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.

Atorvastatin induces adrenal androgen downshift in men with prostate cancer: A post Hoc analysis of a pilot adaptive Randomised clinical trial.

BACKGROUND: Prostate cancer (PCa) progression depends on androgen receptor activity. Cholesterol is required for biosynthesis of all steroid hormones, including androgens. Impact of cholesterol-lowering statins on androgens is unknown. We explored atorvastatin influence on serum and prostatic tissue steroidomic profiles (SP) to expose novel pathways for limiting androgen concentration in men with PCa. METHODS: This is a pre-planned post hoc analysis of ESTO-1 pilot randomised, double-blinded, clinical trial. Statin naïve men, scheduled for radical prostatectomy due to localised PCa, were randomised 1:1 to use daily 80 mg of atorvastatin or placebo before the surgery for a median of 28 days. Participants were recruited and treated at the Pirkanmaa Hospital District, Tampere, Finland. 108 of the 158 recruited men were included in the analysis based on sample availability for hormone profiling. Serum and prostatic tissue steroid profiles were determined using liquid chromatography mass spectrometry. Wilcoxon rank sum test and bootstrap confidence intervals (CI) were used to analyse the difference between placebo and atorvastatin arms. FINDINGS: Most serum and prostatic steroids, including testosterone and dihydrotestosterone, were not associated with atorvastatin use. However, atorvastatin use induced serum SP changes in 11-ketoandrostenedione (placebo 960pM, atorvastatin 617.5pM, p-value <0.0001, median difference -342.5; 95% CI -505.23 - -188.98). In the prostatic tissue, atorvastatin was associated with plausible downshift in 11- ketodihydrotestosterone (placebo 25.0pM in 100 mg tissue/1 mL saline, atorvastatin 18.5pM in 100 mg tissue/1 mL saline, p-value 0.027, median difference -6.53; 95% CI -12.8 - -0.29); however, this association diminished after adjusting for multiple testing. No serious harms were reported. INTERPRETATION: Atorvastatin was associated with adrenal androgen downshift in the serum and possibly in the prostate. The finding warrants further investigation whether atorvastatin could improve androgen deprivation therapy efficacy. FUNDING: Funded by grants from the Finnish Cultural Foundation, Finnish Cancer Society, Academy of Finland, and the Expert Responsibility Area of the Tampere University Hospital. CLINICALTRIALS. GOV IDENTIFIER: NCT01821404.

Fast tensorial JADE.

We propose a novel method for tensorial-independent component analysis. Our approach is based on TJADE and k-JADE, two recently proposed generalizations of the classical JADE algorithm. Our novel method achieves the consistency and the limiting distribution of TJADE under mild assumptions and at the same time offers notable improvement in computational speed. Detailed mathematical proofs of the statistical properties of our method are given and, as a special case, a conjecture on the properties of k-JADE is resolved. Simulations and timing comparisons demonstrate remarkable gain in speed. Moreover, the desired efficiency is obtained approximately for finite samples. The method is applied successfully to large-scale video data, for which neither TJADE nor k-JADE is feasible. Finally, an experimental procedure is proposed to select the values of a set of tuning parameters. Supplementary material including the R-code for running the examples and the proofs of the theoretical results is available online.

Circulatory and prostatic tissue lipidomic profiles shifts after high-dose atorvastatin use in men with prostate cancer.

Prostate cancer patients using cholesterol-lowering statins have 30% lower risk of prostate cancer death compared to non-users. The effect is attributed to the inhibition of the mevalonate pathway in prostate cancer cells. Moreover, statin use causes lipoprotein metabolism changes in the serum. Statin effect on serum or intraprostatic lipidome profiles in prostate cancer patients has not been explored. We studied changes in the serum metabolomic and prostatic tissue lipidome after high-dose 80 mg atorvastatin intervention to expose biological mechanisms causing the observed survival benefit. Our randomized, double-blind, placebo-controlled clinical trial consisted of 103 Finnish men with prostate cancer. We observed clear difference in post-intervention serum lipoprotein lipid profiles between the study arms (median classification error 11.7%). The atorvastatin effect on intraprostatic lipid profile was not as clear (median classification error 44.7%), although slightly differing lipid profiles by treatment arm was observed, which became more pronounced in men who used atorvastatin above the median of 27 days (statin group median classification error 27.2%). Atorvastatin lowers lipids important for adaptation for hypoxic microenvironment in the prostate suggesting that prostate cancer cell survival benefit associated with statin use might be mediated by both, local and systemic, lipidomic/metabolomic profile changes.

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.

On Exploring Hidden Structures Behind Cervical Cancer Incidence.

