Osong Public Health and Research Perspectives

Original Articles

Joint Disease Mapping of Two Digestive Cancers in Golestan Province, Iran Using a Shared Component Model: Parisa Chamanpara, Abbas Moghimbeigi, Javad Faradmal, Jalal Poorolajal; Osong Public Health Res Perspect. 2015;6(3):205-210. Published online June 30, 2015; DOI: https://doi.org/10.1016/j.phrp.2015.02.002

2,756 View
16 Download
7 Crossref

Objectives
Recent studies have suggested the occurrence patterns and related diet factor of esophagus cancer (EC) and gastric cancer (GC). Incidence of these cancers was mapped either in general and stratified by sex. The aim of this study was to model the geographical variation in incidence of these two related cancers jointly to explore the relative importance of an intended risk factor, diet low in fruit and vegetable intake, in Golestan, Iran.
Methods
Data on the incidence of EC and GC between 2004 and 2008 were extracted from Golestan Research Center of Gastroenterology and Hepatology, Hamadan, Iran. These data were registered as new observations in 11 counties of the province yearly. The Bayesian shared component model was used to analyze the spatial variation of incidence rates jointly and in this study we analyzed the data using this model. Joint modeling improved the precision of estimations of underlying diseases pattern, and thus strengthened the relevant results.
Results
From 2004 to 2008, the joint incidence rates of the two cancers studied were relatively high (0.8–1.2) in the Golestan area. The general map showed that the northern part of the province was at higher risk than the other parts. Thus the component representing diet low in fruit and vegetable intake had larger effect of EC and GC incidence rates in this part. This incidence risk pattern was retained for female but for male was a little different.
Conclusion
Using a shared component model for joint modeling of incidence rates leads to more precise estimates, so the common risk factor, a diet low in fruit and vegetables, is important in this area and needs more attention in the allocation and delivery of public health policies.

Citations

Citations to this article as recorded by

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research
Getayeneh Antehunegn Tesema, Zemenu Tadesse Tessema, Stephane Heritier, Rob G. Stirling, Arul Earnest
International Journal of Environmental Research an.2023; 20(7): 5295. CrossRef
Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia
I. Gede Nyoman Mindra Jaya, Budhi Handoko, Yudhie Andriyana, Anna Chadidjah, Farah Kristiani, Mila Antikasari
Mathematics.2023; 11(17): 3641. CrossRef
Evaluating an intervention for neural tube defects in coal mining cites in China: a temporal and spatial analysis
Ningxu Zhang, Yilan Liao, Zhoupeng Ren
International Health.2021; 13(2): 161. CrossRef
Epidemiologic Study of Gastric Cancer in Iran: A Systematic Review

Khadijeh Kalan Farmanfarma, Neda Mahdavifar, Soheil Hassanipour, Hamid Salehiniya
Clinical and Experimental Gastroenterology.2020; Volume 13: 511. CrossRef
Bivariate spatio-temporal shared component modeling: Mapping of relative death risk due to colorectal and stomach cancers in Iran provinces
Vahid Ahmadipanahmehrabadi, Akbar Hassanzadeh, Behzad Mahaki
International Journal of Preventive Medicine.2019; 10(1): 39. CrossRef
Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies
Qingyun Du, Mingxiao Zhang, Yayan Li, Hui Luan, Shi Liang, Fu Ren
International Journal of Environmental Research an.2016; 13(4): 436. CrossRef
Disappeared persons and homicide in El Salvador
Carlos Carcach, Evelyn Artola
Crime Science.2016;[Epub] CrossRef

Gastric and Esophageal Cancers Incidence Mapping in Golestan Province, Iran: Using Bayesian–Gibbs Sampling: Atefeh-Sadat Hosseintabar Marzoni, Abbas Moghimbeigi, Javad Faradmal; Osong Public Health Res Perspect. 2015;6(2):100-105. Published online April 30, 2015; DOI: https://doi.org/10.1016/j.phrp.2015.01.004

