Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement

Shkolnikov VM, Jdanov DA, Majeed A, Islam N. Making sense of national and international disparities in excess mortality from the COVID-19 pandemic. BMJ Global Health. 2024;9 :e015737. Publisher's Version

Bishop FL, Cross N, Dewar-Haggart R, et al. Talking in primary care (TIP): protocol for a cluster-randomised controlled trial in UK primary care to assess clinical and cost-effectiveness of communication skills e-learning for practitioners on patients’ musculoskeletal pain and enablement. BMJ Open. 2024;14 (3). Publisher's Version Abstract

Introduction Effective communication can help optimise healthcare interactions and patient outcomes. However, few interventions have been tested clinically, subjected to cost-effectiveness analysis or are sufficiently brief and well-described for implementation in primary care. This paper presents the protocol for determining the effectiveness and cost-effectiveness of a rigorously developed brief eLearning tool, EMPathicO, among patients with and without musculoskeletal pain.Methods and analysis A cluster randomised controlled trial in general practitioner (GP) surgeries in England and Wales serving patients from diverse geographic, socioeconomic and ethnic backgrounds. GP surgeries are randomised (1:1) to receive EMPathicO e-learning immediately, or at trial end. Eligible practitioners (eg, GPs, physiotherapists and nurse practitioners) are involved in managing primary care patients with musculoskeletal pain. Patient recruitment is managed by practice staff and researchers. Target recruitment is 840 adults with and 840 without musculoskeletal pain consulting face-to-face, by telephone or video. Patients complete web-based questionnaires at preconsultation baseline, 1 week and 1, 3 and 6 months later. There are two patient-reported primary outcomes: pain intensity and patient enablement. Cost-effectiveness is considered from the National Health Service and societal perspectives. Secondary and process measures include practitioner patterns of use of EMPathicO, practitioner-reported self-efficacy and intentions, patient-reported symptom severity, quality of life, satisfaction, perceptions of practitioner empathy and optimism, treatment expectancies, anxiety, depression and continuity of care. Purposive subsamples of patients, practitioners and practice staff take part in up to two qualitative, semistructured interviews.Ethics approval and dissemination Approved by the South Central Hampshire B Research Ethics Committee on 1 July 2022 and the Health Research Authority and Health and Care Research Wales on 6 July 2022 (REC reference 22/SC/0145; IRAS project ID 312208). Results will be disseminated via peer-reviewed academic publications, conference presentations and patient and practitioner outlets. If successful, EMPathicO could quickly be made available at a low cost to primary care practices across the country.Trial registration number ISRCTN18010240.

Razieh C, Shabnam S, Dambha-Miller H, et al. Socioeconomic inequalities in risk of infection with SARS-CoV-2 delta and omicron variants in the UK, 2020-22: analysis of the longitudinal COVID-19 Infection Survey. BMJ Medicine. 2024;3 (1). Publisher's Version Abstract

