Harvesting Freedom: African American Agrarianism in Civil War Era South Carolina

Pursuit of property Policies frustrated freed slaves\u27 quest for land The Reconstruction era continues to fascinate scholars around the world. In 1928, French statesman Georges Clemenceau published his History of American Reconstruction, and it is not unlikely that his par...

Caveats in reporting of national vaccine uptake

Funding: EAVE II is supported 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. Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care, the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058) and the Lifelong Health and Wellbeing study as part of the National Core Studies (MC_PC_20030).Peer reviewe

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records : a national, retrospective, observational cohort study

Acknowledgements This work was supported by the Chief Scientist Office, grant number COV/LTE/20/15. EAVE II is supported by a grant (MC_PC_19075) from the Medical Research Council; and a grant (MC_PC_19004) from BREATHE–The Health Data Research Hub for Respiratory Health, funded through the UK Research and Innovation Industrial Strategy Challenge Fund. LD was supported by a post-doctoral clinical fellowship from the Asthma UK Centre for Applied Research. SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). The authors would like to acknowledge the support of Dave Kelly and Lamorna Brown of Albasoft Ltd., and Sharon Kennedy, Mike Birnie, Safraj Shahul Hameed and Elliott Hall of Public Health Scotland for their involvement in obtaining approvals, provisioning, and linking data and the use of the secure analytical platform within the National Safe Haven. Funding Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.Peer reviewe

Understanding and reporting odds ratios as rate-ratio estimates in case-control studies

Background: We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies. Methods: We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied. Results: We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios. Conclusions: We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies

Risk of winter hospitalisation and death from acute respiratory infections in Scotland: national retrospective cohort study

Objectives We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation and death during the winter period in Scotland. Design A population-based retrospective cohort analysis Setting Scotland Participants 5.4 million residents in Scotland Main outcome measures Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. Results Between September 1, 2022 and January 31, 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1,759 in children and 20,525 in adults) in Scotland. Compared to the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR=4.55 95%CI (4.11-5.04)). Compared to 25-29 years old, the risk of ARI hospitalisation was highest amongst the oldest adults aged ≥80 years (7.86 (7.06-8.76)). Adults from more deprived areas (most deprived vs least deprived, 1.64 (1.57-1.72)), with existing health conditions (≥5 vs 0 health conditions, 4.84 (4.53-5.18)) or with history of all-cause emergency admissions (≥6 vs 0 previous emergency admissions 7.53 (5.48-10.35)) were at higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. Conclusions Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI

Risk of winter hospitalisation and death from acute respiratory infections in Scotland : national retrospective cohort study

Funding : This study is funded by the National Institute for Health and Care Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work also benefits from the infrastructure and partnerships assembled by HDR UK, including through the Data and Connectivity National Core Study, funded by UK Research and Innovation [grant ref MC_PC_20058].Objectives  We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. Design  A population-based retrospective cohort analysis. Setting   Scotland. Participants   5.4 million residents in Scotland. Main outcome measures   Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. Results   Between September 1, 2022 and January 31, 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1,759 in children and 20,525 in adults) in Scotland. Compared to the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR=4.55 95%CI (4.11-5.04)). Compared to 25-29 years old, the risk of ARI hospitalisation was highest amongst the oldest adults aged ≥80 years (7.86 (7.06-8.76)). Adults from more deprived areas (most deprived vs least deprived, 1.64 (1.57-1.72)), with existing health conditions (≥5 vs 0 health conditions, 4.84 (4.53-5.18)) or with history of all-cause emergency admissions (≥6 vs 0 previous emergency admissions 7.53 (5.48-10.35)) were at higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. Conclusions   Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI.Peer reviewe

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records : a national, retrospective, observational cohort study

Background: Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods: In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings: Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation:The prevalence of long COVID presenting in general practice was estimated to be 0.02–1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach

Risk of winter hospitalisation and death from acute respiratory infections in Scotland : a national retrospective cohort study

Objectives We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. Design A population-based retrospective cohort analysis Setting Scotland Participants 5.4 million residents in Scotland Main outcome measures Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. Results Between September 1, 2022 and January 31, 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1,759 in children and 20,525 in adults) in Scotland. Compared to the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR=4.55 95%CI (4.11-5.04)). Compared to 25-29 years old, the risk of ARI hospitalisation was highest amongst the oldest adults aged ≥80 years (7.86 (7.06-8.76)). Adults from more deprived areas (most deprived vs least deprived, 1.64 (1.57-1.72)), with existing health conditions (≥5 vs 0 health conditions, 4.84 (4.53-5.18)) or with history of all-cause emergency admissions (≥6 vs 0 previous emergency admissions 7.53 (5.48-10.35)) were at higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. Conclusions Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease