大气污染与居民肺癌发病及死亡灰色关联分析

目的探讨北京市、上海市和广东省广州市的大气污染与居民肺癌发病及死亡的关系,为肺癌防制政策的制定提供科学依据。方法收集北京、上海和广州市大气污染资料及居民肺癌发病和死亡数据,采用灰色关联分析方法计算绝对关联度、相对关联度和综合关联度,并对大气污染致肺癌的潜伏期作灰色定量分析。结果NO₂和PM2.5对北京市居民肺癌发病及死亡的影响最大,致肺癌的潜伏期分别为4和8年;降尘与PM2.5与上海市居民肺癌发病及死亡的关系最密切,致肺癌的潜伏期分别为2和4年;降尘与NO₂对广州市居民肺癌发病及死亡的影响最大,致肺癌的潜伏期均为5年。结论北京、上海和广州市大气污染指标对居民肺癌发病及死亡的影响各有不同,其致肺癌的潜伏期各不相同,需针对性整治大气污染并提出相关肺癌防治措施。     

医院入院人数变化的改良灰色关联分析

本文应用关联度具有维一性、对称性、可比性和体现负相关的改良灰色关联分析方法对影响入院人数变化的１１个因素进行关联分析。结果表明：（１）医院的规模、数量和门诊人次及病床周转次数与入院人数的关联程度最高，是影响入院人数变化的主要因素；（２）提供医疗卫生服务的能力对入院人数变化的影响大于医疗卫生服务的投入和需求；（３）人口的数量和疾病严重程度对入院人数变化的影响大于传染病的发病。     

影响入院人数的院内因素灰色关联分析

以往在医院管理中涉及到多因素分析等方面时,都是用数理统计方法进行处理,但统计方法要求有大样本、典型概率分布和计算工作量大等不足,使它在实际工作中存在某些局限性。灰色系统理论的灰色关联分析不仅克服了这些缺点,而且还是按系统发展趋势作的分析。因此,通过这种分析,使医院管理者明确了该系统中的因素,哪些是主要的,     

武汉市空气污染与肺癌潜在减寿年数的关系

目的 探索武汉市空气污染与肺癌之间的关系.方法 收集武汉市1986-1995年间空气质量监测资料及烟草生产等方面的资料,以及1991-2000年间武汉市肺癌死亡资料,利用简单相关、灰色关联等方法进行统计分析.结果 武汉市空气中氮氧化物(NOx)浓度呈现上升趋势,二氧化硫(SO2)、NOx、总悬浮颗粒物(TSP)与男性肺癌潜在减寿年数(PYLL)的关联度分别为0.6702、0.7071、0.6199,与女性肺癌PYLL的关联度分别为0.6188、0.8555、0.5842.NOx浓度与男、女性肺癌PYLL均呈正相关,相关系数分别为r男=0.635 23(P=0.0484),r女=0 763 96(P=0.0101).结论 空气污染是肺癌的重要危险因素,机动车尾气的污染是主要因素之一.     

大气污染与肺癌关系的回归分析

本文通过对福州市区１９８４～１９９３年１０年间大气环境污染状况以及肺癌死亡人数的连续追踪监测，采用多元回归、逐步回归等统计手段，对获取的数据进行回顾性分析。结果表明：福州市区肺癌死亡率和大气中总悬浮颗粒物、降尘呈正相关，相关性显著，且其影响存在滞后效应，并根据实际情况提出控制大气污染、降低肺癌死亡率的具体措施。     

住院费用增长原因的灰色关联分析

本文介绍了宫颈癌病人住院费用增长原因灰色关联分析原理方法,并对湖南省肿瘤医院宫颈癌病人住院费用进行了灰色关联分析,文中还介绍了有关的微机程序计算框图。     

传染病发病率的灰色关联分析

掌握传染病的发病情况,发病因素,分析主要传染病及其流行趋势是十分必要的。本文对传染病的发病率进行了灰色关联分析,以探讨传染病发病的影响因素。     

灰色预测和灰色关联分析在医院管理中的应用

目的 论述和探索灰色预测和灰色关联分析在医院管理中应用的方式和途径。方法 以某医院入院人数为例,应用灰色系统理论建立了ＧＭ（１,１）灰色预测模型和面积关联度分析法,对该院１９９２年至２０００年的入院人数及影响入院人数的院内因素进行了预测和分析。结果 所建预测模型拟合精度高（Ｃ＝０．０４６〈０．３５,Ｐ＝１〉０．９５）可用于外推预测;所选的与该院入院人数相关的７个比较因素,其关联程度由强到弱依次为病床周转次数、治愈有效率、在岗医务人员数、病床数、平均住院日、门急诊人次、病死率。结论 灰色预测和灰色关联分析的有机结合在医院管理中有更为广阔的用途。     

