Breast cancer is a major cause of morbidity and cancer related mortality among women. The prevalence of breast cancer was reported as increasing in most of the Asian countries (Aini abdullah et al., 2013;Najafi et al., 2013). Iran is also sharing the same experience. The etiology of breast cancer is largely unknown; therefore there is no established primary prevention strategy. So, the main strategy is the establishment of screening protocols and early detection programs, which at least theoretically can improve the survival rates. Despite the increasing incidence, the survival rates of breast cancer patients in many developed countries were substantially improved. According to national cancer registry project report, breast cancer is the most common cancer and also the most common cause of cancer-related deaths of female population in Iran (Mohagheghi et al., 2009).According to international agency for research on

BackgroundRoad traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran.MethodsA time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package.ResultsA total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants’ accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January).ConclusionOur findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.

This study attempts to find the best model to forecast international tourism demand using a series of key macroeconomic variables in ASEAN countries. Generally, we find that generalized Poisson regression model is the best one for estimating long-run international tourism demand. In addition, we find that inflation and real exchange rate have negative relationship with international tourism demand. On the other hand, foreign direct investment and openness of trade have positive relationship with international tourism demand. Cointegration test result shows that there is a long-run relationship between variables.

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