Life expectancy at birth for the rf population: prediction based on modeling influence exerted by a set of socio-hygienic determinants on age-specific mortality rates exemplified by diseases of the circulatory system
М.V. Glukhikh, S.V. Kleyn, D.А. Kiryanov, М.R. Kamaltdinov
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, 82 Monastyrskaya Str., Perm, 614045, Russian Federation
The article dwells on cause-effect relations between certain socio-hygienic factors and age-specific mortality rates due to cardiovascular diseases. New research trends in hygiene, a multidisciplinary approach to studies in the field and the current state policy make the present work topical.
Our methodical approach to predicting probable age-specific mortality rates due to cardiovascular diseases relied on applying artificial neural networks. We analyzed a set of indicators that described the public healthcare system, sanitary-epidemiological welfare on a given territory, lifestyle, economic conditions, sociodemographic conditions, and primary incidence.
Overall, we obtained 18 models (as per 5-year age-specific periods) of a relationship between socio-hygienic determinants and mortality rates due to cardiovascular diseases. The determination coefficients fell within 0.01–0.75 range and the greatest explanatory power occurred when the age period “30 years and older” was analyzed. We detected comparability of variational series obtained for mortality due to cardiovascular diseases among the whole population and the determination coefficients of the created models. We established predictive estimates of life expectancy at birth (LEB) in case there were changes in the analyzed socio-hygienic determinants by 2024 set within a certain scenario. Thus, changes in the whole set of determinants would result in 514 days added to LEB; lifestyle-related indicators, 205 days; indicators describing sanitary-epidemiological welfare, 126 days; economic indicators, 102 days; sociodemographic indicators, 101 days; primary incidence rates, 40 days; indicators describing the public healthcare system, 19 days. Several determinants were shown to be the most significant for reducing mortality due to cardiovascular diseases among working age population and older age groups. They are indicators describing people’s physical and motor activity, income levels, consumption of vegetables, education, and working conditions. Our research results are consistent with those obtained by other studies with their focus on establishing cause-effect relations between environmental factors and public health.
- Zaytseva N.V., Popova A.Yu., Onishchenko G.G., May I.V. Current problems of regulatory and scientific-medical support for the assurance of the sanitary and epidemiological welfare of population in the Russian Federation as the strategic government task. Gigiena i sanitariya, 2016, vol. 95, no. 1, pp. 5–9. DOI: 10.18821/0016-9900-2016-95-1-5-9 (in Russian).
- Zaytseva N.V., Ustinova O.U., Zemlyanova M.A. A strategic approaches to improving prevention of diseases associ-ated with influence of environmental factors. Zdorov'e naseleniya i sreda obitaniya – ZNiSO, 2013, no. 11 (248), pp. 14–18 (in Russian).
- Advancing Science As A Global Public Good. Action Plan 2019–2021. Paris, International Science Council, 2019, 60 p. DOI: 10.24948/2019.09
- GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2018, vol. 392, no. 10159, pp. 1859–1922. DOI: 10.1016/S0140-6736(18)32335-3
- Vrablik M., Dlouha D., Todorovova V., Stefler D., Hubacek J.A. Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management? Int. J. Mol. Sci., 2021, vol. 22, no. 8, pp. 4182. DOI: 10.3390/ijms22084182
- Andreev E. Is life expectancy at birth really the best measure of mortality in a population? Demograficheskoe obozrenie, 2021, vol. 8, no. 2, pp. 6–26. DOI: 10.17323/demreview.v8i2.12780 (in Russian).
- Aydin A., Atila Ü., Aydın S.G. Use of ANN in Predicting Life Expectancy: The Case of Turkey. Artificial Intelligence Studies, 2018, vol. 1, no. 1, pp. 1–7. DOI: 10.30855/AIS.2018.01.01.01
- Alam M.F., Briggs A. Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model. Expert review of pharmacoeconomics & outcomes research, 2020, vol. 20, no. 6, pp. 629–640. DOI: 10.1080/14737167.2019.1665512
- Yasnitsky L.N., Zaitseva N.V., Gusev A.L., Shur P.Z. Neural network region model for control action choice in the field of hygiene safety. Informatika i sistemy upravleniya, 2011, no. 3 (29), pp. 51–59 (in Russian).
