Background Little is known about longitudinal patterns of the development of IgE to distinct allergen components. Objective We sought to investigate the evolution of IgE responses to allergenic ...components of timothy grass and dust mite during childhood. Methods In a population-based birth cohort (n = 1184) we measured IgE responses to 15 components from timothy grass and dust mite in children with available samples at 3 time points (ages 5, 8, and 11 years; n = 235). We designed a nested, 2-stage latent class analysis to identify cross-sectional sensitization patterns at each follow-up and their longitudinal trajectories. We then ascertained the association of longitudinal trajectories with asthma, rhinitis, eczema, and lung function in children with component data for at least 2 time points (n = 534). Results Longitudinal latent class analysis revealed 3 grass sensitization trajectories: (1) no/low sensitization; (2) early onset; and (3) late onset. The early-onset trajectory was associated with asthma and diminished lung function, and the late-onset trajectory was associated with rhinitis. Four longitudinal trajectories emerged for mite: (1) no/low sensitization; (2) group 1 allergens; (3) group 2 allergens; and (3) complete mite sensitization. Children in the complete mite sensitization trajectory had the highest odds ratios (ORs) for asthma (OR, 7.15; 95% CI, 3.80-13.44) and were the only group significantly associated with comorbid asthma, rhinitis, and eczema (OR, 5.91; 95% CI, 2.01-17.37). Among children with wheezing, those in the complete mite sensitization trajectory (but not other longitudinal mite trajectories) had significantly higher risk of severe exacerbations (OR, 3.39; 95% CI, 1.62-6.67). Conclusions The nature of developmental longitudinal trajectories of IgE responses differed between grass and mite allergen components, with temporal differences (early vs late onset) dominant in grass and diverging patterns of IgE responses (group 1 allergens, group 2 allergens, or both) in mite. Different longitudinal patterns bear different associations with clinical outcomes, which varied by allergen.
Rapid antigen testing in COVID-19 responses García-Fiñana, Marta; Buchan, Iain E
Science (American Association for the Advancement of Science),
05/2021, Letnik:
372, Številka:
6542
Journal Article
Recenzirano
SARS-CoV-2 transmission was reduced with measures centered on rapid antigen testing
The value of rapid antigen testing of people (with or without COVID-19 symptoms) to reduce transmission of severe ...acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been discussed extensively (
1
–
5
) but remains a topic of policy debates (
6
,
7
). Lateral flow devices (LFDs) to test for SARS-CoV-2 antigen are inexpensive, provide results in minutes, and are highly specific (
2
–
4
), and although less sensitive than reverse transcriptase polymerase chain reaction (RT-PCR) tests to detect viral RNA, they detect most cases with high viral load (
2
,
3
,
8
), which are likely the most infectious (
8
,
9
). Successful mass testing relies on public trust, the social and organizational factors that support uptake, contact tracing, and adherence to quarantine. On page 635 of this issue, Pavelka
et al.
(
10
) report the substantial reduction in transmission that population-wide rapid antigen testing had, in combination with other measures, in Slovakia.
Life-space mobility (LSM) is a holistic measure of resilience to physical decline and social isolation in later life. To promote its use as an outcome in geriatric studies and in clinical practice, ...this review paper explains the concept of LSM; outlines available questionnaires for LSM assessment, provides an overview of associations between LSM and other outcomes, and discusses emerging methods to measure LSM using wearable sensors. Based on performed activity around a central geographical anchor, LSM aims to quantify the observed contraction of daily activities associated with ageing. Several questionnaires are available to assess LSM in different contexts: the University of Alabama Life-Space Assessment and the Life-Space Questionnaire (community settings), the Nursing Home Life-Space Diameter (nursing home settings) and Life Space at Home (for house-bound populations). Some studies using GPS trackers to calculate life-space parameters reported promising results. Although these techniques reduce data collection burden, battery life and older people’s willingness to wear a tracker require further improvement before they can be used more widely. Regardless of the assessment method used, LSM was associated with measures of functional and cognitive abilities, nursing home admission and mortality. The current availability of instruments, the ongoing development of less burdensome data collection techniques, and evidence of construct validity support a case for promoting integration of LSM assessments into geriatric research studies and clinical practice. Ultimately, this will provide a more holistic view on older people’s health and wellbeing.
