What should the detection rates of cancers be in breast screening programmes?

Minimum detection rates at screening are sometimes laid down as standards for breast cancer screening programmes, based on underlying incidence of the disease in the age group screened. Detection rates should also depend on desired sensitivity, mean sojourn time, interscreening interval and the screening round – that is, prevalent (first) or incident (second or subsequent). In this paper, we use these quantities to derive expected, minimum and maximum detection rates proportional to the underlying incidence as well as estimated underlying incidence rates from extrapolation of prescreening trends in England and Wales to derive alternative standard minimum, expected and maximum detection rates per 1000 women screened for the UK Breast Screening Programme, as follows: minimum detection rates should be 4.1 and 4.3 at prevalence screen and incidence screens, respectively; expected rates should be 6.9 and 4.8 and maximum rates of 9.6 and 5.5. These are consistent with observed detection rates in the UK programme

Incidence of Leukemia, Lymphoma, and Multiple Myeloma in Czech Uranium Miners: A Case–Cohort Study

OBJECTIVE: Uranium miners are chronically exposed to low levels of radon and its progeny. We investigated whether radon exposure is associated with increased incidence of leukemia, lymphoma, or multiple myeloma in this population. DESIGN: We conducted a retrospective case–cohort study in 23,043 uranium miners and identified a total of 177 incident cases of leukemia, lymphoma, and myeloma. Detailed information on occupational radon exposure was obtained for the cases and a randomly selected subcohort of 2,393 subjects. We used the proportional hazards model with power relative risk (RR) function to estimate and test the effects of cumulative radon exposures on incidence rates. RESULTS: Incidence of all leukemia combined and chronic lymphocytic leukemia (CLL) alone was positively associated with cumulative radon exposure. The RR comparing high radon exposure [110 working level months (WLM); 80th percentile] to low radon exposure (3 WLM; 20th percentile) was 1.75 [95% confidence interval (CI), 1.10–2.78; p = 0.014] for all leukemia combined and 1.98 (95% CI, 1.10–3.59; p = 0.016) for CLL. Myeloid leukemia and Hodgkin lymphoma were also associated with radon, but RRs were not statistically significant. There was no apparent association of radon with either non-Hodgkin lymphoma or multiple myeloma. Exposure to radon and its progeny was associated with an increased risk of developing leukemia in underground uranium miners. CLL, not previously believed to be radiogenic, was linked to radon exposure

Are quantitative trait-dependent sampling designs cost-effective for analysis of rare and common variants?

Use of trait-dependent sampling designs in whole-genome association studies of sequence data can reduce total sequencing costs with modest losses of statistical efficiency. In a quantitative trait (QT) analysis of data from the Genetic Analysis Workshop 17 mini-exome for unrelated individuals in the Asian subpopulation, we investigate alternative designs that sequence only 50% of the entire cohort. In addition to a simple random sampling design, we consider extreme-phenotype designs that are of increasing interest in genetic association analysis of QTs, especially in studies concerned with the detection of rare genetic variants. We also evaluate a novel sampling design in which all individuals have a nonzero probability of being selected into the sample but in which individuals with extreme phenotypes have a proportionately larger probability. We take differential sampling of individuals with informative trait values into account by inverse probability weighting using standard survey methods which thus generalizes to the source population. In replicate 1 data, we applied the designs in association analysis of Q1 with both rare and common variants in the FLT1 gene, based on knowledge of the generating model. Using all 200 replicate data sets, we similarly analyzed Q1 and Q4 (which is known to be free of association with FLT1) to evaluate relative efficiency, type I error, and power. Simulation study results suggest that the QT-dependent selection designs generally yield greater than 50% relative efficiency compared to using the entire cohort, implying cost-effectiveness of 50% sample selection and worthwhile reduction of sequencing costs

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Pitfalls of using the risk ratio in meta‐analysis

For meta-analysis of studies that report outcomes as binomial proportions, the most popular measure of effect is the odds ratio (OR), usually analyzed as log(OR). Many meta-analyses use the risk ratio (RR) and its logarithm, because of its simpler interpretation. Although log(OR) and log(RR) are both unbounded, use of log(RR) must ensure that estimates are compatible with study-level event rates in the interval (0, 1). These complications pose a particular challenge for random-effects models, both in applications and in generating data for simulations. As background we review the conventional random-effects model and then binomial generalized linear mixed models (GLMMs) with the logit link function, which do not have these complications. We then focus on log-binomial models and explore implications of using them; theoretical calculations and simulation show evidence of biases. The main competitors to the binomial GLMMs use the beta-binomial (BB) distribution, either in BB regression or by maximizing a BB likelihood; a simulation produces mixed results. Two examples and an examination of Cochrane meta-analyses that used RR suggest bias in the results from the conventional inverse-variance-weighted approach. Finally, we comment on other measures of effect that have range restrictions, including risk difference, and outline further research

