Predictive Models for Hypertension Incidence in the Population of Western Siberia Under Climate Change Conditions

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Abstract

BACKGROUND: The development of arterial hypertension is a highly relevant issue, especially in high-latitude regions, due to its significant impact on the working population. It often leads to prolonged temporary incapacity to work, increasing the risks of disability and mortality. Climate change, primarily associated with increased temperature variability, has a negative impact on the cardiovascular system.

AIM: The work aimed to develop predictive models for hypertension incidence in Western Siberia (Yamalo-Nenets Autonomous Okrug, YNAO and Tyumen Oblast) under climate change conditions.

METHODS: Monitoring of primary incidence rates of hypertension per 1000 population in YNAO and the Tyumen Oblast for the period 2010–2020 was conducted. The data were obtained from the annual reports on primary morbidity in the working-age adult population from the official website of the Ministry of Health of the Russian Federation, and from the average annual air temperature provided by the Federal Service for Hydrometeorology and Environmental Monitoring. The Dickey–Fuller test was used for time series analysis. Forecasting was performed using the Box–Jenkins method (ARIMA). The forecast was calculated using the Time Series/Forecasting submodule based on the autoregressive integrated moving average (ARIMA) model.

RESULTS: The predictive models confirmed a growing trend the primary of hypertension in the Arctic zone of Western Siberia over the next five years, taking into account climate change.

CONCLUSION: To prevent the increase in hypertension at the regional level, a comprehensive set of preventive measures should be developed to mitigate the impact of climate change and support the sustainable formation of adaptive mechanisms for preserving public health.

About the authors

Sergey V. Andronov

Tomsk State University; Federal Research Center of Nutrition, Biotechnology and Food Safety

Email: sergius198010@mail.ru
ORCID iD: 0000-0002-5616-5897
SPIN-code: 6926-4831

MD, Cand. Sci. (Medicine)

Russian Federation, Tomsk; Moscow

Elena N. Bogdanova

Tomsk State University; Northern (Arctic) Federal University named after M.V. Lomonosov

Author for correspondence.
Email: bogdanova.en@yandex.ru
ORCID iD: 0000-0001-9610-4709
SPIN-code: 8898-1379

MD, Cand. Sci. (Economics), Associate Professor

Russian Federation, Tomsk; Arkhangelsk

Olga M. Shaduyko

Tomsk State University

Email: dolcezzamia@mail.ru
ORCID iD: 0000-0002-2031-4248
SPIN-code: 8042-6815

Dr. Sci. (History)

Russian Federation, Tomsk

Andrey A. Lobanov

Tomsk State University

Email: alobanov89@gmail.com
ORCID iD: 0000-0002-6615-733X
SPIN-code: 5793-4055

MD, Dr. Sci. (Medicine)

