Falls represent one of the most significant public health challenges facing the aging global population, contributing substantially to morbidity, mortality, and healthcare costs. This review synthesizes recent research progress on intervention measures for fall prevention among older adults, encompassing exercise-based interventions, multifactorial approaches, medication management and deprescribing, digital health technologies, nutritional supplementation, cognitive-behavioral strategies, and environmental modifications. Evidence from systematic reviews and meta-analyses published between 2019 and 2026 demonstrates that exercise interventions, particularly those incorporating balance and strength training, remain the most robustly supported strategy for reducing fall rates. Multifactorial interventions show consistent benefits, especially when actively implemented with exercise and environmental modification components. Emerging digital health technologies offer promising tools for fall detection and prediction, though their clinical effectiveness requires further validation. Medication review and deprescribing of fall-risk-increasing drugs (FRIDs) represent a critical yet underutilized intervention. Vitamin D supplementation at 800–1000 IU/day demonstrates efficacy in fall prevention, particularly among vitamin D-deficient individuals, though the evidence regarding high-dose supplementation remains controversial. Despite the accumulating evidence base, significant implementation gaps persist, highlighting the need for integrated, person-centered approaches that address both intrinsic and extrinsic risk factors. Future research should focus on optimizing intervention delivery, enhancing implementation fidelity, and developing hybrid models that combine technological innovation with evidence-based clinical strategies.
Objective: To analyze the current prevalence and clustering patterns of chronic comorbidities among elderly people in China, explore the association between different comorbidity patterns and impairment of basic daily living ability (BADL) and instrumental ability of daily living (IADL), and test the moderating effects of physical exercise and social support, providing empirical evidence for formulating precise elderly health management strategies. Methods: Using cross-sectional data from the 2018 China Health and Elderly Care Tracking Survey (CHARLS), 11,246 elderly people aged 60 and above were selected as study subjects. Latent category analysis (LCA) was used to identify chronic disease comorbidity patterns, and logistic regression models were used to analyze the number of comorbidities and their association with BADEL and IADL impairment, to test interaction effects, and to conduct multiple robustness tests. Results: The chronic disease comorbidity rate among the elderly in China was 59.9%, with severe comorbidities (≥ 5 types) accounting for 10.5%. LCA identified five comorbidity patterns: cardiometabolic (26.8%), musculoskeletal-digestive (20.5%, with Chinese population specificity), respiratory-psychiatric (15.3%), multisystem impairment (14.0%), and relative health (23.4%, with 4.2% still having BADAL impairment and 14.3% having IADL impairment in this group). The number of comorbidities was significantly graded and cumulatively associated with impaired daily living ability. The risk of BADAL and IADL impairment in severely comorbid elderly patients was 9.12 times higher (OR=9.12, 95% CI: 7.28-11.43) and 7.89 times higher (OR=7.89, 95% CI: 6.65-9.36), respectively. There was significant heterogeneity in the impact of different comorbidity modes on function, with multisystem impairment at the highest risk, and respiratory-psychiatric types causing significantly more IADL damage than cardiometabolic types. Interaction analysis showed that physical exercise and social support had a significant buffering effect on the association between comorbidity and IADL impairment (interaction term p<0.05). The burden of comorbidities is heavier among women, rural areas, and elderly people with low socioeconomic status. Conclusion: The phenomenon of chronic disease comorbidities among the elderly in China is common and shows a clear pattern of clustering. The association between comorbidity and impaired daily living ability has hierarchical cumulative effects and pattern heterogeneity; physical exercise and social support can buffer the negative effects of comorbidities. It is recommended to include comorbidity pattern identification in comprehensive elderly assessments, implement integrated interventions targeting high-risk patterns such as respiratory-psychotic types, and focus on health inequalities in rural and vulnerable groups.
