Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e., boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e., edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test.

典型电子垃圾拆解区居民多卤代芳烃的膳食暴露及其癌症风险评估

收集浙江典型电子垃圾拆解区和对照区的饮用水、蔬菜、豆类、米饭、鸡蛋、鱼、鸡肉和猪肉等8类食物样品共191个;采用GC/MS 5975B分析样品中23种PBBs,12种PBDEs和27种PCBs;评估当地居民的PHAHs膳食暴露现状及其癌症风险.结果表明,拆解区居民这三大类PHAHs的平均日摄入量是对照区的2~3倍,通过米饭摄入PHAHs的量占当地居民PHAHs总摄入量的48%以上.拆解区居民因食物摄入PHAHs的癌症风险(3.81×10-4)是对照区(1.50×10-4)的2倍多,其中二英类PCBs的风险值占总癌症风险的45%以上.通过米饭摄入PHAHs是主要的暴露途径,二英类PCBs是引起癌症风险的主要贡献因子,电子垃圾拆解释放出的PHAHs通过食物摄入进入居民体内引起的癌症风险要明显高于对照区.     

基于PBPK模型评价三氯乙烯的职业暴露健康风险

三氯乙烯(TCE)作为脱脂和清洗剂被广泛应用于五金、电镀和电子等行业。TCE的职业暴露会产生一系列健康风险,包括过敏症和致癌等。2012年TCE被美国环保局(US EPA)和国际癌症研究机构列为1类致癌物。采用吸附管采样-热脱附/气相色谱-质谱法分析了大连市某企业车间生产工况下空气中TCE浓度。基于生理学的药代动力学(PBPK)模型预测了呼吸暴露途径下TCE在职业工人体内组织中的动态分布、代谢产物生成情况和致癌风险。TCE在不同组织中预测的最大浓度呈现出脂肪肠充分灌注室支气管非充分灌注室肝脏静脉血动脉血的趋势。预测的与致癌有关的代谢产物最大浓度表现为三氯乙酸二氯乙酸三氯乙醛S-二氯乙烯基-L-半胱氨酸。在监测的TCE水平(39.2±24.4)μg·m-3下,暴露8 h·d-1,连续暴露20年,基于外暴露评价的职业工人致癌风险均值为1.31×10-5,该暴露水平下,基于PBPK模型预测的TCE内暴露与外暴露计算的致癌风险水平相近,但基于具有致癌性主要代谢产物的内暴露致癌风险值是外暴露风险值的1.17～1.73倍。TCE的暴露水平越高,基于内暴露方法和外暴露方法的致癌风险评价结果差异越大。敏感性分析表明,心输出血流量和充分灌注室血流量对PBPK模型输出结果具有重要影响。不确定性分析表明,模型参数变化会显著地影响PBPK模型输出结果,但变异在可接受水平。本研究结果说明,评价TCE暴露对人的致癌风险需要考虑其在体内的分布和代谢过程。     

江南Z市农村饮水有机污染物健康风险评估

应用化学法及吹扫捕集、气相色谱-质谱联用仪(GC-MS),检测Z市农村不同类型饮用水与癌症相关的有机污染物,对污染物的剂量与癌症标化死亡率进行Spearman等级相关分析。该市河水、池塘水、浅井水中部分样品的COD_Mn、BOD₅和NH₃-N指标超过地表水环境质量Ⅲ类标准,微生物指标超过生活饮用水卫生标准,检出NH₃-N及NO₃-N、NO₂-N合成致癌物NAD的2类前体物及NDMA等直接致癌物。Z市农村河水、池塘水、浅井水主要受生活排污和农业生产污染,不宜作生活饮用水。不同类型饮用水有机污染物丰水期高于枯水期(P值均小于0.05)。癌症标化死亡率与不同类型饮用水有机污染浓度呈等级正相关(P值均小于0.05),与NO₂-N、NDMA、NDEA含量呈剂量-效应关系。直接饮用污染的地表水是造成该市农村癌症高发聚集分布的主要原因之一。     

