Introduction The Nepal Family Cohort study uses a life course epidemiological approach to collect comprehensive data on children’s and their parents’ environmental, behavioural and metabolic risk factors. These factors can affect the overall development of children to adulthood and the onset of specific diseases. Among the many risk factors, exposure to air pollution and lifestyle factors during childhood may impact lung development and function, leading to the early onset of respiratory diseases. The global incidence and prevalence of respiratory diseases are rapidly increasing, with the rate of increase in Nepal being the highest. Although the cohort will primarily focus on respiratory health, other health outcomes such as cardiovascular, metabolic and mental health will be assessed to provide a comprehensive overall health assessment. All other health outcomes are self-reported following doctor diagnosis. Some of these health outcomes will be quality controlled during the follow-up by measuring disease specific markers. Our cohort study will likely provide evidence of risk factors and policy recommendations. Methods and analysis Using a life-course epidemiology approach, we established a longitudinal study to address the determinants of lung health and other health outcomes from childhood to adulthood. The baseline data collection (personal data anonymised) was completed in April 2024, and 16 826 participants (9225 children and 7601 parents) from 5829 families were recruited in different geographical and climate areas (hills and plains) of Nepal. We plan to follow up all the participants every 2–3 years. Descriptive analysis will be used to report demographic characteristics and compare rural and semi-urban regions. A linear regression model will assess the association between air pollution, particularly household air pollution (HAP) exposure, and other lifestyle factors, with lung function adjusted for potential confounders. A two-stage linear regression model will help to evaluate lung development based on exposure to HAP. Ethics Ethical approval was obtained from the Nepal Health Research Council, Kathmandu, Nepal, and McMaster University, Hamilton, Canada. Permissions were obtained from two municipalities where the study sites are located. Parents provided signed informed consent and children their assent. Dissemination Findings will be disseminated through traditional academic pathways, including peer-reviewed publications and conference presentations. We will also engage the study population and local media (ie, research blogs and dissemination events) and prepare research and policy briefings for stakeholders and leaders at the local, provincial and national levels.
Background: Smoking is attributed to both micro- and macrovascular complications at any stage of metabolic deregulation including prediabetes, particularly those who develop the disease at a young age. Current global diabetes prevention programmes appear to be glucocentric, and do not fully acknowledge the ramifications of cardiorenal risk factors in smokers. A more holistic approach is needed to prevent vascular complications in people with prediabetes and diabetes. Considering albuminuria as a surrogate marker for both micro- and macrovascular complications, we investigated the relationship between smoking status and albuminuria in people with prediabetes and diabetes, and explored how this relationship is affected by age, antihypertensive, and cholesterol-lowering medications.Methods: A logistic regression model was fitted on UK Biobank dataset with 502,490 participants. A subgroup analysis investigated the effect of age, smoking status, antihypertensive and cholesterol-lowering medications on this relationship in people with prediabetes and diabetes.Findings: Compared with non-smokers, the odds of albuminuria in smokers with prediabetes and diabetes were 1.43 (95% CI 1.16 - 1.77), and 1.29 (95% CI 1.02 – 1.64), respectively. People younger than 50 with prediabetes, and diabetes were at increased risk of albuminuria, compared with those over 50 years old, with OR 1.62 and 1.34, respectively. The odds of albuminuria remained statistically significantly high, in prediabetes and diabetes groups, despite being on anti-hypertensive, and cholesterol-lowering medications. The odds of albuminuria were not attenuated in ex-smokers either with prediabetes or diabetes.Interpretation: Smokers with prediabetes are at a higher risk of albuminuria than those with diabetes. The risk in ex-smokers did not decline to a statistically significant level, presumably due to insufficient lag period since quitting. Current strategies for cholesterol and hypertension management may not be sufficient to reduce the risk of albuminuria in people both with prediabetes and diabetes. Smoking cessation and continued abstinence in people with prediabetes and diabetes should be promoted in order to prevent future vascular complications. Screening for albuminuria should be incorporated in the NHS health check.Funding Information: No external funding was received for this study. Declaration of Interests: DK declares no duality of interest. This publication is undertaken using UK Biobank data under application no – 61894 and is a part of MD thesis. JPS receives funding from the Wellcome Trust/Royal Society via a Sir Henry Dale Fellowship (ref: 211182/Z/18/Z) and an NIHR Oxford Biomedical Research Centre (BRC) Senior Fellowship. For open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. JRA is supported by a NIHR Clinician Scientist Award (CS 2018-18-ST2-007) SdeL reports that through his university, he has had grants not directly relating to this work, from AstraZeneca, GSK, Sanofi, Seqirus, and Takeda for vaccine research and membership of advisory boards for AstraZeneca, Sanofi and Seqirus. