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Since 2016, social media companies and news providers have come under pressure to tackle the spread of political mis- and disinformation (MDI) online. However, despite evidence that online health MDI (on the web, on social media, and within mobile apps) also has negative real-world effects, there has been a lack of comparable action by either online service providers or state-sponsored public health bodies. We argue that this is problematic and seek to answer three questions: why has so little been done to control the flow of, and exposure to, health MDI online; how might more robust action be justified; and what specific, newly justified actions are needed to curb the flow of, and exposure to, online health MDI? In answering these questions, we show that four ethical concerns—related to paternalism, autonomy, freedom of speech, and pluralism—are partly responsible for the lack of intervention. We then suggest that these concerns can be overcome by relying on four arguments: (1) education is necessary but insufficient to curb the circulation of health MDI, (2) there is precedent for state control of internet content in other domains, (3) network dynamics adversely affect the spread of accurate health information, and (4) justice is best served by protecting those susceptible to inaccurate health information. These arguments provide a strong case for classifying the quality of the infosphere as a social determinant of health, thus making its protection a public health responsibility. In addition, they offer a strong justification for working to overcome the ethical concerns associated with state-led intervention in the infosphere to protect public health.

Author(s): Miriam Samuel 1,*, Robin Y. Park 2, Sophie V. Eastwood 3, Fabiola Eto 1, Caroline E. Morton 1,2, Daniel Stow 1, Sebastian Bacon 2, Amir Mehrkar 2, Jessica Morley [...], Background Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. Methods and findings With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before ([delta]-prepandemic) and during ([delta]-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m.sup.2 /year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period ([delta]-change = [delta]-pandemic-[delta]-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain ([delta]-change [greater than or equal to]1.84 kg/m.sup.2 /year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m.sup.2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m.sup.2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. Conclusions We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

Background: COVID-19 pandemic restrictions may have influenced behaviours related to weight. Aim: To describe patterns of weight change among adults living in England with type 2 diabetes (T2D) and/or hypertension during the pandemic. Design and setting: An observational cohort study using the routinely collected health data of approximately 40% of adults living in England, accessed through the OpenSAFELY service inside TPP. Method: Clinical and sociodemographic characteristics associated with rapid weight gain (>0.5 kg/m2/year) were investigated using multivariable logistic regression. Results: Data were extracted on adults with T2D (n = 1 231 455, 43.9% female, and 76.0% White British) or hypertension (n = 3 558 405, 49.7% female, and 84.3% White British). Adults with T2D lost weight overall (median δ = −0.1 kg/m2/year [interquartile range {IQR} −0.7–0.4]). However, rapid weight gain was common (20.7%) and associated with the following: sex (male versus female: adjusted odds ratio [aOR] 0.78 [95% confidence interval {CI} = 0.77 to 0.79]); age (older age reduced odds, for example, aged 60–69 years versus 18–29 years: aOR 0.66 [95% CI = 0.61 to 0.71]); deprivation (least deprived Index of Multiple Deprivation [IMD] quintile versus most deprived IMD quintile: aOR 0.87 [95% CI = 0.85 to 0.89]); White ethnicity (Black versus White: aOR 0.95 [95% CI = 0.92 to 0.98]); mental health conditions (for example, depression: aOR 1.13 [95% CI = 1.12 to 1.15]); and diabetes treatment (non-insulin treatment versus no pharmacological treatment: aOR 0.68 [95% CI = 0.67 to 0.69]). Adults with hypertension maintained stable weight overall (median δ = 0.0 kg/m2/year [IQR −0.6–0.5]); however, rapid weight gain was common (24.7%) and associated with similar characteristics as in T2D. Conclusion: Among adults living in England with T2D and/or hypertension, rapid pandemic weight gain was more common among females, younger adults, those living in more deprived areas, and those with mental health conditions. [ABSTRACT FROM AUTHOR]

Background: The English NHS data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database. Aim: To describe the potential impact of NHS national data opt-outs in 2021 on health data research. Design & setting: We conducted a descriptive analysis of opt-outs using publicly available data and the potential consequences on research are discussed. Method: Trends in opt-outs in England were described by age, sex, and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs. Results: During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021-30 June 2021), 1 339 862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Among females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared with 76% in males (from 2.51% to 4.41%). Across age groups, the highest relative increase was among people aged 40-49 years, which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events. Conclusion: Opt-out rates varied by age, sex, and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions. [ABSTRACT FROM AUTHOR]

Background: The UK delivered its first “booster” COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273. Methods: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1–14, 15–42, 43–69, 70–97, 98–126, 127–152, and 155–182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture. Results: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non- COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID- 19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals. Conclusions: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID‐19 pandemic, we developed the secure, transparent, analytic open‐source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code‐sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility‐by‐design approach in detail. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the effect of the COVID‐19 pandemic on prostate cancer incidence, prevalence, and mortality in England. Patients and Methods: With the approval of NHS England and using the OpenSAFELY‐TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre‐pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results: In 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241–21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740–22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5–71.8) years, in 2021 it was 71.8 (95% CI 71.7–72.0) years as compared to 71.3 (95% CI 71.1–71.4) years in 2019. Conclusions: Given that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the effectiveness of two doses of coronavirus disease 2019 (COVID-19) vaccine beyond six months in England. Topics include increase in the cumulative incidence of receiving a third dose of vaccine during the eight weeks following eligibility, basis of the effectiveness of two doses during later comparison periods, and important factors in interpreting estimated vaccine effectiveness (VE).

