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Objectives To provide direct estimates of risk of cancer after protracted low doses of ionising radiation and to strengthen the scientific basis of radiation protection standards for environmental, occupational, and medical diagnostic exposures. Design Multinational retrospective cohort study of cancer mortality. Setting Cohorts of workers in the nuclear industry in 15 countries. Participants 407 391 workers individually monitored for external radiation with a total follow-up of 5.2 million person years. Main outcome measurements Estimates of excess relative risks per sievert (Sv) of radiation dose for mortality from cancers other than leukaemia and from leukaemia excluding chronic lymphocytic leukaemia, the main causes of death considered by radiation protection authorities. Results The excess relative risk for cancers other than leukaemia was 0.97 per Sv, 95% confidence interval 0.14 to 1.97. Analyses of causes of death related or unrelated to smoking indicate that, although confounding by smoking may be present, it is unlikely to explain all of this increased risk. The excess relative risk for leukaemia excluding chronic lymphocytic leukaemia was 1.93 per Sv ( < 0 to 8.47). On the basis of these estimates, 1-2% of deaths from cancer among workers in this cohort may be attributable to radiation. Conclusions These estimates, from the largest study of nuclear workers ever conducted, are higher than, but statistically compatible with, the risk estimates used for current radiation protection standards. The results suggest that there is a small excess risk of cancer, even at the low doses and dose rates typically received by nuclear workers in this study.

BACKGROUND: In spring 2013, groundwater of a vast area of the Veneto Region (northeastern Italy) was found to be contaminated by perfluoroalkyl substances (PFAS) from a PFAS manufacturing plant active since the late 1960s. Residents were exposed to high concentrations of PFAS, particularly perfluorooctanoic acid (PFOA), through drinking water until autumn 2013. A publicly funded health surveillance program is under way to aid in the prevention, early diagnosis, and treatment of chronic disorders possibly associated with PFAS exposure. OBJECTIVES: The objectives of this paper are: a) to describe the organization of the health surveillance program, b) to report serum PFAS concentrations in adolescents and young adults, and c) to identify predictors of serum PFAS concentrations in the studied population. METHODS: The health surveillance program offered to residents of municipalities supplied by contaminated waterworks includes a structured interview, routine blood and urine tests, and measurement of 12 PFAS in serum by high-performance liquid chromatography-tandem mass spectrometry. We studied 18,345 participants born between 1978 and 2002, 14-39 years of age at recruitment. Multivariable linear regression was used to identify sociodemographic, lifestyle, dietary, and reproductive predictors of serum PFAS concentrations. RESULTS: The PFAS with the highest serum concentrations were PFOA [median 44:4 ng=mL, interquartile range (IQR) 19.3-84.9], perfluorohexanesulfonic acid (PFHxS) (median 3:9 ng=mL, IQR 1.9-7.4), and perfluorooctanesulfonic acid (PFOS) (median 3:9 ng=mL, IQR 2.6-5.8). The major predictors of serum levels were gender, municipality, duration of residence in the affected area, and number of deliveries. Overall, the regression models explained 37%, 23%, and 43% of the variance of PFOA, PFOS, and PFHxS, respectively. CONCLUSIONS: Serum PFOA concentrations were high relative to concentrations in populations with background residential exposures only. Interindividual variation of serum PFAS levels was partially explained by the considered predictors.

Growing public awareness of environmental hazards has led to an increased demand for public health authorities to investigate geographical clustering of diseases. Although such cluster analysis is nearly always ineffective in identifying causes of disease, it often has to be used to address public concern about environmental hazards. Interpreting the resulting data is not straightforward, however, and this paper presents a guide for the non-specialist. The pitfalls include the fact that cluster analyses are usually done post hoc, and not as a result of a prior hypothesis. This is particularly true for investigations prompted by reported clusters, which have the inherent danger of overestimating the disease rate through "boundary shrinkage" of the population from which the cases are assumed to have arisen. In disease surveillance the problem of making multiple comparisons can be overcome by testing for clustering and autocorrelation. When rates of disease are illustrated in disease maps undue focus on areas where random fluctuation is greatest can be minimised by smoothing techniques. Despite the fact that cluster analyses rarely prove fruitful in identifying causation, they may-like single case reports-have the potential to generate new knowledge.

This multilevel study of spatial variability in, and determinants of, birthweight was conducted using individual and ecological data in a geographically defined prospective birth cohort for 1986 in northern Finland. The study area comprises three large areas defined by latitude: Northern Lapland (NL), Southern Lapland (SL) and Oulu province (OP), comprising 74 localities with a total study population of 9216 singleton births. The mean birthweight was 3482 g for NL, 3537 g for SL and 3587 g for OP (NL vs. OP and SL vs. OP: P < 0.05). The crude rate for stillbirths was highest in NL. The women in the northernmost area were socially less privileged and the localities less prosperous compared with those in the southernmost area. Significant spatial clustering of mean birthweights was found (P = 0.0016), with highest birthweight in the south-western part of the study area. A variable expressing the wealth of each locality, the financial capacity category (FCC), had its lowest mean value in NL, with a range of one to six for the localities studied here. A multilevel multiple regression model showed that, after allowing for sex, gestational age, mother's age, height and hypertensive disorders, parity, body mass index, previous low birthweight child and smoking as individual determinants of birthweight, part of the residual variation could be explained by the locality wealth parameter. Using the multilevel model, the differences in mean birthweight across the three latitude areas persisted but were reduced (difference OP vs. NL reduced from 105 g to 86.5 g). The relationship between birthweight and FCC was inverse U-shaped with the highest mean birthweight estimated for localities occurring in the middle of the range (FCC = 3). The wealthiest urban localities (FCC = 6) and the most deprived localities (FCC = 1) both had a predicted birthweight about 60 g below the maximum at FCC = 3, if all other factors were held constant. This result, taken together with the spatial clustering of birthweights, suggests that there may be important social and environmental determinants of birthweight that have yet to be identified.