Search Results (1,920)

Background: Mobile health has been increasingly integrated into tuberculosis care to support patient education, communication, and treatment engagement. However, evidence remains limited regarding whether positive engagement with mHealth is associated with knowledge, attitudes, and practices among patients with multidrug-resistant tuberculosis. This study aimed to examine the association between positive mHealth engagement and knowledge, attitude, practice, and total KAP among patients with multidrug-resistant tuberculosis, and to evaluate the psychometric properties of the engagement score used as the primary exposure variable. Methods: A cross-sectional study was conducted among patients with multidrug-resistant tuberculosis. A positive mHealth engagement score was constructed from 12 mHealth-related items after harmonizing item directionality so that higher scores indicated more favorable engagement. The 12 items reflected five behavioural domains: intensity of use, ease and acceptability of use, functional engagement (communication with providers, access to health information, and perceived benefit for disease self-management), continuity of use, and barriers to sustained engagement. The composite score was computed as the mean of the 12 standardised items, with higher values indicating more positive engagement. Internal consistency was assessed using Cronbach’s alpha and corrected item–total correlations, and structural validity was explored using principal component analysis. Adjusted linear regression models were used to examine associations between the engagement score and Knowledge, Attitude, Practice, and total KAP scores, controlling for age, sex, and occupation. Sensitivity analyses were performed after excluding a poorly performing item, and tertile analyses were used to assess dose–response patterns. Results: The positive mHealth engagement score showed good internal consistency, with a Cronbach’s alpha of 0.852. One item demonstrated poor psychometric performance, and Cronbach’s alpha increased to 0.864 after its exclusion. The data were suitable for dimensionality assessment, with a Kaiser–Meyer–Olkin value of 0.870 and a significant Bartlett’s test. Principal component analysis identified a dominant first component explaining 43.29% of the total variance. Using the refined score, higher positive mHealth engagement was significantly associated with higher Knowledge scores (β = 2.06; 95% CI: 1.28–2.85; p < 0.001), higher Attitude scores (β = 4.68; 95% CI: 3.30–6.06; p < 0.001), and higher total KAP scores (β = 6.68; 95% CI: 4.62–8.74; p < 0.001), whereas no significant association was observed for the Practice score (β = −0.07; 95% CI: −0.63 to 0.49; p = 0.804). In tertile analyses, Knowledge, Attitude, and total KAP scores increased significantly across engagement levels, while Practice scores did not. Conclusions: Positive mHealth engagement was associated with better knowledge, attitudes, and overall KAP among patients with multidrug-resistant tuberculosis, but not with practice. These findings are associative; the cross-sectional design does not permit causal conclusions. The engagement score demonstrated good reliability and acceptable structural validity and may be a useful summary measure for evaluating patient interaction with mHealth interventions in tuberculosis care. Integrated strategies combining mHealth with clinical follow-up, adherence counseling, and structural support may be needed to translate informational and attitudinal gains into practice change. Full article

Background/Objectives: Low back and neck pain are common musculoskeletal complaints among healthcare workers, including anesthesia providers. This study aims to quantify the prevalence of back and neck pain amongst anesthesiology providers to identify risk factors, mechanisms of injury, and recovery practices to guide preventative measures. Methods: A cross-sectional survey of anesthesiology clinicians in a multi-site academic healthcare system in New York City was administered using REDCap version 12.5.9. The recorded survey results were aggregated to determine percentages for each question. Descriptive statistics were used to determine the nature of low back and neck pain and detail causes. Oswestry Disability Index (ODI) and Neck Disability Index (NDI) scores were calculated. Results: The survey instrument was distributed to 380 anesthesiology clinicians at four separate institutions and yielded 191 responses for a response rate of 50.3%. Fifty-three-point-nine percent of survey respondents reported having current back or neck pain, with the majority reporting that it was chronic (87.4%). A substantial proportion of respondents reported not having back or neck pain prior to training (58.3%), and the majority reported that their back or neck pain was work-related (54.1%). Only 14.1% of respondents reported having had training in back or neck pain prevention. The most common location of pain was lumbar (81.6%). The most common inciting event for work-related pain was patient transfer/transport (68.6%). For ODI scoring, 98% of clinicians within the health system were classified as minimal disability and 2% of clinicians as moderate disability. For NDI scoring, 95.8% of clinicians were classified as minimally disabled, with 2.6% classified as moderate disabled. Conclusions: Back and neck pain are common pathologies amongst anesthesia providers. For most clinicians, the pain began to occur during training. Common inciting events include patient transfer/transport, procedure performance, and room setup. This provides a framework with which preventative practices can take place to reduce the prevalence of back and neck pain in anesthesiology and other related health care disciplines. Full article

