Effect of Covid-19 frailty heterogeneity on the future evolution of mortality by stratified weighting

The starting point of our research is the inadequacy of assuming, in the construction of a model of mortality, that frailty is constant for the individuals comprising a demographic population. This assumption is implicitly made by standard life table techniques. The substantial differences in the individual susceptibility to specific causes of death lead to heterogeneity in frailty, and this can have a material effect on mortality models and projections – specifically a bias due to the underestimation of longevity improvements. Given these considerations, in order to overcome the misrepresentation of the future mortality evolution, we develop a stochastic model based on a stratification weighting mechanism, which takes into account heterogeneity in frailty. Furthermore, the stratified stochastic model has been adapted also to capture Covid-19 frailty heterogeneity, that is a frailty worsening due to the Covid-19 virus. Based on different frailty levels characterising a population, which affect mortality differentials, the analysis allows for forecasting the temporary excess of deaths by the stratification schemes in a stochastic environment

Longevity risk and capital markets: the 2021–22 update

This Special Issue of the Journal of Demographic Economics contains 10 contributions to the academic literature all dealing with longevity risk and capital markets. Draft versions of the papers were presented at Longevity 16: The Sixteenth International Longevity Risk and Capital Markets Solutions Conference that was held in Helsingør near Copenhagen on 13-14 August 2021. It was hosted by PerCent at Copenhagen Business School and the Pensions Institute at City, University of London

Core evidence elements for generating medicine safety evidence for pregnancy using population-based data: Recommendations from IMI-ConcePTION

Background: The risks and benefits of medicine use during pregnancy are typically established through post-approval population-based observational studies. Currently, there is heterogeneity in the identification, selection and the definitions of key pregnancy and maternal outcomes of interest, exposures, risk factors, and confounders. There is also a large range of different study designs and statistical tools available for these studies. Objectives: IMI-ConcePTION (https://www.imi-conception.eu/) identified the need to create recommendations for standardized key concepts and research methods. Methods: The core evidence elements guide for population-based observational studies was compiled using expertise in pharmacovigilance, pharmacoepidemiology, perinatal epidemiology, statistics, perinatal clinical pharmacology, and health services research, both internal and external to IMI-ConcePTION. It also included reviews of the literature, best practices, and regulatory guidance documents. Results: The recommendations cover core evidence elements including gestational age, exposures (dose/duration of medicine and etiological window); relevant confounders; pregnancy outcomes (live and non-live births), congenital anomalies, infant/childhood outcomes (including long-term outcomes), and maternal outcomes; research design considerations; analytical methods; statistical power/sample size considerations and study limitations. A list of “default” core evidence elements is also proposed as a minimal set of elements that should be considered in all pregnancy medicine safety surveillance studies. The recommendations also include guidance when assessing the quality of data sources. Conclusions: This core evidence elements recommendations will facilitate setting standards with regards to the definitions used in medicine and pregnancy studies, the quality of the data and the suitability of data sources used for this work. It can also be tailored to address studies with specific research questions, particular medicines/disease areas or specific outcomes. It will promote the conduct of more standardized, high quality and clinically meaningful population-based studies among pregnant women. It will also help with alignment across different studies to improve evidence synthesis

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

An epidemiological investigation of the burden of and facility-level risk factors for SARS-CoV-2 infection and outbreaks in care home staff and residents