Background: Remote and hybrid work arrangements may improve autonomy and reduce commuting, but may also reduce routine movement and promote prolonged sitting. Evidence on sedentary behavior in these settings has expanded, whereas direct evidence on cardiometabolic outcomes remains limited. Objective: To map behavioral evidence on sedentary exposure in remote and hybrid work, describe its measurement and distribution across work arrangements, and characterize cardiometabolic outcomes as an evidence gap. Methods: We conducted a scoping review in accordance with the JBI methodology and reported it following the PRISMA-ScR guideline. PubMed, Embase, Web of Science Core Collection, PsycINFO, CINAHL, and Scopus were searched from database inception to 19 July 2026, supplemented by backward and forward citation searching. Two reviewers independently screened titles and abstracts and assessed potentially eligible full-text reports. Inter-reviewer agreement was high (Cohen's κ=0.82). Data were charted using a standardized form and independently verified by a second reviewer. Methodological appraisal was completed using the latest revised design-specific JBI critical appraisal tools. Results: The searches identified 360 records. After removal of 34 duplicates, 326 records underwent title-and-abstract screening, and 161 reports proceeded to full-text assessment. Following exclusion of 127 full-text reports, 34 reports representing 30 unique studies were included. Thirty-three reports representing 29 unique studies contributed quantitative or intervention evidence, and one intervention-development report contributed contextual evidence. Most studies reported greater occupational or total sitting, longer sedentary bouts, or fewer steps on home-working days, although several device-based studies reported null or mixed findings. Direct cardiometabolic evidence remained sparse and consisted mainly of one small cross-sectional study and short-term intervention trials without clear cardiometabolic improvement. Conclusions: Remote and hybrid work are often associated with greater sedentary exposure, particularly during working hours, but estimates vary across work arrangements and measurement methods. Direct evidence on long-term cardiometabolic outcomes is insufficient and largely cross-sectional. Movement-break, workstation, and organizational strategies may be considered to reduce occupational sitting, but their longer-term health effects warrant evaluation.
In an era of interconnected global health challenges, this editorial synthesizes key insights from the current issue of the Journal of Public Health and Preventive Medicine. It highlights how neurobiological remodeling in depression—from taste blunting to anhedonia—interacts with behavioral prevention; how cognitive science can enhance health equity in intervention design; and how bereavement acts as a catalyst for population-level inequities. The editorial further examines advances in risk prediction for pediatric Epstein-Barr virus-associated hemophagocytic syndrome, the applications and policy needs of digital health technologies in prevention, and public health perspectives on adolescent mental health and occupational risks in emerging industries (platform, remote, and outdoor workers). We argue that effective 2026 prevention strategies must integrate biological mechanisms with social determinants, leverage digital innovation equitably, and prioritize multisectoral action to reduce disparities. By bridging these domains, the field can move toward more resilient, mechanism-informed, and just public health systems worldwide.
Background Major depressive disorder (MDD) is a globally prevalent condition characterized by high heterogeneity. Anhedonia—the inability to experience pleasure—is a core symptom and a predictor of poor prognosis. Emerging evidence suggests that "taste blunting" (reduced taste sensitivity and pleasure) is a measurable sensory component of anhedonia, reflecting a potential disruption in the neural systems that translate sensory signals into hedonic experiences. Methods This review synthesizes current clinical and preclinical evidence to investigate the link between taste dysfunction and the brain's reward system in depression. We analyze neuroimaging data, molecular studies on taste receptors, and the impact of neuroinflammation and neurotransmitter dysregulation on the taste-reward neural circuitry. Results Clinical findings demonstrate that MDD patients exhibit significant reductions in taste intensity and pleasure, which correlate with symptom severity. Neurobiologically, this dysfunction is mapped onto a remodeled mesocorticolimbic reward circuit, involving the nucleus accumbens (NAc), ventral tegmental area (VTA), and prefrontal cortex (PFC). Molecularly, chronic stress leads to the downregulation of peripheral sweet taste receptors and 5-HT receptors in taste cells. Centrally, pro-inflammatory cytokines disrupt dopamine synthesis and corticostriatal connectivity. Furthermore, the activation of "anti-reward" systems, such as the κ-opioid receptor (KOR) system, and imbalances in glutamatergic signaling further exacerbate circuit dysfunction. Conclusions Taste blunting in depression is not a mere peripheral sensory deficit but an external manifestation of a profound disruption within the shared taste-reward neural circuitry. This perspective suggests that sensory assessment of taste may serve as a valuable translational marker for MDD subtypes. Future therapeutic strategies should move beyond monoamine-centric models to target this circuitry directly via glutamatergic modulators, KOR antagonists, and anti-inflammatory interventions.
Background: Low health literacy among diabetic adults exacerbates health inequities in the United States, with disproportionately higher prevalence in racial minorities. Such disparities raise healthcare costs due to excessive medical service utilization. Methods: This systematic review followed the PRISMA 2020 guidelines. Relevant studies published from January 2020 to April 2026 were retrieved from five databases. Two independent reviewers completed study screening, data extraction and quality assessment. Results: Twenty-six eligible studies were included, covering four types of LLM-based interventions. These interventions effectively improved patients’ disease understanding, self-efficacy and treatment compliance, and relieved anxiety. LLMs were proven scalable and cost-efficient to narrow health equity gaps, despite the digital divide acting as a major constraint. Conclusion: LLMs are promising scalable tools to reduce diabetes-related anxiety among low-health-literacy populations. It is recommended to integrate AI technologies into public health strategies to mitigate systemic healthcare disparities.
Objective: Globally endorsed for tackling zoonotic and emerging infections, the One Health framework faces major structural gaps between policy and practice. This study explores its implementation barriers across governance, funding, technology, workforce and community aspects via hantavirus cases, and evaluates related risks of Disease X outbreaks. Methods: Critical policy analysis and case study were conducted, drawing on hantavirus research in Latin America and the Caribbean, existing reviews on One Health challenges, and literature on Disease X preparedness in resource-limited regions. Results: Systemic flaws pervade One Health execution: fragmented cross-sector governance and budgets, insufficient preventive funding, poor data interoperability and unequal genomic surveillance capacity. Segmented professional training hinders interdisciplinary collaboration, while top-down strategies and local cultural misunderstandings reduce public cooperation. Multiple field cases further prove the framework’s operational fragmentation.Conclusion: Hantavirus exposes deep structural defects of One Health. To move beyond empty policy advocacy and prevent future pandemics, cross-sector coordination, unified budgeting, community-involved surveillance, and equitable data governance must be established worldwide.
Objective: To examine the association between blood and urine heavy metal concentrations and the risk of rheumatoid arthritis (RA) in U.S. adults using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020. Methods: We analyzed data from 5,530 participants aged ≥20 years. Heavy metal levels (blood cadmium, blood mercury, urine lead) were measured via mass spectrometry, and RA was diagnosed based on self-reported physician diagnosis. Multivariate logistic regression models were used to assess the relationship between heavy metals and RA risk, adjusting for potential confounders including age, gender, BMI (body mass index), race, education level, marital status, smoking history, diabetes history, hypertension history, coronary heart disease history, and serum albumin levels. Subgroup analyses, interaction tests, and mediation analysis were also conducted. Results: Elevated blood cadmium levels (OR = 1.23, 95% CI: 1.05-1.44) and urine lead levels (OR = 1.31, 95% CI: 1.09-1.57) were positively associated with RA risk, while higher blood mercury levels were negatively associated (OR = 0.78, 95% CI: 0.67-0.91). Dose-response relationships were observed for these metals. The negative correlation between blood mercury and RA was stronger among non-smokers and individuals with diabetes (P<.05 for interaction). Mediation analysis revealed that serum albumin did not mediate the associations between heavy metals and RA risk. Conclusion: Elevated blood cadmium and urine lead levels are associated with increased RA risk, whereas higher blood mercury levels are associated with reduced risk. These relationships are influenced by smoking and diabetes status and appear to be independent of serum albumin levels. Further prospective studies are needed to establish causality and explore the underlying mechanisms.