Angioedema (AE) is a frequent symptom reported by dermatologists and allergists, as well as by general practitioners and physicians in other specialties. Hereditary angioedema (HAE) is an ultra-rare condition, whereas the majority of AE episodes in daily medical practice are secondary to an underlying condition or drug intake. This review discusses the most common causes of acquired angioedema, presents selected aspects of its pathogenesis in the context of available diagnostic tests, and provides an account of reports on possible management options. Acquired angioedema (AAE) poses an actual challenge both in terms of its diagnosis and management. Its variable etiology warrants a diagnostic approach aimed at the exclusion of underlying cancers, lympho- and myeloproliferative diseases, monoclonal gammopathies, as well as autoimmune and infectious conditions. Apart from its variable etiology, the management of AAE is further complicated by the lack of approved and standardized prophylaxis and treatment schemes. Therefore, an appropriate diagnostic approach is required for the efficient prevention of AAE symptoms and the detection of possible underlying pathologies. Full article

Background/Objectives: Vaccine-preventable diseases remain a persistent public health challenge in regions characterized by structural vulnerabilities, including suboptimal vaccination coverage, socioeconomic deprivation, and limited access to healthcare. In structurally vulnerable regions, such as the South-West Romanian region, characterized by persistent vaccination gaps and recurrent outbreaks, these conditions generate a sustained public health burden that requires ongoing preventive risk management strategies. In such contexts, digital risk stratification tools may support preventive decision-making by enabling early identification of patients at increased risk of severe outcomes. This study applied machine learning techniques to routinely collected measles surveillance data from South-West Romania to identify severe disease cases and determine key predictors of severity, offering a pragmatic alternative to outbreak forecasting in a resource-constrained setting. Methods: An open epidemiological dataset of laboratory-confirmed measles cases reported by the Regional Center for Public Health Surveillance Craiova was analyzed. The dataset defined severe cases as those with pneumonia, thrombocytopenia, a hospital stay exceeding three days, or other documented complications requiring medical intervention. Random Forest (RF) and Logistic Regression (LR) classifiers were trained and compared using a 10-fold cross-validation framework across 200 resampling iterations. Model performance was assessed using accuracy, AUC-ROC, sensitivity, specificity, positive predictive value, and F1-score. Feature importance was quantified using permutation-based measures, and the highest-ranked predictors were further evaluated through chi-square tests of independence. Results: RF significantly outperformed LR in accuracy (0.84 vs. 0.82), AUC (0.87 vs. 0.80), specificity (0.87 vs. 0.84), positive predictive value (0.89 vs. 0.86), and F1-score (0.84 vs. 0.83), with p ≤ 0.001 for most metrics. Sensitivity was equivalent between models (approximately 0.81; p = 0.328). Feature importance analysis identified seven key predictors: county of residence, vaccination status, outbreak status, presence of other symptoms, occupation, cough, and conjunctivitis. All seven were significantly associated with disease severity, and six showed significant geographic variation across counties. Vâlcea County had the highest concentration of severe cases. The model was trained on a regional surveillance cohort in which symptomatic and hospitalized cases are over-represented and should be interpreted as a triage-support tool within this surveillance context rather than as a population-level severity estimator. Conclusions: Machine learning, particularly RF, can effectively identify severe measles cases using routinely collected surveillance data in settings where robust outbreak prediction is not feasible. The county of residence functioned as a composite proxy for structural determinants, including healthcare access, vaccination coverage, and socioeconomic deprivation. These findings support the use of ML-based severity classification as a pragmatic tool for clinical risk stratification and targeted public health intervention in resource-constrained environments. Full article