2,795 View
16 Download
4 Crossref

Abstract PDF

Objectives
Recent studies of esophageal cancer (EC) and gastric cancer (GC) have been reported to have high incidence rates of these cancers in Golestan Province of Iran. The present study describes the geographical patterns of EC and GC incidence based on cancer registry data and display statistically significant regions within this province.
Methods
In order to map the distribution of upper gastrointestinal cancer, relative risk (RR) were calculated. Therefore, to estimate a more reliable RR, Poisson regression models were used. The adjusted models (adjusted to urban–rural area, sex, and grouped age proportion) were utilized. We considered two-component random effects for each observation, an unstructured (noncorrelated) and a group of “neighbor” (correlated) heterogeneities. We estimated the model parameters using Gibbs sampling and empirical Bayes method. We used EC and GC data that were registered with Golestan Research Center of Gastroenterology and Hepatology in the years 2004–2008.
Results
The EC and GC maps were drawn for 2004–2008 in the province. Kalaleh and Minoodasht counties have a high RR of EC and GC in the years of study. In almost all years, the areas with a high RR were steady.
Conclusion
The EC and GC maps showed significant spatial patterns of risk in Golestan province of Iran. Further study is needed to multivariate clustering and mapping of cancers RRs with considering diet and socioeconomic factors.

Citations

Citations to this article as recorded by

Design of risk prediction model for esophageal cancer based on machine learning approach
Raoof Nopour
Heliyon.2024; 10(2): e24797. CrossRef
Meat consumption and risk of esophageal and gastric cancer in the Golestan Cohort Study, Iran
Giulia Collatuzzo, Arash Etemadi, Masoud Sotoudeh, Arash Nikmanesh, Hossein Poustchi, Masoud Khoshnia, Akram Pourshams, Maryam Hashemian, Gholamreza Roshandel, Sanford M. Dawsey, Christian C. Abnet, Farin Kamangar, Paul Brennan, Paolo Boffetta, Reza Malek
International Journal of Cancer.2022; 151(7): 1005. CrossRef
Epidemiologic Study of Gastric Cancer in Iran: A Systematic Review

Khadijeh Kalan Farmanfarma, Neda Mahdavifar, Soheil Hassanipour, Hamid Salehiniya
Clinical and Experimental Gastroenterology.2020; Volume 13: 511. CrossRef
Building cancer registries in a lower resource setting: The 10-year experience of Golestan, Northern Iran
Gholamreza Roshandel, Shahryar Semnani, Abdolreza Fazel, Mohammadreza Honarvar, MohammadHossein Taziki, SeyedMehdi Sedaghat, Nafiseh Abdolahi, Mohammad Ashaari, Mohammad Poorabbasi, Susan Hasanpour, SeyedAhmad Hosseini, SeyedMohsen Mansuri, Ataollah Jahan
Cancer Epidemiology.2018; 52: 128. CrossRef

Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method: Maryam Farhadian, Hossein Mahjub, Jalal Poorolajal, Abbas Moghimbeigi, Muharram Mansoorizadeh; Osong Public Health Res Perspect. 2014;5(6):324-332. Published online December 31, 2014; DOI: https://doi.org/10.1016/j.phrp.2014.09.002

2,657 View
17 Download
4 Crossref

Abstract PDF

Objectives
Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented.
Methods
The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA).
Results
The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies.
Conclusion
The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework.

Citations

Citations to this article as recorded by

Diagnosing thyroid disorders: Comparison of logistic regression and neural network models
Shiva Borzouei, Hossein Mahjub, NegarAsaad Sajadi, Maryam Farhadian
Journal of Family Medicine and Primary Care.2020; 9(3): 1470. CrossRef
Thyroid disorder diagnosis based on Mamdani fuzzy inference system classifier
Negar Asaad Sajadi, Hossein Mahjub, Shiva Borzouei, Maryam Farhadian
Koomesh Journal.2020; 22(1): 107. CrossRef
Diagnosis of hypothyroidism using a fuzzy rule-based expert system
Negar Asaad Sajadi, Shiva Borzouei, Hossein Mahjub, Maryam Farhadian
Clinical Epidemiology and Global Health.2019; 7(4): 519. CrossRef
WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis
Kui Deng, Fan Zhang, Qilong Tan, Yue Huang, Wei Song, Zhiwei Rong, Zheng-Jiang Zhu, Kang Li, Zhenzi Li
Analytica Chimica Acta.2019; 1061: 60. CrossRef

Evaluation of Drug Abuse Relapse Event Rate Over Time in Frailty Model: Somaye Hosseini, Abbas Moghimbeigi, Ghodratollah Roshanaei, Farzaneh Momeniarbat; Osong Public Health Res Perspect. 2014;5(2):92-95. Published online April 30, 2014; DOI: https://doi.org/10.1016/j.phrp.2014.02.003