Bereavement is increasingly recognized as a global public health priority, yet existing research and policy frameworks often treat it as a psychologically uniform experience. This paper argues that the health consequences of bereavement are not uniformly distributed but are profoundly shaped by the context in which loss occurs, specifically the circumstances of death, the structural conditions surrounding the bereaved, and the cultural frameworks available for processing grief. Drawing on evidence from the opioid epidemic, armed conflict, climate-related disasters, and occupational health literature, we demonstrate that bereavement functions both as a consequence of systemic public health failures and as an independent cause of downstream morbidity, including post-traumatic stress disorder, complicated grief disorder, cardiovascular events, and immune dysfunction. We examine how the conflation of traumatic and expected bereavement in current epidemiological models constitutes a measurement failure with direct policy implications, and how the imposition of Western clinical grief frameworks on culturally diverse populations compounds existing disparities in bereavement support. Finally, we advance a structural reframing of the public health response that moves away from individual resilience models toward institutional resilience, including mandated bereavement leave, trauma-informed occupational health policy, and culturally adapted community-based support. We argue that bereavement must be reconceptualized as a social determinant of health—one whose inequitable distribution reflects and reproduces broader patterns of structural disadvantage. Policy and research recommendations are offered accordingly.
Long COVID is becoming a widespread issue, as some patients endure multi-system symptoms for months or even years post-acute infection, markedly diminishing their quality of life. A purely reactive approach to symptoms overlooks key opportunities for preventive intervention. From the standpoint of secondary prevention, this article re-examines how to spot people who might deteriorate, how to dynamically monitor their condition, and how to take action before permanent functional loss happens. The definition and phenotypic distinction of long-term COVID-19 are covered in the article, along with the potential role that risk models, biomarkers, and symptom scales can have in early detection. The article focuses on early interventions for the four main symptoms—fatigue, dyspnea, cognitive impairment, and autonomic dysfunction—such as multidisciplinary rehabilitation, cognitive behavioural therapy, and digital home-based rehabilitation. It also examines how proactive follow-up and digital monitoring can identify early signs of functional decline. However, the article does not ignore real-world challenges like accessibility and equity. Overall, lowering the burden of long-term COVID requires an individualised secondary preventive approach that is started shortly after acute infection and continued throughout. However, further implementation study is needed to determine whether this pathway is practical in health systems with various contexts and resources.
Mental disorders among adolescents have reached alarming levels globally, yet most prevention efforts remain locked inside clinical settings. This perspective argues that effective adolescent mental health promotion requires a population-based, multi-sectoral approach that goes beyond diagnosis and treatment. We call for integrating mental health into schools, digital spaces, and community policies – shifting from a reactive clinical model to a proactive public health framework.
Background This study aimed to investigate the association between the uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) and diabetic kidney disease (DKD) among American adults with diabetes. Methods Data from NHANES 2007-2016 were analyzed. Logistic regression and restricted cubic spline (RCS) models were used to evaluate the association between UHR and DKD risk. Additionally, the diagnostic performance of UHR for DKD was assessed using receiver operating characteristic (ROC) curves. Subgroup analyses and interaction tests were performed to examine the reliability and robustness of the findings. Results A total of 4,312 participants were included in the analysis. As a continuous variable, UHR was positively associated with an increased risk of DKD (OR 1.07, 95%CI 1.05–1.09, (P<0.05)). When stratified by quartiles, the fully adjusted model showed that the third and fourth UHR quartiles were associated with significantly higher odds of DKD compared with the lowest quartile (Q3: OR 1.70, 95%CI 1.31-2.22, (P<0.001); Q4: OR 2.44, 95%CI 1.86-3.18, (P<0.001)). RCS analysis indicated a linear relationship between UHR and DKD risk (P for nonlinearity = 0.502). The area under the ROC curve (AUC) of UHR for discriminating DKD was 0.708. Subgroup analyses revealed consistent associations across all subgroups, and interaction tests identified significant effect modifications by age, sex, and smoking status (all P for interaction <0.05). Conclusion UHR is significantly associated with DKD in US adults with diabetes. An elevated UHR may serve as a valuable biomarker for assessing DKD risk in adult patients with diabetes.
