Changes in the Relationship between the Outcomes of Cohabiting Partnerships and Fertility among Young British Women: Evidence from the 1958 and 1970 Birth Cohort Studies

We investigate the effects of a range of time-varying fertility indicators, including pregnancy, and the presence and characteristics of children, on the outcomes of nonmarital unions for two cohorts of British women. We compare the effect of conceptions and births on the odds that a cohabiting partnership is dissolved or that it is converted to marriage for women born in 1958 and 1970. The analysis uses a multilevel competing risks model to allow for multiple partnerships and conceptions, and to distinguish between two outcomes of cohabiting unions (separation and marriage). We also use a multiprocess model, in which the outcomes of cohabitation are modelled simultaneously with fertility, to allow for the potential joint determination of partnership and childbearing decisions. The analysis is based on partnership and birth histories between the ages of 16 and 29, and social background, in the National Child Development Study and the 1970 British Birth Cohort Study

Construction and assessment of risk models in medicine

This thesis investigates the application of classical and contemporary statistical methods in medical research attempting to bridge the gap between statistics and clinical medicine. The importance of using simple and advanced statistical methods in constructing and assessing risk models in medicine will be demonstrated by empirical studies related to vascular complications: namely abdominal aortic aneurysm and diabetic retinopathy. First, data preprocessing and preliminary statistical analysis are examined and their application is investigated using data on abdominal aortic aneurysm. We illustrate that when dealing with missing data, the co-operation between statisticians and clinicians is necessary. Also, we show advantages and disadvantages of exploratory analysis. Second, we describe and compare classification models for AAA selective screening. Tow logistic regression models are proposed. We also show that it is important to assess the performance of classifiers by cross-validation and bootstrapping. We also examine models that include other definitions of abnormality, weighted classification and multiple class models. Third, we consider the application of graphical models. We look at different types of graphical models that can be used for classification and for identifying the underlying data structure. The use of Naïve Bayes classifier (NBC) is shown and subsequently we illustrate the Occam’s window model selection in a statistical package for Mixed Interactions Modelling (MIM). The EM-algorithm and multiple imputation method are used to deal with inconsistent entries in the dataset. Finally, modelling mixture of Normal components is investigated by graphical modelling and compared with an alternative minimisation procedure. Finally, we examine risk factors of diabetic sight threating retinopathy (STR). We show the complexity of data preparation and preliminary analysis as well as the importance of using the clinicians’ opinion on selecting appropriate variables. Blood pressure measurements have been examined as predictors of STR. The fundamental role of imputation and its influence on the conclusions of the study are demonstrated. From this study, we conclude that the application of statistics in medicine is an optimisation procedure where both the statistical and the clinical validity need to be taken into account. Also, the combination of simple and advanced methods should be used as it provides additional information. Data, software and time limitations should be considered before and during statistical analysis and appropriate modifications might be implemented to avoid compromising the quality of the study. Finally, medical research should be regarded for statisticians and clinicians as part of a learning process

Gang Members, Gang Affiliates, and Violent Men: Perpetration of Social Harms, Violence-Related Beliefs, Victim Types, and Locations

Adult gang involvement attracts little empirical attention, so little is known about how they compare to nongang violent men in social harms beyond gang contexts. This study, based on unpublished data of 1,539 adult males, aged 19-34, from the Coid et al., (2013) national survey, compared gang members’ (embedded in a gang; n = 108), affiliates’ (less embedded in a gang; n = 119), and violent men’s (no gang association; n = 1312) perpetration of social harms by assessing their violence-related dispositions and beliefs, victim types, and locations of violence. Results showed that compared to violent men, gang members and affiliates were equally more likely to: cause social harms to a wider range of victims, including family and friends, seek violence, be excited by violence, and carry weapons. Gang members and affiliates were equally more likely than violent men to be violent at home, in friends’ homes, and at work; they also thought more about hurting people, but felt regret for some of their violence. A decreasing gradient was identified in gang members’ (highest), affiliates’ (next highest) and violent men’s (lowest) beliefs in violent retaliation when disrespected, the use of violence instrumentally and when angry, and worry about being violently victimized. Implications of findings are that interventions need to address anger issues across all levels of adult gang membership Importantly, adult gang members’ regrets regarding violence and anxiety about being violently victimized could be key factors that interventions could use to help them relinquish their gang involvement

Differentiating gang members, gang affiliates and violent men on their psychiatric morbidity and traumatic experiences

Objective: Little is known about the differences between gang members and gang affiliates—or those individuals who associate with gangs but are not gang members. Even less is known about how these groups compare with other violent populations. This study examined how gang members, gang affiliates, and violent men compare on mental health symptoms and traumatic experiences. Method: Data included a sample of 1,539 adult males, aged 19 to 34 years, taken from an earlier survey conducted in the United Kingdom. Participants provided informed consent before completing questionnaires and were paid £5 for participation. Logistic regression analyses were conducted to compare participants’ symptoms of psychiatric morbidity and traumatic event exposure. Results: Findings showed that, compared to violent men and gang affiliates, gang members had experienced more severe violence, sexual assaults, and suffered more serious/life-threatening injuries. Compared to violent men, gang members and gang affiliates had made more suicide attempts; had self-harmed more frequently; and had experienced more domestic violence, violence at work, homelessness, stalking, and bankruptcy. Findings further showed a decreasing gradient from gang members to gang affiliates to violent men in symptom levels of anxiety, antisocial personality disorder, pathological gambling, stalking others, and drug and/or alcohol dependence. Depression symptoms were similar across groups. Conclusions: The identified relationship between gang membership, affiliation, and adverse mental health indicates that mental health in gang membership deserves more research attention. Findings also indicate that criminal justice strategies need to consider gang members’ mental health more fully, if gang membership is to be appropriately addressed and reduced

Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity

BACKGROUND: The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated. AIMS: Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring patient similarity. We applied the analysis on a patient cohort diagnosed with chronic obstructive pulmonary disease. METHODS: Using data from the CALIBER data resource, we extracted clinically relevant features for a cohort of patients diagnosed with chronic obstructive pulmonary disease. We used four different data processing pipelines to construct lower dimensional patient representations from which we calculated patient similarity scores. We described the resulting representations, ranked the influence of each individual feature on patient similarity and evaluated the effect of different pipelines on clustering outcomes. Experts evaluated the resulting representations by rating the clinical relevance of similar patient suggestions with regard to a reference patient. RESULTS: Each of the four pipelines resulted in similarity scores primarily driven by a unique set of features. It was demonstrated that data transformations according to each pipeline prior to clustering can result in a variation of clustering results of over 40%. The most appropriate pipeline was selected on the basis of feature ranking and clinical expertise. There was moderate agreement between clinicians as measured by Cohen's kappa coefficient. CONCLUSIONS: Data transformation has downstream and unforeseen consequences in cluster analysis. Rather than viewing this process as a black box, we have shown ways to quantitatively and qualitatively evaluate and select the appropriate preprocessing pipeline

Risk of cardiovascular events following COVID-19 in people with and without pre-existing chronic respiratory disease

BACKGROUND: COVID-19 is associated with cardiovascular outcomes in the general population, but it is unknown whether people with chronic respiratory disease (CRD) have a higher risk of cardiovascular events post-COVID-19 compared with the general population and, if so, what respiratory-related factors may modify this risk in these people.METHODS: Primary and secondary care data from the National Health Service England were used to define a population of adults in England with COVID-19 (index date) between 1 January 2020 and 30 November 2021. Adjusted Cox proportional hazard regression was used to quantify the association between CRD, asthma-related factors, chronic obstructive pulmonary disease (COPD)-related factors, and risk of cardiovascular events. Asthma-specific factors included baseline asthma control, exacerbations, and inhaled corticosteroid (ICS) dose. COPD-specific risk factors included baseline ICS and exacerbations. Secondary objectives quantified the impact of COVID-19 hospitalisation and vaccine dose on cardiovascular outcomes.RESULTS: Of 3 670 455 people, those with CRD had a higher risk of cardiovascular events [adjusted hazard ratio (HRadj), 1.08; 95% confidence interval (CI) 1.06-1.11], heart failure (HRadj, 1.17; 95% CI, 1.12-1.22), angina (HRadj, 1.13; 95% CI, 1.06-1.20) and pulmonary emboli (HRadj, 1.24; 95% CI, 1.15-1.33) compared with people without CRD. In people with asthma or COPD, baseline exacerbations were associated with a higher risk of cardiovascular outcomes (HRadj, 1.36; 95% CI, 1.27-1.00 and HRadj, 1.35; 95% CI, 1.24-1.46, respectively). Regardless of CRD, the risk of cardiovascular events was lower with increasing COVID-19 vaccine dose.CONCLUSIONS: Higher risk of cardiovascular events post-COVID-19 might be explained by the underlying severity of the CRD, and COVID-19 vaccines were beneficial to both people with and those without CRD with regards to cardiovascualr events.</p

Identifying Causal Risk Factors for Violence among Discharged Patients

This study was funded by the UK National Institute for Health Research (NIHR) under its Programme Grants for Applied Research funding scheme (RP-PG-0407-10500)

Adult Attention Deficit Hyperactivity Disorder and Violence in the Population of England: Does Comorbidity Matter?

It is unclear whether the association between Attention Deficit/Hyperactivity Disorder (ADHD) and violence is explained by ADHD symptoms or co-existing psychopathology. We investigated associations of ADHD and its symptom domains of hyperactivity and inattention, among individuals reporting violence in the UK population. Methods We report data from the Adult Psychiatric Morbidity Survey (2007), a representative sample of the household population of England. A randomly selected sample of 7,369 completed the Adult Self-Report Scale for ADHD and the self-reported violence module, including repetition, injury, minor violence, victims and location of incidents. All models were weighted to account for non-response and carefully adjusted for demography and clinical predictors of violence: antisocial personality, substance misuse and anxiety disorders. Results ADHD was moderately associated with violence after adjustments (OR 1.75, p = .01). Hyperactivity, but not inattention was associated with several indicators of violence in the domestic context (OR 1.16, p = .03). Mild and moderate ADHD symptoms were significantly associated with violence repetition, but not severe ADHD where the association was explained by co-existing disorders. Stratified analyses further indicated that most violence reports are associated with co-occurring psychopathology. Conclusions The direct effect of ADHD on violence is only moderate at the population level, driven by hyperactivity, and involving intimate partners and close persons. Because violence associated with severe ADHD is explained by co-existing psychopathology, interventions should primarily target co-existing disorders

Construction and assessment of risk models in medicine

EThOS - Electronic Theses Online ServiceGBUnited Kingdo