Objective To explore the risk of a positive test result for the delta or omicron variant of the SARS-CoV-2 virus in different occupations and deprivation groups in the UK.Design Analysis of the longitudinal COVID-19 Infection Survey.Setting COVID-19 Infection Survey, conducted by the Office for National Statistics and the University of Oxford, UK, a nationwide longitudinal survey to monitor SARS-CoV-2 infection in the community, 26 April 2020 to 31 January 2022.Participants Survey participants recruited from randomly selected households to reflect the UK population (England, Scotland, Wales, and Northern Ireland) were divided into the delta cohort (2 July 2020 to 19 December 2021) and the omicron variant (on or after 20 December 2021), the dominant variants during our study period.Main outcome measures Incidence rate and incidence rate ratio for the presence of the delta and omicron variants by area level deprivation and occupation sector. Multivariable Poisson regression models were fitted to estimate the adjusted incidence rate ratio after adjusting for age, sex, ethnic group, comorbid conditions, urban or rural residence, household size, patient or client facing job, and time (as quarters of the year).Results 329 356 participants were included in the delta cohort and 246 061 in the omicron cohort. The crude incidence rate for the presence of the delta and omicron variants of the SARS-CoV-2 virus were higher in the most deprived group (based on the index of multiple deprivation divided by deciles; delta cohort 4.33 per 1000 person months, 95% confidence interval 4.09 to 4.58; omicron cohort 76.67 per 1000 person months, 71.60 to 82.11) than in the least deprived group (3.18, 3.05 to 3.31 and 54.52, 51.93 to 57.24, respectively); the corresponding adjusted incidence rate ratios were 1.37 (95% confidence interval 1.29 to 1.47) and 1.34 (1.24 to 1.46) during the delta and omicron variant dominant periods, respectively. The adjusted incidence rate ratios for a positive test result in the most deprived group compared with the least deprived group in the delta cohort were 1.59 (95% confidence interval 1.25 to 2.02) and 1.50 (1.19 to 1.87) in the healthcare and manufacturing or construction sectors, respectively. Corresponding values in the omicron cohort were 1.50 (1.15 to 1.95) and 1.43 (1.09 to 1.86) in the healthcare and teaching and education sectors, respectively. Associations between SARS-CoV-2 infection and other employment sectors were not significant or were not tested because of small numbers.Conclusion In this study, the risk of a positive test result for the SARS-CoV-2 virus in the delta and omicron cohorts was higher in the most deprived than in the least deprived group in the healthcare, manufacturing or construction, and teaching and education sectors.Data are available upon reasonable request. Data from the Office of National Statistics (ONS) COVID-19 Infection Survey can be accessed only by ONS accredited researchers through the secure research service. Researchers can apply for accreditation through the research accreditation service and will need approval to access the COVID-19 Infection Survey data. Further details can be found at https://www.ons.gov.uk/aboutus/whatwedo/statistics/requestingstatistics/...

Islam N, van der Schaar M. Use of generative artificial intelligence in medical research. BMJ. 2024;384 :q119.

Kahan BC, Hall SS, Beller EM, et al. Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement. JAMA. 2023;330 (21) :2106-2114. Publisher's Version Abstract

Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal.To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials.Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist.This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported.This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.

Kahan BC, Hall SS, Beller EM, et al. Consensus Statement for Protocols of Factorial Randomized Trials: Extension of the SPIRIT 2013 Statement. JAMA Network Open. 2023;6 (12) :e2346121-e2346121. Publisher's Version Abstract

Trial protocols outline a trial’s objectives as well as the methods (design, conduct, and analysis) that will be used to meet those objectives, and transparent reporting of trial protocols ensures objectives are clear and facilitates appraisal regarding the suitability of study methods. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, no extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement, which provides guidance on reporting of trial protocols, for factorial trials is available.To develop a consensus-based extension to the SPIRIT 2013 Statement for factorial trials.The SPIRIT extension for factorial trials was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework. First, a list of reporting recommendations was generated using a scoping review of methodological articles identified using a MEDLINE search (inception to May 2019), which was supplemented with relevant articles from the personal collections of the authors. Second, a 3-round Delphi survey (January to June 2022, completed by 104 panelists from 14 countries) was conducted to assess the importance of items and identify additional recommendations. Third, a hybrid consensus meeting was held, attended by 15 panelists to finalize selection and wording of the checklist.This SPIRIT extension for factorial trials modified 9 of the 33 items in the SPIRIT 2013 checklist. Key reporting recommendations were that the rationale for using a factorial design should be provided, including whether an interaction is hypothesized; the treatment groups that will form the main comparisons should be identified; and statistical methods for each main comparison should be provided, including how interactions will be assessed.In this consensus statement, 9 factorial-specific items were provided that should be addressed in all protocols of factorial trials to increase the trial’s utility and transparency.

