孕妇凝血指标的数学模型及指标异常变化警戒值探讨

目的探讨孕妇不同孕期凝血指标变化趋势的数学模型及凝血指标异常变化的警戒值估计,帮助临床医生及预防保健人员及早期发现高危人群,有效预防产科出血和凝血并发症的发生。方法对214例孕妇的孕期凝血指标进行回顾性追索,根据聚类分析的结果,建立方差分析、回归分析及ARIMA时间序列分析模型,比较各检测指标在不同孕期的变化规律。结果所建模型均能够很好的模拟不同孕期凝血指标的变化规律,结果显示:PLT指标值在孕晚期出现明显的下降(P0.05),PT与INR指标值在妊娠中期的下降明显(P0.01),APTT在整个妊娠期均下降(P0.01),FG指标值在妊娠晚期出现上升(P0.01),不同孕期凝血指标的警戒值表达式为:μ±2σ。结论所建模型能够较好的模拟不同孕期凝血指标变化规律,不同孕期凝血指标的正常参考值范围及异常警戒值具有一定的医学应用价值。     

1990—2019年中国高血清低密度脂蛋白胆固醇疾病负担趋势分析与预测

目的 描述和分析1990—2019年中国高血清低密度脂蛋白胆固醇（high LDL cholesterol,高LDL - C）疾病负担状况及变化趋势,并预测未来5年的疾病负担,为中国高LDL - C科学防控提供依据。方法 提取2019年全球疾病负担（GBD 2019）中因高LDL - C造成的死亡数、死亡率及DALYs等疾病负担指标,相关指标均采用GBD 2019全球标准人口进行年龄标准化,采用平均年度变化百分比（AAPC）分析率的变化趋势,并应用R 4.1.0对1990—2016年中国因高LDL - C造成的死亡率和DALYs率建立ARIMA模型和NNAR模型,用2017—2019年的数据来评价两模型的拟合效果,最后用拟合效果最好的模型预测2020—2024年中国高LDL - C死亡率和DALYs率。结果 1990—2019年中国高LDL - C造成的死亡率（AAPC = 3.1%,P＜0.05）和DALYs率（AAPC = 2.2%,P＜0.05）整体呈波动上升趋势;标化死亡率和DALYs率增长14.21%和0.56%,男女性别比范围分别为1.33～1.67和1.36～1.76,男性高于女性;年龄别疾病负担≥70岁人群远高于15～49岁和50～69岁群体;ARIMA（0,2,0）和NNAR（1,1）模型预测与实际趋势基本一致,前者预测值与实际值相对误差、均方根误差（RMSE）、平均绝对误差（MAE）以及平均绝对百分误差（MAPE）均较小,预测精度更好。 结论 中国高LDL - C造成的疾病负担呈逐渐上升趋势,在2020—2024年将继续上升。男性、高龄人群疾病负担更加沉重,应采取针对性措施进行干预。     

ARIMA模型在医院卫生消耗材料需求量预测中的应用

目的 阐述ARIMA模型拟合时间序列的方法和步骤,并将其应用于医院卫生消耗材料需求量的预测,为医院设备管理人员提供决策依据。方法 利用SAS软件系统,求解适宜的ARIMA模型,据所得误差评价预测效果。结果 通过对3种卫生消耗材料需求量的预测,相对误差在10％左右,预测效果较为可靠。结论 医院卫生消耗材料需求量的近期预测中引入时间序列的ARIMA模型分析方法,能够对实际工作产生积极的指导意义。     

Understanding and using time series analyses in addiction research

Time series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre‐registering hypotheses and analysis plans before the analyses are undertaken.     

ARIMA模型预测医院感染发病状况研究

目的 探讨ARIMA季节乘积模型在时间序列资料中的应用,建立金坛市中医院医院感染发病率的预测模型. 方法 收集本院2005-2012年住院病人病案资料,应用SFSS18.0软件中的ARIMA模型预测模块对数据进行分析建立ARIMA预测模型,并预测2013年医院感染情况. 结果 ARIMA（1,0,1）（0,1,1）4能够较好的拟合本院医院感染发病率情况,利用此模型预测2013年本院4个季度的医院感染率分别为2.67％、2.03％、2.68％和1.93％. 结论 ARIMA模型能够较好的拟合和预测医院感染的发病情况,可以为医院决策提供科学依据.     

