OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.

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.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

Assessing efficacy in non-inferiority trials with non-adherence to interventions: Are intention-to-treat and per-protocol analyses fit for purpose?

BACKGROUND: Non-inferiority trials comparing different active drugs are often subject to treatment non-adherence. Intention-to-treat (ITT) and per-protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non-adherence. METHODS: The REMoxTB trial evaluated two 4-month experimental regimens compared with a 6-month control regimen for newly diagnosed drug-susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post-randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non-adherence in non-inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse-probability-of-treatment weighting (IPTW), and a doubly-robust (DR) estimator. RESULTS: When non-adherence differed between trial arms, ITT, and PP analyses often resulted in non-trivial bias in the estimated treatment effect, which consequently under- or over-inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non-adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power. CONCLUSIONS: When non-adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches.

Spiritual coping within medical professions: A psychometric analysis of the Numinous Motivations Inventory short form.

AIM: To examine the psychometric properties of a short form version of the Numinous Motivation Inventory (NMI) for use with healthcare providers in measuring their existential engagement with life and to assess its relationship with spiritual coping and emotional dysphoria. DESIGN: Correlational and psychometric study. METHOD: Data were collected from June to December 2022. Participants included 102 physicians, recruited from across the United States. Qualtrics was utilized to collect data, and they were evaluated with the NMI short form, Spiritual Coping Questionnaire and Depression, Anxiety, and Stress scale (DASS-21). RESULTS: Obtained fit statistics from structural equation modelling analysis indicated close fit of the NMI short form with the original model. Multiple regression analyses demonstrated the value of the NMI as a predictor of negative affect independent of spiritual coping. The NMI did not interact with Spiritual Coping, which was independent of negative affect. CONCLUSIONS: The Numinous represents an important aspect of physicians' coping. The constructs can be utilized in training and clinical settings as a valuable and easy-to-use metric for promoting and assessing wellness. The implications of these findings and the value of the NMI were discussed. IMPACT: An understanding of existential drivers can equip one to cope with the stressors of healthcare. The NMI short form has the capability to explore an individual's existential drivers through the understanding of three domains. REPORTING METHOD: Adhered to proper EQUATOR guidelines (GRRAS). PUBLIC CONTRIBUTION: No patient or public contribution.

NHS National Data Opt-Outs: trends and potential consequences for health data research.

BACKGROUND: The English National Health Service (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 discuss the potential consequences on research. 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 to 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. Amongst females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared to 75% in males (2.51%-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. CONCLUSIONS: 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.

Sustained Hypothetical Interventions on Midlife Alcohol Consumption in Relation to All-Cause and Cancer Mortality: The Australian Longitudinal Study on Women's Health.

No randomized controlled trial has evaluated the effect of long-term alcohol interventions on mortality. Results reported in existing observational studies may be subject to selection bias and time-varying confounding. Using data from the Australian Longitudinal Study on Women's Health 1946-1951 birth cohort, collected regularly from 1996-2016, we estimated all-cause and cancer mortality had women been assigned various alcohol interventions (in categories ranging from 0 to >30 g/day ethanol, or reduced to ≤20 g/day if higher) at baseline, and had they maintained these levels of consumption. The cumulative risks for all-cause and cancer mortality were 5.6% (10,118 women followed for 20 years) and 2.9% (18 years), respectively. For all-cause and cancer mortality, baseline ethanol up to 30 g/day showed lower risk and >30 g/day showed higher risk relative to abstention. Had women sustainedly followed the interventions, a similar relationship was observed for all-cause mortality. However, the negative association observed for intakes ≤30 g/day and positive association for intakes >30 g/day was not evident for cancer mortality. Our findings suggest that all-cause mortality could have been lower than observed if this cohort of women had consumed some alcohol (no more than 30 g/day) rather than no consumption, but cancer mortality might not.

MiR-223-3p promotes genomic stability of hematopoietic progenitors after radiation.

When hematopoietic cells are overwhelmed with ionizing radiation (IR) DNA damage, the alternative non-homologous end-joining (aNHEJ) repair pathway is activated to repair stressed replication forks. While aNHEJ can rescue cells overwhelmed with DNA damage, it can also mediate chromosomal deletions and fusions, which can cause mis-segregation in mitosis and resultant aneuploidy. We previously reported that a hematopoietic microRNA, miR-223-3p, normally represses aNHEJ. We found that miR-223-/- mice have increased survival of hematopoietic stem and progenitor cells (HSPCs) after sublethal IR. However, this came at the cost of significantly more genomic aberrancies, with miR-223-/- hematopoietic progenitors having increased metaphase aberrancies, including chromothripsis, and increased sequence abnormalities, especially deletions, which is consistent with aNHEJ. These data imply that when an HSPC is faced with substantial DNA damage, it may trade genomic damage for its own survival by choosing the aNHEJ repair pathway, and this choice is regulated in part by miR-223-3p.

Cellular Responses to Widespread DNA Replication Stress.

