5,093 research outputs found
Congenital diaphragmatic hernia:A critical appraisal of perinatal care
A congenital diaphragmatic hernia (CDH) is a rare birth defect characterised byincomplete closure of the diaphragm. After birth, CDH is associated with significantneonatal morbidity and mortality due to a combination of pulmonary hypoplasia,pulmonary hypertension, and cardiac dysfunction. Despite improvements in clinicalcare, around 30% of these infants do not survive. The research projects reportedin this thesis provide a critical appraisal of important aspects of perinatal care forinfants with CDH.<br/
Causally Interpretable Meta-Analysis of Multivariate Outcomes in Observational Studies
Integrating multiple observational studies to make unconfounded causal or
descriptive comparisons of group potential outcomes in a large natural
population is challenging. Moreover, retrospective cohorts, being convenience
samples, are usually unrepresentative of the natural population of interest and
have groups with unbalanced covariates. We propose a general
covariate-balancing framework based on pseudo-populations that extends
established weighting methods to the meta-analysis of multiple retrospective
cohorts with multiple groups. Additionally, by maximizing the effective sample
sizes of the cohorts, we propose a FLEXible, Optimized, and Realistic (FLEXOR)
weighting method appropriate for integrative analyses. We develop new weighted
estimators for unconfounded inferences on wide-ranging population-level
features and estimands relevant to group comparisons of quantitative,
categorical, or multivariate outcomes. The asymptotic properties of these
estimators are examined, and accurate small-sample procedures are devised for
quantifying estimation uncertainty. Through simulation studies and
meta-analyses of TCGA datasets, we discover the differential biomarker patterns
of the two major breast cancer subtypes in the United States and demonstrate
the versatility and reliability of the proposed weighting strategy, especially
for the FLEXOR pseudo-population.Comment: arXiv admin note: text overlap with arXiv:2212.0912
Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods
In the course of the COVID-19 pandemic and the implementation of associated non-pharmaceutical containment measures, the need for continuous monitoring of the mental health of populations became apparent. When the pandemic hit Germany, a nationwide Mental Health Surveillance (MHS) was in conceptual development at Germany’s governmental public health institute, the Robert Koch Institute. To meet the need for high-frequency reporting on population mental health we developed a prototype that provides monthly estimates of several mental health indicators with smoothing splines. We used data from the telephone surveys German Health Update (GEDA) and COVID-19 vaccination rate monitoring in Germany (COVIMO). This paper provides a description of the highly automated data pipeline that produces time series data for graphical representations, including details on data collection, data preparation, calculation of estimates, and output creation. Furthermore, statistical methods used in the weighting algorithm, model estimations for moving three-month predictions as well as smoothing techniques are described and discussed. Generalized additive modelling with smoothing splines best meets the desired criteria with regard to identifying general time trends. We show that the prototype is suitable for a population-based high-frequency mental health surveillance that is fast, flexible, and able to identify variation in the data over time. The automated and standardized data pipeline can also easily be applied to other health topics or other surveys and survey types. It is highly suitable as a data processing tool for the efficient continuous health surveillance required in fast-moving times of crisis such as the Covid-19 pandemic
A General Form of Covariate Adjustment in Randomized Clinical Trials
In randomized clinical trials, adjusting for baseline covariates has been
advocated as a way to improve credibility and efficiency for demonstrating and
quantifying treatment effects. This article studies the augmented inverse
propensity weighted (AIPW) estimator, which is a general form of covariate
adjustment that includes approaches using linear and generalized linear models
and machine learning models. Under covariate-adaptive randomization, we
establish a general theorem that shows a complete picture about the asymptotic
normality, efficiency gain, and applicability of AIPW estimators. Based on the
general theorem, we provide insights on the conditions for guaranteed
efficiency gain and universal applicability under different randomization
schemes, which also motivate a joint calibration strategy using some
constructed covariates after applying AIPW. We illustrate the application of
the general theorem with two examples, the generalized linear model and the
machine learning model. We provide the first theoretical justification of using
machine learning methods with dependent data under covariate-adaptive
randomization. Our methods are implemented in the R package RobinCar
Trajectories of early childhood skill development and maternal mental health
We investigate the impacts of a perinatal psychosocial intervention on trajectories of maternal mental health and child skills, from birth to age 3. We find improved maternal mental health and functioning (0.17 – 0.29 SD), modest but imprecisely estimated improvements in parental investments (0.07 to 0.11 SD), and transitory improvements in child socioemotional development (0.06 to 0.39 SD). We also find negligible influence of the intervention on physical health and cognitive development. Estimates of a skill production function reveal that the intervention is associated with reduced productivity of maternal mental health and narrowed “depression gaps” in mother and child outcomes
Privacy-preserving patient clustering for personalized federated learning
Federated Learning (FL) is a machine learning framework that enables multiple
organizations to train a model without sharing their data with a central
server. However, it experiences significant performance degradation if the data
is non-identically independently distributed (non-IID). This is a problem in
medical settings, where variations in the patient population contribute
significantly to distribution differences across hospitals. Personalized FL
addresses this issue by accounting for site-specific distribution differences.