应用ARIMA模型预测福建省戊型肝炎疫情

目的建立福建省戊型肝炎（戊肝）分月发病数预测预警的ARIMA时间序列模型。方法利用SAS 9.0软件的PROC ARIMA综合软件包对《疾病监测信息报告管理系统》收集的福建省2004-2010年戊肝分月发病数序列进行ARI-MA模型的建模与分析。结果福建省2004-2010年戊肝分月发病数序列含有以年为周期的季节效应,经12步差分后为平稳非白噪声序列,拟合的相对最优模型为ARIMA（0,0,0）×（0,1,1）12。结论拟合戊肝的相对最优ARIMA模型进行预测和预警,具有实际应用价值。     

The impact of immunization programs on 10 vaccine preventable diseases in Italy: 1900–2015

BackgroundVaccination has determined a dramatic decline in morbidity and mortality from infectious diseases over the last century. However, low perceived risk of the infectious threat and increased concern about vaccines’ safety led to a reduction in vaccine coverage, with increased risk of disease outbreaks.MethodsAnnual surveillance data of nationally communicable infectious diseases in Italy between 1900 and 2015 were used to derive trends in morbidity and mortality rates before and after vaccine introduction, focusing particularly on the effect of vaccination programs. Autoregressive integrated moving average models were applied to ten vaccine-preventable diseases: diphtheria, tetanus, poliomyelitis, hepatitis B, pertussis, measles, mumps, rubella, chickenpox, and invasive meningococcal disease. Results of these models referring to data before the immunization programs were projected on the vaccination period to estimate expected cases. The difference between observed and projected cases provided estimates of cases avoided by vaccination.ResultsThe temporal trend for each disease started with high incidence rates, followed by a period of persisting reduction. After vaccine introduction, and particularly after the recommendation for universal use among children, the current rates were much lower than those forecasted without vaccination, both in the whole population and among the 0-to-4 year olds, which is, generally, the most susceptible age class. Assuming that the difference between incidence rates before and after vaccination programs was attributable only to vaccine, more than 4 million cases were prevented, and nearly 35% of them among children in the early years of life. Diphtheria was the disease with the highest number of prevented cases, followed by mumps, chickenpox and measles.ConclusionsUniversal vaccination programs represent the most effective prevention tool against infectious diseases, having a major impact on human health. Health authorities should make any effort to strengthen public confidence in vaccines, highlighting scientific evidence of vaccination benefits.     

Bayesian age–period–cohort models with versatile interactions and long‐term predictions: mortality and population in Finland 1878–2050

Age–period–cohort (APC) models are widely used for studying time trends of disease incidence or mortality. Model identifiability has become less of a problem with Bayesian APC models. These models are usually based on random walk (RW1, RW2) smoothing priors. For long and complex time series and for long predicted periods, these models as such may not be adequate. We present two extensions for the APC models. First, we introduce flexible interactions between the age, period and cohort effects based on a two‐dimensional conditional autoregressive smoothing prior on the age/period plane. Our second extension uses autoregressive integrated (ARI) models to provide reasonable long‐term predictions. To illustrate the utility of our model framework, we provide stochastic predictions for the Finnish male and female population, in 2010–2050. For that, we first study and forecast all‐cause male and female mortality in Finland, 1878–2050, showing that using an interaction term is needed for fitting and interpreting the observed data. We then provide population predictions using a cohort component model, which also requires predictions for fertility and migration. As our main conclusion, ARI models have better properties for predictions than the simple RW models do, but mixing these prediction models with RW1 or RW2 smoothing priors for observed periods leads to a model that is not fully consistent. Further research with our model framework will concentrate on using a more consistent model for smoothing and prediction, such as autoregressive integrated moving average models with state‐space methods or Gaussian process priors. Copyright © 2013 John Wiley & Sons, Ltd.     

ARIMA与LSTM模型在医院出院人次预测中的比较研究

目的 通过ARIMA乘积季节模型和LSTM神经网络模型拟合某三甲专科医院的月出院人次并进行预测,比较两种模型的预测效果.方法 运用某三甲专科医院2013—2018年度的月出院人次,分别构建ARIMA乘积季节模型和LSTM神经网络模型,然后利用所得的模型对2019年度的月出院人次进行预测并与实际数据进行比较.采用平均绝对...