Firefighters and forest brigades engaged in wildfire suppression are routinely exposed to smoke containing toxic compounds that pose acute and chronic health risks, and it is important to understand how they perceive these risks during their work. This study aimed to evaluate health risk perception among military firefighters and contracted forest brigades in the Federal District, Brazil, the use of respiratory protection equipment (RPE), and institutional support. A questionnaire was administered to 150 firefighters and 22 brigades in 2023 and 2024. Most respondents were between 30 and 40 years old, with firefighters having a significantly higher education level than brigades (p < 0.0001). Most were concerned about smoke exposure and recognized its high health risk, including respiratory diseases and cancer, with brigades showing a higher risk perception than firefighters (p < 0.0001). Despite this high perceived risk, about 80% of firefighters and 86% of brigades reported not using RPE, mainly because it was not provided by their institutions (according to 53.8% of firefighters and 73.7% of brigades). The level of concern about wildfire smoke among participants correlated positively with age, years of experience, perceived necessity of RPE, and willingness to use it if provided. Firefighters rated their institution’s performance on occupational health and safety significantly less positively than brigades (p < 0.0001). The results of this study demonstrated that the lack of preventive and protective practices is not due to low risk perception, but rather to institutional failures in guidance, support, and provision of RPE. Full article

Background: Workplace-related outbreaks of invasive pneumococcal disease (IPD) have been increasingly reported among shipyard workers, yet their epidemiological and clinical features remain incompletely characterized. This systematic review and meta-analysis aimed to synthesize available evidence on IPD outbreaks in shipyard settings. Methods: A systematic search of PubMed/MEDLINE, Scopus, EMBASE, and medRxiv was conducted up to March 2026. Observational studies reporting IPD outbreaks in shipyards were included. Pooled incidence rates and clinical outcomes were estimated using random-effects models, with heterogeneity assessed by I² statistics. Risk of bias was evaluated using the Newcastle–Ottawa Scale. Results: Eight studies describing six outbreaks across four European countries (France, Norway, Northern Ireland, Finland; 2015–2025) were included, encompassing 131 cases among 35,623 workers. The pooled incidence was 368.9 cases per 100,000 workers with an attack rate of 2.36 per 1000 person-months for total cases, compared to 200.49 cases per 100,000 workers (95%CI 103.54–387.85) and 1.10 cases per 1000 person-months (95% CI 0.17–2.03) for laboratory confirmed cases, with considerable heterogeneity across studies. Most cases occurred in men (97.7%), with the median age ranging from 39 to 48 years. Hospitalizations occurred in 79.1% of cases, intensive care unit admission in 13.7%, and the case fatality ratio was 0.8%. Serotype 4 accounted for 67.2% of characterized isolates. Occupational exposures and shared accommodation may have contributed to transmission, although this could not be formally assessed. Conclusions: IPD outbreaks in shipyard settings are characterized by high incidence but relatively favorable outcomes, likely reflecting workforce demographics. However, considerable heterogeneity and methodological limitations across studies constrain the interpretation of pooled estimates. Preventive strategies, including vaccination and workplace-targeted interventions, should be considered as plausible public health measures, with a proactive role for occupational health services. Full article