阿坝州室内氡及子体水平与肺癌患病率关系的研究

本文报道了四川省阿坝州高原山区各县室内氧及子体浓度,并对阿坝州各县肺癌患病进行了流调。全州室内氧浓度范围值0.5～374.1Bqm~(_3)X:22.7Bqm~(-3);Xg:16.3Bqm~(-3),子体浓度范围值0.32～87.6mwl;X:6.0mwl;Xg:3.94mwl。研究表明:肺癌患病率与氡浓度有相关关系,r=0.6,p<0.01。     

江西某地"癌症高发区"地质环境现状及评价

江西某地“癌症高发区”的报道引起了社会的广泛关注。通过对该地土壤、水体、降尘地质环境调查评价,认为调查区土壤环境质量较好,为清洁区,仅镍元素达轻度污染级。地下水质量为较差级,外围为优良级。饮用水源地水质综合评价为较差级,有机氯农药检出2项,多环芳烃检出10项,萘达到轻度污染级。区域地质、水文地质背景不具特殊性,土壤、水体地质环境与周边南昌地区无明显异常,是否与癌症发病有直接的关系,尚需作进一步的调查研究工作。降尘污染较为严重,污染因子主要为镉,应引起重视。     

Prenatal detection of non-cardiac rhabdomyosarcoma

The most prevalent soft tissue tumour in children is rhabdomyosarcoma. These tumours may develop within or outside of muscle anywhere in the body and at any age. We report what is apparently the earliest case of non-cardiac rhabdomyosarcoma detected prenatally.     

氟化物与癌症相关性研究进展

为了探索氟化物与癌症的相关性及其机理,国内外许多研究机构分别从氟化物对癌的诱发作用、流行病学调查和相关性研究以及促进致癌作用等方面进行了长期的研究.结果表明:在一定的条件下,氟化物对肝癌、纤维肉瘤、肿瘤性转化等方面具有诱发作用,与口腔癌、咽癌、结肠癌、直肠癌、肝胆管癌、排泄器官癌、男性骨癌、泌尿系癌、淋巴造血系癌、肺癌、膀胱癌等的发生密切相关,在肿瘤发生过程中,也具有某种促进作用.     

某化工企业1971～2000年职工恶性肿瘤死亡情况的动态分析

对以天然气、食盐为原料生产基础化工产品的综合性化工企业的职工调查显示,恶性肿瘤已成为该企业职工死亡的主要原因之一,总死亡率为1.44‰,与当地对照有显著性,尤以肺癌、肝癌、食道癌为主.动态观察显示,职工恶性肿瘤死亡率从1971～的0.41‰上升为1996～的1.93‰,总体呈现逐年上升的趋势.1991～以来的男、女职工死亡性比率为1.90∶1,两者差异非常显著,目前3位死因都有统计学意义.这说明恶性肿瘤对该企业职工,特别是男性职工的生命健康威胁越来越大,应成为今后卫生工作的重点之一.     

Evaluation of the validity of the US EPA's cancer risk assessment of arsenic for low-level exposures: a likelihood ratio approach

Skin cancers associated with ingesting of arsenic have been documented since the 19th century. A study in the southwestern coastal area of Taiwan where people drank well water containing arsenic is generally recognised as providing the best data available for quantifying the risk, and the US Environmental Protection Agency (EPA) used these data to conduct a risk assessment of arsenic ingestion. However, the lowest exposure category in the Taiwan study included arsenic levels up to 290 µg L^–1, which is nearly six times higher than the current EPA maximum contaminant level (MCL), 50 µg L^–1. Therefore, the EPA risk assessment model extrapolated data on high-level exposures to generate risk estimates for low-level exposures. To evaluate the validity of this model, we conducted a quantitative review of epidemiological studies observing arsenic exposures below 290 µg L^–1. A ratio of the likelihood of the EPA model being inappropriate to that of it being appropriate was calculated for each study population as a measurement of the validity of the EPA model. Although existing human data on low-level exposures are limited, the review suggested that the EPA model is unlikely to be able to predict the risk of skin cancer accurately when the arsenic exposure level is between 170 and 270 µg L^–1.