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Burden of chronic respiratory disease in Asia, 1990–2023:a systematic analysis for the Global Burden of Disease Study 2023
No full textBackgroundChronic respiratory diseases are an important global issue, particularly in Asia, where burden patterns vary widely across countries. With more than half the world's population living in Asia, understanding the national and regional burden of chronic respiratory diseases is essential; however, research on this area remains inadequate. We aimed to investigate the burden of chronic respiratory diseases in Asia at national and regional levels, and to identify key risk factors.MethodsThe Global Burden of Diseases, Injuries, and Risk Factors Study 2023 provides estimates for assessing the burden of chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD), and pulmonary sarcoidosis. We focused on 34 countries in Asia, encompassing the high-income Asia Pacific region and central, east, south, and southeast Asia. Estimates for age-standardised prevalence and disability-adjusted life-year (DALY) rates per 100 000 population, including 95% uncertainty intervals (UIs), were extracted by location, sex, year, and Socio-demographic Index (SDI). The average annual percentage change was calculated and presented as a percentage with 95% CIs. Estimates of modifiable attributable risk factors for DALYs and mortality were also included.FindingsIn Asia, the age-standardised prevalence and DALY rates for chronic respiratory diseases generally declined from 1990 to 2023; however, the trend varied substantially by disease and country. In 2023, the age-standardised prevalence rate of COPD was highest in south Asia (3044·18 [95% UI 2748·67–3303·04] per 100 000 population), while the age-standardised asthma prevalence rate was highest in the high-income Asia Pacific region (4870·24 [4046·70–5962·78] per 100 000 population) and southeast Asia (4778·18 [3970·25–5735·61] per 100 000 population). Despite southeast Asia and the high-income Asia Pacific region having a similar age-standardised asthma prevalence rate, southeast Asia had a higher age-standardised DALY rate (508·67 [95% UI 394·89–669·92] per 100 000 population) compared with the high-income Asia Pacific region (204·40 [129·23–290·41] per 100 000 population). A decrease in the age-standardised DALY rate for chronic respiratory diseases was observed with increasing SDI, contrasting with its prevalence patterns. Age-standardised DALY rates of COPD decreased in all Asian countries except for Georgia (average annual percentage change 1·37 [95% CI 1·26–1·48]) and Kazakhstan (0·73 [0·55–0·93]), and age-standardised DALY rates of asthma decreased in all countries. Smoking and ambient particulate matter pollution were identified as leading attributable risk factors for chronic respiratory diseases across Asia. Household air pollution from solid fuels was a regionally pronounced risk factor for chronic respiratory diseases, particularly in south Asia (age-standardised DALY rate 657·58 [95% UI 485·04–880·45] per 100 000 population). Although smoking was a major risk factor in males, ambient particulate matter pollution and secondhand smoke emerged as important attributable risk factors for chronic respiratory diseases in females.InterpretationCountries with lower SDI had markedly higher DALY rates, highlighting the need to address socioeconomic and health-care inequities. Household air pollution from solid fuels continues to impose a substantial but preventable burden in south Asia, calling for clean energy adoption and improved ventilation.<br/
Remote working and the new geography of local service spending
Get PDFRemote working has rapidly become the new norm in many sectors, at least some of the time. Remote working changes where workers spend much of their time and the geographical location of demand, particularly for local personal services (LPS). Our main contribution is to systematically quantify this change for England and Wales using a new nationally representative survey of nearly 35,000 working age adults, which captures (pre-pandemic) LPS spending while at work and permanent changes in remote working. On average, our work shows neighbourhoods where people commute 20% less often experience a decline in LPS spending of 5%. There is a clear geographic pattern the ”donut” effect) to these spending changes but our granular analysis shows that they are uneven: large decreases in LPS demand are oncentrated in a small number of city-centre neighbourhoods, while increases in LPS demand around the periphery are more dispersed. Further analysis of neighbourhoods by geographical and socio-demographic characteristics shows the least affluent are most likely to benefit the least from remote work, increasing inequality
The Impact of Community-Based Midwife Continuity of Care Models for Women Living in Areas of Social Disadvantage and Ethnic Diversity in the United Kingdom:A Prospective Cohort Study