Chronic hand eczema (CHE) is a persistent and relapsing inflammatory dermatosis characterized by substantial functional impairment, psychosocial distress, and occupational disability. Although epidemiologically common and clinically burdensome, CHE has long suffered from nosological ambiguity, frequently interpreted as a localized manifestation of atopic dermatitis, psoriasis, allergic contact dermatitis, or cumulative irritant dermatitis. The recent regulatory approval of topical delgocitinib, a pan-Janus kinase (JAK) inhibitor specifically indicated for moderate-to-severe CHE inadequately controlled by topical corticosteroids, has reshaped both therapeutic strategy and conceptual framing of the disease. The introduction of a targeted therapy dedicated to CHE has reinforced its clinical identity while simultaneously highlighting its internal biological heterogeneity. Beneath the umbrella term “chronic hand eczema” lie distinct phenotypes characterized by variable barrier dysfunction, immune polarization, and environmental interaction. This review integrates current knowledge on epidemiology, pathophysiology, diagnostic stratification, therapeutic algorithms, phase III registrative evidence, emerging real-world data, and the central role of barrier restoration. Particular attention is devoted to the hand as a specialized barrier organ and to the interplay between inflammation and epidermal structural integrity. In the era of targeted therapy, precise diagnostic framing and barrier-oriented management are indispensable to optimize outcomes. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes. Full article

Long-duration animal trajectories are central to computational ethology, yet constructing large rodent cohorts remains costly, time-intensive, and constrained by animal-use considerations. We present a sensor-aware generative framework that separates latent behavioral dynamics from sensing-induced observation distortion to synthesize observed-domain trajectories that are behaviorally plausible while reproducing proxy-referenced observation distortions. The framework combines a run-level semi-Markov ethology model, occupancy calibration, and state-conditioned kinematic generation with a regime-dependent Ultra-Wideband observation channel that explicitly captures Line-of-Sight and Non-Line-of-Sight sensing conditions. Using four UWB sessions, this proof-of-concept study models three states—exploring, feeding, and burrowing—and evaluates realism through state occupancy, state-conditioned kinematic divergence, residual-domain agreement, and mean-squared displacement across time lags. We further assess whether sensor-aware conditioning improves robustness under LoS/NLoS domain shift in downstream trajectory classification. Sensor-aware conditioning yields stable mixed-domain performance with AUC = 0.995, whereas condition-agnostic baselines decline to AUC = 0.974 and AUC = 0.901. These results support the feasibility of sensor-aware in silico ethology as a proof-of-concept framework for controlled robustness studies and algorithm evaluation under proxy-referenced observation distortion. Because the present evaluation is based on four UWB sessions and uses a smoothed UWB-derived reference trajectory rather than independent ground truth, broader applications to synthetic-cohort generation, disease modeling, and statistical power-analysis workflows should be considered future directions requiring validation in larger datasets. Full article

Background/Objectives: Non-Communicable Diseases (NCD) impose a substantial socioeconomic burden on health systems through direct costs and indirect costs from productivity loss due to health-related absenteeism. While lifestyle factors are crucial for NCD prevention, evidence regarding their association with absenteeism in middle-income countries, such as Brazil, remains limited. In this context, the present study aims to analyze factors associated with the odds, duration, and costs of health-related absenteeism in São Paulo City, Brazil. Methods: Quantitative analysis was performed using microdata from the São Paulo Health Survey 2003, 2008, and 2015 (ISA-Capital). Logistic and negative binomial regression models identified factors associated with the odds and duration of health-related absenteeism. The human capital approach was used to estimate indirect costs (Int$ PPP), while two-part regression models (logit and generalized linear model) and average marginal effects (ME) identified cost-associated factors. Results: Tobacco use and NCD diagnoses (hypertension, type 2 diabetes, and cardiovascular diseases) significantly increased the odds and duration of absenteeism. Conversely, meeting recommended leisure-time physical activity levels was associated with lower indirect costs (ME = −33.94, p < 0.05). Higher costs were significantly driven by tobacco use (ME = 48.68, p < 0.01) and NCD, namely cardiovascular diseases (ME = 62.73), diabetes (ME = 55.18), hypertension (ME = 52.13), and obesity (ME = 36.45), all with p < 0.05. Conclusions: Promoting leisure-time physical activity and tobacco cessation may be important strategies for public health policies aiming to enhance productivity by reducing the frequency, duration, and economic burden of health-related absenteeism, complementing the necessary diagnosis and monitoring of NCD. Full article

