Search

Research on socioeconomic position (SEP) and mild neurocognitive impairment, considered a transient state between normal cognitive function and dementia is limited. The purpose of this study was to determine the role of SEP in transitioning between different cognitive states and mortality risk. Using nationally representative English data and utilising a multistate model association between SEP and the risk of transitioning from no cognitive impairment (NOCI) to Cognitive impairment no dementia (CIND), dementia and death were investigated. The potential reverse transition from CIND to NOCI was also explored. The probabilities of transitioning between cognitive states and time spent in each state differed significantly between those with lower and higher levels of SEP. Higher wealth was associated with a reverse transition from CIND to NOCI [HR = 1.56, CI (1.42,1.72)]. Socioeconomic advantage might protect against the progression to the early stages of neurocognitive disorders (CIND) and facilitate the potential reversion from mild cognitive impairment to a healthy cognitive state in later life. Lower levels of education affect the risk of mortality after the onset of dementia., (© 2024. The Author(s).)

Population screening for breast cancer (BC) is currently offered in the UK for women aged 50 to 71 with the aim of reducing mortality. There is additional screening within the national programme for women identified as having a very high risk of BC. There is growing interest in further risk stratification in breast screening, which would require a whole population risk assessment and the subsequent offer of screening tailored to the individual's risk. Some women would be offered more intensive screening than others or no screening. This might provide a better balance of screening benefits and harms for each individual than the current population age-based programme alone. The UK National Screening Committee (UK NSC) is considering using decision-analytic and other models to evaluate different risk stratification screening strategies and identify remaining gaps in evidence. This paper reports the proceedings of a UK NSC workshop where experts in the field discussed both risk prediction models, as well as decision-analytic models providing a benefit-harm analysis/economic evaluation of risk-stratified screening programmes (see Table 1). The aim of the meeting was to present and discuss the current work of experts, including some data which had not been published at the time of the meeting, to inform the UK NSC. The workshop was not intended to present a balanced evaluation of how to deliver screening in future. Areas for further work identified included methods for comparing models to assess accuracy, the optimum risk assessment tools, the digital screening infrastructure, acceptability of stratification, choice of screening test and reducing inequalities. A move to risk stratification of the whole programme would require a careful phased introduction with continuing assessment of real-world evidence during deployment., (© 2024. The Author(s).)

Background: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors., Methods: The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states., Results: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively., Conclusion: In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied., (© 2024 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.)

Untargeted metabolomics was used to analyze serum and urine samples for biomarkers of autologous blood transfusion (ABT). Red blood cell concentrates from donated blood were stored for 35−36 days prior to reinfusion into the donors. Participants were sampled at different time points post-donation and up to 7 days post-transfusion. Metabolomic profiling was performed using ACQUITY ultra performance liquid chromatography (UPLC), Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The markers of ABT were determined by principal component analysis and metabolites that had p < 0.05 and met ≥ 2-fold change from baseline were selected. A total of 11 serum and eight urinary metabolites, including two urinary plasticizer metabolites, were altered during the study. By the seventh day post-transfusion, the plasticizers had returned to baseline, while changes in nine other metabolites (seven serum and two urinary) remained. Five of these metabolites (serum inosine, guanosine and sphinganine and urinary isocitrate and erythronate) were upregulated, while serum glycourdeoxycholate, S-allylcysteine, 17-alphahydroxypregnenalone 3 and Glutamine conjugate of C6H10O2 (2)* were downregulated. This is the first study to identify a panel of metabolites, from serum and urine, as markers of ABT. Once independently validated, it could be universally adopted to detect ABT.

Background: Neuromyelitis optica spectrum disorder (NMOSD) is a rare, neurological disease that places a significant burden on patients, their carers, and healthcare systems., Objectives: To estimate patient and carer health utilities and costs of NMOSD within the UK setting., Methods: Patients with NMOSD and their carers, recruited via a regional specialist treatment centre, completed a postal questionnaire that included a resource use measure, the EuroQoL (EQ)-5D-5L, EQ-5D-VAS, Vision and Quality of Life Index (VisQoL), Carer Experience Survey (CES) and the Expanded Disability Status Scale (EDSS). The questionnaire asked about respondents' use of health and community care services, non-medical costs, informal care and work capacity. Data were analysed descriptively. Uncertainties in costs and utilities were assessed using bootstrap analysis., Results: 117 patients and 74 informal carers responded to the survey. Patients' mean EQ-5D-5L and VisQoL health utilities (95% central range) were 0.54 (- 0.29, 1.00) and 0.79 (0.11, 0.99), respectively. EQ-5D-5L utility decreased with increasing EDSS score bandings, from 0.80 (0.75, 0.85) for EDSS ≤ 4.0, to 0.20 (- 0.29, 0.56) for EDSS 8.0 to 9.5. Mean, 3-month total costs were £5623 (£2096, £12,156), but ranged from £562 (£381, £812) to £32,717 (£2888, £98,568) for these EDSS bandings. Carer-reported EQ-5D-5L utility and CES index scores were 0.85 (0.82, 0.89) and 57.67 (52.69, 62.66). Mean, 3-month costs of informal care were £13,150 to £24,560., Conclusions: NMOSD has significant impacts on health utilities and NHS and carer costs. These data can be used as inputs to cost-effectiveness analyses of new medicines for NMOSD., (© 2022. The Author(s).)

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen-detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous-time Markov model and the corresponding likelihood function. The model allows for the use of interval-censored, left-truncated and right-censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age-varying hazards and age-varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro-simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)