Innovative platforms for data aggregation, linkage and analysis in the context of pandemic and epidemic intelligence.

During the COVID-19 pandemic, open-access platforms that aggregate, link and analyse data were transformative for global public health surveillance. This perspective explores the work of three of these platforms: Our World In Data (OWID), Johns Hopkins University (JHU) COVID-19 Dashboard (later complemented by the Coronavirus Resource Center), and Global.Health, which were presented in the second World Health Organization (WHO) Pandemic and Epidemic Intelligence Innovation Forum. These platforms, operating mostly within academic institutions, added value to public health data that are collected by government agencies by providing additional real-time public health intelligence about the spread of the virus and the evolution of the public health emergency. Information from these platforms was used by health professionals, political decision-makers and members of the public alike. Further engagement between government and non-governmental surveillance efforts can accelerate the improvements needed in public health surveillance overall. Increasing the diversity of public health surveillance initiatives beyond the government sector comes with several benefits: technology innovation in data science, engagement of additional highly skilled professionals, greater transparency and accountability for government agencies, and new opportunities to engage with members of society.

Patient consultation rate and clinical and NHS outcomes: a cross-sectional analysis of English primary care data from 2.7 million patients in 238 practices.

BACKGROUND: Primary care workload is high and increasing in the United Kingdom. We sought to examine the association between rates of primary care consultation and outcomes in England. METHODS: Cross sectional observational study of routine electronic health care records in 283 practices from the Clinical Practice Research Datalink from April 2013 to March 2014. Outcomes included mortality rate, hospital admission rate, Quality and Outcomes Framework (QOF) performance and patient satisfaction. Relationships between consultation rates (with a general practitioner (GP) or nurse) and outcomes were investigated using negative binomial and ordinal logistic regression models. RESULTS: Rates of GP and nurse consultation (per patient person-year) were not associated with mortality or hospital admission rates: mortality incidence rate ratio (IRR) per unit change in GP/ nurse consultation rate = 1.01, 95% CI [0.98 to 1.04]/ 0.97, 95% CI [0.93 to 1.02]; hospital admission IRR per unit change in GP/ nurse consultation rate = 1.02, 95% CI [0.99 to 1.04]/ 0.98, 95% CI [0.94 to 1.032]. Higher rates of nurse but not GP consultation were associated with higher QOF achievement: OR = 1.91, 95% CI [1.39 to 2.62] per unit change in nurse consultation rate vs. OR = 1.04, 95% CI [0.87 to 1.24] per unit change in GP consultation rate. The association between the rates of GP/ nurse consultations and patient satisfaction was mixed. CONCLUSION: There are few associations between primary care consultation rates and outcomes. Previously identified demographic and staffing factors, rather than practice workload, appear to have the strongest relationships with mortality, admissions, performance and satisfaction. Studies with more detailed patient-level data would be required to explore these findings further.

Best practices for government agencies to publish data: lessons from COVID-19.

Without data, knowing how to respond to the COVID-19 pandemic would have been impossible. Data were crucial to understanding how the disease spread and which efforts successfully protected people. Yet, national agencies often did not publish their data in an optimal way, which made responding to the pandemic challenging. Therefore, learning from what went well and what did not for the future is crucial. Drawing on our first-hand experience of republishing COVID-19 data, we identify seven best practices for how to publish data in an optimal way: collect the data that are relevant; make them comparable; clearly document the data; share them frequently and promptly; publish data at a stable location; choose a reusable format; and license others to reuse the data. These best practices are straightforward, inexpensive, and achievable, with some countries already having implemented most of them during the COVID-19 pandemic. More government agencies following these best practices will enable others to access their data and address the world's public health challenges-including the next pandemic.

A global database of COVID-19 vaccinations.

An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout; the interactions with non-vaccination policy responses; the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality; and global inequalities in vaccine access.

Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis.