Get PDFOBJECTIVE: Addressing inequalities in maternal and newborn health is a UK public health priority. Evidence on effective multi-interventional strategies is urgently needed. This study evaluated the impact of community-based midwife continuity of care (CBMCOC) models for women and babies in ethnically diverse and socially disadvantaged areas of South London.DESIGN: We conducted a prospective cohort study using the eLIXIR, Born in South London, maternity-child data linkage.SETTING: United Kingdom.POPULATION: Pregnant women exposed to CBMCOC and standard care between 2018 and 2020.METHODS: Propensity score matching (1:4) was used to account for differences between CBMCOC and standard care cohorts and control for confounding bias. Conditional logistic regression estimated risk ratios. Subgroup analysis included women of Black, Asian and other ethnic minority groups, and those living in highly deprived areas.OUTCOMES: The primary outcome was preterm birth (< 37 weeks' gestation). Secondary outcomes included other relevant maternal, perinatal, process and clinical variables.RESULTS: Before matching, 12 386 women were exposed to standard care and 1338 to CBMCOC; after matching, 5352 and 1338 were included, respectively. The risk of preterm birth was lower among women exposed to CBMCOC (unmatched: 4.6% vs. 10.3%, RR = 0.50, 95% CI: 0.38-0.64; matched: 4.6% vs. 8.4%, RR = 0.54, 95% CI: 0.40-0.70). Subgroup analyses showed reduced preterm birth rates among ethnic minority women and those in deprived areas when exposed to CBMCOC.CONCLUSIONS: In this diverse population with a range of risk factors, locality-based interventions integrating community-based care and midwife continuity may reduce maternal and newborn health inequalities. Further trials of such models should be conducted.</p
Real world evidence versus randomised controlled trials:is the future of nutritional sciences research in electronic health records?
Get PDFRandomised controlled trials (RCTs) are the gold standard of research studies. They aim to recruit participants with similar characteristics and randomly assign them to a treatment or control/placebo arm. Due to randomisation, RCTs provide comprehensive, unbiased evidence about treatment efficacy and safety and examine cause-and-effect relationships between the intervention and outcome. However, RCTs are expensive, recruitment can be time-consuming and high drop-out rates can reduce internal validity. Depending on the target population, findings are not always generalisable at a population level. Of relevance to nutritional sciences, due to the type of research questions, researchers and participants cannot always be blinded to randomisation. Electronic health records (EHRs) provide a possible solution to some of these constraints. Using data from healthcare systems, may help to reduce costs and overcome logistical challenges as (1) pragmatic trials integrated into routine care enable real-time data analysis and faster translation of findings and (2) once dynamic longitudinal cohorts have been generated, they can be analysed using quasi-experimental designs. These have the potential to provide population level data with higher generalisability, lower attrition, and greater statistical power. EHRs do come with their own challenges, including the lack of a uniform information infrastructure, missing data and data quality. There are also ethical considerations, as patients may not wish for their data to be used in a research capacity, which in turn can affect the generalisability of findings.When it comes to nutritional sciences and generating evidence, there is no one-size fits all approach. EHRs offer great potential for advancing certain research questions, such as when there is a population level intervention, e.g. the soft drinks industry levy or the inclusion of folic acid in non-wholemeal wheat flour. EHRs offer the opportunity to integrate multiple datasets which will enable a comprehensive understanding of a nutrition intervention impact on health and disease in diverse populations and real-world settings. However, RCTs remain imperative for understanding causality. The scope of this review is to examine how RCTs and EHRs can be used to generate evidence in nutritional sciences, highlighting their respective opportunities and challenges
Complete FSM Testing Using Strong Separability
Get PDFApartness is a concept developed in constructive mathematics,which has resurfaced in the areas of model learning and model-basedtesting. We identify some fundamental shortcomings of apartnessin quantitative models, such as in hybrid and stochastic systems. Wepropose a closely-related alternative, called strong separability and showthat using it to replace apartness addresses the identified shortcomings.We adapt a well-known complete model-based testing method, the HarmonizedState Identifiers (HSI) method, to adopt strong separability. Weprove that the adapted HSI method is complete. As far as we are aware,this is the first work to show how complete test suites can be generatedfor quantitative models such as those found in the development ofcyber-physical systems
