恶性肿瘤死亡率年龄分布的数学模型

恶性肿瘤死亡率有随年龄增长而升高的趋势,为了探讨这种趋势的规律性,我们采用指数曲线y=10^a+bx对山东省疾病监测点恶性肿瘤死亡资料进行数学模拟建立了恶性肿瘤死亡率年龄分布的数学模型,并用全国资料进行了验证,以观察这种模型的普遍意义。该模型不仅可从理论上阐明一个人群恶性肿瘤死亡率年龄分布的规律,为恶性肿瘤死亡预测提供一种初步方法,还可用指数曲线方程y=10^a+bx的微分方程计算出各年龄组每增长一岁时恶性肿瘤死亡率的增量,由该模型尚可推导出一个人群恶性肿瘤死亡率随年龄增长的"增长倍敛常数",该常数可以用作比较不同人群或同一人群不同时期恶性肿瘤死亡率受年龄影响程度的指标,为恶性肿瘤病因研究提供线索。     

脑血管病死亡率年龄分布的数学模型

本文采用指数曲线y=10~(a bx),对国内几个不同类型地区和性别人群的脑血管病死亡率资料进行了数学模拟,建立了脑血管病死亡率年龄分布的数学模型,这一模型表明了脑血管病死亡率年龄分布的规律,可以预测人群中脑血管病的死亡率和死亡数。用指数曲线方程y=10~(a bx)的微分方程xy/dx=(1n 10)·b·10~(a bx)可以计算出各年龄组当年龄增长1岁时脑血管病死亡率的增量。进而,可计算出这种增量随年龄组增长而增长的“增长倍数常数”。这两种指标均可作为比较不同人群脑血管病的危害程度.     

脑血管病死亡率年龄分布指数模型拟合研究

目的：探讨济宁市疾病监测点1996、1997年脑血管病死亡率年龄分布特征。方法：应用指数模型y=ae^bx进行拟合研究。结果：描绘出脑血管病死亡率年龄分布的轨迹，阐明脑血管病死亡率随年龄变化而变化的规律，发现脑血管死亡率年龄分布曲线比期望值年龄分布曲线向左移；1997年比1996年的年龄分布高峰向左移。同时，得出脑血管病死亡率增长的速度为增长倍数的常数结果。结论：指数曲线y=ae^bx能较好地表达这种规律性，拟合度检测结果、曲线总趋势比较一致。     

内蒙古自治区全人群脑血管病流行病学调查

目的 探讨内蒙古自治区全人群脑血管病流行病学特点。方法 2013年9月至2014年1月采用与人口规模成比例的PPS抽样方法,对内蒙古自治区全年龄组常住居民19 315人进行现场问卷调查及影像(CT/MRI)等辅助确诊和体格检查。结果 内蒙古自治区全人群脑血管病患病率为1 812.06/10万(男性2 008.86/10万,女性1 613.24/10万)。随年龄增加患病率升高,且男性高于女性,乡村人群高于城区,文化程度低者患病率较高。脑血管病发病率为392.54/10万,男女性别间发病率的差异无统计学意义(χ²=0.380,P=0.846);农村人群高于城市,差异有统计学意义(χ²=13.029,P=0.000),且随年龄增加发病率有逐渐升高的趋势(χ²=410.130,P=0.000)。脑血管病死亡率为149.67/10万,病死率为15.14%。中年组脑出血及脑梗死患病率均高于青年组(< 45岁)人群。结论 内蒙古自治区全人群脑血管病患病率、发病率、死亡率、复发率均较高,并以缺血型为主。     

高血压年龄患病专率的数学模型

从1979年我国全国高血压抽样普查结果看来,不同地区不同性别人群在15~50岁年龄范围内,高血压患病率普遍呈随年龄增长的类似趋势。本文试用指数曲线ŷ=de^bx和ŷ=HBx来模拟各地的这个趋势。所得的数学模型的拟合度是高的。根据以上模型,可以推算出高血压患病率随年龄而增的速率dŷ/dx.各个人群的数学模型中特有的d、b二值(或H、B二值)及推导所得的dŷ/dx,可能是测量和比较不同条件下的人群发病趋势和研究环境因子或人群特征与疾病关系的有价值的指标.     