Finding new etiological components is of great interest in disease epidemiology. We consider time series version of invariant coordinate selection (tICS) as an exploratory tool in the search of hidden structures in the analysis of population-based registry data. Increasing cancer burden inspired us to consider a case study of age-stratified cervical cancer incidence in Finland between the years 1953 and 2014. The latent components, which we uncover using tICS, show that the etiology of cervical cancer is age dependent. This is in line with recent findings related to the epidemiology of cervical cancer. Furthermore, we are able to explain most of the variation of cervical cancer incidence in different age groups by using only two latent tICS components. The second tICS component, in particular, is interesting since it separates the age groups into three distinct clusters. The factor that separates the three clusters is the median age of menopause occurrence.

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach.

BACKGROUND: Fishing communities around Lake Victoria in sub-Saharan Africa have been characterised as a population at high risk of HIV-infection. METHODS: Using data from a cohort of HIV-positive individuals aged 13-49 years, enrolled from 5 fishing communities on Lake Victoria between 2009-2011, we sought to identify factors contributing to the epidemic and to understand the underlying structure of HIV transmission networks. Clinical and socio-demographic data were combined with HIV-1 phylogenetic analyses. HIV-1 gag-p24 and env-gp-41 sub-genomic fragments were amplified and sequenced from 283 HIV-1-infected participants. Phylogenetic clusters with ≥2 highly related sequences were defined as transmission clusters. Logistic regression models were used to determine factors associated with clustering. RESULTS: Altogether, 24% (n = 67/283) of HIV positive individuals with sequences fell within 34 phylogenetically distinct clusters in at least one gene region (either gag or env). Of these, 83% occurred either within households or within community; 8/34 (24%) occurred within household partnerships, and 20/34 (59%) within community. 7/12 couples (58%) within households clustered together. Individuals in clusters with potential recent transmission (11/34) were more likely to be younger 71% (15/21) versus 46% (21/46) in un-clustered individuals and had recently become resident in the community 67% (14/21) vs 48% (22/46). Four of 11 (36%) potential transmission clusters included incident-incident transmissions. Independently, clustering was less likely in HIV subtype D (adjusted Odds Ratio, aOR = 0.51 [95% CI 0.26-1.00]) than A and more likely in those living with an HIV-infected individual in the household (aOR = 6.30 [95% CI 3.40-11.68]). CONCLUSIONS: A large proportion of HIV sexual transmissions occur within house-holds and within communities even in this key mobile population. The findings suggest localized HIV transmissions and hence a potential benefit for the test and treat approach even at a community level, coupled with intensified HIV counselling to identify early infections.

Dynamic logistic regression model and population attributable fraction to investigate the association between adherence, missed visits and mortality: a study of HIV-infected adults surviving the first year of ART.

BACKGROUND: Adherence is one of the most important determinants of viral suppression and drug resistance in HIV-infected people receiving antiretroviral therapy (ART). METHODS: We examined the association between long-term mortality and poor adherence to ART in DART trial participants in Uganda and Zimbabwe randomly assigned to receive laboratory and clinical monitoring (LCM), or clinically driven monitoring (CDM). Since over 50% of all deaths in the DART trial occurred during the first year on ART, we focussed on participants continuing ART for 12 months to investigate the implications of longer-term adherence to treatment on mortality. Participants' ART adherence was assessed by pill counts and structured questionnaires at 4-weekly clinic visits. We studied the effect of recent adherence history on the risk of death at the individual level (odds ratios from dynamic logistic regression model), and on mortality at the population level (population attributable fraction based on this model). Analyses were conducted separately for both randomization groups, adjusted for relevant confounding factors. Adherence behaviour was also confounded by a partial factorial randomization comparing structured treatment interruptions (STI) with continuous ART (CT). RESULTS: In the CDM arm a significant association was found between poor adherence to ART in the previous 3-9 months with increased mortality risk. In the LCM arm the association was not significant. The odds ratios for mortality in participants with poor adherence against those with optimal adherence was 1.30 (95% CI 0.78,2.10) in the LCM arm and 2.18 (1.47,3.22) in the CDM arm. The estimated proportions of deaths that could have been avoided with optimal adherence (population attributable fraction) in the LCM and CDM groups during the 5 years follow-up period were 16.0% (95% CI 0.7%,31.6%) and 33.1% (20.5%,44.8%), correspondingly. CONCLUSIONS: Recurrent poor adherence determined even through simple measures is associated with high mortality both at individual level as well as at the ART programme level. The number of lives saved through effective interventions to improve adherence could be considerable particularly for individuals monitored without using CD4 cell counts. The findings have important implications for clinical practice and for developing interventions to enhance adherence.

Clustering based on adherence data.

Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.