Impact of the COVID-19 pandemic on domiciliary care workers in Wales, UK: a data linkage cohort study using the SAIL Databank.

OBJECTIVES: To quantify population health risks for domiciliary care workers (DCWs) in Wales, UK, working during the COVID-19 pandemic. DESIGN: A population-level retrospective study linking occupational registration data to anonymised electronic health records maintained by the Secure Anonymised Information Linkage Databank in a privacy-protecting trusted research environment. SETTING: Registered DCW population in Wales. PARTICIPANTS: Records for all linked DCWs from 1 March 2020 to 30 November 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary outcome was confirmed COVID-19 infection; secondary outcomes included contacts for suspected COVID-19, mental health including self-harm, fit notes, respiratory infections not necessarily recorded as COVID-19, deaths involving COVID-19 and all-cause mortality. RESULTS: Confirmed and suspected COVID-19 infection rates increased over the study period to 24% by 30 November 2021. Confirmed COVID-19 varied by sex (males: 19% vs females: 24%) and age (>55 years: 19% vs <35 years: 26%) and were higher for care workers employed by local authority social services departments compared with the private sector (27% and 23%, respectively). 34% of DCWs required support for a mental health condition, with mental health-related prescribing increasing in frequency when compared with the prepandemic period. Events for self-harm increased from 0.2% to 0.4% over the study period as did the issuing of fit notes. There was no evidence to suggest a miscoding of COVID-19 infection with non-COVID-19 respiratory conditions. COVID-19-related and all-cause mortality were no greater than for the general population aged 15-64 years in Wales (0.1% and 0.034%, respectively). A comparable DCW workforce in Scotland and England would result in a comparable rate of COVID-19 infection, while the younger workforce in Northern Ireland may result in a greater infection rate. CONCLUSIONS: While initial concerns about excess mortality are alleviated, the substantial pre-existing and increased mental health burden for DCWs will require investment to provide long-term support to the sector's workforce.

Learning to rank query expansion terms for COVID-19 scholarly search.

OBJECTIVE: With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need. MATERIALS AND METHODS: For the sake of training and evaluation, we use the widely adopted TREC-COVID benchmark. Given a query, the proposed framework utilizes a contextual and a domain-specific neural language model to generate a set of candidate query expansion terms that enrich the original query. Moreover, the framework includes a multi-head attention mechanism that is trained alongside a learning-to-rank model for re-ranking the list of generated expansion candidate terms. The original query and the top-ranked expansion terms are posed to the PubMed search engine for retrieving relevant scholarly articles to an information need. The framework, CQED, can have four different variations, depending upon the learning path adopted for training and re-ranking the candidate expansion terms. RESULTS: The model drastically improves the search performance, when compared to the original query. The performance improvement in comparison to the original query, in terms of RECALL@1000 is 190.85% and in terms of NDCG@1000 is 343.55%. Additionally, the model outperforms all existing state-of-the-art baselines. In terms of P@10, the model that has been optimized based on Precision outperforms all baselines (0.7987). On the other hand, in terms of NDCG@10 (0.7986), MAP (0.3450) and bpref (0.4900), the CQED model that has been optimized based on an average of all retrieval measures outperforms all the baselines. CONCLUSION: The proposed model successfully expands queries posed to PubMed, and improves search performance, as compared to all existing baselines. A success/failure analysis shows that the model improved the search performance of each of the evaluated queries. Moreover, an ablation study depicted that if ranking of generated candidate terms is not conducted, the overall performance decreases. For future work, we would like to explore the application of the presented query expansion framework in conducting technology-assisted Systematic Literature Reviews (SLR).

National data linkage assessment of live births and deaths in Mexico: Estimating under-five mortality rate ratios for vulnerable newborns and trends from 2008 to 2019.