Agriculture is a hazardous industry, and working in livestock production has been linked to musculoskeletal disorders (MSDs). However, limited research has examined work-related risk factors contributing to MSDs among livestock workers especially in small and family-owned operations, like most of the companies located in the Friuli-Venezia Giulia (FVG) region of Italy. This cross-sectional study conducted in July 2024 investigates self-reported musculoskeletal pain and discomfort and occupational exposures among dairy and swine farmworkers (N = 50; mean age = 37 years) in FVG. We assessed musculoskeletal exposures, self-reported pain and discomfort, and the use of preventive techniques to maintain musculoskeletal health. Participants reported a high prevalence (80%) of musculoskeletal pain and discomfort, particularly among those working in family operations. While lower back and knee pain were most common, work-related exposures were most strongly associated with pain in the lower back and neck. These findings highlight the need to address occupational health risks related to MSDs in livestock operations, including possible prevention and intervention strategies. This may be especially important for small and family-owned farms where preventive and ergonomic interventions may yield substantial benefits. Full article

Non-alcoholic fatty liver disease affects approximately thirty percent of the global population, yet public awareness remains dangerously low among young adults facing occupational risk factors. This study introduces the Fatty Liver Awareness Game (FLAG), an educational serious game designed to improve liver health literacy among software engineering students at the University of Guayaquil. While evaluated with this specific sample, FLAG is intended for the broader target population of young adults in developing nations who face occupational sedentary risk and limited access to preventive health education. Through a controlled experiment with fifty participants randomly assigned to game-based or traditional lecture instruction, the game demonstrated superior effectiveness, with a twenty-percentage-point advantage in post-test scores and a seventy-two percent reduction in incorrect responses compared to fifty percent in the lecture group. The large effect size (Cohen’s d = 1.43) and reduced performance variability among game participants indicate that interactive, feedback-rich learning environments can outperform passive instruction for this population and content domain. While the present design does not isolate the contribution of individual game elements—such as narrative framing, explanatory feedback, or mini-game interleaving—the results establish FLAG as a replicable model for digital health interventions targeting underserved populations at critical developmental junctures. Future component analyses are needed to determine which specific design features drive the observed advantages. Full article

Purpose: Saudi Arabia’s Vision 2030 designates tourism as a non-oil diversification engine. This study tests Tourism-Led Growth Hypothesis (TLGH) predictions among tourism professionals across five regions of the Kingdom of Saudi Arabia (KSA), proposing the TLGH-GCC (Gulf Cooperation Council) Framework. Design/Methodology/Approach: Sequential explanatory mixed-methods design: Structural Equation Modelling (SEM; N = 612; five regions) as primary evidence, executive interviews (n = 24) explaining mechanisms, and exploratory ARDL (T = 9; non-inferential). Findings: Perceptual support was found for all four hypothesised structural pathways (all p < 0.001), with megaproject investment exhibiting the strongest association with employment generation (β = 0.63) and sustainability governance challenges inversely associated with diversification efficiency. All associations are directionally consistent with TLGH predictions but do not establish causation. Qualitative findings further identified Saudisation alignment and workforce competency development as critical boundary conditions for translating tourism employment growth into sustained economic diversification. Theoretical Contribution: The TLGH-GCC Framework extends TLGH with institutional acceleration, Dutch Disease boundary conditions, and sustainability governance as a diversification determinant. The SGS-6 scale is validated for GCC megaproject contexts. Practical Implications: Regional decentralisation of gigaproject investment, occupational upgrading, and proactive sustainability governance are the highest-leverage Vision 2030 policy interventions. The findings further inform human capital development priorities under Vision 2030, including sector-specific tourism competency frameworks and Saudisation alignment in megaproject workforce planning. Originality/Value: The study addresses a methodological gap in the TLGH literature by combining five-region stratified SEM, executive interviews, and the validated SGS-6 sustainability governance scale within a single GCC-contextualised framework. Full article

