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Chronic obstructive pulmonary disease (COPD) is a growing cause of morbidity and mortality worldwide, and accurate estimates of the prevalence of this disease are needed to anticipate the future burden of COPD, target key risk factors, and plan for providing COPD-related health services. We aimed to measure the prevalence of COPD and its risk factors and investigate variation across countries by age, sex, and smoking status. Participants from 12 sites (n=9425) completed postbronchodilator spirometry testing plus questionnaires about respiratory symptoms, health status, and exposure to COPD risk factors. COPD prevalence estimates based on the Global Initiative for Chronic Obstructive Lung Disease staging criteria were adjusted for the target population. Logistic regression was used to estimate adjusted odds ratios (ORs) for COPD associated with 10-year age increments and 10-pack-year (defined as the number of cigarettes smoked per day divided by 20 and multiplied by the number of years that the participant smoked) increments. Meta-analyses provided pooled estimates for these risk factors. The prevalence of stage II or higher COPD was 10·1% (SE 4·8) overall, 11·8% (7·9) for men, and 8·5% (5·8) for women. The ORs for 10-year age increments were much the same across sites and for women and men. The overall pooled estimate was 1·94 (95% CI 1·80–2·10) per 10-year increment. Site-specific pack-year ORs varied significantly in women (pooled OR=1·28, 95% CI 1·15–1·42, p=0·012), but not in men (1·16, 1·12–1·21, p=0·743). This worldwide study showed higher levels and more advanced staging of spirometrically confirmed COPD than have typically been reported. However, although age and smoking are strong contributors to COPD, they do not fully explain variations in disease prevalence—other factors also seem to be important. Although smoking cessation is becoming an increasingly urgent objective for an ageing worldwide population, a better understanding of other factors that contribute to COPD is crucial to assist local public-health officials in developing the best possible primary and secondary prevention policies for their regions.

This article briefly reviews evidence of health effects associated with exposure to particulate matter (PM) air pollution from five common outdoor emission sources: traffic, coal-fired power stations, diesel exhaust, domestic wood combustion heaters, and crustal dust. The principal purpose of this review is to compare the evidence of health effects associated with these different sources with a view to answering the question: Is exposure to PM from some emission sources associated with worse health outcomes than exposure to PM from other sources? Answering this question will help inform development of air pollution regulations and environmental policy that maximises health benefits. Understanding the health effects of exposure to components of PM and source-specific PM are active fields of investigation. However, the different methods that have been used in epidemiological studies, along with the differences in populations, emission sources, and ambient air pollution mixtures between studies, make the comparison of results between studies problematic. While there is some evidence that PM from traffic and coal-fired power station emissions may elicit greater health effects compared to PM from other sources, overall the evidence to date does not indicate a clear ‘hierarchy’ of harmfulness for PM from different emission sources. Further investigations of the health effects of source-specific PM with more advanced approaches to exposure modeling, measurement, and statistics, are required before changing the current public health protection approach of minimising exposure to total PM mass.

Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.

Allergic diseases such as asthma and rhinitis are common in many countries. Globally the most common allergen associated with symptoms is produced by house dust mites. Although the bed has often been cited as the main site of exposure to mite allergens, surprisingly this has not yet been directly established by measurement due to a lack of suitable methods. Here we report on the development of novel methods to determine the pattern of personal exposure to mite allergen bioaerosols over 24-hour periods and applied this in a small field study using 10 normal adults. Air was sampled using a miniature time-based air-sampler of in-house design located close to the breathing zone of the participants, co-located with a miniature time-lapse camera. Airborne particles, drawn into the sampler at 2L/min via a narrow slot, were impacted onto the peripheral surface of a disk mounted on the hour-hand of either a 12 or 24 hour clock motor. The impaction surface was either an electret cloth, or an adhesive film; both novel for these purposes. Following a review of the time-lapse images, disks were post-hoc cut into subsamples corresponding to eight predetermined categories of indoor or outdoor location, extracted and analysed for mite allergen Der p 1 by an amplified ELISA. Allergen was detected in 57.2% of the total of 353 subsamples collected during 20 days of sampling. Exposure patterns varied over time. Higher concentrations of airborne mite allergen were typically measured in samples collected from domestic locations in the day and evening. Indoor domestic Der p 1 exposures accounted for 59.5% of total exposure, whereas total in-bed-asleep exposure, which varied 80 fold between individuals, accounted overall for 9.85% of total exposure, suggesting beds are not often the main site of exposure. This study establishes the feasibility of novel methods for determining the time-geography of personal exposure to many bioaerosols and identifies new areas for future technical development and clinical applications.

