Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

Epidemiology of cardiomyopathies and incident heart failure in a population-based cohort study

AimsThe population prevalence of cardiomyopathies and the natural history of symptomatic heart failure (HF) and arrhythmia across cardiomyopathy phenotypes is poorly understood. Study aims were to estimate the population-diagnosed prevalence of cardiomyopathies and describe the temporal relationship between a diagnosis of cardiomyopathy with HF and arrhythmia.MethodsPeople with cardiomyopathy (n=4116) were identified from linked electronic health records (~9 million individuals; 2000–2018) and categorised into hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), restrictive cardiomyopathy (RCM) and cardiac amyloidosis (CA). Cardiomyopathy point prevalence, rates of symptomatic HF and arrhythmia and timing relative to a diagnosis of cardiomyopathy were determined.ResultsIn 2018, DCM was the most common cardiomyopathy. DCM and HCM were twice as common among men, with the reverse trend for ARVC. Between 2010 and 2018, prevalence increased for ARVC by 180% and HCM by 9%. At diagnosis, more patients with CA (66%), DCM (56%) and RCM (62%) had pre-existing HF compared with ARVC (29%) and HCM (27%). Among those free of HF at diagnosis of cardiomyopathy, annualised HF incidence was greatest in CA and DCM. Diagnoses of all cardiomyopathies clustered around the time of HF onset.ConclusionsThe recorded prevalence of all cardiomyopathies increased over the past decade. Recognition of CA is generally preceded by HF, whereas individuals with ARVC or HCM more often developed HF after their cardiomyopathy diagnosis suggesting a more indolent course or better asymptomatic recognition. The clustering of HF and cardiomyopathy diagnoses suggests opportunities for presymptomatic or earlier diagnosis.

Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach

Although one often has detailed information about participants in a program, the lack of comparable information on non-participants precludes standard qualitative choice estimation. This challenge can be overcome by incorporating a supplementary sample of covariate values from the general population. This paper presents new estimators based on this sampling strategy, which perform comparably to the best existing supplementary sampling estimators. The key advantage of the new estimators is that they readily incorporate sample weights, so that they can be applied to Census surveys and other supplementary data sources that have been generated using complex sample designs. This substantially widens the range of problems that can be addressed under a supplementary sampling estimation framework. The potential for improving precision by incorporating imperfect knowledge of the population prevalence rate is also explored.

Obstructive Sleep Apnea in a Clinical Population: Prevalence, Predictive Factors and Clinical Characteristics of Patients Referred to a Sleep Center in Mongolia

Obstructive sleep apnea (OSA) disrupts sleep. This study examined factors related to OSA severity. A cross-sectional, prospective, hospital-based study was conducted with 205 patients who underwent polysomnography (PSG). Demographic, anthropometric, clinical, PSG, and sleep quality assessment data were analyzed. Participants (N = 205) were classified into four groups based on apnea–hypopnea index (AHI); no OSA (AHI <5/h; N = 14), mild (mOSA, 5< AHI <15/h; N = 50), moderate (modOSA, 15 <AHI <30/h; N = 41), severe (sOSA, 30 <AHI <60/h; N = 50), and very severe (vsOSA, AHI ≥ 60; N = 50). Men had more severe OSA than women (p < 0.001). Anthropometric characteristics differed with OSA severity (p < 0.001). OSA patients had decreased sleep quality and increased excessive daytime sleepiness (EDS). Body mass index (BMI), neck/waist circumference, and blood pressure (BP) differed between groups (p < 0.001). Patients with vsOSA had the highest Mallampati grades (p < 0.001). Multiple linear regression indicated that OSA severity was related to gender and sleep quality. PSG parameters (oxygen saturation, systolic BP, and arousal/respiratory arousal) were strongly related to OSA severity. In conclusion, about half of study-referred patients had severe/very severe OSA; these groups were predominantly obese men with high BP. OSA severity associated with high BP, BMI, waist circumference, and neck circumference.

Factors Associated with Depression and Anxiety in Adults ≥60 Years Old during the COVID-19 Pandemic: A Systematic Review

COVID-19 represents a threat to public health and the mental health of the aged population. Prevalence and risk factors of depression and anxiety have been reported in previous reviews in other populations; however, a systematic review on the factors associated with depression and anxiety in older adults is not currently present in the literature. We searched PubMed, Embase, Scopus, ProQuest Psychology Database, Science Direct, Cochrane Library and SciELO databases (23 February 2021). The results were obtained by entering a combination of MeSH or Emtree terms with keywords related to COVID-19, elderly, depression and anxiety in the databases. A total of 11 studies were included in the systematic review. Female gender, loneliness, poor sleep quality and poor motor function were identified as factors associated with both depression and anxiety. Aspects related to having a stable and high monthly income represent protective factors for both depression and anxiety, and exercising was described as protective for depression. This study synthesised information and analysed the main factors associated with depression and anxiety in the older population during the COVID-19 pandemic. However, the cross-sectional design of most of the included studies does not allow a causal relationship between the factors analysed and depression or anxiety.