Background: Laos experiences a pronounced north–south gradient in dengue vector seasonality and transmission, yet the climatic drivers underlying this heterogeneity remain unquantified. Objective: To quantify the associations between climatic factors (temperature, precipitation, relative humidity) and Aedes larval indices (Breteau Index, BI) and dengue case incidence across three latitudinally distinct sites in Laos, and to estimate lag effects for evidence-based differentiated vector control. Methods: Monthly BI and dengue case data (January 2024–December 2025) were obtained from longitudinal entomological and epidemiological surveillance in Luang Prabang (north, 19°53′N), Vientiane (central, 17°58′N), and Attapeu (south, 14°48′N). Gridded monthly climate data (mean temperature, total precipitation, mean relative humidity) at 0.1° resolution were extracted from ERA5-Land. Cross-correlation functions (CCF) were used to identify optimal lag months between climate variables, BI, and cases. Distributed lag non-linear models (DLNM) were fitted to estimate the non-linear and delayed effects of climate on BI and dengue incidence, adjusting for seasonal trends. Attributable fractions were calculated for key climatic windows. Results: A clear north–south climatic gradient was observed: Attapeu had the highest annual mean temperature (26.5°C), earliest rainy season onset (March), and highest cumulative rainfall (2,340 mm); Luang Prabang had the lowest temperature (22.8°C) and latest monsoon onset (June). Temperature explained 68–74% of BI variance across sites, with peak effects at 1-month lag. Precipitation showed stronger effects on BI in Luang Prabang (r=0.81 at 1-month lag) than Attapeu (r=0.52 at 0-month lag), reflecting baseline water availability differences. The optimal climatic windows for dengue cases—defined as the climatic conditions associated with the highest predicted dengue risk and vector proliferation in the DLNM framework—were: Attapeu, April–May (temperature 26–29°C, rainfall 180–250 mm/month); Vientiane, May–July (27–30°C, 220–300 mm/month); Luang Prabang, July–September (25–28°C, 250–350 mm/month). Relative humidity ≥75% was necessary but not sufficient for BI elevation. DLNM revealed that a 1°C temperature increase above site-specific thresholds was associated with a 12–18% increase in dengue risk at 1–2 months lag. Cumulative rainfall in the wettest 3-month period accounted for 52–63% of annual dengue cases (attributable fraction). The BI–case lag was consistently 1–2 months across all sites. Conclusion: Temperature and precipitation jointly drive the north–south gradient in dengue vector seasonality in Laos, with site-specific optimal climatic windows. These findings support a differentiated "south-early, central-mid, north-late" vector control calendar: source reduction initiation in Attapeu (March–April), Vientiane (April–May), and Luang Prabang (June–July). Integrating seasonal climate forecasts into dengue early warning systems can enhance proactive control in Laos.
