ABSTRACT
Objective To examine the feasibility' of using multiple seasonal autoregressive integrated moving average (SARIMA) model for predicting pulmonary tuberculosis (TB) incidence, so as to provide scientific evidence for the prevention and treatment of TB. Methods EViews 7.0.01 software was used to create a SARIMA fit model for seasonal incidence of TB on a monthly basis from January 2004 to December 2012, and the predicting performance of the model was tested with TB data from January to December in 2013. Results The established SARIMA (2,0,2) × (0,1,1)12 model could better fit with the previous TB incidence; and it basically well predicted the TB incidence of the 12 months of 2013, with the mean absolute error being 0. 416 992 and the mean absolute error rate being 5.350 8%. Conclusion The established multiplicative SARIMA model can better simulate and predict the trend of TB incidence with time, and it may have a future in predicting the incidence of TB.
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Objective To compare the performance of ARIMA model and GRNN model for predicting the incidence of tuberculosis. Methods ARIMA model was set up by Eviews 7.0.0.1 and GRNN model was set up by neural network toolbox of Matlab 7.1 based on the monthly tuberculosis incidence data from January 2004 to December 2012 in China. Monthly tuberculosis incidence data in 2013 were subjected to the two models for testing, and the results were compared between the two groups. Results The Theil unequal coefficients (TIC) were 0.034 and 0.059 for ARIMA model and GRNN model, respectively, indicating that ARIMA model was better than GRNN model to fit with the monthly incidence of tuberculosis in 2013. The absolute value of the relative error for ARIMA model was only 57.19% of GRNN model. Conclusion ARIMA prediction model is more suitable for predicting the incidence of tuberculosis in China, and it is suggested a combination of models should be used to predict the incidence of tuberculosis.
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Objective To use fuzzy comprehensive evaluation method for rationally evaluating the performance of military hospitals. Methods The performance of a military hospital (2009-2011) was evaluated from the following four domains: patient, finance, internal flow, and learning and growth. Multi-layer fuzzy comprehensive evaluation method was used to obtain the fuzzy membership of all the domains of the subjects, and the evaluation results were subjected to corresponding analysis. Results It was found that the total proportion of "good" and "comparatively good" performance was 76.85% for a military hospital in 2009-2011. The overall performance degree of the hospital was "good" according to the maximal fuzzy membership principle. And corresponding analysis showed that the result was "moderate" for the financial domain, "poor" or "comparatively poor" for the patient domain, "good" for the internal flow domain, and "good" or "comparatively good" for the learning and growth domain. Conclusion Multi-layer fuzzy comprehensive evaluation method can properly handle the problem of multiple parameter integration in a system, and corresponding analysis makes the evaluation results more rational and more objective.
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Objective To set up an evaluation system for hospital performance, so as to meet the requirement of health system reform, to properly allocate health resources, to effectively standardize the administration of military hospitals, and to upgrade their overall performance. Methods According to the current situation, an evaluation system for performance of military hospitals, which including four domains-finance, patients, internal flow, and learning and progression, was established based on the balanced scorecard (BSC) ideology using the hospital resources planning (HRP) system, analytic hierarchy process (AHP) and Delphi method. Results We established an evaluation system for the performance of military hospitals, which including four first level parameters, 13 second level parameters, and 55 third level parameters. Conclusion HRP combined with BSC for evaluating hospital performance can help the proper allocation of health resources in military hospitals, contributing to the sound, cost-saving and highly efficient administration of hospitals. Adjustment should be made in actual application, and attention should be given to the time effectiveness and scientific nature of the parameters.
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Objective To measure the changes of total factor productivity of high leveKLevel 3) comprehensive military hospitals, so as to provide evidence for improving the productivity of these hospitals. Methods The 2007 and 2010 panel data of 36 high level military hospitals, including 3 input parameters and 5 output parameters, were collected in the present study. The Malmquist productivity index (MPI)of data envelopment analysis (DEA) was used for analysis. Results The productivity of 28 (77. 8%) hospitals was increased during 2007-2010. Further analysis showed that the increased productivity was mainly attributable to technology progress in 11(30. 6%) hospitals, to improved efficiency in 2 (5. 56%) hospitals, and to both technology progress and improved efficiency in 15(41. 7%) hospitals. The 8 hospitals with decreased productivity were all due to technology backwardness. Conclusion The total factor productivity of high level comprehensive military hospitals has witnessed a noticeable increase, and the increase is mainly attributable to technology progress and improved efficiency. The decrease of productivity is mainly due to technology backwardness.