BACKGROUND: Linked datasets that enable longitudinal assessments are scarce in low and middle-income countries. OBJECTIVES: We aimed to assess the linkage of administrative databases of live births and under-five child deaths to explore mortality and trends for preterm, small (SGA) and large for gestational age (LGA) in Mexico. METHODS: We linked individual-level datasets collected by National statistics from 2008 to 2019. Linkage was performed based on agreement on birthday, sex, residential address. We used the Centre for Data and Knowledge Integration for Health software to identify the best candidate pairs based on similarity. Accuracy was assessed by calculating the area under the receiver operating characteristic curve. We evaluated completeness by comparing the number of linked records with reported deaths. We described the percentage of linked records by baseline characteristics to identify potential bias. Using the linked dataset, we calculated mortality rate ratios (RR) in neonatal, infants, and children under-five according to gestational age, birthweight, and size. RESULTS: For the period 2008-2019, a total of 24,955,172 live births and 321,165 under-five deaths were available for linkage. We excluded 1,539,046 records (6.2%) with missing or implausible values. We succesfully linked 231,765 deaths (72.2%: range 57.1% in 2009 and 84.3% in 2011). The rate of neonatal mortality was higher for preterm compared with term (RR 3.83, 95% confidence interval, [CI] 3.78, 3.88) and for SGA compared with appropriate for gestational age (AGA) (RR 1.22 95% CI, 1.19, 1.24). Births at <28 weeks had the highest mortality (RR 35.92, 95% CI, 34.97, 36.88). LGA had no additional risk vs AGA among children under five (RR 0.92, 95% CI, 0.90, 0.93). CONCLUSIONS: We demonstrated the utility of linked data to understand neonatal vulnerability and child mortality. We created a linked dataset that would be a valuable resource for future population-based research.

Investigating changes in mental health services utilisation in England and their impact on health outcomes and wellbeing during the COVID-19 pandemic: Protocol for a health data-linkage study.

INTRODUCTION: Linking routinely collected health care system data records for the same individual across different services and over time has enormous potential for the NHS and its patients. The aims of this data linkage study are to quantify the changes to mental health services utilisation in responses to the COVID-19 pandemic and determine whether these changes were associated with health-related outcomes and wellbeing among people living in the most deprived communities in North East and North Cumbria, England. METHODS AND ANALYSIS: We will assemble a retrospective cohort of people having referred or self-referred to NHS-funded mental health services or Improving Access to Psychological Therapies (IAPT) services between 23rd March 2019 and 22nd March 2020 in the most deprived areas in England. We will link together data from retrospective routinely collected healthcare data including local general practitioner (GP) practice data, Hospital Episode Statistics admitted patient care outpatients, and A&E, Community Services Data Set, Mental Health Services Data Set, and Improving Access to Psychological Therapies Data Set. We will use these linked patient-level data to 1) describe the characteristics of the cohort prior to the lockdown; 2) investigate changes to mental health services utilised between multiple time periods of the COVID-19 lockdown including out of lockdown; 3) explore the relationship between these changes and health outcomes/wellbeing and factors that confound and mediate this relationship among this cohort. STRENGTHS AND LIMITATIONS OF THIS STUDY: This study comprises a deprived population-based cohort of people having referred or self-referred to NHS-funded secondary mental health services or Improving Access to Psychological Therapies (IAPT) services over an extended period of the lockdown in England (2019-2022).This study will utilise a new longitudinal data resource that will link together detailed data from a cohort of individual participants and retrospective administrative data relating to the use of primary, secondary, and community care services.The study period covers pre-lockdown, different lockdown and post-lockdown, and out of lockdown periods up to March 2022.Routinely collected administrative data contain limited contextual information and represent an underestimate of total health outcomes for these individuals.Routinely collected datasets can often been incomplete or contain missing data, which can make it difficult to accurately analyse the data and draw meaningful conclusions.Intervention and treatment for mental health conditions are not wholly captured across these data sources and may impact health outcomes.

Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records.

BACKGROUND: The COVID-19 pandemic has directly and indirectly had an impact on health service provision owing to surges and sustained pressures on the system. The effects of these pressures on the management of long-term or chronic conditions are not fully understood. AIM: To explore the effects of COVID-19 on the recorded incidence of 17 long-term conditions. DESIGN AND SETTING: This was an observational retrospective population data linkage study on the population of Wales using primary and secondary care data within the Secure Anonymised Information Linkage (SAIL) Databank. METHOD: Monthly rates of new diagnosis between 2000 and 2021 are presented for each long-term condition. Incidence rates post-2020 were compared with expected rates predicted using time series modelling of pre-2020 trends. The proportion of annual incidence is presented by sociodemographic factors: age, sex, social deprivation, ethnicity, frailty, and learning disability. RESULTS: A total of 5 476 012 diagnoses from 2 257 992 individuals are included. Incidence rates from 2020 to 2021 were lower than mean expected rates across all conditions. The largest relative deficit in incidence was in chronic obstructive pulmonary disease corresponding to 343 (95% confidence interval = 230 to 456) undiagnosed patients per 100 000 population, followed by depression, type 2 diabetes, hypertension, anxiety disorders, and asthma. A GP practice of 10 000 patients might have over 400 undiagnosed long-term conditions. No notable differences between sociodemographic profiles of post- and pre-2020 incidences were observed. CONCLUSION: There is a potential backlog of undiagnosed patients with multiple long-term conditions. Resources are required to tackle anticipated workload as part of COVID-19 recovery, particularly in primary care.