Replicative DNA polymerases are blocked by nearly all types of DNA damage. The resulting DNA replication stress threatens genome stability. DNA replication stress is also caused by depletion of nucleotide pools, DNA polymerase inhibitors, and DNA sequences or structures that are difficult to replicate. Replication stress triggers complex cellular responses that include cell cycle arrest, replication fork collapse to one-ended DNA double-strand breaks, induction of DNA repair, and programmed cell death after excessive damage. Replication stress caused by specific structures (e.g., G-rich sequences that form G-quadruplexes) is localized but occurs during the S phase of every cell division. This review focuses on cellular responses to widespread stress such as that caused by random DNA damage, DNA polymerase inhibition/nucleotide pool depletion, and R-loops. Another form of global replication stress is seen in cancer cells and is termed oncogenic stress, reflecting dysregulated replication origin firing and/or replication fork progression. Replication stress responses are often dysregulated in cancer cells, and this too contributes to ongoing genome instability that can drive cancer progression. Nucleases play critical roles in replication stress responses, including MUS81, EEPD1, Metnase, CtIP, MRE11, EXO1, DNA2-BLM, SLX1-SLX4, XPF-ERCC1-SLX4, Artemis, XPG, FEN1, and TATDN2. Several of these nucleases cleave branched DNA structures at stressed replication forks to promote repair and restart of these forks. We recently defined roles for EEPD1 in restarting stressed replication forks after oxidative DNA damage, and for TATDN2 in mitigating replication stress caused by R-loop accumulation in BRCA1-defective cells. We also discuss how insights into biological responses to genome-wide replication stress can inform novel cancer treatment strategies that exploit synthetic lethal relationships among replication stress response factors.

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

TATDN2 resolution of R-loops is required for survival of BRCA1-mutant cancer cells.

BRCA1-deficient cells have increased IRE1 RNase, which degrades multiple microRNAs. Reconstituting expression of one of these, miR-4638-5p, resulted in synthetic lethality in BRCA1-deficient cancer cells. We found that miR-4638-5p represses expression of TATDN2, a poorly characterized member of the TATD nuclease family. We discovered that human TATDN2 has RNA 3' exonuclease and endonuclease activity on double-stranded hairpin RNA structures. Given the cleavage of hairpin RNA by TATDN2, and that BRCA1-deficient cells have difficulty resolving R-loops, we tested whether TATDN2 could resolve R-loops. Using in vitro biochemical reconstitution assays, we found TATDN2 bound to R-loops and degraded the RNA strand but not DNA of multiple forms of R-loops in vitro in a Mg2+-dependent manner. Mutations in amino acids E593 and E705 predicted by Alphafold-2 to chelate an essential Mg2+ cation completely abrogated this R-loop resolution activity. Depleting TATDN2 increased cellular R-loops, DNA damage and chromosomal instability. Loss of TATDN2 resulted in poor replication fork progression in the presence of increased R-loops. Significantly, we found that TATDN2 is essential for survival of BRCA1-deficient cancer cells, but much less so for cognate BRCA1-repleted cancer cells. Thus, we propose that TATDN2 is a novel target for therapy of BRCA1-deficient cancers.

Timeliness of childhood vaccination in England: A population-based cohort study.

INTRODUCTION: Vaccine surveillance for children in England focuses on coverage at ages 1, 2, and 5 years. Previous studies exploring vaccine timeliness have used different arbitrary categories to define whether vaccines were received 'late' or 'on time'. This paper aims to provide more detailed and holistic information on timing and patterns of vaccine uptake across the childhood immunisation schedule in England. METHODS: We included all children born in England between 2006 and 2014 and registered in the Clinical Practice Research Datalink (CPRD) Aurum, a primary care electronic health record. We described vaccine uptake for representative antigens (pertussis, pneumococcus, measles) by age in days and stratified by ethnicity, region and birth cohort. Alluvial diagrams were used to illustrate common journeys through the vaccination schedule, and we applied survival analysis using accelerated failure time models (AFT) to predict age of vaccine receipt based on timing of previous doses. RESULTS: 573,015 children were followed up until their fifth birthday, when they had 90.16 % coverage for two doses of measles, mumps, rubella (MMR) vaccine and 88.78% coverage for four doses of diphtheria, tetanus, pertussis (DTP) vaccine. Overall, the later the age at which a vaccine was due, the more delay in vaccination. Children of Black Ethnicity or from London showed deviating uptake patterns. If a child received their third DTP dose more than a year later than recommended, they would receive the next dose 2.7 times later than a child who was vaccinated on time. A smaller delay was found for children who did not receive first MMR dose on time. DISCUSSION: We showed that the risk of vaccination delay increased with the age of the child and significant delay of previous doses. Primary care data can help to promptly identify children at higher risk of delayed vaccination.

In Vitro Reconstitutive Base Excision Repair (BER) Assay.