The ability to identify individuals at increased risk of developing tuberculosis (TB) has important implications for public health policy and patient care. We conducted a general population historical cohort study in all Australian States and Territories to establish the risk of TB arising in people on chronic hemo- or peritoneal dialysis. Cases of TB disease in patients receiving chronic dialysis were identified by record linkage using the Australia & New Zealand Dialysis and Transplant Registry (ANZDATA) and State and Territory TB notification databases 2001 to 2006. Main outcome measure was the relative risk of TB in people on dialysis, adjusted for TB incidence in country of birth, sex, age and indigenous status. A total of 6,276 cases of active TB were reported among 19,855,283 people living in Australia between 2001 and 2006. Among 14,506 patients on dialysis, 37 had a notification for TB disease after commencing dialysis, of whom 28 were culture positive. The incidence of TB was 66.8/100,000/year (95% CI 47.7 to 93.2) among people on dialysis and 5.7/100,000/year (95% CI 5.5 to 5.8) in the general population. The adjusted relative risk (aRR) of TB in people on dialysis was 7.8 (95% CI 3.3 to 18.7), and the aRR of culture positive TB was 8.6 (95% CI 3.9 to 19.3). Patients on dialysis are at increased risk of TB. The final decision to screen for, and to treat, LTBI in individual dialysis patients will be influenced by a cumulative assessment of the risk of reactivation of TB and by assessment of risk factors for adverse effects of treatment.

The bed is commonly regarded as the main site of house dust mite exposure; however this has not been directly established by continuous measurements. The objective of this study was to determine the pattern of personal exposure to mite aeroallergen over 24 hours. 12 adults each collected 9 sequential samples (8 during the day, mean 115 mins, and one overnight, mean 514 mins) over 24 hours using a portable air-pump (2L/min) connected to an IOM filter located on the shoulder during the day and on the bed head overnight. Samples were analysed for mite allergen Der p 1 by ELISA. Location and activity were recorded. A mixed model analysis was performed to determine exposure as a function of 14 categories of activity. Personal aeroallergen exposure differed widely over time, both within and between subjects. The highest average exposure (1117 pg/m(3), 95% CI: 289-4314) occurred on public transport and the lowest overnight in bed (45 pg/m(3), 95% CI: 17-17), which contributed only 9.8% (95% CI: 4.4%-15.1%) of total daily exposure. Aeroallergens were not related to bed reservoirs. The study challenges the current paradigm that the bed is the main site of HDM exposure and instead suggests most exposure occurs in association with domestic activity and proximity to other people. Effective mite interventions, designed to improve asthma outcomes, need to first identify and then address the multiple sources of aeroallergen exposure.