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.

BACKGROUND: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. OBJECTIVE: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. METHODS: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. RESULTS: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. CONCLUSIONS: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.

OBJECTIVES: Analyze the content of publications within the medical natural language processing (NLP) domain in 2021. METHODS: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. RESULTS: Four best papers have been selected in 2021. We also propose an analysis of the content of the NLP publications in 2021, all topics included. CONCLUSIONS: The main issues addressed in 2021 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as information extraction and use of information from social networks.

Semantic interoperability: ontological unpacking of a viral conceptual model.

BACKGROUND: Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. RESULTS: In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the "ontological unpacking" method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. CONCLUSIONS: We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the "I" in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.

CovidGraph: a graph to fight COVID-19.

SUMMARY: Reliable and integrated data are prerequisites for effective research on the recent coronavirus disease 2019 (COVID-19) pandemic. The CovidGraph project integrates and connects heterogeneous COVID-19 data in a knowledge graph, referred to as 'CovidGraph'. It provides easy access to multiple data sources through a single point of entry and enables flexible data exploration. AVAILABILITY AND IMPLEMENTATION: More information on CovidGraph is available from the project website: https://healthecco.org/covidgraph/. Source code and documentation are provided on GitHub: https://github.com/covidgraph. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Changing Patterns of Bloodstream Infections in the Community and Acute Care Across 2 Coronavirus Disease 2019 Epidemic Waves: A Retrospective Analysis Using Data Linkage.

BACKGROUND: We examined community- and hospital-acquired bloodstream infections (BSIs) in coronavirus disease 2019 (COVID-19) and non-COVID-19 patients across 2 epidemic waves. METHODS: We analyzed blood cultures of patients presenting to a London hospital group between January 2020 and February 2021. We reported BSI incidence, changes in sampling, case mix, healthcare capacity, and COVID-19 variants. RESULTS: We identified 1047 BSIs from 34 044 blood cultures, including 653 (62.4%) community-acquired and 394 (37.6%) hospital-acquired. Important pattern changes were seen. Community-acquired Escherichia coli BSIs remained below prepandemic level during COVID-19 waves, but peaked following lockdown easing in May 2020, deviating from the historical trend of peaking in August. The hospital-acquired BSI rate was 100.4 per 100 000 patient-days across the pandemic, increasing to 132.3 during the first wave and 190.9 during the second, with significant increase in elective inpatients. Patients with a hospital-acquired BSI, including those without COVID-19, experienced 20.2 excess days of hospital stay and 26.7% higher mortality, higher than reported in prepandemic literature. In intensive care, the BSI rate was 421.0 per 100 000 intensive care unit patient-days during the second wave, compared to 101.3 pre-COVID-19. The BSI incidence in those infected with the severe acute respiratory syndrome coronavirus 2 Alpha variant was similar to that seen with earlier variants. CONCLUSIONS: The pandemic have impacted the patterns of community- and hospital-acquired BSIs, in COVID-19 and non-COVID-19 patients. Factors driving the patterns are complex. Infection surveillance needs to consider key aspects of pandemic response and changes in healthcare practice.

A scoping review of semantic integration of health data and information.

OBJECTIVE: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS: The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION: SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

A Natural Language Processing Tool Offering Data Extraction for COVID-19 Related Information (DECOVRI).

A new natural language processing (NLP) application for COVID-19 related information extraction from clinical text notes is being developed as part of our pandemic response efforts. This NLP application called DECOVRI (Data Extraction for COVID-19 Related Information) will be released as a free and open source tool to convert unstructured notes into structured data within an OMOP CDM-based ecosystem. The DECOVRI prototype is being continuously improved and will be released early (beta) and in a full version.

Association between the COVID-19 pandemic and pertussis derived from multiple nationwide data sources, France, 2013 to 2020.