Hopper NA, Campbell A, Roberts C, et al. A comparison of excess deaths by UK country and region during the first year of the COVID-19 pandemic. European Journal of Public Health. 2023. Publisher's Version Abstract

We compare the impact of the first two waves of the COVID-19 pandemic on risk of age-standardized mortality by sex, UK country, and English region. Each wave is defined as lasting 26 weeks and are consecutive beginning in 2020 week 11. The expected rate is estimated from 2015 to 2019 mean and the projected mortality trend from the same period are used to estimate excess mortality. By both measures, excess mortality was highest and lowest in regions of England, London and the South-West, respectively. Excess mortality was consistently higher for males than females.

Hansford HJ, Cashin AG, Jones MD, et al. Development of the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) guideline. BMJ Open. 2023;13 (9) :e074626. Publisher's Version Abstract

Background Observational studies are increasingly used to inform health decision-making when randomised trials are not feasible, ethical or timely. The target trial approach provides a framework to help minimise common biases in observational studies that aim to estimate the causal effect of interventions. Incomplete reporting of studies using the target trial framework limits the ability for clinicians, researchers, patients and other decision-makers to appraise, synthesise and interpret findings to inform clinical and public health practice and policy. This paper describes the methods that we will use to develop the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) reporting guideline.Methods/design The TARGET reporting guideline will be developed in five stages following recommended guidance. The first stage will identify target trial reporting practices by systematically reviewing published studies that explicitly emulated a target trial. The second stage will identify and refine items to be considered for inclusion in the TARGET guideline by consulting content experts using sequential online surveys. The third stage will prioritise and consolidate key items to be included in the TARGET guideline at an in-person consensus meeting of TARGET investigators. The fourth stage will produce and pilot-test both the TARGET guideline and explanation and elaboration document with relevant stakeholders. The fifth stage will disseminate the TARGET guideline and resources via journals, conferences and courses.Ethics and dissemination Ethical approval for the survey has been attained (HC220536). The TARGET guideline will be disseminated widely in partnership with stakeholders to maximise adoption and improve reporting of these studies.

Islam N, Shabnam S, Khan N, et al. Combinations of multiple long-term conditions and risk of hospitalisation and death during the winter season: population-based study of 48 million people in England. medRxiv. 2023 :2023.09.04.23295015. Publisher's Version Abstract