- Powell-Wiley T.M., Baumer Y., Baah F.O., Baez A.S., Farmer N., Mahlobo C.T., Pita M.A., Potharaju K.A. [et al.]. Social Determinants of Cardiovascular Disease. Circ. Res., 2022, vol. 130, no. 5, pp. 782–799. DOI: 10.1161/CIRCRESAHA.121.319811
- Rangachari P., Govindarajan A., Mehta R., Seehusen D., Rethemeyer R.K. The relationship between Social Determi-nants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009–2018). BMC Public Health, 2022, vol. 22, no. 1, pp. 236. DOI: 10.1186/s12889-022-12653-8
- Zhang Y., Chen C., Pan X., Guo J., Li Y., Franco O.H., Liu G., Pan A. Associations of healthy lifestyle and socioeco-nomic status with mortality and incident cardiovascular disease: two prospective cohort studies. BMJ, 2021, vol. 373, pp. n604. DOI: 10.1136/bmj.n604
- GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020, vol. 396, no. 10258, pp. 1223–1249. DOI: 10.1016/S0140-6736(20)30752-2
- Zaitseva N.V., Kleyn S.V., Glukhikh М.V., Kiryanov D.А., Kamaltdinov М.R. Predicting growth potential in life ex-pectancy at birth of the population in the Russian Federation based on scenario changes in socio-hygienic determinants using an artificial neural network. Health Risk Analysis, 2022, no. 2, pp. 4–16. DOI: 10.21668/health.risk/2022.2.01.eng
- Vinyarskaya I.V., Terletskaya R.N., Basargina E.N., Soboleva K.A., Chernikov V.V. The morbidity rate of diseases of the circulatory system in children in the Russian Federation. Rossiiskii pediatricheskii zhurnal, 2015, vol. 18, no. 5, pp. 60–65 (in Russian).
- Bogatchevskaia S.A., Kapitonenko N.A., Bogatchevskiy A.N. The epidemiological features of congenital heart diseases in the Russian Federation and the Far Eastern Federal District for the last 10 years. Dal'nevostochnyi meditsinskii zhurnal, 2016, no. 1, pp. 96–101 (in Russian).
- Barr D.A. The Childhood Roots of Cardiovascular Disease Disparities. Mayo Clin. Proc., 2017, vol. 92, no. 9, pp. 1415–1421. DOI: 10.1016/j.mayocp.2017.06.013
- Ajala O., Mold F., Boughton C., Cooke D., Whyte M. Childhood predictors of cardiovascular disease in adulthood. A systematic review and meta-analysis. Obes. Rev., 2017, vol. 18, no. 9, pp. 1061–1070. DOI: 10.1111/obr.12561
- Hood C.M., Gennuso K.P., Swain G.R., Catlin B.B. County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. Am. J. Prev. Med., 2016, vol. 50, no. 2, pp. 129–135. DOI: 10.1016/j.amepre.2015.08.024
- Paluch A.E., Gabriel K.P., Fulton J.E., Lewis C.E., Schreiner P.J., Sternfeld B., Sidney S., Siddique J. [et al.]. Steps per Day and All-Cause Mortality in Middle-aged Adults in the Coronary Artery Risk Development in Young Adults Study. JAMA Netw. Open, 2021, vol. 4, no. 9, pp. e2124516. DOI: 10.1001/jamanetworkopen.2021.24516
- Chudasama Y.V., Khunti K., Gillies C.L., Dhalwani N.N., Davies M.J., Yates T., Zaccardi F. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study. PLoS Med., 2020, vol. 17, no. 9, pp. e1003332. DOI: 10.1371/journal.pmed.1003332
- Zhang X., Lu J., Wu C., Cui J., Wu Y., Hu A., Li J., Li X. Healthy lifestyle behaviours and all-cause and cardiovascular mortality among 0.9 million Chinese adults. Int. J. Behav. Nutr. Phys. Act., 2021, vol. 18, no. 1, pp. 162. DOI: 10.1186/s12966-021-01234-4
- Siegel A., Schug J.F., Rieger M.A. Social Determinants of Remaining Life Expectancy at Age 60: A District-Level Analysis in Germany. Int. J. Environ. Res. Public Health, 2022, vol. 19, no. 3, pp. 1530. DOI: 10.3390/ijerph19031530
- Lampert T., Hoebel J., Kroll L.E. Social differences in mortality and life expectancy in Germany. Current situation and trends. Journal of health monitoring, 2019, vol. 4, no. 1, pp. 3–14. DOI: 10.25646/5872
- Pickett K.E., Wilkinson R.G. Income inequality and health: a causal review. Soc. Sci. Med., 2015, vol. 128, pp. 316–326. DOI: 10.1016/j.socscimed.2014.12.031
- Moreno X., Lera L., Moreno F., Albala C. Socioeconomic inequalities in life expectancy and disability-free life ex-pectancy among Chilean older adults: evidence from a longitudinal study. BMC Geriatr., 2021, vol. 21, no. 1, pp. 176. DOI: 10.1186/s12877-021-02126-9
- Wagg E., Blyth F.M., Cumming R.G., Khalatbari-Soltani S. Socioeconomic position and healthy ageing: A systematic review of cross-sectional and longitudinal studies. Ageing Res. Rev., 2021, vol. 69, pp. 101365. DOI: 10.1016/j.arr.2021.101365
- Mannoh I., Hussien M., Commodore-Mensah Y., Michos E.D. Impact of social determinants of health on cardiovascular disease prevention. Curr. Opin. Cardiol., 2021, vol. 36, no. 5, pp. 572–579. DOI: 10.1097/HCO.0000000000000893