Comparative Study of the Performance of an Artificial Intelligence Platform in Detecting Periapical Radiolucencies Across Different Imaging Modalities
No full textDevelopment of a clinical tool to identify patients with early inflammatory arthritis at high risk of employment loss:analysis from the National Early Inflammatory Arthritis Audit
Get PDFObjectivesWork disability is an early consequence of inflammatory arthritis. Preventive interventions exist but access is limited, highlighting the need for risk stratification. We aimed to develop a tool using routinely collected data to identify patients at greatest risk of employment loss. MethodsThis cohort study used data from the National Early Inflammatory Arthritis Audit. Patients ≥16years with early inflammatory arthritis (EIA), enrolled May 2018–April 2025, employed at diagnosis and with three-month follow-up were included. The outcome was self-reported employment loss at 3 months. Predictors were occupation (manual vs non-manual), age, sex, disease activity (DAS28>5.1), mental health (anxiety/depression) and musculoskeletal burden (MSKHQ ≤25v>25). Employment loss was modelled using Poisson regression. Model discrimination, calibration and bootstrap validation were assessed. A risk score was derived and stratified into low, medium and high-risk groups. ResultsOf 11 894 patients with EIA, 6036 were employed at baseline and 1662 had complete work-outcome data. At 3 months, 168(10.1%) reported employment loss. Manual workers had higher risk than non-manual (14.1% vs 7.8%). In multivariable analysis, manual work (IRR: 1.54, 95% CI: 1.15–2.06), older age (per 10years: IRR: 1.65, 95% CI: 1.43–1.90), high musculoskeletal burden (IRR: 1.55, 1.10–2.19) and anxiety/depression (IRR: 1.45, 1.02–2.06) were associated with employment loss, whereas DAS28 was not. The optimal model (age, occupation, musculoskeletal and mental health) showed good discrimination (C-statistic 0.710) and calibration. An 8-point score stratified patients into low (2.5%), medium (9.1%) and high-risk (19.5%). Conclusio Employment loss in EIA is driven by occupation, age, musculoskeletal symptoms and mental health. A risk tool incorporating these domains can stratify patients and guide targeted interventions.</p
Time trends in newly recorded diagnoses of 19 long term conditions before, during, and after the covid-19 pandemic:population based cohort study in England using OpenSAFELY
Get PDFOBJECTIVE: To evaluate temporal changes in rates of newly recorded diagnoses for 19 long term conditions in England in relation to the covid-19 pandemic by disease, age group, sex, socioeconomic status, and ethnicity.DESIGN: Population based cohort study.SETTING: Primary care and hospital admission data, with the approval of NHS England.PARTICIPANTS: 29 995 025 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform.MAIN OUTCOME MEASURES: Temporal trends in age and sex standardised incident and prevalent diagnosis rates for 19 long term conditions between 1 April 2016 and 30 November 2024. Differences between expected and observed diagnosis rates after the onset of the covid-19 pandemic were compared using seasonal autoregressive integrated moving-average models, based on modelled projections of expected rates from pre-pandemic patterns.RESULTS: All 19 conditions showed a sharp decline in newly recorded diagnoses during the first year of the pandemic, followed by variable recovery. As of November 2024, cumulative reductions in diagnoses remained evident for conditions such as depression (734 800 (27.7%) fewer diagnoses than expected; 95% prediction interval (PI) 703 100 to 766 400), asthma (152 900 (16.4%) fewer diagnoses; 95% PI 137 500 to 168 300), chronic obstructive pulmonary disease (COPD) (90 100 (15.8%) fewer diagnoses; 95% PI 81 400 to 98 900), psoriasis (54 700 (17.1%) fewer diagnoses; 95% PI 50 100 to 59 200), and osteoporosis (54 100 (11.5%) fewer diagnoses; 95% PI 47 100 to 61 100). Conversely, diagnoses of chronic kidney disease have increased by 34.8% above expected levels during the pandemic recovery period, corresponding to 359 000 additional diagnoses (95% PI 333 500 to 384 500). Unadjusted subgroup analyses stratified by ethnicity and socioeconomic status indicated that, after an initial decrease, dementia diagnosis rates have risen above pre-pandemic levels for people of white ethnicity and in less deprived socioeconomic areas, but not for those from other ethnicities and more deprived areas.CONCLUSIONS: Since the covid-19 pandemic, there have been fewer diagnoses than expected for conditions such as depression, asthma, COPD, and osteoporosis, in contrast with a rapid increase in diagnoses of chronic kidney disease since 2022. Unadjusted analyses stratified by ethnicity and socioeconomic status suggest differential patterns of recovery, particularly for individuals with dementia. This study highlights the potential for near real time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and investigate inequities in healthcare.</p