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AbstractThe University of Toronto's Troost ILead helps all engineering students blend research and experiential education to find the leader within.

The rise of congressional polarization has led presidents to seek alternative ways to pursue their agendas through staffing. I argue presidents use excepted appointments, which are excepted both from advice and consent and competitive hiring processes, when ideological conflict within the Senate is high. I also argue that Schedule C appointments specifically are concentrated in agencies ideologically similar to the president due to their advisory nature. I find preliminary support for these hypotheses using Office of Personnel Management data on Schedule C appointees from 1998 to 2013. In sum, this study shows that excepted appointments are an important yet understudied tool in the president's administrative toolbox.

From 1938 to 1942, Tlingit and Haida Native men enrolled in a New Deal work relief program known as the Civilian Conservation Corps (CCC) worked with the U.S. Forest Service to restore more than one hundred nineteenth-century totem poles in Southeast Alaska. Reversing decades of assimilation policies that had nearly ended totem pole carving in Alaska at the turn of the century, the CCC restored or replicated nineteenth-century totem poles and reerected them in totem parks designed to attract tourists traveling on the steamship route known as the Inside Passage. This dissertation provides the first extensive analysis of this New Deal program, situating the totem parks as contact zones where Natives and non-Natives met to negotiate the complex (and often cross-purposed) catalysts of the restoration program: modernist primitivism, New Deal nationalist heritage, and indigenous rights movements of the Indian New Deal. Attending to the carving styles as well as to tourist and government photography of the parks, the project positions the totem parks as a case study for a transcultural model of American art history.

Funding Information: This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We thank Dave Kelly from Albasoft (Inverness, UK) for his support with making primary care data available and James Pickett (Health Data Research UK, London, UK); Wendy Inglis-Humphrey, Vicky Hammersley, Laura Brook, Maria Georgiou, and Laura Gonzalez Rienda (University of Edinburgh, Edinburgh, UK); and Pam McVeigh, Amanda Burridge, Sumedha Asnani-Chetal, and Afshin Dastafshan (Public Health Scotland, Glasgow, UK) for their support with project management and administration. ; Peer reviewed ; Publisher PDF

This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23). ; Background As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave. Methods We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a stratified sample of individuals from our cohort who had not previously tested positive, with future cases in each group sampled from a multinomial distribution. We then used their patient characteristics (including age, sex, comorbidities, and socioeconomic status) to predict their probability of hospitalisation or death. Findings Our cohort included 5 384 819 people, representing 98·6% of the entire estimated population residing in Scotland during 2020. Hospitalisation and death among those testing positive for SARS-CoV-2 between March 1 and June 23, 2020, were associated with several patient characteristics, including male sex (hospitalisation hazard ratio [HR] 1·47, 95% CI 1·38–1·57; death HR 1·62, 1·49–1·76) and various comorbidities, with the highest hospitalisation HR found for transplantation (4·53, 1·87–10·98) and the highest death HR for myoneural disease (2·33, 1·46–3·71). For those testing positive, there were decreasing temporal trends in hospitalisation and death rates. The proportion of positive tests among older age groups (>40 years) and those with at-risk comorbidities increased during October, 2020. On Nov 10, 2020, the projected number of hospitalisations for Dec 8, 2020 (28 days later) was 90 per day (95% prediction interval 55–125) and the projected number of deaths was 21 per day (12–29). Interpretation The estimated incidence of SARS-CoV-2 infection based on positive tests recorded in this unique data resource has provided forecasts of hospitalisation and death rates for the whole of Scotland. These findings were used by the Scottish Government to inform their response to reduce COVID-19-related morbidity and mortality. ; Publisher PDF ; Peer reviewed

This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23). ; Background As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave. Methods We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a ...

This work was funded by the Medical Research Council as part of the Lifelong Health and Wellbeing study as part of National Core Studies (MC_PC_20030). SVK acknowledges funding from the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE – The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. BG has received research funding from the NHS National Institute for Health Research (NIHR), the Wellcome Trust, Health Data Research UK, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme. ; Background Uncontrolled infection and lockdown measures introduced in response have resulted in an unprecedented challenge for health systems internationally. Whether such unprecedented impact was due to lockdown itself and recedes when such measures are lifted is unclear. We assessed the short- and medium-term impacts of the first lockdown measures on hospital care for tracer non-COVID-19 conditions in England, Scotland and Wales across diseases, sexes, and socioeconomic and ethnic groups. Methods We used OpenSAFELY (for England), EAVEII (Scotland), and SAIL Databank (Wales) to extract weekly hospital admission rates for cancer, cardiovascular and respiratory conditions (excluding COVID-19) from the pre-pandemic period until 25/10/2020 and conducted a controlled interrupted time series analysis. We undertook stratified analyses and assessed admission rates over seven months during which lockdown restrictions were gradually lifted. Findings Our combined dataset included 32 million people who contributed over 74 million person-years. Admission rates for all three conditions fell by 34.2% (Confidence Interval (CI): -43.0, -25.3) in England, 20.9% (CI: -27.8, -14.1) in ...

Funding: This study is being funded by the UKRI Centre on the Dynamics of Ethnicity 4 (ES/W000849/1). PH, SVK, AHL and KJH are funded by the Medical Research Council (MC_UU_00022/2) and Scottish Government Chief Scientist Office (SPHSU17). SVK is funded by a NRS Senior Clinical Fellowship (SCAF/15/02). EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE-The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK; with additional support from the Scottish Government DG Health and Social Care. ; Introduction Evidence from previous pandemics, and the current COVID-19 pandemic, has found that risk of infection/severity of disease is disproportionately higher for ethnic minority groups, and those in lower socioeconomic positions. It is imperative that interventions to prevent the spread of COVID-19 are targeted towards high-risk populations. We will investigate the associations between social characteristics (such as ethnicity, occupation and socioeconomic position) and COVID-19 outcomes and the extent to which characteristics/risk factors might explain observed relationships in Scotland. The primary objective of this study is to describe the epidemiology of COVID-19 by social factors. Secondary objectives are to (1) examine receipt of treatment and prevention of COVID-19 by social factors; (2) quantify ethnic/social differences in adverse COVID-19 outcomes; (3) explore potential mediators of relationships between social factors and SARS-CoV-2 infection/COVID-19 prognosis; (4) examine whether occupational COVID-19 differences differ by other social factors and (5) assess quality of ethnicity coding within National Health Service datasets. Methods and analysis We will use a national cohort comprising the adult population of Scotland who completed the 2011 Census and were living in Scotland on 31 March 2020 (~4.3 million people). Census data will be linked to the Early Assessment of Vaccine and Anti-Viral Effectiveness II cohort consisting of primary/secondary care, laboratory data and death records. Sensitivity/specificity and positive/negative predictive values will be used to assess coding quality of ethnicity. Descriptive statistics will be used to examine differences in treatment and prevention of COVID-19. Poisson/Cox regression analyses and mediation techniques will examine ethnic and social differences, and drivers of inequalities in COVID-19. Effect modification (on additive and multiplicative scales) between key variables (such as ethnicity and occupation) will be assessed. Ethics and dissemination Ethical approval was obtained from the National Research Ethics Committee, South East Scotland 02. We will present findings of this study at international conferences, in peer-reviewed journals and to policy-makers. ; Publisher PDF ; Peer reviewed