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Showing 2 results for Moradinazar

J Hasanzadeh, F Najafi, M Moradinazar,
Volume 11, Issue 1 (Vol 11, No 1 2015)
Abstract

The time series is a collection of observation data that are arranged according to time. The main purpose of setting up a time series is to predict future values. The first step in time series data is graphed. Using graphs can provide general information such as uptrend or downtrend, seasonal patterns, periodic presence, and outliers in time series graphs. After graphing the data, if a good forecast is required, stationary data can be used. Differencing or decomposition methods can be used to make the data stationary. Then, a correlogram can be used to identify the order moving average and autoregressive model. The parameters of the model are examined using T-test. If the parameters are significant and the residue is independence, the predicted values can be evaluated using the mean absolute percentage error.


Rasoul Gholamiveis, Fatemeh Heydarpour, Mehdi Moradinazar,
Volume 21, Issue 4 (Vol.21, No.4, Winter 2026)
Abstract

Background and Objectives: Prostate cancer is one of the most common cancers among Iranian men and has shown a marked increase in both incidence and mortality over recent decades. This study aimed to analyze temporal trends in prostate cancer incidence and mortality in Iran using an Age–Period–Cohort (APC) analytical framework.
Methods: Data were extracted from the Global Burden of Disease Study 2021 for Iranian males aged 45 years and older during 1992–2021. Data were stratified into eight 5-year age groups and six 5-year time periods. APC analysis was conducted using the second-order difference method, and Joinpoint regression was employed to assess temporal trends in risk factors.
Results: In 2021, the age-standardized incidence rate reached 30.05 per 100,000 population, and the mortality rate reached 10.66 per 100,000 representing increases of 105.96% and 14.99%, respectively, compared to 1992. Age effects were positively associated with both incidence and mortality. The period effect peaked during 2002–2006. Cohort analysis revealed that younger birth cohorts exhibited a slower acceleration in incidence rates. Among risk factors, the disease burden attributable to smoking increased by 17.1%, while the protective effects of low calcium intake and low milk consumption diminished over time.
Conclusion: Rising incidence and mortality are shaped by age, period, and cohort effects. The relative decline in younger cohorts underscores the need for targeted prostate cancer screening programs. Strengthening public health education policies and improving access to diagnostic services can reduce the disease burden.


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