莱芜市农村居民脑血管病死亡率年龄分布指数曲线拟合

本文利用指数曲线方程y=ae~(bx),对莱芜市疾病监测点农村居民脑血管病年龄别死亡率进行了模拟,旨在了解脑血管病年龄死亡率的分布特点,现报告如下。     

人群结直肠癌筛检项目成本效果分析与评价

目的 以卫生经济学角度，探讨各年龄组人群结直肠癌筛检项目成本效果差异。方法 利用浙江省嘉善县结直肠癌筛检项目资料，计算各年龄组人群的筛检依从率和病例检出率，利用χ²检验及趋势χ²检验判断年龄组间的差别。通过对筛检项目成本进行统计，计算各年龄组的成本/效果比。结果 免疫化学粪便潜血试验阳性率及进展期腺瘤、结直肠癌和早期癌的检出率随年龄增长有上升趋势，早诊率随年龄增长有下降趋势。40～49岁组人群筛检成本/效果比最高，敏感性分析发现剔除该组后筛检成本/效果比将降低15%～30%。结论 从卫生经济学角度，结直肠癌筛检起始年龄推迟至50岁更有利于提高筛检效率。     

山西省宫颈癌筛查队列中人乳头瘤病毒基因型别分布10年动态变化规律研究

目的 评估人乳头瘤病毒（HPV）型别分布特征在宫颈癌筛查队列中随时间动态的变化。方法 采用线性反向探针杂交技术,对山西省宫颈癌筛查队列2005-2014年高危型HPV阳性女性宫颈脱落细胞标本进行HPV基因型别检测。利用线性混合模型分析不同型别HPV感染率在总人群中随时间的变化趋势,采用线性趋势χ²检验比较宫颈上皮内瘤变2级及以上（CIN2+）人群中HPV型别随时间的变化,最后分析多重感染率随年龄增长的动态变化规律。结果 2005-2014年筛查总人群中最常见型别为HPV16和52,但HPV16感染率在筛查随访中,从2005年4.6%下降到2010年和2014年的2.2%（F=8.125,P<0.001）。HPV52感染率在筛查随访中相对稳定,HPV33、51和58感染率则先下降后升高。病理结果正常人群中HPV型别分布与总人群相似,CIN2+人群中HPV16感染率下降较为明显,由2005年65.22%,经2010年41.03%下降至2014年的31.58%（χ²=4.420,P=0.036）,HPV33感染率有升高趋势,其余HPV型别感染率在筛查过程中无明显变化。多重感染率随年龄增长而波动。结论 宫颈癌筛查队列在定期筛查和治疗随访中HPV型别分布特点有所变化,尤其是HPV16出现显著下降,提示宫颈癌人群筛查和随访时如采用HPV检测,应考虑型别随时间变化的规律。     

福建省1990--201 0年居民出生期望寿命差异分析

目的分析1990--2010年福建省居民出生期望寿命的变化,探讨不同年龄、死因对期望寿命年代差异的影响。方法利用卫生部死因监测系统中福建省1990--2010年人群的死亡数据估算出生期望寿命,应用Arriaga因素分解法估计期望寿命改变的年龄别、死因别贡献。结果20年问福建省城乡居民期望寿命分别增长了5.82岁和11.67岁,城市人群出生期望寿命高于农村,但农村人群增幅高于城市,两者差距逐步缩小。低年龄组对出生期望寿命增加的贡献率减小,<14岁儿童对农村地区期望寿命的贡献率由78.29%下降至31.23%,使城市居民出生期望寿命降低,高年龄组逐渐成为影响出生期望寿命变化的主体。恶性肿瘤、呼吸系统疾病及脑血管病对城市居民期望寿命增量的影响在减弱,传染病和寄生虫病、神经系统疾病及心血管病的影响增大,分别使城市居民期望寿命增加1.54岁、O.67岁和0.49岁,呼吸系统疾病、消化系统疾病及损伤和中毒对农村居民期望寿命影响也在逐渐减少,而恶性肿瘤、脑血管病、心血管病的影响在逐渐增加,三者使农村居民期望寿命增加了1.23岁;不同死因对各年龄人群期望寿命增量的作用不同。结论福建省居民应降低高年龄组死亡率,提高慢眭非传染性疾病的防治水平,有助于提高人群期望寿命。     

