BMC Research Notes

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

ObjectiveThe objective of this analysis was to explore temporal and regional trends in breast pump prescription claims in outpatient settings in Germany, and to characterize the types of pumps covered. Study designWe conducted a nationwide secondary analysis of outpatient statutory health insurance billing data for breast pump prescriptions from 2011 to 2024, covering nearly 90% of the German population. Billing data from community pharmacies were scaled to full national coverage using regional extrapolation factors and subsequently linked with national and state-level live birth statistics to adjust for birth rates and population size across federal states. A list of breast pumps covered by German national statutory health insurance funds was queried for information on their characteristics. ResultsPrescription of electric pumps dominate outpatient statutory health insurance breast pump claims in Germany, with national statutory health insurance funds covering {euro}15.3 million for pump rentals. Manual pumps dispensed through community pharmacies accounted for {euro}27 thousand in 2024. Between 2011 and 2024, electric pump claims increased by a factor of 2.57, rising from 235.4 to 605.2 claims per 1000 infants newly enrolled in statutory health insurance (average annual growth rate 8.24%). Claims varied substantially across federal states but increased overall. ConclusionsThis is the first epidemiological analysis of statutory health insurance prescription claims for breast pumps in Germany. We found that electric breast pumps are important medical devices supporting outpatient human milk expression in Germany. Prescription claims appear to be very common and have shown an increase over the past 13 years.

Breast reconstruction following mastectomy can restore body image and confer psychosocial benefits but can also result in lasting functional deficits in shoulder range of motion (SROM). Early identification of these deficits is crucial for guiding rehabilitation and improving long-term outcomes, but traditional assessment tools are often costly or impractical for routine clinical use. This longitudinal observational study examined the feasibility and validity of an app-based shoulder mobility assessment (MotionDetect) for detecting post-operative changes in SROM compared to traditional inertial measurement units (IMU)-derived measurements among breast cancer patients undergoing reconstruction. Twenty female participants undergoing bilateral mastectomy with immediate breast reconstruction performed at-home SROM assessments before surgery and once at 6-12 weeks post-operatively using both wearable IMUs and the MotionDetect iPhone application. Maximum shoulder abduction and flexion angles were recorded at each time point. Structured interviews gathered patient feedback on app usability. Statistical analyses assessed changes over time, correlation between measurement modalities, and repeatability. Significant reductions in abduction ROM after surgery were observed using both IMU and iPhone app assessments (both p < 0.031), with strong correlations between modalities (r > 0.80). Both approaches demonstrated excellent intra-class coefficients (ICC) repeatability (ICC > 0.89). Patient interviews indicated high feasibility and acceptability, with minor logistical challenges. Overall, this study indicates MotionDetect is a valid and feasible tool for remotely identifying post-operative SROM limitations in breast cancer patients, enabling early referral for rehabilitation to improve long-term quality of life.

BackgroundDengue outbreaks have become a severe threat to Bangladesh as the infections and mortality numbers are skyrocketing in recent years. Favorable environmental and anthropogenic conditions have established the capital of Bangladesh, Dhaka city as the epicenter of dengue outbreak. Studies have showed that climate change induced extreme weather events are exacerbating Aedes mosquito breeding and dengue virus transmission conditions. Methodology/Principal FindingsIn this study, short-term (0-6 weeks) associations of maximum temperature and heatwave days on dengue cases in Dhaka city were examined through Distributed Lag Non-linear Model (DLNM) methodology for weekly measurement of 2016-2024, taking into account relative humidity, cumulative rainfall, seasonality and hospital closure effect. Two separate negative binomial models were constructed. The maximum temperature model rendered an overall inverted U-shaped association, where the maximum temperature range of 31.5-33.2{degrees}C showed a sustained elevated dengue risk, with highest risk estimate at 33.2{degrees}C [relative risk (RR): 1.186, 95% CI: 1.002, 1.403]. Whereas, results of weekly heatwave days showed an overall protective effect (RR<1) for dengue cases. The lowest risk of infection was found at 3 heatwave days per week, with RR 0.275 (95% CI: 0.178, 0.423). Multiple sensitivity analyses were conducted for both models to evaluate their robustness. Lastly, the optimized models were analyzed under three distinct sub-periods, to capture the association of exposure variables with predominant circulating serotypes. Conclusions/SignificanceThe findings of the study aim to support public health policymakers and healthcare authorities in designing and implementing effective vector control interventions under emerging climatic emergencies. Author SummaryDengue disease is one of the most buringing issue in Bangladesh in recent years. This vector-borne disease is inherently influenced by climatic variables, i.e., temperature, rainfall, humidity, etc. Moreover, these relations are complex and non-linearly associated. Due to shift in climatic conditions, the occurance of extreme weather events are becoming frequent, with increased magnitude and longer duration. In this study, the nonlinear and delayed association of dengue infections due to the exposure of extreme temperature events were assessed in climate-change vulnerable Dhaka city. To do this, a statistical method was used, called distributed lag nonlinear methodology (DLNM). The results showed that dengue infections had an inverted U-shaped (parabolic) relationship with maximum temperature, while compared to mean maximum temperature, and a suppressive association with heatwaves relative to days without heatwaves. The findings aim to work as an early warning system, and support to policymakes and healthcare authorities to tackle the dengue surge in the changing climate.

