Trends in survival of children with severe congenital heart defects by gestational age at birth: A population-based study using administrative hospital data for England.

BACKGROUND: Children with congenital heart defects (CHD) are twice as likely as their peers to be born preterm (<37 weeks' gestation), yet descriptions of recent trends in long-term survival by gestational age at birth (GA) are lacking. OBJECTIVES: To quantify changes in survival to age 5 years of children in England with severe CHD by GA. METHODS: We estimated changes in survival to age five of children with severe CHD and all other children born in England between April 2004 and March 2016, overall and by GA-group using linked hospital and mortality records. RESULTS: Of 5,953,598 livebirths, 5.7% (339,080 of 5,953,598) were born preterm, 0.35% (20,648 of 5,953,598) died before age five and 3.6 per 1000 (21,291 of 5,953,598) had severe CHD. Adjusting for GA, under-five mortality rates fell at a similar rate between 2004-2008 and 2012-2016 for children with severe CHD (adjusted hazard ratio [HR] 0.79, 95% CI 0.71, 0.88) and all other children (HR 0.78, 95% CI 0.76, 0.81). For children with severe CHD, overall survival to age five increased from 87.5% (95% CI 86.6, 88.4) in 2004-2008 to 89.6% (95% CI 88.9, 90.3) in 2012-2016. There was strong evidence for better survival in the ≥39-week group (90.2%, 95% CI 89.1, 91.2 to 93%, 95% CI 92.4, 93.9), weaker evidence at 24-31 and 37-38 weeks and no evidence at 32-36 weeks. We estimate that 51 deaths (95% CI 24, 77) per year in children with severe CHD were averted in 2012-2016 compared to what would have been the case had 2004-2008 mortality rates persisted. CONCLUSIONS: Nine out of 10 children with severe CHD in 2012-2016 survived to age five. The small improvement in survival over the study period was driven by increased survival in term children. Most children with severe CHD are reaching school age and may require additional support by schools and healthcare services.

Congenital diaphragmatic hernia subtypes: Comparing birth prevalence, occurrence by maternal age, and mortality in a national birth cohort.

BACKGROUND: Population-based administrative data have rarely been used to compare the birth prevalence, risk factors for occurrence, and mortality of congenital diaphragmatic hernia (CDH) subtypes. OBJECTIVES: We used a national birth cohort to identify CDH subtypes and compared their birth prevalence, relationship with maternal age after accounting for sociodemographic factors, and 1-year mortality rates. METHODS: Linked hospital admission and death records were used to identify isolated and complex CDH cases (involving additional anomalies) among singleton livebirths in England between 2002 and 2018. The prevalence of each CDH subtype per 10,000 livebirths was estimated overall and by infant, birth and maternal characteristics. The relationship between maternal age and each subtype relative to no CDH was examined using multivariable log-binomial regression to estimate risk ratios (RRs). One-year mortality rates were examined using Kaplan-Meier curves and the hazard ratio (HR) of complex versus isolated CDH was calculated using Cox regression. RESULTS: Among 9.5 million livebirths, we identified 1285 with isolated CDH and 1150 with complex CDH. The overall prevalence of isolated and complex CDH was 1.4 (95% confidence interval [CI] 1.3, 1.4) and 1.2 (95% CI 1.1, 1.3) per 10,000 livebirths, respectively. Only complex CDH was associated with maternal age. Compared with maternal age 25-34 years, complex CDH risk was elevated for maternal age < 20 years (RR 1.31, 95% CI 1.00, 1.72). Risk was highest for maternal age ≥ 40 years (RR 1.61, 95% CI 1.21, 2.15) although accounting for chromosomal anomalies attenuated the risk (RR 1.39, 95% CI 1.00, 1.92). The 1-year mortality rate for complex CDH (33.1%, 95% CI 30.5, 35.9) was slightly higher than for isolated CDH (29.7%, 95% CI 27.3, 32.3) (HR 1.10, 95% CI 0.96, 1.27). CONCLUSIONS: Mechanisms of occurrence differed between and within CDH subtypes and 1-year mortality of complex CDH was slightly higher than for isolated CDH.

Birth prevalence of anorectal malformations in England and 5-year survival: a national birth cohort study.