Russian Federation, Tomsk

References

  1. Report on the peculiarities of the climate in the territory of the Russian Federation for 2019. Moscow: Rosgidromet; 2020. 97 р. (In Russ.) ISBN 978-5-906099-58-7
  2. Report on the peculiarities of the climate in the territory of the Russian Federation for 2022. Moscow: Rosgidromet; 2023. 104 р. (In Russ.) ISBN 978-5-906099-58-7
  3. Romanello M, Di Napoli C, Drummond P, et al. The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. Lancet. 2022;400(10364):1619–1654. doi: 10.1016/S0140-6736(22)01540-9
  4. Khraishah H, Alahmad B, Ostergard RL Jr, et al. Climate change and cardiovascular disease: implications for global health. Nat Rev Cardiol. 2022;19(12):798–812. doi: 10.1038/s41569-022-00720-x
  5. Bennett MT, Gagnon D, Reeves F. Not for the faint of heart: environmental influences on cardiovascular health. Can J Cardiol. 2023;39(9):1163–1165. doi: 10.1016/j.cjca.2023.07.026
  6. Park S, Kario K, Chia YC, et al. The influence of the ambient temperature on blood pressure and how it will affect the epidemiology of hypertension in Asia. J Clin Hypertens (Greenwich). 2020;22(3):438–444. doi: 10.1111/jch.13762
  7. Kunes J, Tremblay J, Bellavance F, Hamet P. Influence of environmental temperature on the blood pressure of hypertensive patients in Montréal. Am J Hyperten. 1991;4(5 Pt 1):422–426. doi: 10.1093/ajh/4.5.422
  8. Reeves F, Potter BJ. Toward a cardio-environmental risk model: environmental determinants of cardiovascular disease. Can J Cardiol. 2023:39(9):1166–1181. doi: 10.1016/j.cjca.2023.06.419
  9. Rios FJ, Montezano AC, Camargo LL, Touyz RM. Impact of environmental factors on hypertension and associated cardiovascular disease. Can J Cardiol. 2023:39(9):1229–1243. doi: 10.1016/j.cjca.2023.07.002
  10. Evdakov VA, Starodubov VI, Oleynik BA, et al. Correlation between regular medical check-up indicators and mortality from coronary heart disease in Kursk, Kurgan regions and in the Russian Federation. Russian Journal of Preventive Medicine and Public Health. 2023;26(8):22–30. doi: 10.17116/profmed20232608122 EDN: UYGOVI
  11. Shaposhnikov DA, Revich BA. On some approaches to calculation of health risks caused by temperature waves. Health Risk Analysis. 2018;(1):22–31. doi: 10.21668/health.risk/2018.1.03 EDN: YUOPGR
  12. Shaposhnikov DA, Revich BA, Meleshko VP, et al. Experience of predicting of expected excess mortality due to climate change: a case study in Arkhangelsk. Ekologiya cheloveka (Human Ecology). 2013;20(8):17–23. EDN: RAHIWP
  13. Shaposhnikov DA, Revich BA. Heat waves and their imdact on mortality risk in Arctic and Subarctic cities. Scientific Proceedings: Institute of Economic Forecasting RAS. 2019;17:269–283. doi: 10.29003/m821.sp_ief_ras2019/269-283 EDN: ZWJQZP
  14. Revich BA. Heat-wave, air quality and mortality in European Russia in summer 2010: preliminary assessment. Ekologiya cheloveka (Human Ecology). 2011;18(7):3–9. EDN: NXVVRJ
  15. Revich BA, Shaposhnikov DA. Influence features of cold and heat waves to the population mortality — the city with sharply continental climate. Siberian Medical Review. 2017;(2):84–90. doi: 10.20333/2500136-2017-2-84-90 EDN: YUBVVH
  16. Rastokina TN, Kudryavtsev AV, Unguryanu TN. Association between atmospheric air temperature and blood pressure among adult population in different seasons. Ekologiya cheloveka (Human Ecology). 2023;30(6):417–427. doi: 10.17816/humeco456483 EDN: GLERDV
  17. Watanabe T, Matsuyama H, Kuzhevskaia I, et al. Long-term trends of extreme climate indexes in the southern part of siberia in comparison with those of surrounding regions. Atmosphere. 2023;14(7):1131. doi: 10.3390/atmos14071131
  18. Dubrovskaya LI. Forecasting of time series in the Statistica package: methodological guidelines. Tomsk: Tomskij gosudarstvennyj universitet; 2012. 36 p. (In Russ.) URL: https://core.ac.uk/download/pdf/287482535.pdf
  19. Box J, Jenkins G. Time series analysis, forecasting and management. Issue 1. Мoscow: Mir; 1974. 406 р. (In Russ.)
