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

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

Climate change · Climatology · Environmental health · Geography · Physical geography · Population · Air Quality and Health Impacts · Climate Change and Health Impacts · Demography · Environmental Science · Global Health Care Issues · Mathematics · Medicine · Geology · Oceanography

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