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC). Prof Khunti has acted as a consultant and speaker for Amgen, AstraZeneca, Bayer, Novartis, Novo Nordisk, Roche, Sanofi-Aventis, Lilly, Servier and Merck Sharp & Dohme. He has received grants in support of investigator and investigator-initiated trials from AstraZeneca, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim and Merck Sharp & Dohme. KK has received funds for research, honoraria for speaking at meetings and has served on advisory boards for AstraZeneca, Lilly, Sanofi-cool=Aventis, Merck Sharp & Dohme and Novo Nordisk. MJD reports personal fees from Novo Nordisk, Sanofi-Aventis, Lilly, Merch Sharp & Dohme, Boehringer Ingelheim, Astra Zeneca, Janssen, Servier, Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceuticals International Inc. She has also received grants from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim, Janssen outside the submitted work 24 KK and MJD are members of the National Institute for Health and Clinical Excellence public health guidance on preventing type 2 diabetes and both are advisers to the UK epartment of Health for the NHS health checks programme. All other authors have nothing to confirm. Ethics Approval Statement: This is a retrospective cross-sectional study using the UK Biobank (UKB) data. UK Biobank received ethics approval from the Northwest Multi-centre Research Ethics Committee (MREC). It has also received approval from the National Information Governance Board for Health & Social Care (NIGB). For this study, ethics approval was also granted by the Research Ethics Committee, Sheffield School of Health and Related Research, University of Sheffield Application No 038586, 09/03/2021).
Abstract ObjectivesThe aim of this perspective is to report the use of synthetic data as a viable method in women’s health given the current challenges linked to obtaining life-course data within a short period of time and accessing electronic healthcare data. Methods We used a 3-point perspective method to report an overview of data science, common applications, and ethical implications. Results There are several ethical challenges linked to using real-world data, consequently, generating synthetic data provides an alternative method to conduct comprehensive research when used effectively. The use of clinical characteristics to develop synthetic data is a useful method to consider. Aligning this data as closely as possible to the clinical phenotype would enable researchers to provide data that is very similar to that of the real-world. Discussion Population diversity and disease characterisation is important to optimally use data science. There are several artificial intelligence techniques that can be used to develop synthetic data. ConclusionSynthetic data demonstrates promise and versatility when used efficiently aligned to clinical problems. Therefore, exploring this option as a viable method in women’s health, in particular for epidemiology may be useful.
BACKGROUND The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as, England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the UK uses the Readv2 terminology in primary care. The availability of data sources is not uniform across the UK. OBJECTIVE To use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We plan to do this for vaccine coverage and two adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Readv2, the World Health Organisation’s International Classification of Disease version 10 (ICD-10) terminology and the UK’s Dictionary of Medicines and Devices (dm+d). METHODS Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the UK devolved nations health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Athena online browser. Lead analysts from each nation then confirm or add to the mappings identified. These mappings will then be used to report AEIs in a common format. We will report rates for windows of 0-2 days and 3-28 days post-vaccine every 28 days. RESULTS We list the mappings between Read v2, SNOMED CT, ICD-10 and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED clinical terms from which we selected 47, and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED codes and 9 from Read v2, from which we selected 10 and 4 clinical terms to include in our repeated cross-sectional studies. CONCLUSIONS This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 are sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.