Background The annual winter season poses substantial challenges to the National Health Service (NHS) in England. Hospitalisation and mortality increase during winter, especially in people with multiple long-term conditions (MLTC or multimorbidity). We aimed to describe which combinations of long-term conditions (LTC) are associated with a higher risk of hospitalisation and death during winter amongst adults in England.Methods In this population-based study, we used linked primary and secondary care data from the General Practice Extraction Service Data for Pandemic Planning (GDPPR) database, Hospital Episode Statistics, and Office for National Statistics death registry. We included individuals aged ≥18 years and alive on 1st December 2021 and used overdispersed Poisson models to estimate the incidence rate ratios of all-cause hospitalisations and deaths associated with the combinations of MLTCs – compared to those with no LTC – during the winter season (1 December 2021 to 31 March 2022).Findings Complete data were available for 48,253,125 adults, of which 15 million (31.2%) had MLTC. Hospitalisation per 1000 person-years was higher in individuals with MLTCs, and varied by combination, e.g.: 96, 1643, and 1552 in individuals with no LTC, cancer+chronic kidney disease (CKD)+cardiovascular disease (CVD)+type 2 diabetes mellitus, and cancer+CKD+CVD+osteoarthritis, respectively. Incidence of death (per 1000 person-years) was 345 in individuals with cancer+CKD+CVD+dementia and 1 with no LTC. CVD+dementia appeared in all the top five MLTC combinations by death and was associated with a substantially higher rate of death than many 3-, 4- and 5-disease combinations.Interpretation Risks of hospitalisation and death vary by combinations of MLTCs and are substantially higher in those with vs. without any LTCs. We have highlighted high-risk combinations for prioritisation and preventive action by policymakers to help manage the challenges imposed by winter pressures on the NHS.Funding National Institute for Health and Care Research (NIHR) through Health Data Research UK rapid funding call for the research activity “Data Science to inform NHS compound winter pressure policy response” (grant number: HDRUK2022.0313)Evidence before this study We searched PubMed, from inception to April 2023, for published population-based studies examining MLTC combinations in cohorts of adults aged 18 years and over. The search terms were “multimorbidity” or ‘’multiple-long-term conditions’’ alongside “groups” or “combinations”. We found no previous studies examining MLTC in relation to death or hospitalisation during the winter season.Added value of this study We have identified distinct combinations of LTCs and estimated the associated risk of hospitalisation and deaths during the winter season using the whole-population primary and secondary care data in England.Implications of all the available evidence Understanding which combinations of MLTCs are associated with the highest risk of hospitalisation and death allows clinicians and policymakers to prioritise resources for preventative measures, such as vaccination to those that will benefit most during winter seasons.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work is supported by the National Institute for Health and Care Research (NIHR) through Health Data Research UK rapid funding call for the research activity - Data Science to inform NHS compound winter pressure policy response (grant number: HDRUK2022.0313). Health Data Research UK is funded by the British Heart Foundation, Chief Scientists Office of the Scottish Government, Health and Care Research Wales, Health & Social Care Research and Development N. Ireland, Engineering and Physical Sciences Research, Economic and Social Research Council, Medical Research Council, National Institute for Health Research, Cancer Research UK. The funder had no role in study design, data collection, analysis or interpretation, or manuscript writing.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This project used anonymised electronic health records and administrative data which were collected and curated by NHS Digital in a Trusted Research Environment. Data access is available for research conditional on the approval of a research proposal and protocol, data access agreements with NHS Digital, and other information governance requirements. This project falls within the remit of the CVD-COVID-UK/COVID-IMPACT research programme, supported by the British Heart Foundation (BHF) Data Science Centre and the Health Data Research UK (HDR UK), which obtained overall ethics from the Northeast-Newcastle and North Tyneside 2 research ethics committee (REC No 20/NE/0161). Additional details of the linkage, approval, and scope of the consortium approved to use this data have been described here: Wood A, Denholm R, Hollings S, Cooper J, Ip S, Walker V, et al. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource. BMJ [Internet]. 2021 Apr 7 [cited 2023 May 26];373. Available from: https://www.bmj.com/content/373/bmj.n826I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe analytical codes and phenotypes used within the NHS Digital Trusted Research Environment are available in the following repository (GitHub link https://github.com/BHFDSC/CCU059_01). This project used anonymised electronic health records and administrative data which were collected and curated by NHS Digital in a Trusted Research Environment. Due to policies on information governance restrictions, the authors are unable to share individual patient data directly, but data access is available for research conditional on the approval of a research proposal and protocol, data access agreements with NHS Digital, and other information governance requirements. The authors and colleagues across the CVD-COVID-UK/COVID-IMPACT consortium have invested considerable time and energy in developing this data resource and would like to ensure that it is used widely to maximise its value. For inquiries about data access, please see https://web.www.healthdatagateway.org/dataset/7e5f0247-f033-4f98-aed3-3d.... Data access approval was granted to the CVD-COVID-UK consortium (under project proposal CCU059) through the NHS Digital online Data Access Request Service (DARS-NIC-391419-J3W9T). NHS Digital data have been made available for research under the Control of Patient Information notice, which mandated the sharing of national electronic health records for COVID-19 research (https://digital.nhs.uk/coronavirus/coronavirus-covid-19-response-informa...)BHFBritish Heart FoundationCKDChronic Kidney DiseaseCVDCardiovascular DiseasesDHSCDepartment of Health and Social CareGDPPRGeneral Practice Extraction Service Data for pandemic planningGPESGeneral Practice Extraction ServiceHDRUKHealth Data Research United KingdomHES APCHospital Episode Statistics Admitted Patient CareIMDIndex of Multiple DeprivationLTCLong term conditionsNHSNational Health ServiceNIHRNational Institute for Health and Care ResearchOAOsteoarthritisONSOffice for National StatisticsPPIEPublic and Patient Involvement and EngagementT2DMType 2 Diabetes MellitusTRETrusted Research Environment