灰色关联分析在水质评价中的应用

灰色关联分析在医学研究中的应用已有报道［１，２］。本文是将该法演变为通过比较待评水样与各评价等级标准的关联度,然后将待评样品进行分类定级以达到多指标综合评价的目的。该方法与灰色聚类分析相比较,计算更简单,更便于推广使用。现将该法介绍如下。１计算步骤与方法１．１建立数据矩阵首先以待评水样的Ｍ个污染指标为参考数列｛ｘ０｝,然后以Ｎ个水质等级各项污染指标标准构成比较数列｛ｘ１｝,｛ｘ２｝,…,｛ｘＮ｝,排成如下（Ｎ+１）×Ｍ矩阵ｘ１．２数据矩阵预处理对上述数据矩阵的原始数据一般都要做预处理,以消除量纲的差异,强…     

2010—2017年厦门市肺癌发病趋势分析及预测

目的 分析2010—2017年厦门市肺癌发病率和发病趋势,预测2018—2020年肺癌发病率,为厦门市制定肺癌防治策略提供依据。方法 收集2010—2017年厦门市肿瘤登记报告的肺癌病例资料,计算肺癌发病率、中标率、世标率、累积发病率。采用Joint - point回归计算年度变化百分比（average annual percent change, APC）,分析其变化趋势。采用灰色模型（grey model, GM）预测2018—2020年肺癌发病率。结果 2010—2017年厦门市肺癌粗发病率为66.05/10万,中标率为51.34/10万, 世标率为51.35/10万,0～64岁累积发病率为2.59%,0～74岁累积发病率为6.25%。2010—2017年厦门市肺癌粗发病率年均上升1.92%（t = 3.41,P＜0.05）,其中女性年均上升5.74%（t = 5.68,P＜0.05）,男性无变化趋势（t = 1.55,P＞0.05）。灰色模型预测结果显示,2018—2020年肺癌粗发病率分别为73.21/10万、75.19/10万、77.22/10万。结论 厦门市肺癌发病率水平较高,女性肺癌发病率上升明显。应采取有效肺癌防治措施。     

Correlation between the arsenic concentrations in the air and the SMR of lung cancer

Objectives  To verify whether the concentrations of arsenic (As) and its compounds in the air (referred to here as arsenic concentrations) affect the standardized mortality ratio (SMR) associated with lung cancer. Methods  Using monitoring survey data on arsenic concentrations published by the Ministry of the Environment, we classified the municipalities for which arsenic concentrations were measured (measured municipalities) into ten groups according to the average arsenic concentration. We then determined the SMR of lung cancer, stomach cancer, pneumonia, cerebrovascular disease and cardiac disease for each group using socio-demographic data, such as the national census and demographic trends. The relationships between these factors were compared and investigated by statistical means. Results  No effect of arsenic concentrations on stomach cancer, cerebrovascular disease or cardiac disease was observed, and while significant differences in pneumonia were observed among several of the male subjects, there were no significant effects of arsenic concentration. However, lung cancer and arsenic concentration showed a significantly positive correlation for both males and females (males: Spearman r = 0.709, P < 0.05; females: Spearman r = 0.758, P < 0.05). The probability of type α error was less than 5% in areas with more than 1.77 ng As/m³ (71st percentile) and less than 1% in areas with more than 2.70 ng As/m³ (91st percentile). These results confirm that the SMR of lung cancer tends to be higher than the national average in areas of higher arsenic concentrations. Conclusions  The SMR of lung cancer is significantly higher in areas with arsenic concentrations of 1.77 ng/m³ or more.     

Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

OBJECTIVE

To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children.

METHODS

Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components.

RESULTS

For every 10:49 μg/m³ increase (interquartile range) in levels of the pollutant PM₁₀ there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%.

CONCLUSIONS

Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.     

大气污染物致肺癌的潜伏期灰色定量分析

目的：测算大气污染物致肺癌的潜伏期。方法：利用灰色系统理论中的灰色关联度模型，根据最大关联度值测算出总体的潜伏期。结果：总悬浮颗粒物（TSP）的最大关联度值为0.886，所对应的是1985－1989年的比较数列与1994－1998年的参考数列的关系最为密切。二氧化硫（SO2）的最大关联度值为0.919，的是1986－1990年的比较数列与1994－1998年的参考数列的关系最为密切。结论：H市SO2和TSP致肺癌的潜伏期分别为7年和8年。该方法不受混杂因素的制约，且计算较为简便，为大气污染物致肺癌的潜伏期研究提供一种可行和实用的新途径。     

Smoking and air pollution exposure and lung cancer mortality in Zhaoyuan County

Background

Simultaneous exposure to high levels of air pollution and high tobacco consumption at the same place is rare. The aim of the present study was to evaluate the impact of the two factors on the risk of developing lung cancer.