两级催化曲线模型在肺吸虫病流行病学上的应用

本文应用两级催化模型对浙江省永嘉县的小长坑、黄过坑两村人群肺吸虫感染进行拟合,得曲线方程:ŷ（小）=1.0480（e^-0.01375t-e^-0.3t）;ŷ（黄）=1.0661（e^-0.0155t-e^-0.25t）。模型较成功地模拟了两村人群感染肺吸虫的年龄分布情况,定量测知该两村人群肺吸虫平均感染力分别为0.30、0.25。同时,通过模型分析了两村肺吸虫感染状况、人群感染的年龄分布特征等。认为感染力“a”值是一个定量估计流行区肺吸虫感染程度的指标,可作为比较各地流行状况,评价防治效果等。     

河北省卢龙县婴幼儿轮状病毒腹泻与气象因素的关系

目的探索河北省卢龙县轮状病毒腹泻与气象因素之间的关系。方法监测2000-01/2003-06河北省卢龙县每月5岁以下轮状病毒腹泻住院患者数以及当地每月的温度、湿度和降雨量。计算卢龙县轮状病毒腹泻的月住院率,并对轮状病毒腹泻和温度、湿度及降雨量进行相关和回归分析。结果RV腹泻与温度之间的相关系数为-0.75,与湿度及降雨量无相关性。RV腹泻发病和温度之间呈现指数曲线的关系,指数曲线方程：y=13.2-2.2x＋0.11x^2-0.0016x^3。结论卢龙县婴幼儿轮状病毒腹泻主要受温度的影响,与温度呈负相关,不受湿度和降雨量的影响。     

Confirmation of delayed menarche based on regression evaluation of age at menarche for age at MPV of height in female ball game players

A general delay in menarche in female athletes has been confirmed based on comparisons of mean ages between athletes and non-athletes; however, it has not been possible to judge such delays individually. If delayed menarche could be evaluated for an individual, the athlete could be advised as to necessary precautions. In this study, the age at maximum peak velocity (MPV) of height, adopted as an index of physical maturation, was identified by the wavelet interpolation method (WIM). The relationship between the age at menarche and age at MPV of height in female athletes and non-athletes was then examined. For the athlete group, health examination records of 90 female ball game players in the first year of university in the Tokai area, all of whom had participated in national level competitions, were reviewed for the period from the first grade of elementary school until the final year of high school (from 1985 to 1996). A similar examination was conducted for the control group, among whom a final group of 78 female non-athletes were selected. The age at menarche was determined by questionnaires, and the longitudinal data for height and weight were obtained from the health examination records. Based on a comparison of the difference between the age at MPV of height and age at menarche in ball game players and the control group, a tendency was seen for the difference between the two ages to narrow as the age at MPV of height rose. A corrected regression evaluation of age at menarche against age at MPV of height was derived in the control group, and the evaluation system was applied to ball game players. The delay in menarche in ball game players could be individually evaluated. The trend line 1 applied conveniently with the above corrected regression evaluation is derived from the following equation. Trend line 1: y=0.589x+6.61 The calculation method of trend line 1 is as follows. First, substitute 9.80 years of age at MPV of height: as the mean in early maturation for the regression equation (y=0.682x+4.55). The estimated age at menarche, y=0.682×9.80+4.55=11.2336, is calculated. Next, substituting 12.05 years of age at MPV of height as the mean in late maturation for the regression equation (y=0.682x+4.55), the estimated age at menarche, y=0.682×12.05+4.55=12.7681, is calculated. 1.5 SD (1.5×0.76=1.14, 1.5×0.62=0.93) of difference between age at MPV of height and age at menarche in both maturation bands is added to the above-mentioned estimated age at menarche in the early and late maturation bands. The trend line 1 that passes through the two points (y1, y2) in the early and late maturation bands with the added 1.5 SD is determined. In other words, y1=11.2336+1.14=a×9.80+b...(1) y2=12.7681+0.93=a×12.05+b...(2) From equations (1) and (2), a=0.589, b=6.61, trend line 1 y=0.589x+6.61 is derived.     

A new predictive equation for resting energy expenditure in healthy individuals

A predictive equation for resting energy expenditure (REE) was derived from data from 498 healthy subjects, including females (n = 247) and males (n = 251), aged 19-78 y (45 +/- 14 y, mean +/- SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was measured by indirect calorimetry. Multiple-regression analyses were employed to drive relationships between REE and weight, height, and age for both men and women (R2 = 0.71): REE = 9.99 x weight + 6.25 x height - 4.92 x age + 166 x sex (males, 1; females, 0) - 161. Simplification of this formula and separation by sex did not affect its predictive value: REE (males) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) + 5; REE (females) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) - 161. The inclusion of relative body weight and body-weight distribution did not significantly improve the predictive value of these equations. The Harris-Benedict Equations derived in 1919 overestimated measured REE by 5% (p less than 0.01). Fat-free mass (FFM) was the best single predictor of REE (R2 = 0.64): REE = 19.7 x FFM + 413. Weight also was closely correlated with REE (R2 = 0.56): REE = 15.1 x weight + 371.     