Background The COVID-19 pandemic significantly impacted care homes, highlighting their vulnerability to infection. I described the burden of infection and investigated facility-level risk factors for SARS-CoV-2 infections and outbreaks within care homes. Methods I helped to rapidly establish the VIVALDI cohort study in ~330 care homes for older people in England (ISRCTN14447421), which hosted my analyses. I reviewed the literature to investigate risk factors for SARS-CoV-2 in care homes. Using data from asymptomatic SARS-CoV-2 testing and anti-nucleocapsid (from infection) and anti-spike (from vaccination) antibodies in care home staff and residents, I estimated prevalence and spread of SARS-CoV-2 infection across homes and modelled longevity of antibody responses following infection and vaccination. Finally, I designed a built environment survey and evaluated environmental risk factors for ingress and transmission of SARS-CoV-2. Results Within VIVALDI, over one-quarter of staff and one-third of residents were infected over 15 months from the pandemic start, increasing to two-thirds after two years. I showed that nucleocapsid-antibodies were negative in half of participants eight months post-infection, suggesting waning immunity, however spike-antibody waning rates following vaccination were comparable between staff and residents. I demonstrated rapid spread of the emergent B.1.1.7 variant in care homes, suggesting introduction of infection from the community. Community incidence of SARS-CoV-2 was also the main risk factor for infection ingress (measured by outbreak incidence) but not transmission (measured by infection incidence, outbreak size, and duration), which was associated with environmental factors like bedroom and storey number, building type, indoor temperature, air quality, and ventilation. Conclusion Care homes experienced high SARS-CoV-2 rates despite stringent control measures, with comparable antibody responses between staff and residents that wane following infection. Although preventing infection entry is challenging, environmental modifications may limit spread. Building on lessons from VIVALDI, controlling infection in care homes should be a research priority

Data integration and simulation of population immunity at the beginning of a pandemic

Accurate knowledge of population exposure at the outset of a pandemic has critical ramifications for preparedness plans for future epidemic waves. In this thesis, I developed a mechanistically informed statistical model to integrate multiple epidemiological datasets in different settings and in different population and to estimate key epidemiological parameters as well as population exposure using Bayesian inference. First, I present a dynamic model to link together three key metrics for evaluating the progress of COVID-19 epidemic in England: seroprevalence, PR-PCR test positivity and death. While estimating the IgG antibody seroreversion rate and region-specific infection fatality ratios, I find that epidemic progression resulted in an increasing gap between measured serology prevalence levels and cumulative population exposure to the virus. Ultimately, this may mean that twice as many, or more, people have been exposed to the virus relative to the number of people who are seropositive by the end of 2020. Moreover, I demonstrate that the model could reconstruct the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as inputs, with the second wave during the winter of 2020 validated by the estimates from the ONS Coronavirus Infection Survey. Comparing with the inferred exposure, I find that the UK official COVID-9 online dashboard reported COVID-19 cases only accounted for less than ten percent by the end of October 2020. I then generalise the model to account for the undocumented COVID-19-related mortality and sparse measurements of seroprevalence. I apply this in the context of Afghanistan COVID- 19 epidemic and find the population exposure in nine regions of Afghanistan were all higher than the seroprevalence survey suggested by July 2020. Finally, I assess the impact of shielding among pregnant patients by comparing their exposure with the estimated exposure of the general population. To approach this, I develop a dynamic model to link RT-PCR and antibody testing results from patients who gave birth and then apply Bayesian inference to estimate transmission parameters and exposure among pregnant patients. I find that after considering the duration of each pregnancy pre-COVID onset and after, the impact of shielding on reducing the level of exposure among pregnant patients during early 2020 who gave birth in this New York City hospital were approximately 50%

Association of frailty status with the causes, characteristics and outcomes of patients with cardiovascular disease using electronic health record data

Introduction Cardiovascular disease (CVD) is the commonest cause of morbidity and mortality worldwide. Frailty is a clinical syndrome of physiological decline, resulting in adverse health outcomes. While the bidirectional relationship between CVD and frailty has been established, there is limited data on the contemporary association of frailty status with the causes, characteristics and outcomes of patients with CVD. This thesis aimed to investigate 1) the prevalence of frailty in CVD patients, 2) the clinical characteristics of frail CVD patients, 3) which CVD patients with frailty present with and 4) the outcomes of frail CVD patients.Methods Two studies were conducted. For the first study, CVD encounters from the 2016-2018 Nationwide Emergency Department (ED) Sample were stratified by their Hospital Frailty Risk Score (HFRS) into low risk (15). For the second study, CVD hospital admissions from the 2015-2019 National Inpatient Sample were stratified by their HFRS into low, intermediate and high risk. These samples were filtered by specific diagnoses: acute myocardial infarction, acute ischaemic stroke, atrial fibrillation (AF), heart failure, pulmonary embolism, acute haemorrhagic stroke and cardiac arrest.Results Over 20 million ED encounters and 16 million hospitalisations were identified. Frailty was present in a significant proportion of ED and hospital admissions for CVD. Increasing HFRS was associated with older age, female sex and increased comorbidities. Increasing frailty was associated with increased odds of mortality across most CVD. The largest effect size was observed in high HFRS patients diagnosed with AF for both studies (ED adjusted odds ratio (aOR) 27.14, 95% confidence interval (CI) 25.03-29.43 and in-hospital aOR 17.69, 95% CI 16.08-19.45 in-hospital compared to their low HFRS counterparts).ConclusionPatients have varying frailty risk according to CVD phenotype. Increased frailty is associated with increased all-cause mortality in patients with most CVD admissions. Knowledge of these trends is fundamental for the early recognition and optimal management of frailty in CVD patients