IntroductionAge-group specific disparities for dentalcare use persist in the United States. The COVID-19 led to delays in non-urgent dentalcare. We provide national estimates on dentalcare use and influencing factors for the U.S. population before and during the COVID-19. MethodsWe used nationally representative Medical Expenditure Panel Survey for over pre-COVID-19 years (2018-2019) and COVID-19 years (2020-2021) We estimated yearly survey-weighted trends in mean non-zero dental visits by age followed Poisson regression, controlling for a comprehensive set of confounders across five domains of influence. Dentalcare visits were defined as visits to any dentalcare provider. ResultsOverall analytic sample included non-institutionalized community living persons (unweighted n=6518, weighted N[~]320 million) grouped as ages 0-17, 18-44, 45-64, 65-74 and 75+ present in all four years The prevalence ratio (PR) for dental visits was slightly higher for ages 75+ in comparison to ages 65-74 across years 2018-2021 and increased from 1.73 (95% CI: 1.4, 2.1) to 1.84 (95% CI: 1.5, 2.3) to 2.13 (95% CI: 1.7, 2.7) from 2018 to 2020 but rebounding to near pre-pandemic level in 2021 to 1.66 (95% CI, 1.3, 2.0). Consistent factors during COVID-19 pandemic years 2020-2021 that increased dental visits included dental insurance, high income, and having a usual source of care (p<0.01). ConclusionsDentalcare use rebounded for older adults in 2021 but remained below pre-pandemic levels. Practical ImplicationsIncreasing dentalcare visits across ages remains a key policy priority. Continued monitoring of dentalcare use trends beyond COVID-19 among older adults is critical to improve their oral health.

ObjectiveWe aimed to prospectively validate and compare published prediction models and clinician-assigned triage categories for early trauma care. DesignProspective multicentre cohort study. SettingThree public hospitals in urban India: one secondary care hospital in Mumbai and one tertiary care teaching hospitals in Delhi and Kolkata each. ParticipantsAdult patients aged over 18 years presenting to the emergency department with a history of trauma between 2016 and 2022. A total of 13,041 patients were included in the final analysis. MethodsWe externally validated five published trauma prediction models (GAP, Gerdin, KTS, MGAP, and RTS) and clinician-assigned triage categories based on initial assessment. The primary outcome was 30-day all-cause mortality. Models were recalibrated using a separate updating sample prior to evaluation, and model performance was assessed in terms of discrimination (AUC), calibration (calibration slope and plots), and decision curve analysis. ResultsAll models and clinician gestalt-based triage demonstrated excellent discrimination (AUC range: 0.90-0.96) and good calibration after updating. The GAP model achieved the highest AUC (0.96, 95% 0.94-0.97), and RTS demonstrated the highest sensitivity (0.70). ConclusionSimple, physiology-based prediction models and clinician gestalt both demonstrated excellent performance in predicting 30-day mortality among adult trauma patients in Indian emergency departments. These findings provide a practical foundation for further development of trauma triage systems.