BACKGROUND Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. OBJECTIVE This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). METHODS We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency’s national data. We used paired <i>t</i> tests to compare populations, reported with 95% CI. RESULTS The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; <i>P</i><.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; <i>P</i><.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; <i>P</i><.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. CONCLUSIONS The PCSC’s sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage.
Endometriosis is a complex chronic condition characteristic of chronic pelvic pain, dysmenorrhea, anxiety and fatigue. This can often lead to multimorbidity which is defined by the presence of two or more long term conditions. Delayed diagnosis of endometriosis is a crucial issue that leads to poor quality of life and clinical management. There are a variety of limitations linked to conducting endometriosis research including lack of dedicated funding. Additionally, accessing existing electronic healthcare records can be challenging due to governance and regulatory restrictions. Missing data issues are another concern that has been commonly identified among real-world studies. Considering these challenges, data science technique could provide a solution by way of using synthetic datasets that could be generated using known characteristics of endometriosis to explore the possibility of predicting multimorbidity. This study aimed to develop an exploratory machine learning model that can predict multimorbidity among women with endometriosis using real-world and synthetic data. A sample size of 1012 was used from two endometriosis specialized centres in the UK. In addition, 1000 synthetic data records per centre were generated using the widely used Synthetic Data Vault’s Gaussian Copula model based on patients’ records’ characteristics. Three standard classification models, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), were used for classification. The average accuracies for all three models (LR, SVM and RF), given as “model accuracy-centre1: accuracy-centre2” were found to be: LR 64.26%:69.04%, SVM 67.35%:68.61%, and RF 58.67%:73.76% on real-world data, and LR 69.9%:72.29%, SVM 69.39%:70.13, and RF 68.88%:74.62 on synthetic data, respectively. The findings of this report show machine learning models trained on synthetic data performed better than models trained on real-world data. Our findings suggest synthetic data holds great promise for shows value to conduct clinical epidemiology and clinical trials that could devise better precision treatments and possibly reduce the burden of multimorbidity.
Respiratory syncytial virus (RSV) commonly causes lower respiratory tract infections and hospitalization in children. In 2019-2020, the Europe-wide RSV ComNet standardized study protocol was developed to measure the clinical and socioeconomic disease burden of RSV infections among children aged <5 years in primary care. RSV has a recognized seasonality in England.We aimed to describe (1) the adaptations of the RSV ComNet standardized study protocol for England and (2) the challenges of conducting the study during the COVID-19 pandemic.This study was conducted by the Oxford-Royal College of General Practitioners Research and Surveillance Centre-the English national primary care sentinel network. We invited all (N=248) general practices within the network that undertook virology sampling to participate in the study by recruiting eligible patients (registered population: n=3,056,583). Children aged <5 years with the following case definition of RSV infection were included in the study: those consulting a health care practitioner in primary care with symptoms meeting the World Health Organization's definition of acute respiratory illness or influenza-like illness who have laboratory-confirmed RSV infection. The parents/guardians of these cases were asked to complete 2 previously validated questionnaires (14 and 30 days postsampling). A sample size of at least 100 RSV-positive cases is required to estimate the percentage of children that consult in primary care who need hospitalization. Assuming a swab positivity rate of 20% in children aged <5 years, we estimated that 500 swabs are required. We adapted our method for the pandemic by extending sampling planned for winter 2020-2021 to a rolling data collection, allowing verbal consent and introducing home swabbing because of increased web-based consultations during the COVID-19 pandemic.The preliminary results of the data collection between International Organization for Standardization (ISO) weeks 1-41 in 2021 are described. There was no RSV detected in the winter of 2020-2021 through the study. The first positive RSV swab collected through the sentinel network in England was collected in ISO week 17 and then every week since ISO week 25. In total, 16 (N=248, 6.5%) of the virology-sampling practices volunteered to participate; these were high-sampling practices collecting the majority of eligible swabs across the sentinel network-200 (43.8%) out of 457 swabs, of which 54 (N=200, 27%) were positive for RSV.Measures to control the COVID-19 pandemic meant there was no circulating RSV last winter; however, RSV has circulated out of season, as detected by the sentinel network. The sentinel network practices have collected 40% (200/500) of the required samples, and 27% (54/200) were RSV positive. We have demonstrated the feasibility of implementing a European-standardized RSV disease burden study protocol in England during a pandemic, and we now need to recruit to this adapted protocol.DERR1-10.2196/38026.
