BACKGROUND:Short-term exposure to ambient fine particulate matter (PM2.5) is an established risk factor for cardiovascular morbidity and mortality. However, existing air pollution alert systems are inefficient in protecting general populations, partly because of a lack of precise understanding of the nonlinear exposure-response relationship between PM2.5 and cardiovascular mortality. OBJECTIVES:The aim of this study was to construct a globally representative nonlinear exposure-response function for short-term PM2.5 exposure and cardiovascular mortality through a systematic meta-analysis of published global literature. On the basis of this function, the goal was to identify an optimal public health alert threshold that balances health protection benefits with societal disruption. METHODS:A systematic review and meta-analysis of 100 epidemiologic studies (comprising 123 effect estimates) up to May 2025 was conducted. An innovative 3-stage meta-regression model, combining spline functions and structural causal modeling theory, was used to estimate the nonlinear curve. On the basis of this curve, and integrated with global gridded data on PM2.5 concentrations, population, and baseline mortality, the cardiovascular mortality burden attributable to PM2.5 from 2000 to 2023 was quantified, and a receiver-operating characteristic-like curve analysis was used to determine the optimal alert value. RESULTS:The meta-analysis confirmed that for every 10 μg/m3 increase in short-term PM2.5 concentration, the pooled risk ratio for cardiovascular mortality was 1.0090 (95% CI: 1.0074-1.0106). The constructed exposure-response curve exhibited a supralinear pattern: the marginal risk was high at low concentrations, flattened at moderate concentrations (∼75-150 μg/m3), and rose sharply again above 150 μg/m3. In 2023, the global number of cardiovascular deaths attributable to PM2.5 pollution episodes (exceeding the World Health Organization's first-stage interim target of 75 μg/m3) was estimated at 59,399 (95% CI: 38,126-82,413). The optimal alert value was estimated as 136 μg/m3 (95% CI: 129-148), which could prevent 73.2% (95% CI: 71.8%-76.6%) of attributable deaths while affecting only 32% of at-risk person-days. CONCLUSIONS:A significant nonlinear relationship exists between short-term PM2.5 exposure and cardiovascular mortality. The optimal alert value identified in this study provides critical evidence for developing more scientific, efficient, and health-oriented air pollution warning systems, thereby maximizing public health benefits while minimizing social disruption.

Wind-blown dust (dust PM2.5) is a major contributor to fine particulate matter (PM2.5) in low- and middle-income countries (LMICs), yet its impact on under-five mortality (U5M) remains underexplored. In particular, due to the lack of an exposure-response function (ERF) focusing on dust PM2.5, the relevant burden is evaluated based on pre-established ERFs for total PM2.5 mass. Our study aimed to evaluate the association between long-term dust PM2.5 exposure and U5M, estimating the attributable mortality burden in LMICs. Using high-resolution PM2.5 mass-maps, well-validated dust ratio data, and 125 demographic and health surveys, we applied a fixed-effects Cox model to examine the association between life-course dust PM2.5 exposure and the survival status of 1 411 851 children from 53 LMICs. Subsequently, we developed a nonlinear ERF by integrating the marginal effects of within-strata exposure variation, and extrapolated this function to estimate the U5M burden attributable to dust PM2.5 across 100 LMICs, comparing results with two existing ERFs for total PM2.5. Each 10-μg/m³ increase in dust PM2.5 exposure was associated with a 7.13% (95% confidence interval [CI]: 4.54-9.78) increase in U5M risk. The ERF indicated no threshold effect at low concentrations and a steeper slope at higher levels. Based on this function, we estimated that dust PM2.5 contributed to ∼1.74 million, 1.30 million, and 1.07 million U5Ms in 2000, 2010, and 2017, respectively. Notably, these estimates exceeded those derived from pre-established ERFs for total PM2.5 mass in most countries. Our findings underscore the significant contribution of dust PM2.5 to the U5M burden and emphasize the importance of early-warning systems to effectively safeguard child health.

