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Background: High incidence rates of gastrointestinal tract cancers have been reported in the Caspian region of Iran. This study aimed to: 1) describe the geographical spatial patterns of gastrointestinal tract cancer incidence based on cancer registry data and, 2) determine whether geographical clusters of statistical significance exist.

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Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims.

Introduction: Crimean-Congo hemorrhagic fever (CCHF) is endemic in southeast Iran. In this study we present the epidemiological features of CCHF and its relationship with climate factors in over a 13-year span. Methodology: Surveillance system data of CCHF from 2000 to 2012 were obtained from the Province Health Centre of Zahedan University of Medical Sciences in southeast Iran. The climate data were obtained from the climate organization. The seasonal auto-regression integrated moving average (SARIMA) model was used for time series analysis to produce a model as applicable as possible in predicting the variations in the occurrence of the disease. Results: Between 2000 and 2012, 647 confirmed CCHF cases were reported from Sistan-va-Baluchistan province. The total case fatality rate was about 10.0%. Climate variables including mean temperature (°C), accumulated rainfall (mm), and maximum relative humidity (%) were significantly correlated with monthly incidence of CCHF (p <0.05). There was no clear pattern of decline in the reported number of cases within the study's time span. The first spike in the number of CCHF cases in Iran occurred after the first surge of the disease in Pakistan. Conclusions: This study shows the potential of climate indicators as predictive factors in modeling the occurrence of CCHF, even though it has to be appreciated whether there is any need for a practically applicable model. There are also other factors, such as entomological indicators and virological finding that must be considered.

Physical activity has no effect on knee pain and may have either a very small effect or no effect on functional performance in adults with knee osteoarthritis.

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