OBJECTIVE: To determine the birth prevalence, maternal risk factors and 5-year survival for isolated and complex anorectal malformations. DESIGN: National birth cohort using hospital admission data and death records. SETTING: All National Health Service England hospitals. PATIENTS: Live-born singletons delivered from 2002 through 2018, with evidence in the first year of life of a diagnosis of an anorectal malformation and repair during a hospital admission, or anorectal malformation recorded on the death certificate. Cases were further classified as isolated or complex depending on the presence of additional anomalies. MAIN OUTCOME MEASURES: Birth prevalence of anorectal malformations per 10 000 live births, risk ratios for isolated and complex anorectal malformation by maternal, infant and birth characteristics, and 5-year survival. RESULTS: We identified 3325 infants with anorectal malformations among 9 474 147 live-born singletons; 61.7% (n=2050) of cases were complex. Birth prevalence was 3.5 per 10 000 live births (95% CI 3.4 to 3.6). Complex anorectal malformations were associated with maternal age extremes after accounting for other sociodemographic factors. Compared with maternal ages 25-34 years, the risk of complex anorectal malformations was 31% higher for ≥35 years (95% CI 17 to 48) and 13% higher for ≤24 years (95% CI 0 to 27). Among 2376 anorectal malformation cases (n=1450 complex) born from 2002 through 2014, 5-year survival was lower for complex (86.9%; 95% CI 85.1% to 88.5%) than isolated anorectal malformations (98.2%; 95% CI 97.1% to 98.9%). Preterm infants with complex anorectal malformations had the lowest survival (73.4%; 95% CI 68.1% to 78.0%). CONCLUSIONS: Differences in maternal risk factors for isolated and complex anorectal malformations may reflect different underlying mechanisms for occurrence. Five-year survival is high but lowest for preterm children with complex anorectal malformations.

The identification and validity of congenital malformation diagnoses in UK electronic health records: A systematic review.

PURPOSE: To describe the methods used to identify and validate congenital malformation diagnoses recorded in UK electronic health records, and the results of validation studies. METHODS: Medline and Embase were searched for publications between 1987 and 2019 that involved identifying congenital malformations from UK electronic health records using diagnostic codes. The methods and code-lists used to identify congenital malformations, and the methods and results of validations, were examined. RESULTS: We retrieved 54 eligible studies; 36 identified congenital malformations from primary care data and 18 from secondary care data alone or in combination with birth and/or death records. Identification in secondary care data relied on codes from the 'Q' chapter for congenital malformations in ICD-10. In contrast, studies using primary care data frequently used additional codes outside of the 'P' chapter for congenital malformation diagnoses in Read, although the exact codes used were not always clear. Eight studies validated diagnoses identified in primary care data. The positive predictive value was highest (80%-100%) for congenital malformations overall, major malformations, and heart defects although the validity of the reference standard used was often uncertain. It was lowest for neural tube defects (71%) and developmental hip dysplasia (56%). CONCLUSIONS: Studies identifying congenital malformations from primary care data provided limited details about the methods used. The few validation studies were limited to diagnoses recorded in primary care. Further assessments of all measures of validity in both data sources and of other malformation subgroups are needed, using robust reference standards and adhering to reporting guidelines.

UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records.

BACKGROUND: Characterising the size and distribution of the population at risk of severe COVID-19 is vital for effective policy and planning. Older age, and underlying health conditions, are associated with higher risk of death from COVID-19. This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom. METHODS: We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to estimate the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region with binomial exact confidence intervals. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. RESULTS: On 5 March 2019, 24.4% of the UK population were at risk due to a record of at least one underlying health condition, including 8.3% of school-aged children, 19.6% of working-aged adults, and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1.6% of the population had a new diagnosis of cancer in the past 5 y. CONCLUSIONS: The population at risk of severe COVID-19 (defined as either aged ≥70 years, or younger with an underlying health condition) comprises 18.5 million individuals in the UK, including a considerable proportion of school-aged and working-aged individuals. Our national estimates broadly support the use of Global Burden of Disease modelled estimates in other countries. We provide age- and region- stratified prevalence for each condition to support effective modelling of public health interventions and planning of vaccine resource allocation. The high prevalence of health conditions among older age groups suggests that age-targeted vaccination strategies may efficiently target individuals at higher risk of severe COVID-19.

Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction.

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

Seasonal Influenza Vaccination During Pregnancy and the Risk of Major Congenital Malformations in Live-born Infants: A 2010-2016 Historical Cohort Study.

BACKGROUND: Available evidence indicates that seasonal inactivated influenza vaccination during pregnancy protects both the mother and her newborn and is safe. Nevertheless, ongoing safety assessments are important in sustaining vaccine uptake. Few studies have explored safety in relation to major congenital malformations (MCMs), particularly in the first trimester when most organogenesis occurs. METHODS: Anonymized UK primary care data (the Clinical Practice Research Datalink), including a recently developed Pregnancy Register, were used to identify live-born singletons delivered between 2010 and 2016. Maternal influenza vaccination was determined using primary care records and stratified by trimester. Ascertainment of MCMs from infant primary care records was maximized by linkage to hospitalization data and death certificates. The relationship between vaccination and MCMs recorded in the year after delivery and in early childhood was then assessed using multivariable Cox regression. RESULTS: A total of 78 150 live-birth pregnancies were identified: 6872 (8.8%) were vaccinated in the first trimester, 11 678 (14.9%) in the second, and 12 931 (16.5%) in the third. Overall, 5707 live births resulted in an infant with an MCM recorded in the year after delivery and the adjusted hazard ratio when comparing first-trimester vaccination to no vaccination was 1.06 (99% CI, .94-1.19; P = .2). Results were similar for second- and third-trimester vaccination and for analyses considering MCMs recorded beyond the first birthday. CONCLUSIONS: In this large, population-based historical cohort study there was no evidence to suggest that seasonal influenza vaccine was associated with MCMs when given in the first trimester or subsequently in pregnancy.

Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: A matched cohort study using linked English electronic health records data.

BACKGROUND: People with active cancer are recognised as at risk of COVID-19 complications, but it is unclear whether the much larger population of cancer survivors is at elevated risk. We aimed to address this by comparing cancer survivors and cancer-free controls for (i) prevalence of comorbidities considered risk factors for COVID-19; and (ii) risk of severe influenza, as a marker of susceptibility to severe outcomes from epidemic respiratory viruses. METHODS: We included survivors (≥1 year) of the 20 most common cancers, and age, sex and general practice-matched cancer-free controls, derived from English primary care data linked to cancer registrations, hospital admissions and death registrations. Comorbidity prevalences were calculated 1 and 5 years from cancer diagnosis. Risk of hospitalisation or death due to influenza was compared using Cox models adjusted for baseline demographics and comorbidities. FINDINGS: 108,215 cancer survivors and 523,541 cancer-free controls were included. Cancer survivors had more diabetes, asthma, other respiratory, cardiac, neurological, renal, and liver diseases, and less obesity, compared with controls, but there was variation by cancer site. There were 205 influenza hospitalisations/deaths, with cancer survivors at higher risk than controls (adjusted HR 2.78, 95% CI 2.04-3.80). Haematological cancer survivors had large elevated risks persisting for >10 years (HR overall 15.17, 7.84-29.35; HR >10 years from cancer diagnosis 10.06, 2.47-40.93). Survivors of other cancers had evidence of raised risk up to 5 years from cancer diagnosis only (HR >5 years 2.22, 1.31-3.74). INTERPRETATION: Risks of severe COVID-19 outcomes are likely to be elevated in cancer survivors. This should be taken into account in policies targeted at clinical risk groups, and vaccination for both influenza, and, when available, COVID-19, should be encouraged in cancer survivors.

Disease severity determines health-seeking behaviour amongst individuals with influenza-like illness in an internet-based cohort.

BACKGROUND: Seasonal influenza epidemics place considerable strain on health services. Robust systems of surveillance are therefore required to ensure preparedness. Sentinel surveillance does not accurately capture the community burden of epidemics as it misses cases that do not present to health services. In this study, Flusurvey (an internet-based community surveillance tool) was used to examine how severity of disease influences health-seeking behaviour in the UK. METHODS: Logistic regression with random effects was used to investigate the association between health-seeking and symptom severity, duration of illness and reduction in self-reported health-score over four flu seasons between 2011 and 2015. RESULTS: The majority of individuals did not seek care. In general, there was very strong evidence for an association between all severity indicators and visiting a health service (p < 0.0001). Being female (OR 1.62, 95% CI 1.23-2.14, p = 0.0003) and a self-diagnosis of the flu (OR 3.39, 95% CI 2.38-4.83, p < 0.0001) were also associated with increased likelihood of visiting a health service. During the 2012-13 and 2014-15 flu seasons, there was a significantly larger proportion of individuals with more severe sets of symptoms and a longer duration of illness. Despite this, the proportion of individuals with particular sets of symptoms visiting a health service showed only very slight variation across years. CONCLUSIONS: Traditional surveillance systems capture only the more severe episodes of illness. However, in spite of variation in flu activity, the proportion of individuals visiting a health service remains relatively stable within specific sets of symptoms across years. These data could be used in combination with data on consultation rates to provide better estimates of community burden.

Thermodynamic mapping of effector protein interfaces with RalA and RalB.

RalA and RalB are members of the Ras family of small G proteins and are activated downstream of Ras via RalGEFs. The RalGEF-Ral axis represents one of the major effector pathways controlled by Ras and as such is an important pharmacological target. RalA and RalB are approximately 80% identical at the amino acid level; despite this, they have distinct roles both in normal cells and in the disease state. We have used our structure of RalB-RLIP76 to guide an analysis of Ral-effector interaction interfaces, creating panels of mutant proteins to probe the energetics of these interactions. The data provide a physical mechanism that underpins the effector selective mutations commonly employed to dissect Ral G protein function. Comparing the energetic landscape of the RalB-RLIP76 and RalB-Sec5 complexes reveals mutations in RalB that lead to differential binding of the two effector proteins. A panel of RLIP76 mutants was used to probe the interaction between RLIP76 and RalA and -B. Despite 100% sequence identity in the RalA and -B contact residues with RLIP76, differences still exist in the energetic profiles of the two complexes. Therefore, we have revealed properties that may account for some of the functional separation observed with RalA and RalB at the cellular level. Our mutations, in both the Ral isoforms and RLIP76, provide new tools that can be employed to parse the complex biology of Ral G protein signaling networks. The combination of these thermodynamic and structural data can also guide efforts to ablate RalA and -B activity with small molecules and peptides.