This prospective cohort study included 408,760 older adults to investigate complex interaction between waist circumference (WC), blood glucose (BG) or blood pressure (BP), and sex in relation to elderly mortality. We used Cox regression models incorporating a tensor product interaction function to model joint impacts of WC and four cardiometabolic markers on mortality, and developed a two-dimensional exposure-response function (ERF) to quantify the population adaptability to cardiometabolic dysfunction across different WC levels. The linear and nonlinear effects of BG and BP on mortality varied by WC, with significant synergistic interactions. The two-dimensional ERF quantified variations in excess mortality risk across different WC and cardiometabolic marker combinations. Individuals with higher WC exhibited a forward shift in risk thresholds, indicating reduced adaptability to elevated BG and BP levels. Our findings highlight the need for targeted cardiometabolic health management strategies to enhance adaptability and reduce the burden of cardiometabolic diseases in aging populations.

The integration of electronic health records (EHRs) into clinical research represents a pivotal advance toward improving data quality, operational efficiency, and regulatory compliance. This review systematically explores recent technological progress in automated data extraction and integration from EHRs, with a focus on three core domains: the development of electronic case report forms (eCRFs), dynamic data exchange mechanisms between EHR and electronic data capture (EDC) systems, and automated standardization strategies to achieve interoperability. eCRFs have evolved from static, manually populated templates to dynamic, technology-enhanced tools that streamline data collection and ensure alignment with clinical protocols and regulatory requirements. Emerging systems increasingly leverage artificial intelligence techniques—including large language models and natural language processing—to automate the generation and population of eCRFs from unstructured EHR data. In parallel, dynamic data pipelines enable real-time, accurate data transfer from EHRs to EDCs, improving data fidelity and reducing clinician workload. Furthermore, the adoption of common data models and standardization frameworks supports multi-center interoperability for large-scale translational research. Despite these advances, widespread implementation is impeded by persistent challenges such as data heterogeneity, governance constraints, and limited scalability. Ongoing efforts to address existing technical challenges are essential optimize the reuse of EHR data in clinical research workflows.

INTRODUCTION:Long-term exposure to fine particulate matter (PM2.5) has been linked to many adverse health outcomes, which can vary significantly depending on the chemical profile of the PM2.5. However, many meta-analyses of the health effects of a specific component of PM2.5 have ignored the effects of other components, leading to omitted variable bias (OVB). This study developed a new method to address this problem and conducted a simulation using black carbon (BC) as an example. METHOD:We used data from two published meta-analyses as input for our model, with supplementary information obtained from a reanalysis product of PM2.5 components. Based on the classical OVB formula, we developed a post hoc adjusted model and verified its performance via a simulation study. We obtained pooled estimates of the effect of BC on all-cause mortality, with adjustment for the effect of non-black carbon (NBC) components. Finally, based on the estimated effects of BC and NBC, we investigated global patterns in PM2.5 toxicity (i.e., the per-unit effect of PM2.5) and the degree of OVB associated with ignoring the differential effects of BC and NBC. RESULTS:The post hoc adjusted model included 46 individual estimates of the effects of BC or NBC on all-cause mortality. Results from the model indicate that a 10 μg/m³ increase in BC and NBC was associated with a 49 % (95 % confidence interval [CI]: 26 - 76 %) and 6 % (95 % CI: 3 - 10 %) increase in mortality risk, respectively. Based on global average total PM2.5 mass composition values (6.1 % and 93.9 % for BC and NBC, respectively), we estimated that the relative risk of all-cause mortality increased by 1.09 (95 % CI: 1.06 - 1.12) per 10 μg/m3 increment in long-term PM2.5 exposure. Estimation of the effects of BC on mortality based on observations obtained within one city yielded a median OVB of 147 % (95 % CI: -151 - 700) when using a single-pollutant model. CONCLUSION:In meta-analyses on the health impacts of PM2.5 components, ignoring the differential effects of BC and NBC causes significant biases in estimating associations with all-cause mortality. Our study presents a novel method to adjust for OVB in meta-analyses, and we find that BC more harmful than NBC components of PM2.5 by using the novel method.

