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Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Background The Cystic Fibrosis (CF) Registry collects clinical data on all patients attending specialist CF centres in the UK. These data have been used to make comparisons between centres on key outcomes such as forced expiratory volume in 1 second (FEV1) using simple rankings, which promote the assumption that those with the highest measures provide ‘better’ care. Objectives To explore whether or not using statistical ‘process control’ charts that move away from league tables and adjusting for case mix (age, where appropriate; sex; CF genotype; pancreatic sufficiency; and socioeconomic status) could identify exceptional CF care services in terms of clinically meaningful outcomes. Then, using insight from patients and clinicians on what structures, processes and policies are necessary for delivering good CF care, to explore whether or not care is associated with observed differences in outcomes. Design Cross-sectional analyses. Setting Specialist CF centres in the UK. Participants Patients aged ≥ 6 years attending specialist CF centres and clinicians at these centres. Main outcome measures FEV1% predicted. Data sources Annual reviews taken from the UK CF Registry (2007–15). Results We studied FEV1 in many different ways and in different periods. In our analyses of both adult and paediatric centres, we observed that some centres showed repeated evidence of ‘special-cause variation’, with mean FEV1 being greater than the mean in some cases and lower than the mean in others. Some of these differences were explained by statistical adjustment for different measures of case mix, such as age, socioeconomic status, genotype and pancreatic sufficiency. After adjustment, there was some remaining evidence of special-cause variation for some centres. Our data at these centres suggest that there may be an association with the use of intravenous antibiotics. Workshops and focus groups with clinicians at paediatric and adult centres identified a number of structures, processes and policies that were felt to be associated with good care. From these, questionnaires for CF centre directors were developed and disseminated. However, the response rate was low, limiting the questionnaires’ use. Focus groups with patients to gain their insights into what is necessary for the delivery of good care identified themes similar to those identified by clinicians, and a patient questionnaire was developed based on these insights. Limitations Our data analyses suggest that differences in intravenous antibiotic usage may be associated with centre-level outcomes; this needs to be explored further in partnership with the centres. Our survey of centre directors yielded a low response, making it difficult to gain useful knowledge to inform further discussions with sites. Conclusions Our findings confirm that the CF Registry can be used to identify differences in clinical outcomes between centres and that case mix might explain some of these differences. As such, adjustment for case mix is essential when trying to understand how and why centres differ from the mean. Future work Future work will involve exploring with clinicians how care is delivered so that we can understand associations between care and outcomes. Patients will also be asked for their perspectives on the care they receive. Funding The National Institute for Health Research Health Services and Delivery Research programme.

Background Limited evidence suggests that exposure to traffic related air pollution is associated with graft failure among lung transplant recipients. We explored associations between pollution and transplant failure among lung and other solid organ transplant recipients in Great Britain through a retrospective cohort study. Methods All patients who received a lung, heart, liver, or kidney transplant between 2000 and 2008 in Great Britain were included, as recorded in the National Health Service Blood and Transplant (NHSBT) register and followed to March 2015. Using residential addresses at time of transplant we calculated distance to nearest (major) road and modelled annual average exposures to airborne nitrogen oxides and particulate matter of diameter ≤10µm and ≤2.5µm for each transplant recipient. All-cause mortality or graft failure (kidney) during follow up was the main outcome; median follow-up was around 10 years for each organ type. We fitted Cox regression models with adjustment for age, sex, year of transplant and donor age/smoking status. Results 780 lung, 1213 heart, 3650 liver and 11966 graft kidney transplant patients were analysed. We did not find any consistent associations between mortality or graft failure and any of the analysed air pollutants or road metrics. Although, exposure to particulate matter was associated with renal transplant failure in univariable analyses but not after adjustment for confounders. Conclusions Our analysis does not confirm previously reported associations between traffic-related air pollution exposure and the risk of transplant failure.