The detection and assessment of malnutrition, sarcopenia and cachexia, in older adults with cancer

Background: Older adults with cancer are a complex and growing population requiring tailored care to achieve optimum treatment outcomes. However, their care is complicated by underrecognisedand under-treated nutrition-related wasting disorders: malnutrition, sarcopenia, and cachexia.Aim: I aimed to understand better the prevalence, detection, assessment, and patients’ experiences of malnutrition, sarcopenia, and cachexia in older adults with cancer.Methods: I conducted three studies: i) a systematic review with narrative synthesis and metaanalysis investigating markers of malnutrition in older adults with cancer, ii) a systematic review with a qualitative synthesis investigating patients’ views and experiences ofmalnutrition screening, and iii) a mixed-methods study screening for the three conditions, with concurrent qualitative interviews, to determine the feasibility of screening for, and the prevalence and overlap of, malnutrition, sarcopenia, and cachexia in a group of older adults with cancer, and to investigate patients’ views and experiences of the conditions, and the screening processes. Interviews were thematically analysed through a phenomenological lens, with feedback loop analysis investigating relationships between themes. A modified critical interpretive synthesis was used to integrate overall thesis findings.Findings: Review findings highlighted the homogeneity of markers of malnutrition in older adults with cancer. Decreased food intake and Prognostic Nutrition Index (PNI) were significantly associated with patient outcomes, but PNI, and other markers, could not distinguish between inflammatory or energy-deficient causes of weight loss. A lack of patient understanding of the causes and consequences of malnutrition was identified in the second review. Mixed-methods quantitative data show malnutrition, sarcopenia, and cachexia to be highly prevalent, overlapping conditions, with more than one condition coexisting in 57%. Screening tools identified established disease rather than ‘risk’. However, although common, nutritional and functional problems were often overlooked, overshadowed, and misunderstood by both patients and (in patients’ perceptions) by clinicians; misattributed to ageing, cancer, or comorbidities. Patients viewed these conditions as both personal impossibilities, yet accepted inevitabilities.Conclusion: Perceptions, identification, and management of these conditions needs to improve; with their importance recognised by clinicians and patients so those truly ‘at risk’ are identified whilst conditions are more remediable to interventions

Abdominal aortic aneurysms : sex and gender disparities in surveillance, treatment and outcome

Background In the field of abdominal aortic aneurysm (AAA), a lot of what is unknown converge in two specific topics: untreated patients and women. Best management strategies related to surveillance, treatment and outcome are all centered around the fact that surgical repair is indicated when the risk of rupture exceeds the risk of adverse surgical outcomes. The difficulty of this risk-benefit balance is a central and recurrent theme in scientific and clinical contexts. In clinical research, surgical outcomes have tended to gain much more attention than the outcomes of patients surgically untreated for their AAA. The scarce representation of women in randomized trials, screening-based materials and also observational efforts has left us with inferior knowledge on best management for women. Many crucial pieces of information related to surgical non-eligibility, rupture risk and mortality lack sex and gendered specification. An overarching example is the scientific uncertainty around the optimal repair threshold for women. The overall aim of this thesis was to increase the scientific understanding of clinical AAA disease and its special implications in women through all the phases of care: surveillance, treatment and outcome. Methods and Results In Study I and II, surgically untreated patients were investigated. Study IIII and IV were studies on surgically treated patients. The aim of the population-based Study I was to characterize patients untreated for their AAA. All patients (≥40 years) diagnosed with an AAA in Sweden 2001-2015 were included by identification from the National Patient Register. There were 19 569 patients who never proceeded to surgical repair during the 14-year period (60% of all diagnosed patients in the nationwide population). In this untreated population, the proportion of women was 23%. The comorbid loads of women and men were similar: equally many women and men had two or more concomitant diseases (15.9% vs. 15.3%, p = 0.46). Within 5 years, 798 ruptures occurred, more frequently in women than men (9.7% women vs. 6.9% men, p <0.001). Female sex was an independent risk factor for rupture (HR 1.23, 95% CI 1.07-1.42, p <0.001, while adjusting for age, comorbidities, and disposable income). More women died because of rupture (11.9% vs. 8.7%, p<0.001). In both women and men, mortality was high – median survival was 5 years. In Study II, the excess surgical non-eligibility in women was under scrutiny in a multicenter design with detailed patient- and aneurysm-related data. From the vascular outpatient clinics of Stockholm (Sweden), Trondheim (Norway) and Graz (Austria), 200 women and 200 men with AAAs in surveillance were consecutively identified (no prior infrarenal repair), starting from Jan 1st 2014. Through manual chart review, every patient was followed-up for 7 years. Women and men had similar median diameters at inclusion and treatment decisions. When applying sex-specific repair thresholds (50 mm women, 55 mm men), fewer women underwent repair (47% vs. 57%). More women were classified as truly untreated (untreated despite AAA reaching threshold, 26% vs. 8%, p <0.001). In a reanalysis with a 55 mm threshold, truly untreated status remained twice as common among women (16% vs. 8%). There were no apparent distinguishing reasons for truly untreated statuses among women: 50% women and men remained truly untreated due to comorbidities alone, and 36% had comorbidities combined with morphological challenges. Imaging analysis showed similar eligibility for endovascular repair in women and men. Among truly untreated women, ruptures were common (18%) and mortality was very high (86%). In Study III, surgical outcomes were examined in relative survival terms among the 12 907 patients who underwent elective repair. A relative survival analysis compares the survival of a study cohort to that of an age, sex- and year-matched general population. The impact of two major changes in best management, 1) endovascular repair and 2) the cardiovascular recognition of patients as high risk, were investigated in a time-resolved analysis (Period 1: open repair dominated [2001-2004], Period 2: transition-period [2005- 2011], Period 3: endovascular first strategy [2012-2015]). In this treated population, the proportion of women was 17%. Despite declining trends in procedural mortality, the relative survival remained constantly compromised and low for all treated patients. The relative survival of women was worse (4-year relative survival 0.78-0.80 compared to 0.89-0.91 for men, from Period 1 to 3). There was no periodic shift in causes of death: both women and men died mostly of cardiovascular causes (49%). Study IV was an observational appraisal of the women-specific repair threshold. The association between AAA size at repair (smaller or larger than the threshold for men, 55 mm) and mortality was investigated among all the 1380 women with elective repairs registered in the Swedish National Registry for Vascular Surgery 2008-2022 (35% repairs at small sizes). Mortality rates of women were high (3.5% at 90 days, 6.7% at 1 year, 15.4% at 3 year) with no clear differences in the two size strata. There was no consistent association between AAA size at repair and mortality up to 3 years in multivariate models (logistic regression and propensity score models). Sex discrepancies in mortality exclusively stemmed from high mortality among younger-than-average women undergoing repair. Conclusions Untreated status is the dominating status in the AAA care trajectory, a finding often overseen. This population of untreated patients with AAA disease is characterized as having a very high 5-year mortality rate. The disadvantaged situation for women at every step of the care trajectory was elucidated, with high rates of surgical non-eligibility, high rupture rates, high mortality rates due to rupture, and high postoperative mortality. The internal balance between these excess risks in women seems delicate and complex. The surgical non-eligibility in women remains a reality without clear causes, opening up for future studies with broader considerations of patient- and aneurysm-related factors as well as health care equity. The so far chosen strategy of early repair at smaller AAA sizes does not emerge as uncontroversial and does not ameliorate surgical outcomes in women. Something is also leading to relatively worse longevity after repair in women. Going forward, improved health in women with AAA disease will be obtained by proportionate inclusion of women into studies, routine sex- and gender-specific reporting, increasing consideration of patient input and subgroups of women, and database collaborations facilitating women-specific studies