Abstract Objective: Timely access to essential patient information is critical for informed, data-driven decision-making in clinical nursing to enhance care efficiency and quality. Despite advancements in electronic health records, many medical reports remain in paper form, posing challenges for data analysis and application. This study aimed to develop ChatSchema, a pipeline based on Large Multimodal Models (LMMs) for extracting structured information from paper-based medical reports, and evaluated its effectiveness in a pilot setting. Method: ChatSchema was a two-stage approach to extract and structure data from paper-based medical reports. The classification stage leveraged Optical Character Recognition (OCR) of the pictured medical reports, pre-correction, desensitization, and prompt engineering to categorize report types. In the extraction stage, OCR-converted text was transformed into a structured format using a predefined schema, with LMMs applied for standardizing fields and converting data types. A dataset of 100 annotated medical reports was collected from Peking University First Hospital, and the effectiveness of ChatSchema was evaluated in terms of precision, recall, F1-score, and overall accuracy. To validate ChatSchema’s effectiveness, we compared it against a baseline method that provided only schema and task instructions. For sensitivity analysis, two basic LMMs, GPT-4o and Gemini 1.5 Pro, were utilized and compared in the development of ChatSchema. Results: A ground-truth dataset comprising 2,945 test item-result pairs was extracted from 100 annotated medical reports. ChatSchema was capable of correctly extracting and structuring data from paper-based medical reports, showing remarkable accuracy across various data types, unit standardization, field mapping, and basic LMMs. Overall, ChatSchema achieved a high F1-score of 95.8%, an accuracy of 97.2%, a precision of 95.8%, and a recall of 95.8%. ChatSchema surpassed the baseline model by 12.9% in accuracy and by 12.3% in F1-score using the GPT-4o model. Conclusion: Our findings demonstrate that ChatSchema is highly effective for extracting and structuring data from paper-based medical reports. By providing accurate, structured information extraction, ChatSchema has the potential to enhance patient care and support clinical decision-making, showing significant promise for broader application in nursing and healthcare settings.

OBJECTIVE:The impact of desert-originated dust has been underestimated in fine particulate matters (PM2.5)-related disease burden studies. This study aimed to assess the association of long-term dust PM2.5 exposure and all-cause mortality among older adults in China. METHODS:A cohort study using electronic health records (2010-2020) across Weinan, a city in northwest China, which experiences persistently high PM2.5 levels and frequent sand and dust storms, included 1,553,724 adults aged ≥45 years. Annual average dust and non-dust PM2.5 exposures were matched for each participant based on their residential locations. Cox regression models were used to estimate mortality risks associated with long-term exposure to both PM2.5 sources. A tensor product interaction function was applied to develop a two-dimensional joint exposure-response function linking mortality to both dust and non-dust PM2.5 exposures to quantify the burden of deaths attributable to PM2.5 sources. RESULTS:During 8,951,372 person-years of follow-up, 45,273 deaths (2.91 %) occurred. Three-year average exposure to dust and non-dust PM2.5 were 14.85 and 36.78 μg/m3, respectively. Each 1 μg/m3 increase in dust PM2.5 was associated with a 29.0 % (95 % confidence interval [CI]: 28.3, 29.8 %) increase in mortality risk among older adults, versus 13.4 % (95 % CI: 13.1, 13.7 %) for non-dust PM2.5. While dust PM2.5 constituted only 25.9 % of total PM2.5 exposure, it contributed 40.1 % of PM2.5-related deaths in 2019. CONCLUSION:Dust PM2.5 has a stronger adverse association with mortality among older adults than non-dust PM2.5. These findings provide critical insights into PM2.5 source-specific health impacts, informing targeted air pollution control strategies and public health interventions.

Previous studies investigating the health benefits of green space primarily focused on its quantity, while individual accessibility has been insufficiently considered. This study aimed to investigate the associations between green space within accessible isochrones and mortality and the effect modification by regional urbanicity. Using a nationally representative survey of 47,086 participants with prospective death records according to ICD-10 codes (up to December 2017) and high spatial-resolution remote sensing data, this cohort study investigated the associations of green space (characterized using green land cover proportion and the Normalized Difference Vegetation Index [NDVI]) within 15-min walking and cycling isochrones with all-cause and cause-specific (cardiovascular disease, respiratory disease, and cancer) mortality. We also explored the associations across regions with different levels of urbanicity-related built environment factors using interaction models. Green space within 15-min walking and cycling isochrones was associated with lower risks of mortality. For instance, a 10 % increase in green land cover proportion within 15-min isochrones was associated with 5 % (hazard ratio [HR] = 0.95, 95 % CI: 0.90, 1.00 for walking) and 12 % (HR = 0.88, 95 % CI: 0.81, 0.96 for cycling) reductions in all-cause mortality risk. Stronger protective effects of green space on mortality were found in areas with higher nighttime light index (NLI), population density, road density, and impervious land cover proportion (P for interaction <0.05). Similar effect modification by urbanicity-related built environment factors was also found for associations of green space with cardiovascular disease mortality. Accessible green space within 15-min walking and cycling regions was associated with lower mortality risks, especially in regions with higher urbanicity levels. The findings underscore the importance of considering both the accessibility of green space and regional urbanicity in land planning to maximize the health benefits of green space.