Background: Low- and middle-income countries (LMICs) face severe shortages of professional health workers, limiting non-communicable disease (NCD) management. Community health workers (CHWs) offer a scalable solution, yet evidence on their cost-effectiveness remains heterogeneous. This systematic review and data re-analysis updates and extends the 2025 scoping review by O'Donovan et al., synthesizing cost-effectiveness evidence for CHW-led NCD interventions in LMICs (2015–2024) from an implementation science lens. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, Scopus, and Web of Science for economic evaluations of CHW programs targeting NCDs in LMICs. We included 20 studies (52 scenarios) from the core 2025 review, supplemented by comparative data from horizontal integrated CHW programs. Costs were re-analyzed from societal and health-system perspectives; incremental cost-effectiveness ratios (ICERs) were pooled narratively and subgrouped by program type (vertical NCD-focused vs. horizontal integrated), disease area, and equity dimensions where reported. Markov-style long-term projections and sensitivity considerations were derived from study-level modeling. Results: CHW interventions were cost-effective in 35/44 (80%) scenarios, with ICERs ranging from dominated (cost-saving) to US$4,080 per DALY averted. For cardiovascular disease (CVD)/hypertension (22 scenarios), ICERs spanned US$411–US$4,080 per DALY; diabetes (12 scenarios) showed similar variability, with per-capita costs of US$0.23–US$1.33. Vertical (NCD-only) programs exhibited higher upfront costs but comparable long-term value to horizontal integrated models. Equity-weighted analyses in select studies indicated greater relative benefits for the poorest quintiles. Probabilistic sensitivity analysis across studies confirmed robustness in >70% of simulations. Conclusion: CHW-led NCD management is highly cost-effective and aligns with universal health coverage (UHC) goals. Implementation science highlights the need to differentiate vertical vs. horizontal models and prioritize equity. We propose a practical “CHW-NCD Investment Return Toolkit” for policymakers.
Multimorbidity refers to the presence of two or more chronic diseases in a single individual. It has surpassed the single-disease model and become a major public health challenge worldwide. In recent years, notable progress has been made in disease clustering analyses based on large cohorts and electronic health records. However, significant challenges remain, including high methodological heterogeneity, limited clustering stability, and unclear pathways for clinical application. This paper systematically reviews the epidemiological transition of multimorbidity, compares the applicability and limitations of common clustering methods such as hierarchical clustering, latent class analysis, K-centroids clustering, and network analysis, summarizes the heterogeneous characteristics of clustering patterns across populations and regions, and explores translational pathways from disease clustering to precision prevention. Based on this, we propose future research directions, including methodological standardization, integration of longitudinal data, development of stratified precision prevention strategies, and establishment of policy translation mechanisms. The goal is to provide a theoretical framework and methodological guidance for precision prevention in the era of multimorbidity.
Abstract Background Human papillomavirus (HPV) infection is a major public health concern in Cameroon, where cervical cancer remains the second leading cause of cancer-related morbidity and mortality among women. Despite the availability of effective preventive measures, their uptake remains suboptimal and is influenced by population-level knowledge and awareness. This study aimed to synthesize existing evidence on HPV-related knowledge and its associated factors in Cameroon. Methods This review included studies assessing knowledge of HPV as a sexually transmitted infection (STI), its causal role in cervical cancer, and overall good HPV knowledge. A comprehensive and systematic search was conducted across PubMed, Scopus, Web of Science, Embase, the Cochrane Library, and local online databases. Study quality was appraised using the Joanna Briggs Institute critical appraisal tool. Pooled prevalence estimates were calculated using random-effects models (DerSimonian and Laird). Heterogeneity was assessed using the I ² statistic and explored through subgroup analyses. Results A total of 32 studies involving 13,□457 participants were included. The pooled prevalence of overall good HPV knowledge was 27.4% (95% CI: 7.6–63.2; 7 studies; n = 3,312), with considerable heterogeneity ( I ² = 99.3%). Knowledge of HPV as a cause of cervical cancer was 27.9% (95% CI: 15.8–44.4; 26 studies; n = 8,688), while knowledge of HPV as an STI was 47.1% (95% CI: 31.4–63.5; 18 studies; n = 9,040). Healthcare workers demonstrated the highest levels of knowledge (80.2% for HPV as an STI; 78.7% for HPV as a cause of cervical cancer), whereas students (43.4% and 10.2%, respectively) and women from the general population (30.6% and 19.9%, respectively) showed substantially lower levels. Factors associated with poor knowledge included Christian affiliation (OR = 1.46; 95% CI: 0.08–26.06) and secondary level education (OR = 1.32; 95% CI: 0.66–2.63), although these associations were non-significant. Conclusions This study reveals that, HPV-related knowledge in Cameroon remains low, particularly regarding the causal link between HPV and cervical cancer. These findings highlight the urgent need for targeted, context-specific educational interventions and strengthened public health strategies to improve awareness and uptake of HPV prevention measures. Systematic review registration PROSPERO CRD420261283152.
