PURPOSE:To investigate whether antihypertensive medications modify the risk of heat-related illness during heatwaves among adults with hypertension in Japan. METHODS:We identified adults with hypertension in Tsukuba City administrative claims data between April 2014 and March 2019. Average daily temperature was derived from Japan Meteorological Agency data. We used a self-controlled case series (SCCS) design to estimate incidence rate ratios (IRRs) for heat-related illness during 5-day pre-heatwave, heatwave and 5-day post-heatwave periods compared to baseline within individuals. The main analysis was stratified by person-time prescribed antihypertensives (angiotensin-converting enzyme [ACE] inhibitors/angiotensin II receptor blockers [ARBs], calcium channel blockers [CCBs] or non-loop diuretics) and person-time not prescribed antihypertensives, with drug-temperature interaction assessed. Subgroup analyses were conducted for each antihypertensive class. All models were adjusted for age and season. RESULTS:We observed a greater increase in the rate of heat-related illness during heatwaves versus baseline among person-time prescribed antihypertensives (IRR: 1.64, 95% CI: 1.53-1.75) than among person-time not prescribed antihypertensives (IRR: 1.42, 95% CI: 1.27-1.58), with evidence of drug-temperature interaction (p < 0.01). When examining individual drug classes, ACE inhibitors/ARBs and CCBs were associated with an increased risk of heat-related illness during heatwaves, whereas there was no evidence of an effect with diuretics. CONCLUSIONS:Our findings indicate that people taking antihypertensives are more vulnerable to heat-related illnesses during heatwaves. More research is required to explore whether this is a causal effect of the medication or due to differences between those who are treated and untreated with antihypertensives that are related to the outcome.

Background There is growing interest in widening the use of sodium-glucose co-transporter 2 inhibitors (SGLT2i) to all people with type 2 diabetes mellitus (T2DM). However, pivotal randomized controlled trials (RCTs) evaluated these drugs only in highly selected populations, often lacking generalizability to real-world populations. Understanding the effects of SGLT2i in populations where RCT evidence may be lacking is essential to help inform guideline development. To address this, we estimated the effect of empagliflozin in real-world users, many of whom would not have been eligible for the pivotal EMPA-REG RCT.Methods We designed a trial emulation in UK primary care data, based on the EMPA-REG RCT, to assess the effect of empagliflozin in a more clinically relevant population. Adults with T2DM initiating empagliflozin (intervention) or dipeptidyl peptidase-4 inhibitors (active control) between January 1, 2014 and December 31, 2022 were included. Eligibility was extended to both RCT-eligible and RCT-ineligible individuals. The effect of empagliflozin on all-cause mortality was estimated using an adjusted Cox proportional hazards model, with stratified analyses by RCT eligibility.Findings The majority of people prescribed empagliflozin would not have met the EMPA-REG RCT eligibility criteria (11,011/13,239, 83.2% RCT-ineligible). During follow-up, all-cause mortality occurred in 551 out of 13,239 (4.2%) in the empagliflozin group and 6,589 out of 49,264 (13.4%) in the active control group (adjusted HR 0.76, 95% CI 0.69 to 0.83). There was no evidence of differential treatment effect by RCT eligibility status (p-interaction=0.27).Interpretation Patients prescribed empagliflozin in real-world settings differ substantially from those enrolled in the EMPA-REG RCT. Using electronic health records, we demonstrate that the mortality benefit observed in EMPA-REG extends to a broader, more diverse real-world population, including those excluded from the original RCT. These findings provide a novel source of real-world evidence supporting the wider use of empagliflozin in routine clinical practice.

Background: Living with children has been associated with greater risks of SARS-CoV-2 infection, COVID-19 hospitalisation, and COVID-19 death. We examined how these associations varied during 2021–22 and according to the COVID-19 vaccination status of adults. Methods: We carried out a population-based cohort study, with the approval of NHS England. Primary care data and pseudonymously-linked hospital and death records from England, between 20th December 2020 and 21st February 2022, were used for adults (≥18 years) registered at a general practice on 20th December 2020. Adjusted hazard ratios (HRs) for SARS-CoV-2 infection, COVID-19 hospitalisation, or COVID-19 death, by presence of children in the household were calculated. Results: The cohort included 9,417,278 adults aged ≤65 years and 2,866,602 adults aged >65 years. Adults aged ≤65 years living with children of any age (versus no children) had greater risks of SARS-CoV-2 infection and COVID-19 hospitalisation (but not COVID-19 death), both when schools were open and closed (e.g. HR=1.50, 95% CI:1.49-1.51, for SARS-CoV-2 infection in the ‘Omicron dominant’ period, when schools were open, in adults living with children aged 0–11 years only). These associations also existed for adults aged >65 years, and there was some evidence that adults living with children also had greater risks of COVID-19 death. Vaccinated adults living with children had greater risks of SARS-CoV-2 infection, but lower risks of COVID-19 hospitalisation and death, than unvaccinated adults not living with children. Conclusions: In an era of widespread adult vaccination, adults living with children remained at increased risk of SARS-CoV-2 infection and COVID-19 hospitalisation.