  20. Afanasyev VN, Voronov EV. Statistical analysis of budget assignments for socio-economic purposes dependence on the general tariff rates established on account of the distributed lag. Izvestia Orenburg State Agrarian University. 2005;(4):117–119. EDN: MUJCWT
  21. Ratner SV, Ratner MD. Evaluation of efficiency of regional ecologic management systems. Izvestia Volgograd State Technical University. 2017;(7):8–16. EDN: ZGQRJV
  22. Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association. 1979;74(366):427–431. doi: 10.1080/01621459.1979.10482531
  23. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974;19(6):716–723. doi: 10.1109/TAC.1974.1100705
  24. Rahman M, Garcia E, Lim CC, et al. Temperature variability associations with cardiovascular and respiratory emergency department visits in Dhaka, Bangladesh. Environ Int. 2022;164:107267. doi: 10.1016/j.envint.2022.107267
  25. Rusticucci, M, Bettolli L.M, Harris MLA. Association between weather conditions and the number of patients at the emergency room in an Argentine hospital. Int J Biometeorol. 2002;46(1):42–51. doi: 10.1007/s00484-001-0113-z
  26. Lecha Estela LB. Biometeorological classification of daily weather types for the humid tropics. Int J Biometeorol. 1998;42(2):77–83. doi: 10.1007/s004840050088
  27. Plavcová E, Kysel J. Effects of sudden air temperature and pressure changes on mortality in the Czech Republic. Epidemiol Mikrobiol Imunol. 2009;58(2):73–83.
  28. Tollefsen NH, Dickstein K. Are emergency admissions to medical departments dependent on weather? Tidsskr Nor Laegeforen. 2000;120(30):3678–3679.
  29. Sohail H, Kollanus V, Tiittanen P, et al. Heat, heatwaves and cardiorespiratory hospital admissions in Helsinki, Finland. Int J Environ Res Public Health. 2020;17(21):7892. doi: 10.3390/ijerph17217892
  30. Gasparrini A, Guo Y, Sera F, et al. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet Health. 2017;1(9):e360–e367. doi: 10.1016/S2542-5196(17)30156-0
  31. Liu C, Yavar Z, Sun Q. Cardiovascular response to thermoregulatory challenges. Am J Physiol Heart Circ Physiol. 2015;309(11):H1793–H1812. doi: 10.1152/ajpheart.00199.2015
  32. Alahmad B, Khraishah H, Shakarchi AF, et al. Cardiovascular mortality and exposure to heat in an inherently hot region: implications for climate change. Circulation. 2020;141(15):1271–1273. doi: 10.1161/CIRCULATIONAHA.119.044860
  33. Lin S, Luo M, Walker RJ, et al. Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology. 2009;20(5):738–746. doi: 10.1097/EDE.0b013e3181ad5522
  34. Zhang M, Zhang Ya, Zhang J, Lin Sh. Time series analysis of the impact of meteorological conditions and air quality on the number of medical visits for hypertension in Haikou City, China. Atmosphere. 2024:15(3):370. doi: 10.3390/atmos15030370
  35. Drapkina OM, Zyryanov SK, Shepel RN, et al. Meteoropathy: myth or reality? assessment of the relationship between cardiovascular diseases and weather conditions according to data from a Moscow hospital. Cardiovascular Therapy and Prevention. 2024;23(5):45–54. doi: 10.15829/1728-8800-2024-4002 EDN: DVOFQR
  36. Skutecki R, Jalali R, Dragańska E, et al. UTCI as a bio-meteorological tool in the assessment of cold-induced stress as a risk factor for hypertension. Sci Total Environ. 2019:688:970–975. doi: 10.1016/j.scitotenv.2019.06.28
  37. Barnett AG, Sans S, SalomaaV, et al. The effect of temperature on systolic blood pressure. Blood Press Monit. 2007;12(3):195–203. doi: 10.1097/MBP.0b013e3280b083f4
  38. Turner LR, Barnett AG, Connell D, Tong S. Ambient temperature and cardiorespiratory morbidity: a systematic review and meta-analysis. Epidemiology. 2012;23(4):594–606. doi: 10.1097/EDE.0b013e3182572795
  39. Gronlund CJ, Zanobetti A, Schwartz JD, et al. Heat, heat waves, and hospital admissions among the elderly in the United States, 1992–2006. Environ Health Perspect. 2014;122(11):1187–1192. doi: 10.1289/ehp.1206132