Abstract It is estimated 1.5 billion of the global population suffer from chronic pain with prevalence increasing with demographics including age. It is suggested long-term exposure to chronic could cause further health challenges reducing people’s quality of life. Therefore, it is imperative to use effective treatment options. We explored the current pharmaceutical treatments available for chronic pain management to better understand drug efficacy and pain reduction. A systematic methodology was developed and published in PROSPERO (CRD42021235384). Keywords of opioids, acute pain , pain management , chronic pain , opiods , NSAIDs , and analgesics were used across PubMed, Science direct, ProQuest, Web of science, Ovid Psych INFO, PROSPERO, EBSCOhost, MEDLINE, ClinicalTrials.gov and EMBASE. All randomised controlled clinical trials (RCTs), epidemiology and mixed-methods studies published in English between the 1st of January 1990 and 30th of April 2022 were included. A total of 119 studies were included. The data was synthesised using a tri-partied statistical methodology of a meta-analysis (24), pairwise meta-analysis (24) and network meta-analysis (34). Mean, median, standard deviation and confidence intervals for various pain assessments were used as the main outcomes for pre-treatment pain scores at baseline, post-treatment pain scores and pain score changes of each group. Our meta-analysis revealed the significant reduction in chronic pain scores of patients taking NSAID versus non-steroidal opioid drugs was comparative to patients given placebo under a random effects model. Pooled evidence also indicated significant drug efficiency with Botulinum Toxin Type-A (BTX-A) and Ketamine. Chronic pain is a public health problem that requires far more effective pharmaceutical interventions with minimal better side-effect profiles which will aid to develop better clinical guidelines. The importance of understanding ubiquity of pain by clinicians, policy makers, researchers and academic scholars is vital to prevent social determinant which aggravates issue.
ABSTRACT Introduction Menopause marks the end of the menstruation period which can incur naturally or due to surgery where the ovaries or the uterus is removed, or the use of other treatments like chemotherapy. Menopause elicits both physiological and psychological changes such as joint or pelvic pain, headaches or migraine, cognitive function and mental health problems such as anxiety. In order to assess the mental health impact of menopause, the physiological, psychological and sociological composites need to be evaluated. It is increasingly recognised that the associated symptoms experienced by women and trans-men are specific to menopause transition, making it challenging to diagnose and treat using conventional methods. We developed a menopause tool called MenopAuse mental hEalth (MARiE) rating tool following a co-production workshop. The MenopAuse mental hEalth (MARIE) project’s overall aim is to explore the mental health impact of menopause through several work stream packages and assess the MARiE tool. The current work package (WP 2a and 2b) that is represented within this study aim to further explore menopause symptoms and then validate and, determine the efficacy of the MARiE tool. Methods We will conduct a prospective mixed methods study in the United Kingdom (UK) among women and trans-men ≥18 years old that are perimenopausal, menopausal or post-menopausal. The quantitative portion will use the Hospital Anxiety and Depression Scale, Insomnia Severity Index Scale, Menopause Rating Scale, Greene Climacteric Scale, Health related quality of life, Quebec Pain Disability Scale, and Burnout Assessment Tool in addition to the MARiE tool within the scope of WP2a and WP2b, respectively. The qualitative component will use a topics guide. Research Ethics and Health Research Authority approval has been obtained with a reference of 22/EE/0159. Dissemination The findings will be submitted for publication in peer-reviewed journals and presented at women’s health, primary care, and mental health themed conferences.
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