BackgroundWhile veterinary surgeons are known to have particularly high rates of injury compared to other sectors, little is known about rates of injury among veterinary students. This study aims to understand animal-related injury rates, injury context and mechanisms, attitudes to reporting injuries, and behaviour change among UK and Irish veterinary students. MethodsA survey was distributed to students across all veterinary schools operating in the UK and Ireland in 2021. Questions explored participants experience of injury through asking about their most recent and most severe injuries via quantitative and free-text questions. Data were analysed using descriptive statistics, logistic regression, and qualitative content analysis. Results533 responses were included in the analyses. Overall, 47.5% of students reported having been injured by an animal during the veterinary degree, 35.5% of students reported being injured within the last 12 months. Most recent injuries were caused by companion animals (38.0%), livestock (37.6%), and equids (23.5%). For their most severe injuries, 48.7% involved livestock, 28.7% companion animals, and 22.1% equids. The content analysis highlighted that students normalised injuries and infrequently reported injuries to the university. It was very rare for students to take time off from their studies or placements, due to course pressures. ConclusionsThese findings reflect concerningly high levels of injury, which are being under-reported and reflect a culture of injury acceptance and expectation among students. Veterinary schools should consider lessons learnt in other work environments which have been successful in changing safety culture.

BackgroundThe management of residual axillary disease after neoadjuvant therapy (NAT) remains controversial, as current recommendations often treat ypN1 breast cancer as a homogeneous entity despite potential prognostic heterogeneity. Evidence supporting uniform axillary surgical strategies across different levels of residual nodal burden is limited. We investigated whether survival associations related to axillary surgical evaluation differ according to residual nodal burden in ypN1 disease, using an adjuvant cohort to validate a SEER-based proxy for surgical extent. MethodsPatients with 1-3 positive lymph nodes were identified in the SEER database (2000-2022) and stratified into neoadjuvant (NAT; n=30,560) and adjuvant (AT; n=197,586) cohorts. Axillary surgical evaluation was categorized as limited (2-3 examined nodes) or extensive ([&ge;]10 examined nodes). Survival was analyzed using Kaplan-Meier methods and log-logistic accelerated failure-time models, adjusted with inverse probability of treatment weighting. ResultsIn the ypN1 cohort, limited axillary evaluation was not associated with inferior overall survival among patients with a single residual positive node (IPTW-adjusted HR: 1.15, p=0.134; time ratio [TR]: 0.86, p=0.184). In contrast, limited evaluation was associated with worse survival in patients with two positive nodes (HR: 1.70, 95%CI 1.54-1.87; TR: 0.58, 95%CI 0.53-0.64). The findings were similar when using breast cancer-specific survival as the endpoint. ConclusionsSurvival associations related to axillary surgical evaluation after NAT vary according to residual nodal burden. Axillary de-escalation appears feasible in patients with a single residual positive node but cannot be extrapolated to those with multiple residual nodes, underscoring heterogeneity within ypN1 disease.

BackgroundApproximately 1 in 5 Canadians experience a mental health illness in any given year. While most individuals can be successfully treated within a primary care setting, a subset of individuals present with a severity and complexity requiring specialist care. Unfortunately, a shortage of psychiatrists (especially in rural regions) can result in wait times of months to years. MethodsWe designed the Alberta Network for Community Health Outreach and Rural Mental Health. ANCHOR-MH is a 12-week program that includes a unique educational intervention, collaborative case conferencing, and a community of practice between family medicine (FM) physicians and psychiatrists. We enrolled two pilot cohorts of n=20 FM physicians each and measured participants confidence and comfort in diagnosing, managing, and treating psychiatric conditions. We also conducted qualitative analyses of their experience. ResultsData from participants that completed both the pre- and post-program survey (n=34) showed increased confidence in screening for, diagnosing, and managing psychiatric issues, as well as increased comfort discussing mental health concerns with patients and families and reduced stigma towards certain psychiatric conditions. Qualitative thematic analysis (n=39) reflected this increased confidence, revealed an increased sense of connectedness to the mental healthcare landscape, and highlighted specific examples of practice changes. Participants broadly agreed that the program improved their ability to provide mental healthcare and would improve psychiatric outcomes within their practice. InterpretationANCHOR-MH improved FM physicians confidence and ability to deliver mental healthcare in their primary care settings. Increasing the reach of this program may improve mental healthcare in underserved communities.