Key Points Question Is exposure to ambient air pollution in the first trimester associated with thyroid function throughout pregnancy? Findings Among 9931 pregnant women in 4 European cohorts and 1 US cohort, an increase of 5 μg/m3 in exposure to particulate matter with an aerodynamic diameter of 2.5 μm or less was associated with 20% higher odds of hypothyroxinemia. Meaning The findings of this study raise the possibility that exposure to particulate matter might disrupt thyroid function in pregnant women., This cohort study evaluates the short-term association of exposure to air pollution in the first trimester with thyroid function throughout pregnancy among European and US cohorts., Importance Air pollutants interact with estrogen nuclear receptors, but their effect on thyroid signaling is less clear. Thyroid function is of particular importance for pregnant women because of the thyroid’s role in fetal brain development. Objective To determine the short-term association of exposure to air pollution in the first trimester with thyroid function throughout pregnancy. Design, Setting, and Participants In this cohort study, 9931 pregnant women from 4 European cohorts (the Amsterdam Born Children and Their Development Study, the Generation R Study, Infancia y Medio Ambiente, and Rhea) and 1 US cohort (Project Viva) with data on air pollution exposure and thyroid function during pregnancy were included. The recruitment period for the Amsterdam Born Children and Their Development Study was January 2003 to March 2004; for Generation R, April 2002 to January 2006; for Infancia y Medio Ambiente, November 2003 to January 2008; for Rhea, February 2007 to February 2008; and for Project Viva, April 1999 to November 2002. Statistical analyses were conducted from January 2018 to April 2019. Main Outcomes and Measures Residential air pollution concentrations (ie, nitrogen oxide and particulate matter [PM]) during the first trimester of pregnancy were estimated using land-use regression and satellite-derived aerosol optical depth models. Free thyroxine, thyrotropin, and thyroid peroxidase antibody levels were measured across gestation. Hypothyroxinemia was defined as free thyroxine below the fifth percentile of the cohort distribution with normal thyrotropin levels, following the American Thyroid Association guidelines. Results Among 9931 participants, the mean (SD) age was 31.2 (4.8) years, 4853 (48.9%) had more than secondary educational levels, 5616 (56.6%) were nulliparous, 404 (4.2%) had hypothyroxinemia, and 506 (6.7%) tested positive for thyroid peroxidase antibodies. Concentrations of nitrogen dioxide and PM with an aerodynamic diameter of 2.5 μm or less (PM2.5) were lower and had less variation in women in the US cohort than those in European cohorts. No associations of nitrogen oxide with thyroid function were found. Higher exposures to PM2.5 were associated with higher odds of hypothyroxinemia in pregnant women (odds ratio per 5-μg/m3 change, 1.21; 95% CI, 1.00-1.47). Although exposure to PM with an aerodynamic diameter of 10 μm or less was not significantly associated with hypothyroxinemia, the coefficient was similar to that for the association of PM2.5 with hypothyroxinemia (odds ratio per 10-μg/m3 change, 1.18; 95% CI, 0.93-1.48). Absorbances of PM2.5 and PM with aerodynamic diameter from 2.5 to 10 μg and were not associated with hypothyroxinemia. There was substantial heterogeneity among cohorts with respect to thyroid peroxidase antibodies (P for heterogeneity

Background: The association between air pollution exposure and emotional and behavioural problems in children is unclear. We aimed to assess prenatal and postnatal exposure to several air pollutants and child's depressive and anxiety symptoms, and aggressive symptoms in children of 7-11 years. Methods: We analysed data of 13182 children from 8 European population-based birth cohorts. Concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM) with diameters of

Objectives The UK CF Registry annual reports include comparisons between centres on key outcomes such as FEV1 using rankings. While illustrating the distribution between centres, they promote the assumption that those with the highest measures provide “better” care. We hypothesised a more scientific approach based on statistical “process control” using funnel plots and adjustment for case-mix may help to identify exceptional CF care services in terms of clinically meaningful outcomes. Methods We extracted data from annual reviews (2007–2012) on the CF Registry. Our outcomes included FEV1 (% predicted) at 15 years and change in FEV1 between 18 and 21 years. Funnel plots were generated with confidence limits at 2 and 3 standard deviations (SD). Centres with mean values outside these limits are said to display “special cause variation” -variability outside what one would expect. Outcomes were then adjusted for case mix (including gender, genotype, pancreatic sufficiency and socio-economic deprivation) and analysed using funnel plots. Results 31 paediatric centres provided FEV1 data on 15 year olds between 2007 and 2012. Funnel plots of unadjusted FEV1 (% predicted) showed few centres with evidence of special cause variation (2SD limits). Initial case-mix adjustment reduced the number of centres outside these limits to 3. We also identified 28 adult centres providing sufficient data to calculate change in FEV1 (% predicted) between 18–21 years. While there was some evidence of special cause variation (at 2SD limits) in prior to case-mix adjustment, after adjustment none were outside the 2SD limits. None of the centres were outside the 3SD limits in either analysis. Conclusion In conclusion the work to-date illustrates that funnel plots can be used to explore potential differences in FEV1 between specialist centres. Case-mix adjustment models should develop into a useful tool for making centre comparisons which can continue to be used by stakeholders. This is early work, however, and we need to bear in mind that by examining outcomes in small populations risk missing true differences due to low statistical power. Further work is required to assess whether any observed differences are due to chance or are related to the care patients receive.

Objectives The UK CF Registry annual reports include comparisons between centres on key outcomes such as FEV 1 using rankings. While illustrating the distribution between centres, they promote the assumption that those with the highest measures provide "better" care. We hypothesised a more scientific approach based on statistical "process control" and adjustment for case-mix may identify exceptional CF care services in terms of clinically meaningful outcomes. Methods We extracted FEV 1 and BMI on patients aged 12–21 from annual review data (2007–2012). Funnel plots were generated with confidence limits at 2 and 3 standard deviations (SD). Centres with mean values outside these limits are said to display "special cause variation" – variability outside what one would expect. Outcomes were then adjusted for case mix (including gender, genotype, pancreatic sufficiency and socio-economic deprivation) and analysed using funnel plots. Results Using a primary outcome of FEV 1 at age 15, data for 1066 patients from 31 paediatric centres were available. Funnel plots of unadjusted data showed 8 centres with evidence of special cause variation (at 2SD limits). Initial adjustment for case-mix reduced the number of centres outside these limits to 3. None of the centres were outside the 3SD limits in either analysis. Similar patterns were found for BMI percentile in this age group. Conclusion In conclusion the work to-date illustrates that funnel plots can be used to identify differences in FEV 1 and BMI between specialist paediatric centres. Case-mix adjustment models should develop into a useful tool for making centre comparisons which can continue to be used by stakeholders.

Environment International 131, 104927 (2019)