Jin D, Trichia E, Islam N, Lewington S, Lacey B. Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank. Journal of Epidemiology and Community Health. 2023 :jech-2023-220801. Publisher's Version Abstract

Background Metabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as compared with established atherosclerotic cardiovascular disease (CVD) risk scores.Methods UK Biobank is a prospective study of 0.5 million participants, aged 40–69 at recruitment. Analyses were restricted to 74 780 participants with metabolic profiling (measured using nuclear magnetic resonance) and without CVD at baseline. Cox regression was used to compare model performance before and after addition of metabolites to QRISK3 (an established CVD risk score used in primary care in England); analyses derived three models, with metabolites selected by association significance or by employing two different machine learning approaches.Results We identified 5097 incident CVD events within the 10-year follow-up. Harrell’s C-index of QRISK3 was 0.750 (95% CI 0.739 to 0.763) for women and 0.706 (95% CI 0.696 to 0.716) for men. Adding selected metabolites did not significantly improve measures of discrimination in women (Harrell’s C-index of three models are 0.759 (0.747 to 0.772), 0.759 (0.746 to 0.770) and 0.759 (0.748 to 0.771), respectively) or men (0.710 (0.701 to 0.720), 0.710 (0.700 to 0.719) and 0.710 (0.701 to 0.719), respectively), and neither did it improve reclassification or calibration.Conclusion This large-scale study applied both conventional and machine learning approaches to assess the potential benefit of metabolic profiling to well-established CVD risk scores. However, there was no evidence that metabolic profiling improved CVD risk prediction in this population.Data are available upon reasonable request. Data from the UK Biobank are available to researchers after registration at the UK Biobank server. The data cleaning and coding used to generate the findings of this study are available from the corresponding author on reasonable request.

Jin D, Trichia E, Islam N, et al. Lipoprotein Characteristics and Incident Coronary Heart Disease: Prospective Cohort of Nearly 90 000 Individuals in UK Biobank. Journal of the American Heart Association. 2023 :e029552. Publisher's Version

Hansford HJ, Cashin AG, Jones MD, et al. Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review. JAMA Netw Open. 2023;6 (9) :e2336023.Abstract

IMPORTANCE: Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice. OBJECTIVE: To assess the reporting of observational studies that explicitly aimed to emulate a target trial. EVIDENCE REVIEW: We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation. FINDINGS: A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation. CONCLUSION AND RELEVANCE: In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

Alicandro G, La Vecchia C, Islam N, Pizzato M. A comprehensive analysis of all-cause and cause-specific excess deaths in 30 countries during 2020. European Journal of Epidemiology. 2023.Abstract

The impact of COVID-19 on mortality from specific causes of death remains poorly understood. This study analysed cause-of-death data provided by the World Health Organization from 2011 to 2019 to estimate excess deaths in 2020 in 30 countries. Over-dispersed Poisson regression models were used to estimate the number of deaths that would have been expected if the pandemic had not occurred, separately for men and women. The models included year and age categories to account for temporal trends and changes in size and age structure of the populations. Excess deaths were calculated by subtracting observed deaths from expected ones. Our analysis revealed significant excess deaths from ischemic heart diseases (IHD) (in 10 countries), cerebrovascular diseases (CVD) (in 10 countries), and diabetes (in 19 countries). The majority of countries experienced excess mortality greater than 10%, including Mexico (+ 38·8% for IHD, + 34·9% for diabetes), Guatemala (+ 30·0% for IHD, + 10·2% for CVD, + 39·7% for diabetes), Cuba (+ 18·8% for diabetes), Brazil (+ 12·9% for diabetes), the USA (+ 15·1% for diabetes), Slovenia (+ 33·8% for diabetes), Poland (+ 30·2% for IHD, + 19·5% for CVD, + 26 1% for diabetes), Estonia (+ 26·9% for CVD, + 34·7% for diabetes), Bulgaria (+ 22·8% for IHD, + 11·4% for diabetes), Spain (+ 19·7% for diabetes), Italy (+ 18·0% for diabetes), Lithuania (+ 17·6% for diabetes), Finland (+ 13·2% for diabetes) and Georgia (+ 10·7% for IHD, + 19·0% for diabetes). In 2020, 22 out of 30 countries had a significant increase in total mortality. Some of this excess was attributed to COVID-19, but a substantial increase was also observed in deaths attributed to cardiovascular diseases and diabetes.