  40. Martínez-Solanas È, Basagaña X. Temporal changes in the effects of ambient temperatures on hospital admissions in Spain. PLoS ONE. 2019;14(6):e0218262. doi: 10.1371/journal.pone.0218262
  41. Rocklov J, Forsberg B. The effect of temperature on mortality in Stockholm 1998–2003: a study of lag structures and heatwave effects. Scand J Public Health. 2008;36(5):516–523. doi: 10.1177/1403494807088458
  42. Baccini M, Biggeri A, Accetta G, et al. Heat effects on mortality in 15 European cities. Epidemiology. 2008;19(5):711–719. doi: 10.1097/EDE.0b013e318176bfcd
  43. Hong YC, Kim H, Oh SY, et al. Association of cold ambient temperature and cardiovascular markers. Sci Total Environ. 2012;435–436:74–79. doi: 10.1016/j.scitotenv.2012.02.070
  44. Lavigne E, Gasparrini A, Wang X, et al. Extreme ambient temperatures and cardiorespiratory emergency room visits: assessing risk by comorbid health conditions in a time series study. Environmental Health. 2014;13(1):5. doi: 10.1186/1476-069X-13-5
  45. Ha S, Talbott EO, Kan H, et al. The effects of heat stress and its effect modifiers on stroke hospitalizations in Allegheny County, Pennsylvania. Int Arch Occup Environ Health. 2014;87(5):557–565. doi: 10.1007/s00420-013-0897-2
  46. Isaksen TB, Yost MG, Hom EK, et al. Increased hospital admissions associated with extreme-heat exposure in King County, Washington, 1990–2010. Rev Environ Health. 2015;30(1):51–64. doi: 10.1515/reveh-2014-0050
  47. Kenney WL, Craighead DH, Alexander LM. Heat waves, aging, and human cardiovascular health. Med Sci Sports Exerc. 2014;46(10):1891–1899. doi: 10.1249/MSS.0000000000000325
  48. Revich B, Shaposhnikov D. Excess mortality during heat waves and cold spells in Moscow, Russia. Occup Environ Med. 2008:65(10):691–696. doi: 10.1136/oem.2007.033944
  49. Bayentin L, El Adlouni S, Ouarda TB, et al. Spatial variability of climate effects on ischemic heart disease hospitalization rates for the period 1989–2006 in Quebec, Canada. Int J Health Geogr. 2010;9:5. doi: 10.1186/1476-072X-9-5
  50. Ye X, Wolff R, Yu W, et al. Ambient temperature and morbidity: A review of epidemiological evidence. Environ Health Perspect. 2012;120(1):19–28. doi: 10.1289/ehp.1003198
  51. Rowland ST, Boehme AK, Rush J, et al. Can ultra short-term changes in ambient temperature trigger myocardial infarction? Environ Int. 2020;143:105910. doi: 10.1016/j.envint.2020.105910
  52. Bogdanova E, Andronov S, Lobanov A, et al. The impact of climate change on the food (in)security of the siberian indigenous peoples in the Arctic: environmental and health risks. Sustainability. 2021;13(5):1–23. doi: 10.3390/su13052561
  53. Hajat S, Kosatky T. Heat-related mortality: a review and exploration of heterogeneity. J Epidemiol Community Health. 2010;64(9):753–760. doi: 10.1136/jech.2009.087999

Supplementary files

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2. Fig. 3. Estimation statistics of the distributed lag model between temperature and hypertension incidence in the Yamalo-Nenets Autonomous Okrug.

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3. Fig. 5. Estimation statistics of the distributed lag model between temperature and hypertension incidence in the Tyumen Oblast.

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4. Fig. 1. Time series of primary incidence of hypertension per 1000 population and average annual air temperature (°C) in the Yamalo-Nenets Autonomous Okrug (YNAO) prior to transformation.

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5. Fig. 2. Time series of primary incidence of hypertension per 1000 population and average annual air temperature(°C) in the Tyumen Oblast before transformation.

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6. Fig. 4. ARIMA forecast of hypertension incidence in the Yamalo-Nenets Autonomous Okrug (YNAO).

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7. Fig. 6. ARIMA forecast of hypertension incidence in the Tyumen Oblast.

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