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- 저자 : Momenyan, Somayeh (검색결과 2 건)
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Relationship between Urbanization and Cancer Incidence in Iran Using Quantile Regression
Momenyan, Somayeh,Sadeghifar, Majid,Sarvi, Fatemeh,Khodadost, Mahmoud,Mosavi-Jarrahi, Alireza,Ghaffari, Mohammad Ebrahim,Sekhavati, Eghbal Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.no.sup3
Quantile regression is an efficient method for predicting and estimating the relationship between explanatory variables and percentile points of the response distribution, particularly for extreme percentiles of the distribution. To study the relationship between urbanization and cancer morbidity, we here applied quantile regression. This cross-sectional study was conducted for 9 cancers in 345 cities in 2007 in Iran. Data were obtained from the Ministry of Health and Medical Education and the relationship between urbanization and cancer morbidity was investigated using quantile regression and least square regression. Fitting models were compared using AIC criteria. R (3.0.1) software and the Quantreg package were used for statistical analysis. With the quantile regression model all percentiles for breast, colorectal, prostate, lung and pancreas cancers demonstrated increasing incidence rate with urbanization. The maximum increase for breast cancer was in the 90th percentile (${\beta}$=0.13, p-value<0.001), for colorectal cancer was in the 75th percentile (${\beta}$=0.048, p-value<0.001), for prostate cancer the 95th percentile (${\beta}$=0.55, p-value<0.001), for lung cancer was in 95th percentile (${\beta}$=0.52, p-value=0.006), for pancreas cancer was in 10th percentile (${\beta}$=0.011, p-value<0.001). For gastric, esophageal and skin cancers, with increasing urbanization, the incidence rate was decreased. The maximum decrease for gastric cancer was in the 90th percentile(${\beta}$=0.003, p-value<0.001), for esophageal cancer the 95th (${\beta}$=0.04, p-value=0.4) and for skin cancer also the 95th (${\beta}$=0.145, p-value=0.071). The AIC showed that for upper percentiles, the fitting of quantile regression was better than least square regression. According to the results of this study, the significant impact of urbanization on cancer morbidity requirs more effort and planning by policymakers and administrators in order to reduce risk factors such as pollution in urban areas and ensure proper nutrition recommendations are made.
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Somayeh Momenyan,Amir Kavousi 한국역학회 2018 Epidemiology and Health Vol.40 No.-
OBJECTIVES: Understanding the geographic variation of HIV/AIDS mortality risk and human immunodeficiency virus (HIV) infection could help identify high-burden areas. The aim of our study was to evaluate the effects of predictors of the time interval between HIV diagnosis to death, while accounting for spatial correlations across counties, and to assess patterns of spatial inequalities in the risk of HIV/AIDS mortality in Hamadan Province, Iran. METHODS: This retrospective study was conducted on 585 patients. The outcome in this study was the time period between the date of HIV/AIDS diagnosis and the date of death. A Weibull regression model with spatial random effects was used. RESULTS: According to multivariate analysis, there were significant associations between age, tuberculosis co-infection, and marital status and the risk of death. In terms of spatial inequalities, a cluster of counties was identified with a somewhat higher death hazard in the north, northwest, northeast, and central regions. Additionally, a cluster with a somewhat lower hazard was identified in the south, southwest, southeast, and west regions. CONCLUSIONS: The spatial pattern of HIV/AIDS death risk could reflect inequalities in access to antiretroviral therapy and public health services. Our results underscore the importance of attention to vulnerable groups in urban areas.
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