Revisiting the Geographical Distribution of Thyroid Cancer Incidence in Fukushima Prefecture: Analysis of Data From the Second- and Third-round Thyroid Ultrasound Examination.

BACKGROUND: After the first-round (Preliminary Baseline Survey) ultrasound-based examination for thyroid cancer in response to the accident at the Fukushima Daiichi Nuclear Power Plant in 2011, two rounds of surveys (Full-scale Survey) have been carried out in Fukushima Prefecture. Using the data from these surveys, the geographical distribution of thyroid cancer incidence over 6 or 7 years after the disaster was examined. METHODS: Children and adolescents who underwent the ultrasound-based examinations in the second- and/or third-round (Full-scale) survey in addition to the first-round survey were included. With a discrete survival model, we computed age, sex, and body mass index standardized incidence ratios (SIRs) for municipalities. Then, we employed spatial statistics to assess geographic clustering tendency in SIRs and Poisson regression to assess the association of SIRs with the municipal average absorbed dose to the thyroid gland at the 59-municipality level. RESULTS: Throughout the second- and third-round surveys, 99 thyroid cancer cases were diagnosed in the study population of 252,502 individuals. Both flexibly shaped spatial scan statistics and maximized excess events test did not detect statistically significant spatial clustering (P = 0.17 and 0.54, respectively). Poisson regression showed no significant dose-response relationship: the estimated relative risks of lowest, middle-low, middle-high, and highest areas were 1.16 (95% confidence interval [CI], 0.52-2.59), 0.55 (95% CI, 0.31-0.97), 1.05 (95% CI, 0.79-1.40), and 1.24 (95% CI, 0.89-1.74). CONCLUSION: There was no statistical support for geographic clustering or regional association with radiation dose measures of the thyroid cancer incidence in the cohort followed up to the third-round survey (fiscal years 2016-2017) in Fukushima Prefecture.

Uncovering trends in training progression for a national cohort of psychiatry trainees: discrete-time survival analysis.

BACKGROUND: The global rise in mental health issues calls for a strong psychiatry workforce. Yet, psychiatry training worldwide is facing recruitment challenges, causing unfilled consultant posts and possibly threatening the quality of patient care. An in-depth understanding of trainees' progression through training is warranted to explore what happens to recruited trainees during training. AIMS: To uncover current trends in psychiatry trainees' progression through training in the UK. METHOD: This national retrospective cohort study with data from the UK Medical Education Database used discrete-time survival analysis to analyse training progression for those trainees who started their core psychiatry post in 2012-2017 (2820 trainees; 59.6% female, 67.6% UK graduates (UKGs)). The impact of sociodemographic characteristics on training progression were also investigated. RESULTS: The overall probability of completing training in 6 years (minimum years required to complete psychiatry training in the UK) was 17.2% (ranging from 4.8% for non-UKG females to 29% for UKG males). The probability to not progress was highest (57.1%) from core to specialty training. For UKGs, trainees from ethnicities other than White, trainees with a disability, and trainees who had experienced childhood social deprivation (measured as entitlement to free school meals) had a significantly (P ≤ 0.02) lower probability of completing training in 6 years. CONCLUSIONS: Less than one in five psychiatry trainees are likely to complete training in 6 years and this probability varies across groups of doctors. Completing psychiatry training in 6 years is, therefore, the exception rather than the norm and this has important implications for trainees, those planning psychiatry workforces or responsible for psychiatry training.

Modelling the reproductive power function.

This paper discusses methods of estimating the reproductive power and the accompanying survival function of communicable events, e.g. infectious disease transmission. The early stage of an outbreak can be described by the infectiousness of the outbreak process, but in later stages of the outbreak, this is complicated by factors such as changing contact patterns and the impact of control measures. It is important to take these factors into account in order to get a good, if approximate, model for an outbreak process. This paper proposes a non-homogeneous birth process and regression model for the reproductive power function, similar to models in discrete survival analysis. A baseline reproductive power function gives a description of the outbreak when covariates are at their baseline values. As an illustration these methods are applied to an avian influenza (H5N1) outbreak among poultry in Thailand.