Objective: To clarify the species composition, biting rhythm, seasonal fluctuation, and insecticide resistance of dengue vectors in residential areas of northern (Luang Prabang), central (Vientiane), and southern (Attapeu) Laos, and to analyze residents' knowledge, attitudes, and practices (KAP) regarding dengue prevention and their association with infection. Methods: From 2024 to 2025, human landing catches (HLC) with full personal protection and larval container indices were conducted in three sites. WHO adult contact tube bioassays were performed in Luang Prabang and Vientiane to determine resistance to five insecticides. Structured questionnaires were administered, and laboratory-confirmed dengue infection history was analyzed using multivariable logistic regression. Results: In Luang Prabang, 1,847 adult mosquitoes (2 subfamilies, 5 genera, 17 species) were collected; Aedes aegypti and Ae. albopictus accounted for 1.85% (34) and 2.38% (44), respectively. Biting peaks occurred at 09:00 and 18:00. BI/HI/CI peaked in August–September, with case peaks in October. In Vientiane, 2,156 adults (2 subfamilies, 6 genera, 18 species) were collected; Ae. aegypti and Ae. albopictus accounted for 2.89% (62) and 1.56% (34). Biting peaks were at 08:30–09:30 and 17:30–18:30; BI/HI/CI peaked in May–July, with case peaks in July–August. In Attapeu, 2,341 adults (2 subfamilies, 6 genera, 19 species) were collected; Ae. aegypti and Ae. albopictus accounted for 4.12% (96) and 2.86% (67). Biting peaks were at 08:00–09:00 and 17:00–18:00; BI/HI/CI peaked in April–May, with case peaks in June. In Luang Prabang, 24 h corrected mortality of Ae. aegypti to permethrin, lambda-cyhalothrin, and deltamethrin was 67.53%, 25.47%, and 72.63% (resistant); to fenitrothion and propoxur was 100% and 98.82% (susceptible). In Vientiane, mortality to permethrin and deltamethrin was 58.24% and 65.38% (resistant), with resistance more severe than in Luang Prabang. Multivariable analysis showed that children/adolescents (<18 years) had higher infection risk across all three sites (OR=2.15–2.78). Active larval source reduction significantly lowered infection risk (OR=0.35–0.48). Significant north-south gradients were observed in vector density, seasonal peaks, and pyrethroid resistance. Conclusion: Significant north-south gradients exist in vector ecology, seasonal peaks, and insecticide resistance in Laos. Larval source management remains key to effective prevention, and differentiated, site-specific integrated vector management is urgently needed.
Health inequities persist globally despite decades of biomedical progress. Traditional approaches to reducing disparities have focused on improving healthcare access, addressing social determinants, and developing culturally competent interventions. Yet one critical dimension remains underexplored: how cognitive processes—attention, perception, reasoning, and decision-making—shape the effectiveness of health interventions across different populations. This article argues that integrating insights from cognitive science into health intervention design is essential for advancing health equity. We review evidence demonstrating how cognitive biases, mental models, and information processing constraints contribute to differential health outcomes, particularly among populations with lower health literacy, higher cognitive load, or culturally distinct explanatory frameworks. Drawing on empirical studies from behavioral economics, health communication, and community-based participatory research, we propose a Cognitive-Informed Equity Framework (CIEF) that integrates three core principles: cognitive accessibility auditing, mental model mapping, and debiasing intervention design. We illustrate the framework’s application through case studies in diabetes self-management, vaccine uptake, and medication adherence. By reimagining health interventions through a cognitive lens, researchers and practitioners can develop more context-sensitive, equitable, and effective strategies for reducing health disparities.