Fox D, Zhang Q, Islam N, et al. Defining a childhood type 1 diabetes cohort, clinical practice measures and outcomes within administrative data in British Columbia. Canadian Journal of Diabetes. 2023.Abstract

ABSTRACT Objectives We used administrative data to (1) establish a cohort of individuals with childhood-onset type 1 diabetes (T1D) in British Columbia (BC) and (2) define T1D-related clinical practice measures. Methods We applied a validated diabetes case finding definition and differentiating algorithm to linked administrative data (1992/93–2019/20). Cases were removed when they did not meet inclusion criteria for childhood-onset T1D. Clinical practice measures were defined based on clinical practice guidelines. Results We developed an administrative cohort that includes 5901 individuals with childhood diagnosed T1D between 04/01/1996 and 03/31/2020. The mean age was 22.31 (8.21). Clinical practice measures that were derived included diabetes outpatient visits (N=4935), hemoglobin A1C tests (N=4935), and screening for thyroid function (N=4457), retinopathy (N=1602), and nephropathy (N=2369). Conclusions We established an administrative cohort of ∼6000 individuals with childhood-onset T1D with 20+ years of follow-up data that can be used to describe the association between clinical practice measures and clinical outcomes.

Marconi AM, Castillo Salgado C, Sarrouf EB, et al. Socioeconomic inequities of COVID-19 mortality in vulnerable Comunas of the City of Buenos Aires. Scientific Reports. 2023;13 :13642.Abstract

During the COVID-19 pandemic, the gap in health inequities was exposed and increased, showing how different vulnerable groups were affected. Our aim was to examine the correlation between an area-based health inequity index and mortality due to COVID-19 in people 60 years old or above in the City of Buenos Aires in 2020. We developed a Health Inequity Composite Index (HICI), including six core indicators. Each indicator value per Comuna was first standardized to a Z-score. All six Z-scores were summed into a final composite Z-score to rank the Comunas from lowest to highest social inequities. Comunas from the northern part of the city had lower inequities whereas those in the south had higher levels of inequities. COVID-19 age-standardized mortality rate in people 60 years or above was higher in the Comunas from the south and lower in those from the north. Finally, we found a strong positive correlation (Rho = 0.83, p < 0.0001 CI95% = 0.65–0.99) between HICI and age-standardized mortality rates from COVID-19 in people 60 years or above. Our finding of a strong correlation between the levels of health inequity and mortality calls for a concerted effort in narrowing or eliminating existing inequities.

Oguntade AS, Islam N, Malouf R, et al. Body Composition and Risk of Incident Heart Failure in 1 Million Adults: A Systematic Review and Dose–Response Meta‐Analysis of Prospective Cohort Studies. Journal of the American Heart Association. 2023 :e029062. Publisher's Version

Loder E, Islam N. Race and gender inequity in awards and recognition. BMJ. 2023;381. Publisher's Version

Shabnam S, Razieh C, Dambha-Miller H, et al. Socioeconomic inequalities of Long COVID: a retrospective population-based cohort study in the United Kingdom. Journal of the Royal Society of Medicine. 2023 :01410768231168377. Publisher's Version Abstract

Objectives: To estimate the risk of Long COVID by socioeconomic deprivation and to further examine the inequality by sex and occupation.

