Atmospheric fine particulate matter and breast cancer mortality: a population-based cohort study.

OBJECTIVES: Atmospheric fine particulate matter (PM2.5) has multiple adverse effects on human health. Global atmospheric levels of PM2.5 increased by 0.55 µg/m3/year (2.1%/year) from 1998 through 2012. There is evidence of a causal relationship between atmospheric PM2.5 and breast cancer (BC) incidence, but few studies have investigated BC mortality and atmospheric PM2.5. We investigated BC mortality in relation to atmospheric PM2.5 levels among patients living in Varese Province, northern Italy. METHODS: We selected female BC cases, archived in the local population-based cancer registry, diagnosed at age 50-69 years, between 2003 and 2009. The geographic coordinates of each woman's place of residence were identified, and individual PM2.5 exposures were assessed from satellite data. Grade, stage, age at diagnosis, period of diagnosis and participation in BC screening were potential confounders. Kaplan-Meir and Nelson-Aalen methods were used to test for mortality differences in relation to PM2.5 quartiles. Multivariable Cox proportional hazards modelling estimated HRs and 95% CIs of BC death in relation to PM2.5 exposure. RESULTS: Of 2021 BC cases, 325 died during follow-up to 31 December 2013, 246 for BC. Risk of BC death was significantly higher for all three upper quartiles of PM2.5 exposure compared to the lowest, with HRs of death: 1.82 (95% CI 1.15 to 2.89), 1.73 (95% CI 1.12 to 2.67) and 1.72 (95% CI 1.08 to 2.75). CONCLUSIONS: Our study indicates that the risk of BC mortality increases with PM2.5 exposure. Although additional research is required to confirm these findings, they are further evidence that PM2.5 exposure is harmful and indicate an urgent need to improve global air quality.

Comparison with manual registration reveals satisfactory completeness and efficiency of a computerized cancer registration system.

Automated software for cancer registration, called Open Registry and developed by ourselves was adopted by the Varese (population-based) Cancer Registry starting from 1997. Since the use of automated cancer registration is increasing, it is important to assess the quality and completeness of the automated data being produced. In this study, we assessed the completeness of the automatically generated data by comparison with a gold standard of all cases identified by manual and automatic systems for the year 1997 when the automated system was introduced, and the manual system was still in operation. We also evaluated the efficiency of the automated system. 5027 cases were generated automatically; 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Sixty-nine cases (1.3%) were not recorded automatically, the most common reason (0.8%) being that the incidence record was dated 1998, even though the case was incident in 1997. A total of 98.7% of all cases found were picked up by the automated system. A completeness figure of 98.7% indicates that the automatic procedure is a valid alternative to manual methods for routine case generation. The fact that 59% of cases were registered automatically indicates that the system can speed up data production and enhance registry efficiency.

Consistency and accuracy of diagnostic cancer codes generated by automated registration: comparison with manual registration.

BACKGROUND: Automated procedures are increasingly used in cancer registration, and it is important that the data produced are systematically checked for consistency and accuracy. We evaluated an automated procedure for cancer registration adopted by the Lombardy Cancer Registry in 1997, comparing automatically-generated diagnostic codes with those produced manually over one year (1997). METHODS: The automatically generated cancer cases were produced by Open Registry algorithms. For manual registration, trained staff consulted clinical records, pathology reports and death certificates. The social security code, present and checked in both databases in all cases, was used to match the files in the automatic and manual databases. The cancer cases generated by the two methods were compared by manual revision. RESULTS: The automated procedure generated 5027 cases: 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Among the cases accepted automatically, discrepancies in data items (surname, first name, sex and date of birth) constituted 8.5% of cases, and discrepancies in the first three digits of the ICD-9 code constituted 1.6%. Among flagged cases, cancers of female genital tract, hematopoietic system, metastatic and ill-defined sites, and oropharynx predominated. The usual reasons were use of specific vs. generic codes, presence of multiple primaries, and use of extranodal vs. nodal codes for lymphomas. The percentage of automatically accepted cases ranged from 83% for breast and thyroid cancers to 13% for metastatic and ill-defined cancer sites. CONCLUSION: Since 59% of cases were accepted automatically and contained relatively few, mostly trivial discrepancies, the automatic procedure is efficient for routine case generation effectively cutting the workload required for routine case checking by this amount. Among cases not accepted automatically, discrepancies were mainly due to variations in coding practice.

Childhood leukemia and road traffic: A population-based case-control study.

To assess the effect of road traffic exhaust on the risk of childhood leukemia, we carried out a population-based case-control study in the Province of Varese, northern Italy, covered by a population-based cancer registry. All 120 incident cases from 1978-97 were included in the study. Four controls per case, matched by age and gender, were sampled from population files. As index of exposure to traffic exhaust we estimated the annual mean concentration of benzene outside the home using a Gaussian diffusion model. This model uses traffic density (vehicles/day) on nearby main roads, distance between roads and residence, and information on vehicle emissions and weather conditions to estimate benzene concentration. Compared to children whose homes was not exposed to road traffic emissions (<0.1 microg/m(3) of benzene as estimated by the model), the risk of childhood leukemia was significantly higher (relative risk [RR] = 3.91; 95% confidence interval [CI] = 1.36-11.27) for heavily exposed children (over 10 microg/m(3) estimated annual average). For the intermediate exposure group (0.1-10 microg/m(3)) the relative risk was 1.51 (95% CI = 0.91-2.51). These data, considered with other available evidence, suggest that motor traffic emissions can be involved in the etiology of childhood leukemia.