Air pollution from indoor and outdoor sources constitutes a substantial health risk to young children in sub-Saharan Africa (SSA). Although some systematic reviews have assessed air pollution and children's respiratory health in SSA, none have considered both ambient and indoor exposures. This systematic review and meta-analysis assessed the effect of air pollution (ambient and indoor) on respiratory hospitalization and mortality among children under five years in SSA. We retrieved relevant articles from PubMed, Embase, Scopus, African Journals Online (AJOL), Web of Science, and medRxiv. The protocol was registered with Prospero (CRD42023470010). We used guidelines from the preferred reporting items for systematic review and meta-analysis (PRISMA-2020) to guide the systematic review process. Risk of bias was assessed using the Office of Health Assessment and Translation (OHAT) quality appraisal tool. For exposures where there were sufficient studies/data we conducted meta-analyses using random effects models and used Stata version 17 software for analysis. For the systematic review we screened 5619 titles and abstracts, reviewed 315 full texts, and included 31 articles involving 2,178,487 participants. Eleven studies examined exposure to solid fuel use in households and its association with all-cause mortality, while four studies explored the impact of passive smoking on mortality among children under five. Only two studies assessed ambient air pollution's effects on all-cause and respiratory-related mortality. Additionally, 13 studies reported varying associations between respiratory hospitalization and household tobacco smoke exposure. Meta-analyses on studies of solid fuel use and mortality and passive smoking and hospitalizations showed that children exposed to indoor solid fuels combustion had higher odds of mortality compared to non-exposed children (OR = 1.31; 95% CI: 1.16-1.47). The meta-analysis of exposure to second-hand smoke found an increased risk of respiratory hospitalization due to pneumonia, although the results were not significant (OR = 1.29; 95% CI: 0.45-3.68), and our certainty of evidence assessment indicated insufficient support to conclusively establish this association. Our review reveals that solid fuel use and ambient PM2.5 exposure were associated with increased mortality risk in children under five years in SSA. The meta-analysis showed evidence of an increased risk of under-five years mortality associated with solid fuel use in households. Associations between secondhand smoke and pneumonia hospitalization were less clear. We conclude that significant research gaps remain in understanding the impact of discrete sources of air pollution on the causation of respiratory illness in young children living in SSA. Prioritizing interventions targeting indoor sources is essential, along with further studies which use standardized and objective exposure and outcome measures to study these associations.

Nearly one quarter of the global population has been infected with Mycobacterium tuberculosis , and there are more than 10 million new infections per year. In an open-label trial, 9 months of isoniazid was compared with 4 months of rifampin as therapy for latent tuberculosis infection.

Asthma in older people is common and is characterised by underdiagnosis and undertreatment. Ageing is associated with unique issues that modify expression, recognition, and treatment of the disease. In particular, asthma and chronic obstructive pulmonary disease (COPD) both overlap and converge in older people. This concurrence, together with absence of precise diagnostic methods, makes diagnosis complex. A multidimensional assessment that addresses airway problems, comorbidities, risk factors, and management skills will draw attention to key needs for intervention. Increased attention to the complications of asthma and obstructive airway disease in older people is needed, specifically to develop effective systems of care, appropriate clinical practice guidelines, and a research agenda that delivers improved health outcomes.

Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue. We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted a systematic review to synthesize current knowledge on this topic. Our search across 3 major databases (Scopus, PubMed, Association for Computing Machinery [ACM] Digital Library) yielded 1146 records, of which we included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies version 2) was performed for the risk-of-bias assessment of all included studies. Radiographic biomarkers (n=129, 84.9%) and deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121 (n=19, 12.5%) architectures being the most common DL approach. The majority of studies focused on model development (n=143, 94.1%) and used a single modality approach (n=141, 92.8%). AI methods demonstrated good performance in all studies: mean accuracy=91.93% (SD 8.10%, 95% CI 90.52%-93.33%; median 93.59%, IQR 88.33%-98.32%), mean area under the curve (AUC)=93.48% (SD 7.51%, 95% CI 91.90%-95.06%; median 95.28%, IQR 91%-99%), mean sensitivity=92.77% (SD 7.48%, 95% CI 91.38%-94.15%; median 94.05% IQR 89%-98.87%), and mean specificity=92.39% (SD 9.4%, 95% CI 90.30%-94.49%; median 95.38%, IQR 89.42%-99.19%). AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. AI performance across various reference standards showed mean accuracies of 91.44% (SD 7.3%), 93.16% (SD 6.44%), and 88.98% (SD 9.77%); mean AUCs of 90.95% (SD 7.58%), 94.89% (SD 5.18%), and 92.61% (SD 6.01%); mean sensitivities of 91.76% (SD 7.02%), 93.73% (SD 6.67%), and 91.34% (SD 7.71%); and mean specificities of 86.56% (SD 12.8%), 93.69% (SD 8.45%), and 92.7% (SD 6.54%) for bacteriological, human reader, and combined reference standards, respectively. The transfer learning (TL) approach showed increasing popularity (n=89, 58.6%). Notably, only 1 (0.7%) study conducted domain-shift analysis for TB detection. Findings from this review underscore the considerable promise of AI-based methods in the realm of TB detection. Future research endeavors should prioritize conducting domain-shift analyses to better simulate real-world scenarios in TB detection. PROSPERO CRD42023453611; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023453611.