Flexible machine-learning (ML) models to generate imputations within the Multiple Imputation (MI) framework has recently gained traction, particularly in non-randomised observational settings. For randomised controlled trials (RCTs), it is unclear whether ML approaches to MI, in combination with Rubin’s Rules, provide valid inference in terms bias, confidence interval coverage, mean squared error (MSE), Type I error and power of the treatment effect estimator. We conducted two simulation studies in RCT settings that have an incomplete continuous outcome but fully observed covariates and treatment assignment. We compared Complete Cases, standard MI (MI-norm), MI with predictive mean matching (MI-PMM) and ML-based approaches to MI, including classification and regression trees (MI-CART), Random Forests (MI-RF) and SuperLearner when outcomes are missing completely at random or missing at random conditional on treatment/covariate. The first simulation explored a cross-sectional outcome with non-linear covariate-outcome relationships in the presence/absence of covariate-treatment interactions. The second simulation explored skewed repeated measures, motivated by a trial with digital outcomes. For the cross-sectional simulation without interaction, we found that Complete Cases yielded valid inference; MI-norm performed similarly, except when there is a non-linear covariate-outcome relationship and missingness depends on the covariate. ML approaches led to smaller MSE in specific non-linear settings, but provided unreliable inference for others. MI-PMM, which is the default setting in the R package mice, led to unreliable inference under some settings. In the presence of complex treatment-covariate interactions, performing MI separately by arm, either with MI-norm, MI-RF or MI-CART, provided inference that had comparable or better properties compared to Complete Cases when the analysis model omits the interaction term. In the repeated measures setting, ML approaches to MI and MI-PMM led to bias and under- or over-coverage, particularly when missingness depended on treatment. Based on the simulation findings, Complete Cases and MI-norm are more appropriate than ML approaches to MI for making inference, especially for late phase RCTs where Type I error control is crucial. While ML approaches may provide gains in MSE for complex covariate-outcome relationships, results should be interpreted with the caveat that Rubin’s Rules are not guaranteed to be valid when used with ML imputation methods, and can lead to bias in the estimated effect and/or its standard error.

Background:We aimed to compare the risks of developing new chronic respiratory conditions, or exacerbating existing ones in survivors of 20 common cancers vs. cancer-free individuals. Methods:We conducted a population-based matched cohort study using English electronic primary care records, linked to cancer registry, hospital admissions, and death records data from 1999 to 2019. Adults aged ≥ 18 years with incident cancer were matched 1:10 to cancer-free individuals on age, sex and general practice. Outcomes included new-onset and exacerbations of asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and interstitial lung disease (ILD) ascertained from electronic health record (EHR) data throughout follow-up. Relative risks were estimated using Cox models, adjusted for confounders, like smoking. Absolute risks were estimated using adjusted incident rate differences and standardised cumulative incidence curves for new-onset disease and mean cumulative count for exacerbations. Findings:789,254 adults with incident cancer were included. Compared with cancer-free individuals, ILD risk was raised in 18 cancers (adjusted hazard ratio [adjHR] > 5 in CNS, oesophageal and lung cancers, and adjHR > 2 in seven further cancers); raised risks persisted beyond five years after diagnosis in 11 cancers. Elevated risks of developing COPD were observed in 11 cancers (adjHR 4.69 increased risk for lung cancer; other adjHRs 1.08-1.71); for seven of these cancers, raised risks persisted beyond five years. New-onset bronchiectasis risk was increased in oesophageal, lung and haematological cancers while new-onset asthma risk was raised in non-Hodgkin lymphoma only. Exacerbations rates of pre-existing respiratory diseases were raised in survivors of oesophageal, liver, lung, pancreatic, central nervous system, and haematological cancers. Interpretation:Survivors of most types of cancer had substantial and sometimes prolonged raised risk of a range of respiratory conditions. Future research should explore the mechanisms underlying these associations, including the roles of post-cancer tobacco use and specific anti-cancer treatments. Targeted management, smoking cessation, and vaccination may be warranted for those at highest risk. Funding:Medical Research Council and Wellcome trust.

Background The population of cancer survivors is growing. Some cancers and their treatments may lead to long-term adverse respiratory issues. This systematic review aims to summarise the evidence on the association between cancer survivorship and long-term respiratory health, across a range of cancer types.Methods We searched Cochrane, Embase and MEDLINE up until 23 February 2025 for cohort or nested case-control studies comparing incident respiratory outcomes in people with a history of cancer versus population-based cancer-free controls. We required studies to include follow-up time beyond the period of active cancer treatment. Outcomes included acute respiratory infections and chronic respiratory conditions. Study quality was assessed using The Scottish Intercollegiate Guidelines Network methodology checklists.Results We identified 34 eligible cohort studies. Cancer survivors’ cohort sizes ranged from 1325 to >8 million. Only 4 out of 34 studies adjusted for smoking, leading to most studies being rated as low quality. Four of the 21 studies of acute respiratory infections were rated as acceptable/high quality, and of these, all observed raised risks, notably among survivors of haematological, head and neck, lung and oesophageal cancers. Of 19 studies of chronic respiratory conditions, 1 was rated as high quality, finding increased risks of chronic obstructive pulmonary disease (COPD) and pneumonitis in survivors of head and neck cancer. The remaining studies found increased risks of adverse outcomes from acute respiratory infections in 17 of 21 cancer types for which data were available, and of COPD in cervical, head and neck, lung, oesophageal, oral, stomach, thyroid and vulva cancers.Discussion These findings suggest increased risks of a range of respiratory conditions in survivors of some cancers. Much of the evidence is compromised by a lack of control for key potential confounders, like smoking. Future studies should address this limitation and investigate the drivers of respiratory risks in cancer survivors. Improved evidence could inform mitigation strategies and lead to better survivorship care plans.PROSPERO registration number CRD42022311557.

Background:COVID-19 severe enough to require hospitalisation is commonly associated with acute kidney injury. However, it remains unclear whether COVID-19 leads to long-term kidney outcomes in the broader population. Methods:We undertook a population-based, matched cohort study. With the approval of NHS England, we used primary and secondary care electronic health records from England using the OpenSAFELY-TPP platform. We compared people with and without COVID-19 using fully-adjusted, stratified, cause-specific Cox models for kidney failure, 50% reduction in kidney function, and death. Findings:Overall, all outcomes were increased after COVID-19 over the course of follow-up (HR for kidney failure 1.93 [95% CI 1.84-2.03]). Hazards of kidney failure were greatest after hospitalisation (HR 7.74 [95% CI 7.00-8.56]) and remained increased beyond 180 days of follow-up. There was no evidence of increased risk in those not hospitalised (HR 0.85 [95% CI 0.79-0.90]). Increased kidney failure was more pronounced in black ethnicity (HR 4.50 [95% CI 2.92-6.92]) compared to white ethnicity (HR 1.82 [95% CI 1.71-1.94]). Amongst those hospitalised with COVID-19, there was no attenuation of kidney failure between the first wave (HR 8.74 [95% CI 6.88-11.08]) and the Omicron wave (HR 8.36 [95% CI 6.81-10.27]). Interpretation:We observed increased long-term kidney outcomes in people hospitalised with COVID-19, as well as notable ethnic differences. Our results suggest strategies to minimise severe COVID-19 should continue to be optimised among vulnerable groups, and that kidney function should be proactively monitored after hospital discharge. Funding:National Institute for Health and Care Research.