Pneumoconiosis, characterized by progressive pulmonary fibrosis, remains a predominant occupational disease in China, with coal workers’ pneumoconiosis (CWP) and silicosis being the primary subtypes. Despite extensive research, its underlying pathogenic mechanisms are not yet fully understood. Mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) are crucial subcellular microdomains that govern Ca²⁺ transport, sustain cellular bioenergetics, and maintain systemic homeostasis. Emerging evidence has linked the structural and functional dysregulation of MAMs to the pathogenesis of various fibrotic disorders. Apoptosis, a highly regulated cell death process, is a key driver in pneumoconiosis progression, in which Ca²⁺ imbalance serves as a critical signaling cascade. Mitofusin 2 (MFN2), a core regulator of MAMs’ structural integrity, mediates mitochondrial fusion and directly bridges the ER with the outer mitochondrial membrane, thereby stabilizing ER–mitochondrial coupling. However, whether MFN2 mitigates fibrosis by preserving MAMs’ integrity and subsequently suppressing Ca²⁺-dependent apoptosis remains elusive. In this study, we established SD rat and A549 cell models of CWP. Our results demonstrated that MFN2 expression was downregulated after coal dust exposure, accompanied by MAMs impairment, Ca²⁺ imbalance, and increased apoptosis, which ultimately drove the pathological progression of pulmonary fibrosis. Notably, MFN2 overexpression restored MAMs’ structure and Ca²⁺ homeostasis, alleviated abnormal apoptosis, and subsequently inhibited fibrosis. This study highlights the importance of the MFN2–MAMs–Ca²⁺–apoptosis axis and identifies MFN2 as a potential therapeutic target for pneumoconiosis. Full article

Mining activities are characterised by a multiplicity of inherent occupational hazards. Exposure to mineral dust such as silica, asbestos, and coal dust is common in mining, leading to pneumoconiosis. Exposure to respirable silica-containing dust is one of the common respiratory hazards associated with adverse health effects such as silicosis, lung cancer, renal failure, scleroderma, systemic lupus erythematosus (SLE) and chronic obstructive pulmonary disease (COPD), to mention but just a few. In southern Africa, there is a rising epidemic of silicosis, human immunodeficiency virus (HIV) and tuberculosis (TB). Excessive exposure to silica-containing dust exacerbates the TB and silicosis epidemic in mining areas. There is poor control of dust exposure and a lack of occupational hygiene assessments of silica dust in mining in southern Africa. In southern Africa, there remains a persistent knowledge gap regarding the extent of occupational exposures to respirable chemical substances, such as silica dust. Consequently, occupational hygiene air monitoring was conducted in mining companies across four low-income Southern Africa Development Community (SADC) countries, Lesotho, Mozambique, Malawi and Zambia, to provide a baseline exposure dataset. The hazardous nature of work associated with mining activities still persists in these low-income countries, with 53% (n = 72) of quarries and 20% (n = 19) of coal mines having respirable quartz exposures exceeding the reference occupational exposure limit (OEL) of 0.1 milligrams per cubic meter (mg/m³). The highest exposure ranges for quartz were recorded in surface aggregate quarries, with the maximum concentration recorded at 2.739 mg/m³. The highest number of air samples (93%, n = 111), which were in compliance with the OEL of 3 mg/m³ for respirable dust, were recorded in the copper, diamond, ruby, cement quarry and gold mines. This exploratory study confirms the variable extent of mineworker exposure to respirable dust and corresponding quartz fractions emanating from different mining activities. The collected exposure data provides a baseline overview of exposures within the mining industry in the SADC region. It also serves as a vital input for future regional exposure surveillance databases, as well as preliminary data for directing future research towards regional exposure prevention initiatives. Full article