BackgroundMany patients with triple-negative breast cancer (TNBC), particularly those who are older, Black, or insured by Medicaid, do not receive guideline-concordant treatment, despite its association with up to 4x higher survival. Early identification of patients at risk for rapid relapse may enable timely interventions and improve outcomes. This study applies machine learning (ML) to real-world data to predict risk of rapid relapse in TNBC. MethodsWe trained various ML models (logistic regression, decision trees, random forests, XGBoost, naive Bayes, support vector machines) using National Cancer Database (NCDB) data and fine-tuned them using electronic health record (EHR) data from a cancer registry. Class imbalance was addressed using synthetic minority oversampling technique (SMOTE). Model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristics area under the curve ROC AUC, accuracy, and F1 scores. Transfer learning, cross-validation, and threshold optimization were applied to enhance the ensemble models performance on clinical data. ResultsInitial models trained on NCDB data exhibited high NPV but low sensitivity and PPV. SMOTE and hyperparameter tuning produced modest improvements. External testing on EHR data from a cancer registry had similar model performance. After applying transfer learning, cross-validation, and threshold optimization using the clinical data, the ensemble model achieved higher performance. The optimized ensemble model achieved a sensitivity of 0.87, specificity of 0.99, PPV of 0.90, NPV of 0.98, ROC AUC of 0.99, accuracy of 0.98, and F1-score of 0.88. This optimized model, leveraging readily available clinical data, demonstrated superior performance compared to initial NCDB-trained models and those reported in extant literature. ConclusionsTransfer learning and threshold optimization effectively adapted ML models trained on NCDB data to an independent real-world clinical dataset from a single site, producing a high-performing model for predicting rapid relapse in TNBC. This model, potentially translatable to fast health interoperability resources (FHIR)-compatible workflows, represents a promising tool for identifying patients at high risk. Future work should include prospective external validation, evaluation of integration into clinical workflows, and implementation studies to determine whether the model improves care processes such as timely patient navigation and treatment planning. Author SummaryIn this study, we set out to understand which patients with triple-negative breast cancer might experience a rapid return of their disease. Many people with this aggressive form of cancer do not receive the treatments that are known to improve survival, especially patients who are older, Black, or insured through public programs. Being able to identify those at highest risk early in their care could help health teams provide timely support and ensure that patients receive the treatments they need. To do this, we used information from a large national cancer database to build computer-based models that learn from patterns in patient data. We then refined these models using real medical records from a cancer center to make sure they worked well in everyday clinical settings. After adjusting and improving the models, we developed a tool that can correctly identify most patients who are likely to have a rapid return of their cancer. Our hope is that this type of tool could eventually be built into routine care and help guide timely follow-up, support services, and treatment planning. More testing in real clinical environments will be important to understand how well the tool improves care and outcomes for patients.

Artificial intelligence (AI) models with medical images as input data are increasingly proposed to support clinical decisions in lung cancer screening. To assess how these models are developed, evaluated, and reported, and to identify gaps in best statistical practices, we conducted a cross-sectional meta-research study of OpenAlex-indexed studies (January 1, 2023, to June 30, 2025) that developed image-based AI tools to detect lung cancer, predict prognosis, or estimate future risk. Thirty-six studies met our inclusion criteria. Study quality and reporting were appraised using three approaches: subjective ratings from two statisticians and two clinicians, scoring from two AI agents (GPT-5 and Gemini 2.5 Pro), and a guideline-based checklist from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Convolutional neural networks were used in most of the included studies (69%). Area under the curve was the most frequently reported metric (81%). Our meta-research study also highlights common lapses in these 36 studies, including limited external test set use (39%), insufficient subgroup analyses (28%), and a substantial lack of adherence to established prediction-model reporting guidelines. AI-based quality scoring aligned better with CHARMS-based scores than did human scoring. Spearman correlations with CHARMS were weaker for statisticians/clinicians (p [&le;] 0.46) than for the two AI agents (GPT-5 p = 0.66; Gemini 2.5 Pro p = 0.56). Overall, future research should prioritize standardized reporting, use of external test sets, and model performance assessment across subpopulations. Large language models (LLMs) offer a supportive role in providing guideline-driven appraisals to complement human judgment in evaluating AI-based prediction models. 1-2 Sentence DescriptionThis cross-sectional meta-research study synthesizes recent studies that developed artificial intelligence (AI)-driven predictive models using medical images to detect lung cancer, predict prognosis, or estimate future risk, highlighting methodological trends, limitations in model testing and subgroup analyses, and advocating for the need for greater transparency, reliability, quality assessment, and adherence to established reporting guidelines in such studies. Quality assessment of the models carried out by LLMs, human statisticians and clinicians indicates chatbots are more aligned with recommended guidelines than humans.