Methods

Data on the number of deaths due to lung cancer and on population from 1970 to 2009 were obtained from Zhaoyuan County. Data on the smoking populations were obtained at random sampling survey during the time in Zhaoyuan. Data on the components of atmospheric surveillance were obtained from the local environmental protection offices. Logarithmic linear regression and general log-linear Poisson age-period-cohort (APC) models were used to estimate age, period, cohort, gender, smoking, and air pollution effects on the risk of lung cancer mortality.

Results

The standardized mortality rates of lung cancer drastically increased from 8.43 in per 100 000 individuals in the 1970–1974 to 25.67 in per 100 000 individuals in the 2005–2009 death survey. The annual change of lung cancer mortality was 3.20%. In the log linear regression model, the age, proportion of smokers, gender, period, and air pollution are significantly associated with lung cancer mortality. The APC analysis shows that the relative risks (RRs) of gender, smoking, and air pollution are 2.29 (95% confidence interval (CI): 2.16–2.43), 3.05 (95% CI = 2.76–3.36), and 1.42 (95% CI = 1.19–1.69), respectively. Compared with the period 1970–1974, high RRs were found during 1995–2009. Compared with the birth cohort 1950–1954, the RRs increased in the birth cohorts of 1910 to the 1940. Compared the aged 35–59 and 60–84 in the1980–1984 death survey (not exposed to air pollution) with that in the 2005–2009 death survey (exposed to air pollution), The two age groups exposed to air pollution, 25 years later, had an increased mortality rates for lung cancer by 2.27 and 3.55 times for males and by 1.47 and 3.35 times for females.

Conclusion

The mortality rates of lung cancer drastically increased in the past 35 years. The trend of lung cancer mortality may be in a great extent possibly due to the effects of combined smoking and air pollution exposure.     

大气污染与主要癌症相关性研究进展

大气污染是癌症明确的危险因素,对人类的健康构成严重威胁。本文介绍了大气污染的组成成分和WHO与我国的空气质量标准,并总结了国内外对于大气污染与肺癌、乳腺癌和膀胱癌等主要癌症的关联性的最新研究。     

HLA-DP基因多态性与肺癌遗传易感性研究

目的 探讨人类白细胞抗原（Human Leukocyte Antigens,HLA）DP基因多态性与中国汉族人群肺癌遗传易感性的关联。方法 应用TaqMan探针方法检测401例肺癌患者和843例对照者 rs3077和rs9277535位点基因型,比较不同基因型与肺癌患病风险的关系。结果 采用多因素Logistic回归分析,结果显示rs3077和rs9277535位点突变基因型AA显著增加肺癌的发病风险（调整OR = 1.69,95%CI = 1.16～2.51;调整OR = 1.58,95%CI = 1.10～2.25）;单倍型分析显示,与GG单倍型相比,携带AA单倍型的个体可增加肺癌发病风险（调整OR = 1.41,95%CI = 1.61～1.71）;与携带rs3077 GG+GA and rs9377357 GG基因型的个体相比,携带1～4个危险等位基因的个体发生肺癌的风险显著（P趋势 ＜0.01）。结论 HLA基因多态性与中国汉族人群肺癌的发病风险存在关联。     

上海市区大气中真菌污染状况的研究

[目的 ]　调查上海市区大气中真菌污染状况及对策研究。 [方法 ]　采用空气自然沉降法于 1993年 7月～ 1994年 4月调查上海市区大气真菌污染状况。 [结果 ]　单细胞真菌功能区年平均数 7.2 4个 /皿 ,明显低于特殊区年平均数 2 7.30个 /皿 ,功能区单细胞真菌污染以交通、商业区较高 ,居住工业混合区、清洁区较低。多细胞真菌地区分布在不同季节不一致。真菌分布有明显季节性规律 ,单细胞真菌以冬春季为高峰 ,夏秋季降低 ,而多细胞真菌则夏秋季升高 ,冬春季较低。除夏季外 ,大气中单细胞、多细胞真菌数均呈日高夜低的日周期变化规律 ,前者以 10～ 14时为高峰 ,后者高峰随季节变化较大。真菌的空间分布一般以 1.2米处高于 15米处。 [结论 ]　上海市区大气真菌污染有一定地区分布 ,季节性分布规律。单细胞和多细胞真菌分布规律不同。