Background: Germ Defence (www.germdefence.org) is an evidence-based interactive website that promotes behaviour change for infection control within households. To maximise the potential of Germ Defence to effectively reduce the spread of COVID-19, the intervention needed to be implemented at scale rapidly. Methods: With NHS England approval, we conducted an efficient two-arm (1:1 ratio) cluster randomised controlled trial (RCT) to examine the effectiveness of randomising implementation of Germ Defence via general practitioner (GP) practices across England, UK, compared with usual care to disseminate Germ Defence to patients. GP practices randomised to the intervention arm (n = 3292) were emailed and asked to disseminate Germ Defence to all adult patients via mobile phone text, email or social media. Usual care arm GP practices (n = 3287) maintained standard management for the 4-month trial period and then asked to share Germ Defence with their adult patients. The primary outcome was the rate of GP presentations for respiratory tract infections (RTI) per patient. Secondary outcomes comprised rates of acute RTIs, confirmed COVID-19 diagnoses and suspected COVID-19 diagnoses, COVID-19 symptoms, gastrointestinal infection diagnoses, antibiotic usage and hospital admissions. The impact of the intervention on outcome rates was assessed using negative binomial regression modelling within the OpenSAFELY platform. The uptake of the intervention by GP practice and by patients was measured via website analytics. Results: Germ Defence was used 310,731 times. The average website satisfaction score was 7.52 (0–10 not at all to very satisfied, N = 9933). There was no evidence of a difference in the rate of RTIs between intervention and control practices (rate ratio (RR) 1.01, 95% CI 0.96, 1.06, p = 0.70). This was similar to all other eight health outcomes. Patient engagement within intervention arm practices ranged from 0 to 48% of a practice list. Conclusions: While the RCT did not demonstrate a difference in health outcomes, we demonstrated that rapid large-scale implementation of a digital behavioural intervention is possible and can be evaluated with a novel efficient prospective RCT methodology analysing routinely collected patient data entirely within a trusted research environment. Trial registration: This trial was registered in the ISRCTN registry (14602359) on 12 August 2020. [ABSTRACT FROM AUTHOR]

Methylphenidate (MPH) often ameliorates attention-deficit/hyperactivity disorder (ADHD) behavioral dysfunction according to "indirect" informant reports and rating scales. The standard of care behavioral MPH titration approach seldom includes "direct" neuropsychological or academic assessment data to determine treatment efficacy. Documenting "cool" executive-working memory (EWM) and "hot" self-regulation (SR) neuropsychological impairments could aid in differential diagnosis of ADHD subtypes and determining cognitive and academic MPH response. In this study, children aged 6 to 16 with ADHD inattentive type (IT; n = 19) and combined type (n = 33)/hyperactive-impulsive type (n = 4) (CT) participated in double-blind placebo-controlled MPH trials with baseline and randomized placebo, low MPH dose, and high MPH dose conditions. EWM/SR measures and behavior ratings/classroom observations were rank ordered separately across conditions, with nonparametric randomization tests conducted to determine individual MPH response. Participants were subsequently grouped according to their level of cool EWM and hot SR circuit dysfunction. Robust cognitive and behavioral MPH response was achieved for children with significant baseline EWM/SR impairment, yet response was poor for those with adequate EWM/SR baseline performance. Even for strong MPH responders, the best dose for neuropsychological functioning was typically lower than the best dose for behavior. Findings offer one possible explanation for why long-term academic MPH treatment gains in ADHD have not been realized. Implications for academic achievement and medication titration practices for children with behaviorally diagnosed ADHD will be discussed. (Contains 2 figures and 1 table.)

Background: The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. Aim: To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. Design and setting: With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. Method: Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. Results: Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). Conclusion: Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS. [ABSTRACT FROM AUTHOR]

In this article, we compare the artificial intelligence strategies of China and the European Union, assessing the key similarities and differences regarding what the high-level aims of each governance strategy are, how the development and use of AI is promoted in the public and private sectors, and whom these policies are meant to benefit. We characterize China's strategy by its primary focus on fostering innovation and a more recent emphasis on "common prosperity," and the EU's on promoting ethical outcomes through protecting fundamental rights. Building on this comparative analysis, we consider the areas where the EU and China could learn from and improve upon each other's approaches to AI governance to promote more ethical outcomes. We outline policy recommendations for both European and Chinese policymakers that would support them in achieving this aim. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible.Aim: To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples.Design and Setting: Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY.Method: Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described.Results: Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as 'no change'. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline.Conclusion: An open-source software framework was delivered to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September, with some important tests less affected and recording of respiratory disease codes was mixed. [ABSTRACT FROM AUTHOR]

Background: On 8 December 2020 NHS England administered the first COVID-19 vaccination.Aim: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout.Design and Setting: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY.Method: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described.Results: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose.Conclusion: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups. [ABSTRACT FROM AUTHOR]

Background: Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created.Aim: To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time.Design and Setting: Population-based cohort study in English primary care.Method: Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week.Results: Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4).Conclusion: Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis. [ABSTRACT FROM AUTHOR]

In this perspective we want to highlight the rise of what we call "digital phenotyping" or inferring insights about peopleãs health and behavior from their digital devices and data, and the challenges this introduces. Indeed, the collection, processing, and storage of data comes with significant ethical, security and data governance considerations. The COVID-19 pandemic has laid bare the importance of scientific data and modeling, both to understand the nature and spread of the disease, and to develop treatment. But digital devices have also played a (controversial) role, with track and trace systems and increasingly "vaccine passports" being rolled out to help societies open back up. These systems epitomize a wider and longer-standing trend towards seeing almost any form of personal data as potentially health data, especially with the rise of consumer health trackers and other gadgets. Here, we offer an overview of the risks this introduces, drawing on the earlier revolution in genomic sequencing, and propose guidelines to help protect privacy whilst utilizing personal data to help get society back up to speed. [ABSTRACT FROM AUTHOR]