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Objective To reduce the cancer burden in the Jinchang cohort and provide evidence for developing cancer prevention strategies and performing effectiveness evaluation in the Jinchang cohort.We are fitting thirteen years of cancer mortality data from the Jinchang cohort by using six kinds of predicting methods to compare relative fitness and to select good predicting methods for the prediction of cancer mortality trends.Methods The mortality data of cancer in Jinchnag cohort from 2001-2013 were fitted using six kinds of predicting methods:dynamic series,linear regression,exponential smoothing,autoregressive integrated moving average (ARIMA) model,grey model (GM),and Joinpoint regression.Weight coefficients of combination models were calculated by four methods:the arithmetic average method,the variance inverse method,the mean square error inverse method,and the simple weighted average method.Results The cancer mortality was fitted and compared by using six kinds of forecasting methods;the fitting precision of the Joinpoint linear regression had the highest accuracy (87.64％),followed by linear regression (87.32％),the dynamic series (86.99％),GM (1,1) (86.25％),exponential smoothing (85.72％) and ARIMA (1,0,0) (81.98％),respectively.Prediction accuracy of the combination model derived from GM (1,1) and linear regression (＞99％) was higher than that of the combination model derived from ARIMA (1,0,0) and GM (1,1).The combination model derived from the GM (1,1) and linear regression,with weight coefficients based on the arithmetic average method and the mean square error inverse method,had the best prediction effect of the four weight calculation methods.Conclusion Prediction accuracy of the combination model,with accuracy ＞95％,was higher than that of the single prediction methods.
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Objective To reduce the cancer burden in the Jinchang cohort and provide evidence for developing cancer prevention strategies and performing effectiveness evaluation in the Jinchang cohort.We are fitting thirteen years of cancer mortality data from the Jinchang cohort by using six kinds of predicting methods to compare relative fitness and to select good predicting methods for the prediction of cancer mortality trends.Methods The mortality data of cancer in Jinchnag cohort from 2001-2013 were fitted using six kinds of predicting methods:dynamic series,linear regression,exponential smoothing,autoregressive integrated moving average (ARIMA) model,grey model (GM),and Joinpoint regression.Weight coefficients of combination models were calculated by four methods:the arithmetic average method,the variance inverse method,the mean square error inverse method,and the simple weighted average method.Results The cancer mortality was fitted and compared by using six kinds of forecasting methods;the fitting precision of the Joinpoint linear regression had the highest accuracy (87.64％),followed by linear regression (87.32％),the dynamic series (86.99％),GM (1,1) (86.25％),exponential smoothing (85.72％) and ARIMA (1,0,0) (81.98％),respectively.Prediction accuracy of the combination model derived from GM (1,1) and linear regression (＞99％) was higher than that of the combination model derived from ARIMA (1,0,0) and GM (1,1).The combination model derived from the GM (1,1) and linear regression,with weight coefficients based on the arithmetic average method and the mean square error inverse method,had the best prediction effect of the four weight calculation methods.Conclusion Prediction accuracy of the combination model,with accuracy ＞95％,was higher than that of the single prediction methods.
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<p><b>OBJECTIVE</b>To understand the current status of disease burden caused by gastric cancer in Jinchang cohort.</p><p><b>METHODS</b>In this historical cohort study, the data of gastric cancer deaths from 2001 to 2013 and the medical records of gastric cancer cases from 2001 to 2010 in Jinchang cohort were collected to analyze the mortality, potential years of life lost (PYLL), working PYLL (WPYLL) associated with gastric cancer, and the medical expenditure data were used to evaluate the direct economic burden. Spearman correlation analysis and the average growth rate were used to describe the change trend of disease burden of gastric cancer.</p><p><b>RESULTS</b>A total of 213 gastric cancer deaths occurred in Jinchang cohort from 2001 to 2013. The average annual crude mortality rate of gastric cancer was 38.30 per 100,000 in Jinchang cohort during 2001-2013 and no obvious change was observed. The crude mortality rate in males was 6.84 times higher than that in females. Gastric cancer death mainly occurred in age group 50-79 years (82.62%), while the mortality rates was increasing among the people under 50 years with an average annual increase rate of 0.77%. The annual average PYLL (APYLL) and average WPYLL (AWPYLL) caused by gastric cancer decreased by 8.43% and 10.46%, respectively. No obvious change in medical expenditure of gastric cancer cases was observed in Jinchang Cohort during 2001-2010, and the medical expenditure and average daily cost of hospitalization were 8102.23 Yuan, and 463.45 Yuan per capita, respectively.</p><p><b>CONCLUSIONS</b>The burden of disease for gastric cancer was heavy in Jinchang cohort. The PYLL and WPYLL had no change, while the APYLL and AWPYLL showed a increasing trend during the last ten years. Direct economic burden of inpatients with gastric cancer had no change.</p>
Subject(s)
Aged , Female , Humans , Male , Middle Aged , China , Epidemiology , Cohort Studies , Cost of Illness , Health Expenditures , Hospitalization , Economics , Stomach Neoplasms , Economics , MortalityABSTRACT
<p><b>OBJECTIVE</b>The purpose of this study was to explore the spatial distribution and spatial clustering of hand-foot-mouth disease (HFMD) in Gansu, 2012.</p><p><b>METHODS</b>Spatial autocorrelation and Spatial scanning analysis were used to conduct spatial statistical analyses for the HFMD at the county/district level.</p><p><b>RESULTS</b>HFMD cases did not show a random distribution but with significant spatial aggregation. When Local Autocorrelation analysis was applied at the county/district level, with nine hot spot areas as Jiayuguan, Yumen, Dunhuang, Jinta, Suzhou, Chengguan, Anning, Xigu and Gaolan, were discovered. Four statistically significant HFMD clusters were identified by spatial scan statistics.</p><p><b>CONCLUSION</b>HFMD was noticed geographically clustered in Gansu in 2012. Results from this study indicated that the spatial autocorrelation and spatial scanning analysis could effectively detect the areas which presenting significant clusters. Cluster Detection System (CDS) could provide evidence for the development of an effective measure concerning the prevention and control of HFMD.</p>
Subject(s)
Humans , China , Epidemiology , Cluster Analysis , Hand, Foot and Mouth Disease , Epidemiology , Spatial AnalysisABSTRACT
OBJECTIVE To understand the epidemic condition on the resistant plasmids and their homology of the Staphylococcus aureus obtained from two hospitals in Gansu Province.METHODS The antibiotic sensitivity test,plasmid elimination,plasmid profile,and restriction endonuclease profile were used in analyzing the S.aureus resistant plasmids.RESULTS We isolated 15 strains of S.aureus from 196 samples in two different hospitals,the isolation rate was 7.7%.From them 86.7% isolates were resistant to the seven commonly used antibiotics in different degree.Only 13.3% strains were just resistant to one antimicrobial,the multi-resistant strains amounted for 46.7%.There were 12 isolates carried plasmids(80.0%).The results of plasmid elimination showed 11 strains carried resistant plasmids,and the one with 38 kb was a popular plasmid.The endonuclease profiles revealed that the same source strains had certain homology,there were similiar endonuclease profiles among strains in different wards.CONCLUSIONS The popular plasmids in different wards have certain homology,and they can be transferred among different strains of bacteria.