AIM:To investigate ethnic differences in the comparative effectiveness of sulfonylureas (SU), dipeptidyl peptidase-4 inhibitors (DPP4i) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) on cardiovascular outcomes. MATERIALS AND METHODS:We identified adults with type 2 diabetes in UK electronic health records initiating SU, DPP4i or SGLT2i (2015-2022). The outcomes were major adverse cardiovascular events (MACE: myocardial infarction, stroke, heart failure hospitalisation, cardiovascular death). Cox models estimated hazard ratios for DPP4i versus SU, SGLT2i versus SU and SGLT2i versus DPP4i. Wald tests assessed interaction by ethnicity. RESULTS:Among 91 116 included individuals (72.3% White, 14.2% South Asian, 6.0% Black), 34.2% initiated an SU, 42.0% DPP4i and 23.8% SGLT2i. There was weak evidence of interaction by ethnicity for DPP4i versus SU on MACE (p = 0.12), with stronger effects observed for DPP4i in the Black group (hazard ratio [HR]: 0.64, 95% confidence interval [CI]: 0.46-0.89) than White (HR: 0.91, 95% CI: 0.84-0.98) or South Asian (HR: 0.93, 95% CI: 0.75-1.16) groups. There was evidence of interaction by ethnicity for DPP4i versus SU on heart failure hospitalisation (p = 0.05), with a stronger effect observed for DPP4i in the Black group (HR: 0.50, 95% CI: 0.30-0.84). There was no clear evidence of ethnic differences for other treatment comparators or cardiovascular outcomes. CONCLUSIONS:We found weak evidence suggesting a greater effect of DPP4i than SUs against MACE in Black people, particularly for heart failure hospitalisation, but no evidence of other ethnic differences in treatment effects.

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW". The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.

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.

Background:Codelists are required to extract meaningful information on characteristics and events from routinely collected health data such as electronic health records. Research using routinely collected health data relies on codelists to define study populations and variables, thus, trustworthy codelists are important. Here, we provide a checklist, in the style of commonly used reporting guidelines, to help researchers adhere to best practice in codelist development and sharing. Methods:Based on a literature search and a workshop with researchers experienced in the use of routinely collected health data, we created a set of recommendations that are 1. broadly applicable to different datasets, research questions, and methods of codelist creation; 2. easy to follow, implement and document by an individual researcher, and 3. fit within a step-by-step process. We then formatted these recommendations into a checklist. Results:We have created a 10-step checklist, comprising 28 items, with accompanying guidance on each step. The checklist advises on which metadata to provide, how to define a clinical concept, how to identify and evaluate existing codelists, how to create new codelists, and how to review, check, finalise, and publish a created codelist. Conclusions:Use of the checklist can reassure researchers that best practice was followed during the development of their codelists, increasing trust in research that relies on these codelists and facilitating wider re-use and adaptation by other researchers.

Abstract Background and Aims Cardiovascular disease (CVD) is a leading cause of death globally, and individuals with chronic kidney disease (CKD) are at increased risk. Results from the ONTARGET trial, in conjunction with the ALTITUDE and VA-Nephron-D studies, led to an end of recommendations for dual ACE inhibitor and ARB therapy due to an increase in acute kidney injury. However, these studies had low power to address long-term kidney outcomes and there remains uncertainty about whether dual therapy could be effective at reducing adverse cardiovascular and renal outcomes in patients with CKD. Observational data provides an opportunity to explore such hypotheses in subgroups underrepresented in trials and with power and follow-up enabling estimation of risk of rare outcomes. We aimed to use ONTARGET to perform a reference trial emulation analysis, before extending analysis to explore treatment effectiveness of dual ARB and ACEi use vs ACEi alone in preventing cardiovascular and renal outcomes among those with CKD. Method Using routinely-collected data from the UK Clinical Practice Research Datalink (CPRD) Aurum linked with Hospital Episode Statistics secondary care data, we applied the ONTARGET trial eligibility criteria to patients prescribed an ARB/ACE inhibitor between 1/1/2001-31/7/2019. As the number of patients receiving prescriptions for both medications on the same day was likely to be small in routine care, we used an operational definition to capture dual users. Outcomes included ONTARGET primary cardiovascular composite outcome of cardiovascular-related death, myocardial infarction, stroke, or hospitalisation for heart failure and a composite renal outcome of ≥50% reduction in GFR or end-stage kidney disease (ESKD). Within the trial-eligible cohort, outcomes of interest were compared between groups prescribed dual therapy vs ACE inhibitors alone using a propensity-score—weighted time-to-event analysis using a Cox proportional hazards model. Conditional on successfully benchmarking results against the ONTARGET trial, we explored treatment effect heterogeneity by chronic kidney disease (CKD) at baseline, with CKD defined as estimated glomerular rate (eGFR) < 60 ml/min/1.73 m2. Results 412,406 trial-eligible patients in CPRD were included in analysis. Among those with non-missing eGFR at baseline, 37% had CKD (Table 1). We found similar effectiveness of dual therapy and ACE inhibitors in reducing the risk of the primary composite cardiovascular outcome (HR 0.98 (95% CI: 0.93, 1.03), consistent with the ONTARGET trial results (HR 0.99 (95% CI: 0.92, 1.07), with no evidence of heterogeneity by CKD (P-value for interaction = 0.14). However, dual therapy use was associated with a greater risk for the composite renal outcome compared to ACE inhibitor, HR 1.24 (95% CI: 1.14, 1.35), with no evidence of heterogeneity by CKD (P = 0.93) (Fig. 1). Analysing components of the composite renal outcome separately gave consistent results (≥50% reduction in GFR: HR 1.22 (95% CI: 1.12, 1.33); ESKD: HR 1.34 (95% CI: 1.24, 1.57)), as did a post-hoc sensitivity analysis by proteinuria status (no proteinuria: HR 1.28 (95% CI: 1.03, 1.59); proteinuria: 1.27 (95% CI: 1.08, 1.49)). Conclusion We found evidence that dual therapy use was associated with increased risk of renal outcomes in both those with and without CKD at baseline. Applying a reference trial emulation approach and successfully benchmarking findings against ONTARGET, where confounding was not present due to randomisation, provides confidence in the validity of these results.