2,629 View
14 Download
9 Crossref

Abstract PDF

Objectives
Drug dependence as a chronic disorder is reversible over time and has a cost burden for individuals, families, and society. An individual who has stopped taking drugs for a long time may start taking drugs again. The variables affecting the reuse of drugs are not well known. Therefore a study of the factors that increase the length of time away from drugs is essential.
Methods
This study used data collected by the Bushehr addiction treatment centers (Tolloe and Pasargadae) from 100 men with drug addiction from March 2006 to September 2010. The shared frailty model was used to study the influence of variables on the duration of time away from drug use. The most common method for entering intra-class (personal) correlation is the survival frailty model, which uses parametric survival data for the evaluation of recurrent events. A Weibull distribution for time to event with gamma shared frailty was used.
Results
The mean (standard deviation) age and age at onset of opium use of the sample were 33.85 (8.11) and 20.65 (6.87), respectively. About 30% of the men studied had chronic disease and 36% had a mental illness. The mean (frequency mean) of the amount of opium used were 4.73 (3.8) g and 2.54 (1.14) times per day. The desire to end drug use was 97% and 3% for the men with drug addiction and their families, respectively, at the time when the men stopped using opium. The age at onset of opium use [p = 0.046, hazards ratio (HR) = 1.30], history of chronic disease (p = 0.005, HR = 249.635), and marital status (p = 0.06, HR = 0.027) are important in the reuse of opium.
Conclusion
We found that opium addiction is related to other chronic diseases and to the age at onset of opium use. A prospective study following up individuals with drug addiction who try to stop drug use in addiction treatment centers could help to determine the risk factors of resuming drug use.

Citations

Citations to this article as recorded by

Does substance use disorder treatment completion reduce the risk of treatment readmission in Chile?
José Ruiz-Tagle Maturana, Andrés González-Santa Cruz, Teresita Rocha-Jiménez, Álvaro Castillo-Carniglia
Drug and Alcohol Dependence.2023; 248: 109907. CrossRef
Long-term relapse prevention strategies among poly-substance users in Ghana: New insights for clinical practice
Richard Appiah
Journal of Ethnicity in Substance Abuse.2022; 21(3): 1104. CrossRef
Is the relapse concept in studies of substance use disorders a ‘one size fits all’ concept? A systematic review of relapse operationalisations
Fredrik D. Moe, Christian Moltu, James R. McKay, Sverre Nesvåg, Jone Bjornestad
Drug and Alcohol Review.2022; 41(4): 743. CrossRef
Evaluating drug use relapse event rate and its associated factors using Poisson model
Alireza Amirabadizadeh, Samaneh Nakhaee, Saeedeh Ghasemi, Maria Benito, Vahideh Bazzazadeh Torbati, Omid Mehrpour
Journal of Substance Use.2021; 26(1): 60. CrossRef
The Association of Loneliness and Non-prescribed Opioid Use in Patients With Opioid Use Disorder
John McDonagh, Cory B. Williams, Benjamin J. Oldfield, Dabely Cruz-Jose, Douglas P. Olson
Journal of Addiction Medicine.2020; 14(6): 489. CrossRef
Precipitants of Substance Abuse Relapse in Ghana
Richard Appiah, Samuel A. Danquah, Kingsley Nyarko, Angela L. Ofori-Atta, Lydia Aziato
Journal of Drug Issues.2017; 47(1): 104. CrossRef
The Role of Neuroticism and Psychological Flexibility in Chronic Fatigue and Quality of Life in Patients with Type 2 Diabetes
Farzaneh Momeniarbat, Javad Karimi, Nosrolah Erfani, Javad Kiani
Romanian Journal of Diabetes Nutrition and Metabol.2017; 24(2): 137. CrossRef
A Prospective Study to Investigate Predictors of Relapse among Patients with Opioid Use Disorder Treated with Methadone
Leen Naji, Brittany B. Dennis, Monica Bawor, Carolyn Plater, Guillaume Pare, Andrew Worster, Michael Varenbut, Jeff Daiter, David C. Marsh, Dipika Desai, Lehana Thabane, Zainab Samaan
Substance Abuse: Research and Treatment.2016; 10: SART.S37030. CrossRef
Survival Analysis of Drug Abuse Relapse in Addiction Treatment Centers
Aziz Kassani, Mohsen Niazi, Jafar Hassanzadeh, Rostam Menati
International Journal of High Risk Behaviors and A.2015;[Epub] CrossRef

First
Prev
Page of 1
Next
Last

Search