VS, Mitikhina IA. Population Models of the Mental Health of the Population of Russia: Assessment of the Influence of Living Conditions and Psychiatric Care Resources. Neuroscience and Behavioral Physiology. 2019;49(2):233-239. https://doi.org/10.1007/s11055-019-00720-4
Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peer J. Computer Science. 2021;7:e623. https://doi.org/10.7717/peerj-cs.623
Kiselev AS, Zharikov NM, Ivanova AE, et al. Mental health of the population. Vladivostok: Izd-vo Dal’nevost. Univ.-ta. 1993. (In Russ.).
Kapitsa SP. Demographic revolution In Russia. Vek globalizatsii, 2008;1:128-143. (In Russ.).
Kapitsa SP. Paradoxes of growth. The laws of global human development. M.: Al’pina nonfikshn. 2012. (In Russ.).
Dudina OM, Arselgova MA. Social and economic consequences of unemployment. Ekonomika truda. 2019;6(2):923-940. (In Russ.). https://doi.org/10.18334/et.6.2.40810
Mental health Atlas 2020. World Health Organization, Geneva. 2021.
Riznichenko GYu. Lectures on mathematical models in biology. Moscow-Izhevsk: RCD. 2011. (In Russ.).
Rotshtejn VG, Yastrebov VS, Bogdan MN, et al. Modern psychiatric care system: epidemiological aspect. S.S. Korsakov Journal of Neurology and Psychiatry. 2001;101(3):42-47. (In Russ.).
Rotshtejn VG, Bogdan MN, Suetin ME. The theoretical aspect of the epidemiology of anxiety and affective disorders. Psihiatriya i psihofarmakoterapiya. 2005;7(2):94-95. (In Russ.).
Wittchen HU, Jacobi F, Rehm J, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. European Neuropsychopharmacology. 2011;21:655-679. https://doi.org/10.1016/j.euroneuro.2011.07.018