Understanding and reporting odds ratios as rate-ratio estimates in case-control studies

Background: We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies. Methods: We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied. Results: We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios. Conclusions: We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies

BNT162b2 COVID-19 vaccination uptake, safety, effectiveness and waning in children and young people aged 12–17 years in Scotland

This study is part of the EAVE II project. EAVE II is funded by the MRC (MC_PC_19075) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through the Health Data Research UK. This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058). This work was also supported by The Alan Turing Institute via ‘Towards Turing 2.0’ EPSRC Grant Funding. Additional support has been provided through Public Health Scotland, the Scottish Government Director-General Health and Social Care and the University of Edinburgh. The original EAVE project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme (11/46/23). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the UK government. We thank Dave Kelly from Albasoft (Inverness, UK) for his support with making primary care data available, and Wendy Inglis-Humphrey, Vicky Hammersley, and Laura Brook (University of Edinburgh, Edinburgh, UK) for their support with project management and administration.Peer reviewedPublisher PD

Risk of winter hospitalisation and death from acute respiratory infections in Scotland : a national retrospective cohort study

Objectives We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. Design A population-based retrospective cohort analysis Setting Scotland Participants 5.4 million residents in Scotland Main outcome measures Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. Results Between September 1, 2022 and January 31, 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1,759 in children and 20,525 in adults) in Scotland. Compared to the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR=4.55 95%CI (4.11-5.04)). Compared to 25-29 years old, the risk of ARI hospitalisation was highest amongst the oldest adults aged ≥80 years (7.86 (7.06-8.76)). Adults from more deprived areas (most deprived vs least deprived, 1.64 (1.57-1.72)), with existing health conditions (≥5 vs 0 health conditions, 4.84 (4.53-5.18)) or with history of all-cause emergency admissions (≥6 vs 0 previous emergency admissions 7.53 (5.48-10.35)) were at higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. Conclusions Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI

ARIA 2016 : Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA-disseminated and implemented in over 70 countries globally-is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.Peer reviewe

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Background: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries. Objectives: To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App. Methods: An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach. Results: A total of 12143 users were registered. A total of 6949 users reported at least one VAS data recording. Among them, 1887 users reported >= 7 VAS data. About 1195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR >= 70% and PDC = 70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR Conclusion and clinical relevance: Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting.Peer reviewe

Caveats in reporting of national vaccine uptake

During the work of the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) group, we reported on vaccine uptake, safety, effectiveness, and waning in specific age groups in Scotland (e.g., 12-17 years) to enable policymakers to make decisions based on evidence generated nearly in real-time [1]. At first, these imperatives appeared methodologically straightforward. However, we soon realised that the seemingly simplest task in theory – i.e., reporting the vaccine uptake – was in fact challenging in practice. We report several caveats that need to be considered when reporting vaccine uptake for a specific age-group at a national level. We also propose a simple stepwise approach for reporting the methods of calculating vaccine uptake in a specific age-group in the context of tracking a large population

Risk of severe COVID-19 outcomes after autumn 2022 COVID-19 booster vaccinations: a pooled analysis of national prospective cohort studies involving 7.4 million adults in England, Northern Ireland, Scotland and Wales

BackgroundUK COVID-19 vaccination policy has evolved to offering COVID-19 booster doses to individuals at increased risk of severe Illness from COVID-19. Building on our analyses of vaccine effectiveness of first, second and initial booster doses, we aimed to identify individuals at increased risk of severe outcomes (i.e., COVID-19 related hospitalisation or death) post the autumn 2022 booster dose.MethodsWe undertook a national population-based cohort analysis across all four UK nations through linked primary care, vaccination, hospitalisation and mortality data. We included individuals who received autumn 2022 booster doses of BNT162b2 (Comirnaty) or mRNA-1273 (Spikevax) during the period September 1, 2022 to December 31, 2022 to investigate the risk of severe COVID-19 outcomes. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for the association between demographic and clinical factors and severe COVID-19 outcomes after the autumn booster dose. Analyses were adjusted for age, sex, body mass index (BMI), deprivation, urban/rural areas and comorbidities. Stratified analyses were conducted by vaccine type. We then conducted a fixed-effect meta-analysis to combine results across the four UK nations.FindingsBetween September 1, 2022 and December 31, 2022, 7,451,890 individuals ≥18 years received an autumn booster dose. 3500 had severe COVID-19 outcomes (2.9 events per 1000 person-years). Being male (male vs female, aHR 1.41 (1.32–1.51)), older adults (≥80 years vs 18–49 years; 10.43 (8.06–13.50)), underweight (BMI &lt;18.5 vs BMI 25.0–29.9; 2.94 (2.51–3.44)), those with comorbidities (≥5 comorbidities vs none; 9.45 (8.15–10.96)) had a higher risk of COVID-19 hospitalisation or death after the autumn booster dose. Those with a larger household size (≥11 people within household vs 2 people; 1.56 (1.23–1.98)) and from more deprived areas (most deprived vs least deprived quintile; 1.35 (1.21–1.51)) had modestly higher risks. We also observed at least a two-fold increase in risk for those with various chronic neurological conditions, including Down's syndrome, immunodeficiency, chronic kidney disease, cancer, chronic respiratory disease, or cardiovascular disease.InterpretationMales, older individuals, underweight individuals, those with an increasing number of comorbidities, from a larger household or more deprived areas, and those with specific underlying health conditions remained at increased risk of COVID-19 hospitalisation and death after the autumn 2022 vaccine booster dose. There is now a need to focus on these risk groups for investigating immunogenicity and efficacy of further booster doses or therapeutics.<br/

Setting research priorities for global pandemic preparedness: an international consensus and comparison with ChatGPT's output

Background: In this priority-setting exercise, we sought to identify leading research priorities needed for strengthening future pandemic preparedness and response across countries. Methods: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals. Results: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences. Conclusions: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.</p