Objective: The number of confirmed cases of Hantavirus Pulmonary Syndrome (HPS) in Argentina surged to 101 in the 2025–2026 epidemic season, nearly doubling from the previous season. There was a significant geographical shift in the epicenter of the epidemic from the traditional Patagonian region to the province of Buenos Aires and the NOA region (Salta, Jujuy) in the northwest. This study aims to use the Google Earth Engine (GEE) cloud platform to integrate ERA5 reanalysis climate data and NOAA surface observation data, construct a Bayesian spatiotemporal hierarchy model, and quantitatively analyze the spatiotemporal correlation and causal path between climate anomalies (high temperature, drought, heavy rainfall) and hantavirus outbreaks. Methods: The data of confirmed cases of HPS from 2020 to 2026, released by the Argentine Ministry of Health (SNVS 2.0), WHO, and PAHO, were collected, and the geographical coordinates and time of onset of HPS were extracted. The ERA5-Land hourly dataset (0.1°×0.1° resolution) and NOAA Global Surface Summary of the Day (GSOD) site data were called by GEE to calculate the monthly temperature anomalies (TA), Standardized Precipitation Index (SPI), and Soil Moisture Anomaly (SMA), and frequency of extreme weather events. Bayesian Spatiotemporal Poisson Regression (Bayesian Spatiotemporal Poisson Regression) combined with the INLA (Integrated Nested Laplace Approximation) method was used to estimate the lag effect (lag of 1–6 months) and spatial heterogeneity of climate factors on the onset of HPS. Causal Mediation Analysis was used to identify the causal chains of "climate anomalies→ vegetation productivity→ rodent population expansion→ human exposure". Results: The spatial distribution of HPS cases in the 2025–2026 season showed significant unstable characteristics (Moran's I = 0.42, p < 0.001). Bayesian models showed that SPI (RR = 1.68, 95% CrI: 1.31–2.14) with a lag of 3–4 months (RR = 1.68, 95% CrI: 1.31–2.14) and TA (RR = 1.45, 95% CrI: 1.12–1.87 per 1°C increase) had a significant positive effect on the onset of HPS, and there was a synergistic interaction between the two (RERI = 0.79). Spatial regression revealed that the HPS risk in Buenos Aires was mainly associated with flood events after extreme rainfall (2.3-fold higher risk in flood months after SPI > 1.5), while outbreaks in NOA were closely related to sudden heavy rainfall ("drought-flood" compound extreme events) after persistent drought. Causal mediation analysis showed that the vegetation index (NDVI) played a significant mediating effect between climate anomalies and HPS risk (41.2% in Buenos Aires and 56.7% in the NOA region). Conclusion: This study realizes the quantitative causal inference of climate anomalies and hantavirus outbreaks at the national scale in Argentina for the first time, and confirms the causal pathway of the "drought-flood" compound extreme climate event driving rodent population expansion and human exposure risk increase through the trophic cascade. The GEE-based real-time monitoring framework can provide a methodological basis for the establishment of the HPS Climate-Driven Early Warning System in Argentina.
Cardiometabolic multimorbidity (CMM), defined as the co-occurrence of hypertension, type 2 diabetes mellitus, dyslipidemia, obesity and related disorders in the same individual, has become an increasingly severe global public health challenge in the era of multimorbidity. Recent large-scale cohort studies have gone beyond simple counting of disease counts and identified heterogeneous phenotypic clusters, such as metabolically healthy obesity (MHO) and metabolically unhealthy normal weight (MUNW), which show significantly different risks of progressing to overt cardiometabolic multimorbidity. This review compares mainstream clustering analysis methods, explores the common pathophysiological mechanisms underlying these phenotypes, including insulin resistance, chronic low-grade inflammation and neuroendocrine disorders, and summarizes evidence-based integrated management strategies tailored to different comorbidity clusters. By integrating phenotype-specific characteristics with multi-target lifestyle interventions, pharmacotherapy and digital health interventions, this framework promotes a shift from fragmented specialty-based management to precision collaborative management, which is expected to reduce cardiovascular events, renal function decline and premature mortality.