BackgroundSocioeconomic status (SES) strongly shapes oral health, yet the magnitude of these gradients, their pathways and the influence of US Medicaid policies remain uncertain. We quantified SES gradients in adult oral health, examined potential mechanisms and assessed whether state Medicaid expansion and adult dental benefit generosity were associated with aggregate oral health indicators. MethodsWe analysed adults aged [&ge;]20 years in NHANES 1999-2019 and state adult populations in BRFSS 2011-2025, supplemented with international oral health and Medicaid policy data. Individual outcomes were DMFT and self-rated oral health (good vs fair/poor). State-level outcomes were past-year dental visit rates, any permanent tooth loss and complete edentulism among adults [&ge;]65 years. SES measures included poverty-income ratio (PIR) and education; mediators included annual dental visits, unmet dental need and sleep duration. Analytic methods comprised survey-weighted regression, concentration and slope indices of inequality, Oaxaca-Blinder decomposition and state-level difference-in-differences models with state and year fixed effects and state-clustered standard errors. ResultsHigher PIR and education were independently associated with lower DMFT and higher odds of good self-rated oral health in all age groups. PIR coefficients for DMFT were {approx}-0.25 (ages 20-44), -0.79 (45-64) and -1.07 ([&ge;] 65); corresponding odds ratios for good oral health were {approx}1.48, 1.46 and 1.33. Predicted probabilities of good oral health increased monotonically across PIR quartiles. Concentration indices indicated that DMFT burden was concentrated among low-income adults (CI {approx} -0.105), whereas good oral health was concentrated among high-income adults (CI {approx} 0.094). The Slope Index suggested that moving from the lowest to highest income rank corresponded to {approx}2.48 fewer affected teeth; the Relative Index indicated {approx}eight-fold higher odds of reporting good oral health. Oaxaca-Blinder decomposition showed a Q4-Q1 DMFT gap of 1.31 teeth, with roughly one quarter explained by observed variables, mainly differences in dental access. State-level difference-in-differences models did not identify large, precisely estimated changes in dental visit rates, tooth loss or edentulism associated with Medicaid expansion or adult dental benefit generosity. ConclusionMarked SES-related oral health inequalities persist among US adults, particularly in midlife, and are strongly linked to differential dental access and socially patterned behaviours. Medicaid expansion and adult dental benefit generosity, as implemented, did not produce substantial detectable shifts in state-level oral health indicators. Reducing inequalities will require improved financial protection for dental care and broader action on income, education and other social determinants of health.

Mendelian randomization is currently mainly implemented through the use of genetic variants as instrumental variables to investigate the causal effect of an exposure on an outcome of interest. Mendelian randomization studies are robust to confounding bias and reverse causation, but they remain susceptible to selection bias; for example, this can happen if the exposure or outcome are associated with selection into the study sample. Negative controls are sometimes used to detect biases (typically due to confounding) in observational studies. Here, we focus specifically on Mendelian randomization analyses and discuss under what conditions a variable can be used as a negative control outcome to detect selection mechanisms that could bias Mendelian randomization estimates. We show that the main requirement is that the negative control outcome relates to confounders of the exposure and outcome. Counter-intuitively, the effect of the negative control on selection is of secondary concern; for example, a variable that does not affect selection can be a valid negative control for an outcome that does. We also investigate under what conditions age and sex can be used as negative control outcomes in Mendelian randomization analyses. In a real-data application, we investigate the pairwise causal relationships between 19 traits, utilizing data from the UK Biobank. Treating biological sex as a negative control outcome, we identify selection bias in analyses involving commonly used traits such as alcohol consumption, body mass index and educational attainment.