The Impact of Heat Islands on Mortality in Paris during the August 2003 Heat Wave

Background: Heat waves have a drastic impact on urban populations, which could increase with climate change

Association of Exposure to Phthalates with Endometriosis and Uterine Leiomyomata: Findings from NHANES, 1999-2004

BACKGROUND. Phthalates are ubiquitous chemicals used in consumer products. Some phthalates are reproductive toxicants in experimental animals, but human data are limited. OBJECTIVE. We conducted a cross-sectional study of urinary phthalate metabolite concentrations in relation to self-reported history of endometriosis and uterine leiomyomata among 1,227 women 20-54 years of age from three cycles of the National Health and Nutrition Examination Survey (NHANES), 1999-2004. METHODS. We examined four phthalate metabolites: mono(2-ethylhexyl) phthalate (MEHP), monobutyl phthalate (MBP), monoethyl phthalate (MEP), and monobenzyl phthalate (MBzP). From the last two NHANES cycles, we also examined mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP). We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for potential confounders. RESULTS. Eighty-seven (7%) and 151 (12%) women reported diagnoses of endometriosis and leiomyomata, respectively. The ORs comparing the highest versus lowest three quartiles of urinary MBP were 1.36 (95% CI, 0.77-2.41) for endometriosis, 1.56 (95% CI, 0.93-2.61) for leiomyomata, and 1.71 (95% CI, 1.07-2.75) for both conditions combined. The corresponding ORs for MEHP were 0.44 (95% CI, 0.19-1.02) for endometriosis, 0.63 (95% CI, 0.35-1.12) for leiomyomata, and 0.59 (95% CI, 0.37-0.95) for both conditions combined. Findings for MEHHP and MEOHP agreed with findings for MEHP with respect to endometriosis only. We observed null associations for MEP and MBzP. Associations were similar when we excluded women diagnosed > 7 years before their NHANES evaluation. CONCLUSION. The positive associations for MBP and inverse associations for MEHP in relation to endometriosis and leiomyomata warrant investigation in prospective studies

Secondary Sex Ratio among Women Exposed to Diethylstilbestrol in Utero

BACKGROUND. Diethylstilbestrol (DES), a synthetic estrogen widely prescribed to pregnant women during the mid-1900s, is a potent endocrine disruptor. Previous studies have suggested an association between endocrine-disrupting compounds and secondary sex ratio. METHODS. Data were provided by women participating in the National Cancer Institute (NCI) DES Combined Cohort Study. We used generalized estimating equations to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the relation of in utero DES exposure to sex ratio (proportion of male births). Models were adjusted for maternal age, child's birth year, parity, and cohort, and accounted for clustering among women with multiple pregnancies. RESULTS. The OR for having a male birth comparing DES-exposed to unexposed women was 1.05 (95% CI, 0.95-1.17). For exposed women with complete data on cumulative DES dose and timing (33%), those first exposed to DES earlier in gestation and to higher doses had the highest odds of having a male birth. The ORs were 0.91 (95% C, 0.65-1.27) for first exposure at ≥ 13 weeks gestation to < 5 g DES; 0.95 (95% CI, 0.71-1.27) for first exposure at ≥ 13 weeks to ≥ 5 g; 1.16 (95% CI, 0.96-1.41) for first exposure at < 13 weeks to < 5 g; and 1.24 (95% CI, 1.04-1.48) for first exposure at < 13 weeks to ≥ 5 g compared with no exposure. Results did not vary appreciably by maternal age, parity, cohort, or infertility history. CONCLUSIONS. Overall, no association was observed between in utero DES exposure and secondary sex ratio, but a significant increase in the proportion of male births was found among women first exposed to DES earlier in gestation and to a higher cumulative dose.National Cancer Institute (N01-CP-21168, N01-CP-51017, N01-CP-01289

A study of soft tissue sarcomas after childhood cancer in Britain

Among 16 541 3-year survivors of childhood cancer in Britain, 39 soft tissue sarcomas (STSs) occurred and 1.1 sarcomas were expected, yielding a standardised incidence ratio (SIR) of 16.1. When retinoblastomas were excluded from the cohort, the SIR for STSs was 15.9, and the cumulative risk of developing a soft tissue tumour after childhood cancer within 20 years of 3-year survival was 0.23%. In the case–control study, there was a significant excess of STSs in those patients exposed to both radiotherapy (RT) and chemotherapy, which was five times that observed among those not exposed (P=0.02). On the basis of individual radiation dosimetry, there was evidence of a strong dose–response effect with a significant increase in the risk of STS with increasing dose of RT (P<0.001). This effect remained significant in a multivariate model. The adjusted risk in patients exposed to RT doses of over 3000 cGy was over 50 times the risk in the unexposed. There was evidence of a dose–response effect with exposure to alkylating agents, the risk increasing substantially with increasing cumulative dose (P=0.05). This effect remained after adjusting for the effect of radiation exposure