BACKGROUND Although various studies have been conducted on the effects of seasonal climate changes or emotional variables on the risk of AMI, many of them have limitations to determine the predictable model. The currents study is conducted to assess the effects of meteorological and emotional variables on the incidence and epidemiological occurrence of acute myocardial infarction (AMI) in Sari (capital of Mazandaran, Iran) during 2011-2018. METHODS In this study, a time series analysis was used to determine the variation of variables over time. All series were seasonally adjusted and Poisson regression analysis was performed. In the analysis of meteorological data and emotional distress due to religious mourning events, the best results were obtained by autoregressive moving average (ARMA) (5,5) model. RESULTS It was determined that average temperature, sunshine, and rain variables had a significant effect on death. A total of 2375 AMI's were enrolled. Average temperate (°C) and sunshine hours a day (h/day) had a statistically significant relationship with the number of AMI's (β = 0.011, P = 0.014). For every extra degree of temperature increase, the risk of AMI rose [OR = 1.011 (95%CI 1.00, 1.02)]. For every extra hour of sunshine, a day a statistically significant increase [OR = 1.02 (95% CI 1.01, 1.04)] in AMI risk occurred (β = 0.025, P = 0.001). Religious mourning events increase the risk of AMI 1.05 times more. The other independent variables have no significant effects on AMI's (P > 0.05). CONCLUSION Results demonstrate that sunshine hours and the average temperature had a significant effect on the risk of AMI. Moreover, emotional distress due to religious morning events increases AMI. More specific research on this topic is recommended.

A time-series prediction model of acute myocardial infarction in northern of Iran: the risk of climate change and religious mourning.

Economic time series can be impacted by seasonal factors. If present but not identified, seasonality may lead to incorrect conclusions derived from the analysis. Seasonality is not always easily identifiable as time series are shaped by other factors as well, such as one-off events or natural disasters. There is a variety of methods to deal with seasonality in data. An attempt was made to compare the outcome of two popular methods: X-12-ARIMA and TRAMO/SEATS. They were applied to analyse seasonality for the business climate index in the construction industry in Poland. Both procedures were used to produce a seasonally adjusted series for the business climate index. Comparison of model’s diagnostics proved that TRAMO/SEATS performed slightly better for the analysed series within a chosen time range, which is consistent with some more general results found in the literature.

/pdf/seasonality-testing-for-macroeconomic-time-series-comparison-3jh79kcohm.pdf

Seasonality testing for macroeconomic time series – comparison of X-12-ARIMA and TRAMO/SEATS procedures

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Seasonality testing for macroeconomic time series – comparison of X-12-ARIMA and TRAMO/SEATS procedures