BACKGROUND: Hydroxychloroquine or chloroquine with or without azithromycin have been widely promoted to treat coronavirus disease 2019 (COVID-19) following early in vitro antiviral effects against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: The aim of this systematic review and meta-analysis was to assess whether chloroquine or hydroxychloroquine with or without azithromycin decreased COVID-19 mortality compared with the standard of care. DATA SOURCES: PubMed, Web of Science, Embase Cochrane Library, Google Scholar and MedRxiv were searched up to 25 July 2020. STUDY ELIGIBILITY CRITERIA: We included published and unpublished studies comparing the mortality rate between patients treated with chloroquine or hydroxychloroquine with or without azithromycin and patients managed with standard of care. PARTICIPANTS: Patients ≥18 years old with confirmed COVID-19. INTERVENTIONS: Chloroquine or hydroxychloroquine with or without azithromycin. METHODS: Effect sizes were pooled using a random-effects model. Multiple subgroup analyses were conducted to assess drug safety. RESULTS: The initial search yielded 839 articles, of which 29 met our inclusion criteria. All studies except one were conducted on hospitalized patients and evaluated the effects of hydroxychloroquine with or without azithromycin. Among the 29 articles, three were randomized controlled trials, one was a non-randomized trial and 25 were observational studies, including 11 with a critical risk of bias and 14 with a serious or moderate risk of bias. After excluding studies with critical risk of bias, the meta-analysis included 11 932 participants for the hydroxychloroquine group, 8081 for the hydroxychloroquine with azithromycin group and 12 930 for the control group. Hydroxychloroquine was not significantly associated with mortality: pooled relative risk (RR) 0.83 (95% CI 0.65-1.06, n = 17 studies) for all studies and RR = 1.09 (95% CI 0.97-1.24, n = 3 studies) for randomized controlled trials. Hydroxychloroquine with azithromycin was associated with an increased mortality (RR = 1.27; 95% CI 1.04-1.54, n = 7 studies). We found similar results with a Bayesian meta-analysis. CONCLUSION: Hydroxychloroquine alone was not associated with reduced mortality in hospitalized COVID-19 patients but the combination of hydroxychloroquine and azithromycin significantly increased mortality.

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies.

OBJECTIVES: We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions. METHODS: Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework. RESULTS: A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively. CONCLUSION: Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths. TRIAL REGISTRATION NUMBER: CRD42020218115.

A cross-country database of COVID-19 testing.

Our understanding of the evolution of the COVID-19 pandemic is built upon data concerning confirmed cases and deaths. This data, however, can only be meaningfully interpreted alongside an accurate understanding of the extent of virus testing in different countries. This new database brings together official data on the extent of PCR testing over time for 94 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The database is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries' responses to COVID-19.

Factors associated with potentially serious incidental findings and with serious final diagnoses on multi-modal imaging in the UK Biobank Imaging Study: A prospective cohort study.

BACKGROUND: Feedback of potentially serious incidental findings (PSIFs) to imaging research participants generates clinical assessment in most cases. Understanding the factors associated with increased risks of PSIFs and of serious final diagnoses may influence individuals' decisions to participate in imaging research and will inform the design of PSIFs protocols for future research studies. We aimed to determine whether, and to what extent, socio-demographic, lifestyle, other health-related factors and PSIFs protocol are associated with detection of both a PSIF and a final diagnosis of serious disease. METHODS AND FINDINGS: Our cohort consisted of all UK Biobank participants who underwent imaging up to December 2015 (n = 7334, median age 63, 51.9% women). Brain, cardiac and body magnetic resonance, and dual-energy x-ray absorptiometry images from the first 1000 participants were reviewed systematically by radiologists for PSIFs. Thereafter, radiographers flagged concerning images for radiologists' review. We classified final diagnoses as serious or not using data from participant surveys and clinical correspondence from GPs up to six months following imaging (either participant or GP correspondence, or both, were available for 93% of participants with PSIFs). We used binomial logistic regression models to investigate associations between age, sex, ethnicity, socio-economic deprivation, private healthcare use, alcohol intake, diet, physical activity, smoking, body mass index and morbidity, with both PSIFs and serious final diagnoses. Systematic radiologist review generated 13 times more PSIFs than radiographer flagging (179/1000 [17.9%] versus 104/6334 [1.6%]; age- and sex-adjusted OR 13.3 [95% confidence interval (CI) 10.3-17.1] p<0.001) and proportionally fewer serious final diagnoses (21/179 [11.7%]; 33/104 [31.7%]). Risks of both PSIFs and of serious final diagnoses increased with age (sex-adjusted ORs [95% CI] for oldest [67-79 years] versus youngest [44-58 years] participants for PSIFs and serious final diagnoses respectively: 1.59 [1.07-2.38] and 2.79 [0.86 to 9.0] for systematic radiologist review; 1.88 [1.14-3.09] and 2.99 [1.09-8.19] for radiographer flagging). No other factor was significantly associated with either PSIFs or serious final diagnoses. Our study is the largest so far to investigate the factors associated with PSIFs and serious final diagnoses, but despite this, we still may have missed some associations due to sparsity of these outcomes within our cohort and small numbers within some exposure categories. CONCLUSION: Risks of PSIFs and serious final diagnosis are substantially influenced by PSIFs protocol and to a lesser extent by age. As only 1/5 PSIFs represent serious disease, evidence-based PSIFs protocols are paramount to minimise over-investigation of healthy research participants and diversion of limited health services away from patients in need.