Compassion fatigue is a well-documented hazard among healthcare and veterinary professionals, yet the psychological toll on informal caregivers of feral cat colonies, likely numbering several tens of thousands in Portugal, remains largely unexplored. This cross-sectional study examines internal and external factors associated with the secondary traumatic stress component of compassion fatigue among 172 informal caregivers in Portugal. Secondary traumatic stress refers to work-related secondary exposure to individuals who have experienced extremely stressful or traumatic events. Structured telephone interviews assessed sociodemographics, colony management, compassion satisfaction, resilience, spiritual well-being, and perceived social support. Univariate and multivariable linear regression identified predictors of compassion fatigue. Results indicate that 47% of participants experienced moderate compassion fatigue, and 10% reported high levels. Multivariable analysis revealed that caring for large colonies (more than 25 cats) and being unemployed were significantly associated with higher fatigue. Conversely, older age, higher perceived family support, and the resilience dimension of serenity served as protective factors. Interestingly, finding meaning in life was positively correlated with fatigue, suggesting that caregivers who perceive their role as central to their life purpose may become more emotionally invested, increasing vulnerability to distress when unable to help animals. Official colony registration and formal institutional support did not significantly alleviate fatigue. These findings highlight that institutional support alone is insufficient to mitigate fatigue among informal caregivers, who experience significant distress driven by both practical burdens and profound emotional involvement. The most frequently reported concern among caregivers was the inability to cover the costs of feeding and veterinary care for the cats. Interventions must address both external needs (e.g., support to cover veterinary and feeding expenses for the cats) and internal coping mechanisms. Implementing psychosocial support alongside trap-neuter-return programs may also improve caregiver well-being and foster sustainable urban feral cat management. This underscores a One Health perspective, demonstrating that animal health is closely interconnected with human well-being and environmental health.

This paper presents a smoothing method to estimate age-specific human contact patterns and their variations over different periods. Specifically, it examines how age-specific contact patterns shift under varying conditions, such as holiday periods and levels of public health intervention. The method uses Bayesian P-splines to smooth age-specific contact rates and leverages Laplace approximations for fast Bayesian inference, significantly reducing computational complexity. The proposed methodology is applied to the CoMix data from Belgium, a social contact survey collected during the COVID-19 pandemic. Results indicate significantly reduced contacts during periods in which strict social policies were in place, particularly among adults, and notable reductions among young individuals during holidays. This research advances our understanding of how human contact adapts in response to varying social and policy conditions, which can guide more realistic and adaptive infectious disease transmission models.

BackgroundApproximately 300 million people worldwide live with a rare disease, and the majority of rare diseases manifest in childhood. For families, the period before diagnosis is often protracted and distressing, marked by repeated consultations, inconclusive investigations, conflicting medical opinions, and the absence of a recognisable name for their childs condition. While the clinical and epidemiological dimensions of the diagnostic odyssey have been documented, the narrative and experiential dimensions of how parents live through and make sense of prolonged diagnostic uncertainty remain underexplored, particularly in low- and middle-income country contexts. AimTo explore the narrative experiences of parents navigating diagnostic uncertainty for children with rare diseases in Brazil. MethodsA narrative inquiry informed by the three-dimensional narrative space framework of Clandinin and Connelly was conducted. Sixteen parents (twelve mothers and four fathers) of children who had experienced a diagnostic delay of at least two years were recruited from two rare disease referral centres in Sao Paulo and Belo Horizonte. Data were collected through two narrative interviews per participant, supplemented by participant-produced timelines and family photographs. Analysis followed a narrative analytical approach attending to temporality, sociality, and place. FindingsThree narrative threads were woven across the parents stories: (a) "Living in the space before the name," capturing the disorienting experience of caring for a child whose suffering could not be categorised, explained, or predicted; (b) "Fighting to be believed," describing the relentless advocacy required to sustain medical attention in a system that struggled to accommodate conditions falling outside familiar diagnostic categories; and (c) "Rewriting the story," illuminating how the eventual arrival (or non-arrival) of a diagnosis reshaped parents understanding of their child, their family, and themselves. ConclusionDiagnostic uncertainty for parents of children with rare diseases is not a passive waiting period but an active, effortful, and identity-transforming experience. The findings highlight the need for healthcare systems to provide structured psychosocial support during the pre-diagnostic period and for clinicians to develop communication practices that acknowledge, rather than dismiss, the legitimacy of undiagnosed suffering.