Design: We conducted a retrospective population-based cohort study using data from the ONS COVID-19 Infection Survey between 26 April 2020 and 31 January 2022. This is the largest nationally representative survey of COVID-19 in the UK with longitudinal data on occupation, COVID-19 exposure and Long COVID.

Setting: Community-based survey in the UK.

Participants: A total of 201,799 participants aged 16 to 64 years and with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Main outcome measures: The risk of Long COVID at least 4 weeks after SARS-CoV-2 infection by index of multiple deprivation (IMD) and the modifying effects of socioeconomic deprivation by sex and occupation.

Results: Nearly 10% (n = 19,315) of participants reported having Long COVID. Multivariable logistic regression models, adjusted for a range of variables (demographic, co-morbidity and time), showed that participants in the most deprived decile had a higher risk of Long COVID (11.4% vs. 8.2%; adjusted odds ratio (aOR): 1.46; 95% confidence interval (CI): 1.34, 1.59) compared to the least deprived decile. Significantly higher inequalities (most vs. least deprived decile) in Long COVID existed in healthcare and patient-facing roles (aOR: 1.76; 95% CI: 1.27, 2.44), in the education sector (aOR: 1.68; 95% CI: 1.31, 2.16) and in women (aOR: 1.56; 95% CI: 1.40, 1.73) than men (aOR: 1.32; 95% CI: 1.15, 1.51).

Conclusions: This study provides insights into the heterogeneous degree of inequality in Long COVID by deprivation, sex and occupation. These findings will help inform public health policies and interventions in incorporating a social justice and health inequality lens.

Islam N, Jdanov DA. Age and sex adjustments are critical when comparing death rates. BMJ. 2023;381. Publisher's Version

Chudasama YV, Khunti K, Coles B, et al. Life expectancy following a cardiovascular event in individuals with and without type 2 diabetes: A UK multi-ethnic population-based observational study. Nutrition, Metabolism and Cardiovascular Diseases. 2023. Publisher's Version Abstract

Background and Aims We aimed to evaluate the life expectancy following the first cardiovascular disease (CVD) event by type 2 diabetes (T2D) status and ethnicity. Methods and Results We used the Clinical Practice Research Datalink database in England (UK), linked to the Hospital Episode Statistics information, to identify individuals with and without T2D who survived a first CVD event between 1st Jan 2007 and 31st Dec 2017; subsequent death events were extracted from the Office for National Statistics database. Ethnicity was categorised as White, South Asian (SA), Black, or other. Flexible parametric survival models were used to estimate survival and predict life expectancy. 59,939 individuals with first CVD event were included: 7,596 (12.7%) with T2D (60.9% men; mean age at event: 69.7 years [63.2 years in SA, 65.9 in Black, 70.2 in White]) and 52,343 without T2D (56.7% men; 65.9 years [54.7 in Black, 58.2 in SA, 66.3 in White]). Accounting for potential confounders (sex, deprivation, lipid-lowering medication, current smoking, and pre-existing hypertension), comparing individuals with vs without T2D the mortality rate was 53% higher in White (hazard ratio [HR]: 1.53 [95% CI: 1.44, 1.62]), corresponding to a potential loss of 3.87 (3.30, 4.44) life years at the age of 50 years in individuals with T2D. No evidence of a difference in life expectancy was observed in individuals of SA (HR: 0.82 [0.52, 1.29]; -1.36 [-4.58, 1.86] life years), Black (HR: 1.26 [0.59, 2.70]; 1.21 [-2.99, 5.41] life years); and other (HR: 1.64 [0.80, 3.39]; 3.89 [-2.28, 9.99] life years) ethnic group. Conclusion Following a CVD event, T2D is associated with a different prognosis and life years lost among ethnic groups.

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Nazrul Islam

MBBS, MSc, MPH, PhD

Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement

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