Flexible transition probability model for assessing cost-effectiveness of breast cancer screening extension to include women aged 45-49 and 70-74.

Breast cancer is the most common cancer among Western women. Fortunately, organized screening has reduced breast cancer mortality. New recommendation by the European Union suggests extending screening with mammography from 50-69-year-old women to 45-74-year-old women. However, before extending screening to new age groups, it's essential to carefully consider the benefits and costs locally as circumstances vary between different regions and/or countries. We propose a new approach to assess cost-effectiveness of breast cancer screening for a long-ongoing program with incomplete historical screening data. The new model is called flexible stage distribution model. It is based on estimating the breast cancer incidence and stage distributions of breast cancer cases under different screening strategies. The model parameters, for each considered age group, include incidence rates under screening/non-screening, probability distribution among different stages, survival by stages, and treatment costs. Out of these parameters, we use the available data to estimate survival rates and treatment costs, while the modelling is done for incidence rates and stage distributions under screening policies for which the data is not available. In the model, an ongoing screening strategy may be used as a baseline and other screening strategies may be incorporated by changes in the incidence rates. The model is flexible, as it enables to apply different approaches for estimating the altered stage distributions. We apply the proposed flexible stage distribution model for assessing incremental cost of extending the current biennial breast cancer screening to younger and older target ages in Finland.

Nationwide infection control strategy lowered seasonal respiratory infection rate: occupational health care perspective during the COVID-19 epidemic in Finland.

BACKGROUND: Respiratory infection is the 4th most common reason for absence from work in Finland. There is limited knowledge of how social distancing affects the spread of respiratory infections during respiratory epidemics. We assessed the effect of nationwide infection control strategies against coronavirus disease in 2020 on various respiratory infections (International Statistical Classification of Diseases and Related Health Problems code J06) in occupational outpatient clinics. METHODS: We used occupational healthcare data of respiratory infection J06 diagnoses from 2017 to 2020 obtained from the largest health service provider in Finland. The data was divided into three 252 day-long pieces and was weekday-matched and smoothed by 7-day-moving average. The difference in the J06 diagnosis rate between the follow-up years was measured using Pearson correlation. Possible confounding by sex, age, and region was investigated in a stratified analysis. Confounding by respiratory syncytial virus was analysed using nationwide data of confirmed cases obtained from the national registry. RESULTS: In the second quarter of 2020, the trend in the daily number of J06 diagnoses was significantly different from the follow-up years 2019 and 2018. The number of J06 diagnoses peaked between March and April 2020 with roughly 2-fold higher count compared to normal. The timing of these peaks matched with the government issued infection control strategies and lockdowns. Based on stratified analysis, the increase in the number of J06 diagnoses was not confounded by region, age, or sex. Moreover, the rapid increase in the number of J06 diagnoses was not governed by the respiratory syncytial virus. CONCLUSION: Nationwide infection control strategies were effective to slow down the spread of common respiratory infectious diseases in the occupational population.

Gene regulatory network inference from sparsely sampled noisy data.

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO's superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.