BACKGROUND:“Obesity paradox” refers to an association between obesity and reduced mortality (contrary to an expected increased mortality). A common explanation is collider stratification ...biasunmeasured confounding induced by selection bias. Here, we test this supposition through a realistic generative model.
METHODS:We quantify the collider stratification bias in a selected population using counterfactual causal analysis. We illustrate the bias for a range of scenarios, describing associations between exposure (obesity), outcome (mortality), mediator (in this example, diabetes) and an unmeasured confounder.
RESULTS:Collider stratification leads to biased estimation of the causal effect of exposure on outcome. However, the bias is small relative to the causal relationships between the variables.
CONCLUSIONS:Collider bias can be a partial explanation of the obesity paradox, but unlikely to be the main explanation for a reverse direction of an association to a true causal relationship. Alternative explanations of the obesity paradox should be explored. See Video Abstract at http://links.lww.com/EDE/B51.
Big data, high-performance computing, and (deep) machine learning are increasingly becoming key to precision medicine—from identifying disease risks and taking preventive measures, to making ...diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models require the correct specification of cause and effect, and the calculation of so-called counterfactuals, that is, alternative scenarios. In biomedical research, observational studies are commonly affected by confounding and selection bias. Without robust assumptions, often requiring a priori domain knowledge, causal inference is not feasible. Data-driven prediction models are often mistakenly used to draw causal effects, but neither their parameters nor their predictions necessarily have a causal interpretation. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal approaches and learn causal structures. Here we discuss how target trials (algorithmic emulation of randomized studies), transportability (the licence to transfer causal effects from one population to another) and prediction invariance (where a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings) are linchpins to developing and testing intervention models.Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.
North-South disparities in English mortality 1965–2015 Buchan, Iain E; Kontopantelis, Evangelos; Sperrin, Matthew ...
Journal of epidemiology and community health (1979),
09/2017, Letnik:
71, Številka:
9
Journal Article
Glycemic variability is emerging as a measure of glycemic control, which may be a reliable predictor of complications. This systematic review and meta-analysis evaluates the association between HbA1c ...variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes.
Medline and Embase were searched (2004-2015) for studies describing associations between HbA1c variability and adverse outcomes in patients with type 1 and type 2 diabetes. Data extraction was performed independently by two reviewers. Random-effects meta-analysis was performed with stratification according to the measure of HbA1c variability, method of analysis, and diabetes type.
Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 95% CI 1.08-2.25, two studies), cardiovascular events (1.98 1.39-2.82), and retinopathy (2.11 1.54-2.89). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 1.15-1.57, two studies), macrovascular events (1.21 1.06-1.38), ulceration/gangrene (1.50 1.06-2.12), cardiovascular disease (1.27 1.15-1.40), and mortality (1.34 1.18-1.53). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.
HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.
Alex Crozier and colleagues evaluate the potential opportunities and challenges of expanding the symptom list linked to self-isolation and testing as vaccines are rolled out
Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing ...covariate values, it is not known whether it should be imputed. Similarly no clear recommendations exist on: the utility of incorporating a secondary outcome, if available, in the imputation model; the level of protection offered when data are missing not-at-random; the implications of the dataset size and missingness levels.
We used realistic assumptions to generate thousands of datasets across a broad spectrum of contexts: three mechanisms of missingness (completely at random; at random; not at random); varying extents of missingness (20-80% missing data); and different sample sizes (1,000 or 10,000 cases). For each context we quantified the performance of a complete case analysis and seven multiple imputation methods which deleted cases with missing outcome before imputation, after imputation or not at all; included or did not include the outcome in the imputation models; and included or did not include a secondary outcome in the imputation models. Methods were compared on mean absolute error, bias, coverage and power over 1,000 datasets for each scenario.
Overall, there was very little to separate multiple imputation methods which included the outcome in the imputation model. Even when missingness was quite extensive, all multiple imputation approaches performed well. Incorporating a secondary outcome, moderately correlated with the outcome of interest, made very little difference. The dataset size and the extent of missingness affected performance, as expected. Multiple imputation methods protected less well against missingness not at random, but did offer some protection.
As long as the outcome is included in the imputation model, there are very small performance differences between the possible multiple imputation approaches: no outcome imputation, imputation or imputation and deletion. All informative covariates, even with very high levels of missingness, should be included in the multiple imputation model. Multiple imputation offers some protection against a simple missing not at random mechanism.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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