BackgroundInterventions to mitigate the COVID-19 pandemic may impact other respiratory diseases.AimsWe aimed to study the course of pertussis in France over an 8-year period including the beginning of the COVID-19 pandemic and its association with COVID-19 mitigation strategies, using multiple nationwide data sources and regression models.MethodsWe analysed the number of French pertussis cases between 2013 and 2020, using PCR test results from nationwide outpatient laboratories (Source 1) and a network of the paediatric wards from 41 hospitals (Source 2). We also used reports of a national primary care paediatric network (Source 3). We conducted a quasi-experimental interrupted time series analysis, relying on negative binomial regression models. The models accounted for seasonality, long-term cycles and secular trend, and included a binary variable for the first national lockdown (start 16 March 2020).ResultsWe identified 19,039 pertussis cases from these data sources. Pertussis cases decreased significantly following the implementation of mitigation measures, with adjusted incidence rate ratios of 0.10 (95% CI: 0.04-0.26) and 0.22 (95% CI: 0.07-0.66) for Source 1 and Source 2, respectively. The association was confirmed in Source 3 with a median of, respectively, one (IQR: 0-2) and 0 cases (IQR: 0-0) per month before and after lockdown (p = 0.0048).ConclusionsThe strong reduction in outpatient and hospitalised pertussis cases suggests an impact of COVID-19 mitigation measures on pertussis epidemiology. Pertussis vaccination recommendations should be followed carefully, and disease monitoring should be continued to detect any resurgence after relaxation of mitigation measures.

CovidGraph: Integrating COVID-19 Data.

CovidGraph, developed by the HealthECCO community, is a platform designed to foster research and data exploration to fight COVID-19. It is built on a graph database and encompasses data sources from different biomedical data domains including publications, clinical trials, patents, case statistics, molecular data and systems biology models. The tool provides multiple interfaces for data exploration and thus serves as a single point of entry for data driven COVID-19 research. Availability and Implementation: CovidGraph is available from the project website: https://healthecco.org/covidgraph/. The source code and documentation are provided on GitHub: https://github.com/covidgraph.

Smart Mushroom-Inspired Imprintable and Lightly Detachable (MILD) Microneedle Patterns for Effective COVID-19 Vaccination and Decentralized Information Storage.

The key to controlling the spread of the coronavirus disease 2019 (COVID-19) and reducing mortality is highly dependent on the safe and effective use of vaccines for the general population. Current COVID-19 vaccination practices (intramuscular injection of solution-based vaccines) are limited by heavy reliance on medical professionals, poor compliance, and laborious vaccination recording procedures, resulting in a waste of health resources and low vaccination coverage, etc. In this study, we developed a smart mushroom-inspired imprintable and lightly detachable (MILD) microneedle platform for the effective and convenient delivery of multidose COVID-19 vaccines and decentralized vaccine information storage. The mushroom-like structure allows the MILD system to be easily pressed into the skin and detached from the patch base, acting as a "tattoo" to record the vaccine counts in situ without any storage equipment, offering quick accessibility and effortless readout, saving a great deal of valuable time and energy for both patients and health professionals. After loading inactivated SARS-CoV-2 virus-based vaccines, MILD system induced a high level of antibodies against the SARS-CoV-2 receptor-binding domain (RBD) in vivo without eliciting systemic toxicity and local damage. Collectively, this smart delivery platform serves as a promising carrier to improve COVID-19 vaccination efficacy through its dual capabilities of vaccine delivery and in situ data storage, thus exhibiting great potential for helping to contain the COVID-19 pandemic or a resurgence.

Improving morbidity information in Portugal: Evidence from data linkage of COVID-19 cases surveillance and mortality systems.

BACKGROUND: COVID-19 rapidly spread around the world, putting health systems under unprecedented pressure and continuous adaptations. Well-established health information systems (HIS) are crucial in providing data to allow evidence-based policymaking and public health interventions in the pandemic response. This study aimed to compare morbidity information between two databases for COVID-19 management in Portugal and identify potential complementarities. METHODS: This is an observational study using records from both COVID-19 cases surveillance (National Epidemiological Surveillance System; SINAVE) and related deaths (National e-Death Certificates Information System; SICO) systems, which were matched on sex, age, municipality of residence and date of death. After the linkage, morbidity reported in SINAVE and identified in SICO, through the application of Charlson and Elixhauser comorbidity indexes algorithms, were compared to evaluate agreement level. RESULTS: Overall, 2285 matched cases were analyzed, including 53.9% males with a median age of 84 years. According to the method of data reporting assessment, the presence of any morbidity ranged between 26.3% and 62.5%. The reporting of ten morbidities could be compared between the information reported in SINAVE and SICO databases. The proportion of simultaneous reporting in both databases ranged between 5.7% for diabetes and 0.0% for human immunodeficiency virus infection or coagulopathy. Minimal or no agreement was found when assessing the similarity of the morbidity reporting in both databases, with neoplasms showing the highest level of agreement (0.352, 95% IC: 0.277-0.428; p < 0.001). CONCLUSION: Different information about reported morbidity could be found in two HIS used to monitor COVID-19 cases and related deaths, as data are independently collected. These results show that the interoperability of SICO and SINAVE databases would potentially improve available HIS and improve available information to decision-making and address COVID-19 pandemic management.

The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices.

BACKGROUND: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.