BackgroundThe COVID-19 pandemic required South African public sector HIV viral load (VL) laboratories to scale up Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing while maintaining essential HIV services. This placed additional pressure on diagnostic services. This dual mandate introduced significant occupational and environmental challenges (OEC) for staff that remain underexplored. ObjectiveThis study aimed to investigate the OEC and effects that staff experienced during the implementation of COVID-19 testing at public sector VL laboratories in South Africa. MethodsA quantitative, cross-sectional study utilised a census approach among technical and support staff. Data were collected via a structured REDCap questionnaire using 5-point Likert scales. Pre- and post-implementation challenges were assessed across four domains: workload, environmental conditions (space, ventilation, waste), communication, and PPE availability. Statistical analyses included the Wilcoxon Signed-Rank and Spearmans correlation tests. ResultsPerceived occupational challenges increased significantly across all domains post-implementation. Staff workload saw the highest rise (mean score 3.02 to 3.53). Adverse health effects were pervasive; 80.2% of staff reported burnout/fatigue, and 76.5% reported increased anxiety/stress. A strong positive correlation was observed between post-COVID-19 challenges and adverse mental and physical health outcomes (rho = 0.449, p < 0.001). Furthermore, 35.8% of staff considered resigning due to increased job demands. ConclusionIntegrating COVID-19 testing exacerbated systemic weaknesses, causing measurable psychological injury and threatening workforce retention. Findings suggest that the diagnostic workforce requires formal crisis surge staffing models and institutionalised mental health support to safeguard personnel and maintain essential services during future health emergencies.

Policing is one of the most rewarding occupations; however, it is stressful and demanding. This study was designed to explore stress, stress management, and coping mechanisms among Nigerian Police Officers working across four geopolitical zones in Nigeria. Using an exploratory design, forty in-depth interviews (IDIs) were conducted with police officers. Data was collected using an interview guide. The interviews were conducted in English and the participants indigenous languages (by preference), audio-recorded, and transcribed verbatim. Data were analyzed using the thematic approach. A range of contextual stressors were identified as barriers to the health and well-being of police officers in Nigeria. The police often lack the tools and equipment needed to perform their official duties effectively. This includes items like uniforms, bulletproof vests, and even operational vehicles. Shortage of manpower, lack of operational tools, poor welfare for police officers, and poor remuneration were also among their concerns. The participants were able to identify signs of stress that are common among police officers, which are majorly weaknesses, lack of sleep, dizziness, headache, anxiety, exhaustion, and anger. The common coping mechanisms include regular exercise, adequate rest, and relaxation through recreational activities, regular medical checkups, and seeking support from colleagues, among others. Nigerian police officers face many challenges that affect their health and daily routines. This analysis identifies potential opportunities to improve officers welfare in these contexts.

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

BackgroundQuality-of-life (QoL) assessment is essential in breast cancer care, yet limited evidence describes how interrelated QoL domains change during pharmacotherapy. This study aimed to evaluate correlations among functional and symptom scales using the EORTC QLQ-C30 and QLQ-BR23, highlighting their ability to reveal multidimensional QoL patterns. MethodsA prospective observational study was conducted in two second-referral hospitals in Indonesia, enrolling 106 female breast cancer patients. QoL was assessed before and after pharmacotherapy using QLQ-C30 and QLQ-BR23. Changes in scores ({Delta}) were computed, and interdomain relationships were analyzed using Spearmans rho. ResultsPhysical functioning correlated with role functioning ({rho} = 0.55, p <0.001), emotial functioning ({rho} = 0.33, p <0.001), and social functioning ({rho} = 0.31, p = 0.002). Role and social functioning were likewise correlated ({rho} = 0.32, p = 0.001), indicating that improvements across functional domains tended to occur in parallel. Symptom scales showed strong positive clustering, including fatigue with pain ({rho} = 0.37, p <0.001), insomnia ({rho} = 0.35, p <0.001), and systemic side effects ({rho} = 0.48, p <0.001). Functional and symptom domains generally exhibited inverse relationships: physical functioning negatively correlated with fatigue ({rho} = -0.40), pain ({rho} = -0.43), both p <0.001, and systemic side effects ({rho} = -0.26; p = 0.01). ConclusionThe QLQ-C30 and QLQ-BR23 instruments effectively captured structured, clinically meaningful interdependencies. Functional improvements consistently aligned with symptom reductions, revealing coherent functional-symptom clustering. These findings underscore the sensitivity of QoL instruments to detect multidimensional patient-reported changes during breast cancer pharmacotherapy.