摘要
目的对我国HIV/ADIS流行趋势进行分析和预测,为今后制定艾滋病防治策略提供科学依据。方法以全国2006-2011年艾滋病疫情报告数据为基础建立GM(1,1)模型,在此基础上根据新陈代谢原理对2012-2015年发病趋势进行预测。结果最终建立了三个GM(1,1)模型用于HIV/AIDS、AIDS、HIV流行趋势预测,三个模型拟合优度检验均为1级。预测分析结果显示,2012-2015年我国HIV/AIDS发病数分别为81832例、91576例、103618例、117494例,AIDS发病数分别为24442例、30467例、37174例、45500例,HIV发病数分别为57926例、62349例、68795例、76050例。结论2006年以来我国HIV/AIDS疫情逐年上升,预测分析结果表明未来4年仍将延续持续上升的长期趋势,艾滋病防治形势严峻,需要进一步加大防治力度。
Objective To analyze and forecast the epidemic tendency of HIV/AIDS in China, which may provide a scientific basis for developing prevention and control staregies for HIV/AIDS in the future. Methods We fit GM（ 1,1 ） models based on the epidemic data of HIV/ ADS from 2006 to 2011 in China, and then predicted the incidence levels in 2012 -2015 in accordance with the metabolic principles. Results Three metabolic GM（ 1,1 ） models was fitted for HIV/ADIS, AIDS and HIV, re- spectively. The fitting effects of these models were all ideal. The predicted case numbers in 2012 - 2015 would be 81832,91576,103618,117494 for HIV/AIDS, 24442,30467,37174,45500 for AIDS, and 57926,62349, 68795,76050 for HIV, respectively. Conclusion Since 2006, the inci- dence of HIV/AIDS in China has gone up rapidly year by year. What＇s more, the forecast analysis showed that the long-term trend of rising would be continued in next four years. The study indicats that the situation of pre-gression coefficient or its standard error from PS regression, were close to parameters estimated from standard regression model, compared to the com- mon logistic regression. These differences of parameter estimates were grad- ually disappeared along with increase of sample size. （2） Given sample size of 1000 and 500 and 4% positive proportion of outcome variable, we estimated regression coefficient and its standard error from three models a- long with degree of collinearity. The trend of parameters estimated from PS regression was parallel with the trend of standard model. It means the differ- ence between these two models is consistent. However, the change of re- gression coefficient and standard error estimated from the common logistic regression were parallel with changes of two models mentioned above when r is in a low level. But it changes its direction at r = 0.5 （ n = 1000） or r = 0. 3 （ n = 500 ）. Conclusion The parameters estimated from PS regres- sion were more reliable than the common logistic regression, especially un- der the conditions of small sample size and data with severe collinearity. Therefore, PS regression could be one of excellent methods in dealing with collinearity data.
出处
《中国卫生统计》
CSCD
北大核心
2013年第6期821-823,828,共4页
Chinese Journal of Health Statistics
基金
江苏省“十二五”科教兴卫工程“突发公共卫生事件应急处置创新平台”(ZX201109)
江苏省预防医学科研项目(YZ201020)
关键词
艾滋病
新陈代谢GM(1
1)模型
预测
Propensity score
Collineaxity data
Monte Carlo simulation
Logistic regression