Background Codelists are required to extract meaningful information on characteristics and events from electronic health records (EHRs). EHR research relies on codelists to define study populations and variables, thus, trustworthy codelists are important. Here, we provide a checklist, in the style of commonly used reporting guidelines, to help researchers adhere to best practice in codelist development and sharing. Methods Based on a literature search and a workshop with experienced EHR researchers we created a set of recommendations that are 1. broadly applicable to different datasets, research questions, and methods of codelist creation; 2. easy to follow, implement and document by an individual researcher, and 3. fit within a step-by-step process. We then formatted these recommendations into a checklist. Results We have created a 9-step checklist, comprising 26 items, with accompanying guidance on each step. The checklist advises on which metadata to provide, how to define a clinical concept, how to identify and evaluate existing codelists, how to create new codelists, and how to review, finalise, and publish a created codelist. Conclusions Use of the checklist can reassure researchers that best practice was followed during the development of their codelists, increasing trust in research that relies on these codelists and facilitating wider re-use and adaptation by other researchers.

Background Codelists are required to extract meaningful information on characteristics and events from routinely collected health data such as electronic health records. Research using routinely collected health data relies on codelists to define study populations and variables, thus, trustworthy codelists are important. Here, we provide a checklist, in the style of commonly used reporting guidelines, to help researchers adhere to best practice in codelist development and sharing. Methods Based on a literature search and a workshop with researchers experienced in the use of routinely collected health data, we created a set of recommendations that are 1. broadly applicable to different datasets, research questions, and methods of codelist creation; 2. easy to follow, implement and document by an individual researcher, and 3. fit within a step-by-step process. We then formatted these recommendations into a checklist. Results We have created a 10-step checklist, comprising 28 items, with accompanying guidance on each step. The checklist advises on which metadata to provide, how to define a clinical concept, how to identify and evaluate existing codelists, how to create new codelists, and how to review, check, finalise, and publish a created codelist. Conclusions Use of the checklist can reassure researchers that best practice was followed during the development of their codelists, increasing trust in research that relies on these codelists and facilitating wider re-use and adaptation by other researchers.

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required. For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance. This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.

Cardiovascular disease is a leading cause of death globally. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), compared in the ONTARGET trial (Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial), each prevent cardiovascular disease. However, trial results may not be generalizable, and their effectiveness in underrepresented groups is unclear. Using trial emulation methods within routine-care data to validate findings, we explored the generalizability of ONTARGET results. For people prescribed an ACEi/ARB in the UK Clinical Practice Research Datalink GOLD dataset from January 1, 2001, to July 31, 2019, we applied trial criteria and propensity-score methods to create an ONTARGET trial-eligible cohort. Comparing ARB with ACEi, we estimated hazard ratios for the primary composite trial outcome (cardiovascular death, myocardial infarction, stroke, or hospitalization for heart failure) and secondary outcomes. Because the prespecified criteria were met, confirming trial emulation, we then explored treatment heterogeneity among 3 trial-underrepresented subgroups: females, persons aged ≥75 years, and those with chronic kidney disease. In the trial-eligible population (n = 137 155), results for the primary outcome demonstrated similar effects of ARB and ACEi (hazard ratio = 0.97; 95% CI, 0.93-1.01), meeting the prespecified validation criteria. When extending this outcome to trial-underrepresented groups, similar treatment effects were observed by sex, age, and chronic kidney disease. This suggests that ONTARGET trial findings are generalizable to trial-underrepresented subgroups. This article is part of a Special Collection on Pharmacoepidemiology.