A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice

Summary Background Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group. Aim of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice. Subjects and methods RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 ± 5.7 y, BMI 26.4 ± 3.7 kg/m²) and 107 male (age 66.9 ± 5.1 y, BMI 26.3 ± 3.1 kg/m²) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR. Results In females there was no significant difference between our measured RMR (5504 ± 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 ± 440 kJ/d), whereas in males measured RMR (6831 ± 779 kJ/d) was significantly higher than calculated RMR (6490 ± 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 · body weight [kg] − 15.3 · age [y] + 746 · sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R²) of the variance in RMR and predicted RMR within ± 486 kJ/d (SEE). Conclusion On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice. Received: 5 November 2001, Accepted: 28 February 2002     

Evaluation of the capacity of the APR-DRG classification system to predict hospital mortality

Inpatient mortality has increasingly been used as an hospital outcome measure. Comparing mortality rates across hospitals requires adjustment for patient risks before making inferences about quality of care based on patient outcomes. Therefore it is essential to dispose of well performing severity measures. The aim of this study is to evaluate the ability of the All Patient Refined DRG system to predict inpatient mortality for congestive heart failure, myocardial infarction, pneumonia and ischemic stroke. Administrative records were used in this analysis. We used two statistics methods to assess the ability of the APR-DRG to predict mortality: the area under the receiver operating characteristics curve (referred to as the c-statistic) and the Hosmer-Lemeshow test. The database for the study included 19,212 discharges for stroke, pneumonia, myocardial infarction and congestive heart failure from fifteen hospital participating in the Italian APR-DRG Project. A multivariate analysis was performed to predict mortality for each condition in study using age, sex and APR-DRG risk mortality subclass as independent variables. Inpatient mortality rate ranges from 9.7% (pneumonia) to 16.7% (stroke). Model discrimination, calculated using the c-statistic, was 0.91 for myocardial infarction, 0.68 for stroke, 0.78 for pneumonia and 0.71 for congestive heart failure. The model calibration assessed using the Hosmer-Leme-show test was quite good. The performance of the APR-DRG scheme when used on Italian hospital activity records is similar to that reported in literature and it seems to improve by adding age and sex to the model. The APR-DRG system does not completely capture the effects of these variables. In some cases, the better performance might be due to the inclusion of specific complications in the risk-of-mortality subclass assignment.     

求和自回归滑动平均模型结合圆分布法分析脑卒中死亡率动态规律

通过1999年1月至2006年12月天津市脑卒中逐月死亡率数据,应用圆分布法探讨脑卒中死亡率的季节分布,动态变化规律,建立监测与预测的时间序列模型.通过模型辨识、参数估计及其检验、白噪声检验、模型的拟合度分析等过程,建立求和自回归滑动平均模型(ARIMA)的季节乘积模型(P,d,q)(P,D,Q)s.脑卒中死亡率以年为周期,一年中1月为高发月份.建立ARIMA(0,1,0)×(0,1,1)12:模型:(1-B)(1-B12)lnx1=0.001+(1-0.537B12)εt.结论:ARIMA乘积模型结合圆分布法是对脑卒中死亡率进行时间序列分析的重要方法;应用该方法可对脑卒中流行趋势及死亡率进行预测,为卫生资源合理分配、公共卫生政策计划制定和防治结果考核提供科学依据.     

天津市城乡居民脑卒中流行病学调查:6年前瞻性研究

采用前瞻的方法，对天津市城乡各一个监测点脑卒中发病死亡的状况进行６年前瞻性研究。结果表明：城乡脑卒中发病率和死亡率无明显差异（Ｐ〉０．０５）；但均随着年龄的增加而逐渐升高。总发病密度城乡分别为４４３．８６／１０万人年和４０２．０５／１０万人年，总死亡率则分别为２４８．２０／１０万人年和１９０．２２／１０万人年。在发病类型上无明显的城乡差异；城市脑出血死亡率在４５岁以后明显高于脑梗塞死亡率，农村则是