The mammalian cell genome is continuously exposed to endogenous and exogenous insults that modify its DNA. These modifications can be single-base lesions, bulky DNA adducts, base dimers, base alkylation, cytosine deamination, nitrosation, or other types of base alteration which interfere with DNA replication. Mammalian cells have evolved with a robust defense mechanism to repair these base modifications (damages) to preserve genomic stability. Base excision repair (BER) is the major defense mechanism for cells to remove these oxidative or alkylated single-base modifications. The base excision repair process involves replacement of a single-nucleotide residue by two sub-pathways, the single-nucleotide (SN) and the multi-nucleotide or long-patch (LP) base excision repair pathways. These reactions have been reproduced in vitro using cell free extracts or purified recombinant proteins involved in the base excision repair pathway. In the present chapter, we describe the detailed methodology to reconstitute base excision repair assay systems. These reconstitutive BER assay systems use artificially synthesized and modified DNA. These reconstitutive assay system will be a true representation of biologically occurring damages and their repair.

Multiple imputation approaches for epoch-level accelerometer data in trials.

Clinical trials that investigate physical activity interventions often use accelerometers to measure step count at a very granular level, for example in 5-second epochs. Participants typically wear the accelerometer for a week-long period at baseline, and for one or more week-long follow-up periods after the intervention. The data is aggregated to provide daily or weekly step counts for the primary analysis. Missing data are common as participants may not wear the device as per protocol. Approaches to handling missing data in the literature have defined missingness on the day level using a threshold on daily weartime, which leads to loss of information on the time of day when data are missing. We propose an approach to identifying and classifying missingness at the finer epoch-level and present two approaches to handling missingness using multiple imputation. Firstly, we present a parametric approach which accounts for the number of missing epochs per day. Secondly, we describe a non-parametric approach where missing periods during the day are replaced by donor data from the same person where possible, or data from a different person who is matched on demographic and physical activity-related variables. Our simulation studies show that the non-parametric approach leads to estimates of the effect of treatment that are least biased while maintaining small standard errors. We illustrate the application of these different multiple imputation strategies to the analysis of the 2017 PACE-UP trial. The proposed framework is likely to be applicable to other digital health outcomes and to other wearable devices.

Prevalent new user designs: A literature review of current implementation practice.

PURPOSE: Prevalent new user (PNU) designs extend the active comparator new user design by allowing for the inclusion of initiators of the study drug who were previously on a comparator treatment. We performed a literature review summarising current practice. METHODS: PubMed was searched for studies applying the PNU design since its proposal in 2017. The review focused on three components. First, we extracted information on the overall study design, including the database used. We summarised information on implementation of the PNU design, including key decisions relating to exposure set definition and estimation of time-conditional propensity scores. Finally, we reviewed the analysis strategy of the matched cohort. RESULTS: Nineteen studies met the criteria for inclusion. Most studies (73%) implemented the PNU design in electronic health record or registry databases, with the remaining using insurance claims databases. Of 15 studies including a class of prevalent users, 40% deviated from the original exposure set definition proposals in favour of a more complex definition. Four studies did not include prevalent new users but used other aspects of the PNU framework. Several studies lacked details on exposure set definition (n = 2), time-conditional propensity score model (n = 2) or integration of complex analytical techniques, such as the high-dimensional propensity score algorithm (n = 3). CONCLUSION: PNU designs have been applied in a range of therapeutic and disease areas. However, to encourage more widespread use of this design and help shape best practice, there is a need for improved accessibility, specifically through the provision of analytical code alongside guidance to support implementation and transparent reporting.

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.

BACKGROUND: Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement. OBJECTIVE: Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy. METHODS: We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement. RESULTS: Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different. CONCLUSIONS: We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18690.

Challenges in Estimating the Effectiveness of COVID-19 Vaccination Using Observational Data.

The COVID-19 vaccines were developed and rigorously evaluated in randomized trials during 2020. However, important questions, such as the magnitude and duration of protection, their effectiveness against new virus variants, and the effectiveness of booster vaccination, could not be answered by randomized trials and have therefore been addressed in observational studies. Analyses of observational data can be biased because of confounding and because of inadequate design that does not consider the evolution of the pandemic over time and the rapid uptake of vaccination. Emulating a hypothetical "target trial" using observational data assembled during vaccine rollouts can help manage such potential sources of bias. This article describes 2 approaches to target trial emulation. In the sequential approach, on each day, eligible persons who have not yet been vaccinated are matched to a vaccinated person. The single-trial approach sets a single baseline at the start of the rollout and considers vaccination as a time-varying variable. The nature of the confounding depends on the analysis strategy: Estimating "per-protocol" effects (accounting for vaccination of initially unvaccinated persons after baseline) may require adjustment for both baseline and "time-varying" confounders. These issues are illustrated by using observational data from 2 780 931 persons in the United Kingdom aged 70 years or older to estimate the effect of a first dose of a COVID-19 vaccine. Addressing the issues discussed in this article should help authors of observational studies provide robust evidence to guide clinical and policy decisions.