Clustered FL, a Personalized FL variant, was used to address this problem by
clustering patients into groups across hospitals and training separate models
on each group. However, privacy concerns remained as a challenge as the
clustering process requires exchange of patient-level information. This was
previously solved by forming clusters using aggregated data, which led to
inaccurate groups and performance degradation. In this study, we propose
Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel
Clustered FL framework that can cluster patients using patient-level data while
protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic
technique, to securely calculate patient-level similarity scores across
hospitals. We then evaluate PCBFL by training a federated mortality prediction
model using 20 sites from the eICU dataset. We compare the performance gain
from PCBFL against traditional and existing Clustered FL frameworks. Our
results show that PCBFL successfully forms clinically meaningful cohorts of
low, medium, and high-risk patients. PCBFL outperforms traditional and existing
Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC
improvement of 7.8%
Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
Temporally dense single-person "small data" have become widely available
thanks to mobile apps and wearable sensors. Many caregivers and self-trackers
want to use these data to help a specific person change their behavior to
achieve desired health outcomes. Ideally, this involves discerning possible
causes from correlations using that person's own observational time series
data. In this paper, we estimate within-individual average treatment effects of
physical activity on sleep duration, and vice-versa. We introduce the model
twin randomization (MoTR; "motor") method for analyzing an individual's
intensive longitudinal data. Formally, MoTR is an application of the g-formula
(i.e., standardization, back-door adjustment) under serial interference. It
estimates stable recurring effects, as is done in n-of-1 trials and single case
experimental designs. We compare our approach to standard methods (with
possible confounding) to show how to use causal inference to make better
personalized recommendations for health behavior change, and analyze 222 days
of Fitbit sleep and steps data for one of the authors.Comment: 27 pages, 2 figures, 5 tables; appendix include
Part 1: Supporting the Reduction of Suicide in the General Population of Wales via the use of Structured Professional Judgement; Part 2: Identifying the Factors Moderating Suicidal Thoughts and Suicide Attempts During the COVID-19 Pandemic.
Part 1: Early identification of individuals at risk of suicide represents a crucial component of effective suicide prevention. However, many of the current suicide risk assessment procedures are limited in their ability to identify and prevent future suicide attempts. This thesis aimed to investigate whether the structured professional judgement approach was an effective method of suicide risk assessment within an accident and emergency department. Chapter 1 outlined the major challenges facing the field of suicide risk assessment and introduced the structured professional judgement approach to risk assessment. Chapter 2 reviewed the various methods used to assess the risk of suicide within accident and emergency services, evaluated the efficacy of the structured professional judgement approach and outlined the new structured professional judgement scheme, the Risk of Suicide Protocol, that was investigated in this thesis. Chapter 3 compared the Risk of Suicide Protocol and assessment as usual in their ability to identify future suicide attempts in 107 participants referred for a suicide risk assessment with the accident and emergency-based Psychiatric Liaison Team. Chapter 3 also evaluated the inter-rater reliability of the Risk of Suicide Protocol, with two independent assessors completing assessments on the same 12 patients. Chapter 7 reviewed the research relating to the RoSP and discussed the wider meaning and clinical implications of the findings. The findings demonstrated that risk judgements made using the Risk of Suicide Protocol were significantly better at identifying future suicide attempts compared to assessment as usual. Additionally, the risk judgements made using the Risk of Suicide Protocol demonstrated excellent inter-rater reliability. These results indicate that the Risk of Suicide Protocol is a valid and reliable assessment for the structured clinical evaluation of suicide risk within an accident and emergency department. Overall, this thesis demonstrates that the Risk of Suicide Protocol represents a valuable method for the evaluation of suicide risk and may offer an important solution to some of the challenges facing the field of suicide risk assessment. Part 2: The COVID-19 pandemic resulted in a wide range of difficulties for populations across the world, with research indicating that the pandemic had negatively impacted population mental health. This thesis aimed to identify and understand the factors influencing suicidal thoughts and attempts during the COVID-19 pandemic. Chapters 1 and 4 reviewed how the COVID-19 pandemic affected population mental health and suicidality and explored the rationale for this research. Chapters 5 and 6 reported the results of an online survey administered to a large sample of adults (N > 13,000) living in Wales between the 18th of January 2021 to the 7th of March 2021. Chapter 5 aimed firstly, to identify the demographic groups most vulnerable to suicidal thoughts and attempts and secondly, to examine whether various pandemic related stressors (e.g., social isolation, food insecurity) were associated with suicidal thoughts and attempts. Chapter 6 investigated whether hope, social connectedness, resilience or pandemic acceptance could protect against the presence of suicidal thoughts during the COVID-19 pandemic. Chapter 7 reviewed the research and considered the wider implications of the findings. The findings from chapter 5 revealed that men, younger adults and socioeconomically deprived individuals were more likely to experience suicidal thoughts during the pandemic, with younger adults also more likely to attempt suicide. Chapter 5 also found that domestic abuse, food insecurity, difficulty accessing healthcare, social isolation, relationship problems, financial problems and being made redundant were the pandemic related stressors most strongly related to suicidal thoughts and attempts. Chapter 6 found that hope, resilience and pandemic acceptance all protected against suicidal thoughts during the pandemic, with higher levels of hope, resilience and pandemic acceptance weakening the relationship between pandemic stress and suicidal thoughts. Overall, this thesis has enhanced our understanding of the factors influencing suicidal thoughts and attempts during the COVID-19 pandemic. The findings provide valuable insights that can be used to inform outreach and support structures in their efforts to prevent suicide
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