The city built environment plays a crucial role in influencing population vulnerability to temperature extremes, yet population-based evidence has been limited. We included 21,494 urban residents from a nationally representative cohort study. Temperature extremes were defined using residential address-specific thresholds lasting for ≥ 3 days. Street view images within participants’ residences (500-m radius) were evaluated using semantic segmentation by DeepLabV3 Plus-ResNet101 pretrained by the Cityscapes dataset. Cox proportional hazard models and interaction models were applied to explore the moderating effects of street view-derived built environments on the effects of temperature extremes on mortality. Each additional day of heatwave (95th) and coldspell (5th) duration per year was associated with a 6

Persistent proteinuria is a hallmark of glomerulonephritis and serves as an important diagnostic and prognostic marker. This study proposes a novel digital approach—the quantitative urine protein-to-creatinine ratio (UPCR) home-testing kit (UTK)—for proteinuria screening based on advanced computer vision algorithms and evaluates its effectiveness. Randomly selected spot urine samples were collected from 199 participants recruited from a tertiary hospital in Beijing, China. The UTK method utilized contour detection and image segmentation algorithms to extract the topological information of the urinalysis strip for color calibration, and employed a 3-dimensional color space interpolation algorithm to obtain quantitative UPCR readings. Using the laboratory results as a benchmark, we evaluated the diagnostic performance of UTK in proteinuria screening in terms of validity, reliability, predictive values, and area under the receiver operating characteristic curve (AUC). The mean age of the 199 participants was 51.6 ± 16.2 years and 88 (44.2%) of them were male. The median UPCR was 222.5 mg/g (interquartile range: 106.5–844.6), and 20 (10.1%) participants were identified as having proteinuria. The UTK method performed well in proteinuria screening, with a high accuracy rate of 91.0%, 85.0% sensitivity, a 91.6% specificity, and an AUC of 0.966. The Bland-Altman plot showed a high level of agreement of the quantitative UPCR results between the UTK and laboratory methods. The proposed digital solution for quantitative UPCR analysis based on advanced computer vision technologies showed good performance in proteinuria screening. This user-friendly and cost-effective UPCR measure method presents a promising new strategy for enhancing the efficiency of the primary monitoring and management of glomerulonephritis.

The long-term impacts of climate change on human health have garnered growing attention, but little is known about the effect of climate change on cancer outcomes. This study aims to investigate the long-term effects of anomalous precipitation on all-cause mortality of cancer patients, based on national multicenter cohort data. We conducted a multicenter cohort analysis based on mortality data of 92,638 patients diagnosed with cancer in UK Biobank. For each patient, we calculated the annual average precipitation as well as anomalous precipitation, defined as the deviation from the long-term local average. A multicenter Cox regression model was conducted to estimate the associations between all-cause mortality of cancer patients and average or anomalous precipitation. Based on a varying-coefficient model, we developed a two-dimensional exposure–response function (ERF) linking mortality to both long-term average and anomalous precipitation, to assess heterogeneity across precipitation zones. Each 0.1 mm/day increase in absolute values of precipitation anomalies was associated with a 5.2

This study examined associations between anomalous temperatures and under-five mortality (U5M) in low- and middle-income countries (LMICs). Between 1998 and 2019, data were collected on 1,745,132 live births across 56 LMICs. The median age was 27.0 months (interquartile range: 12.0, 43.0), and 51.0

Flooding has become more frequent and intense due to climate change, yet its long-term health impacts, especially on chronic kidney disease (CKD), remain underexplored. This study examines the association between long-term flood exposure and CKD among adults in China using data from 47,204 participants in a nationally representative survey. Flood exposure was defined as satellite-detected inundation occurring within 10 km of participants' residences during the one to seven years preceding the survey. CKD was defined by an estimated glomerular filtration rate (eGFR) of <60 mL/min/1.73 m2 and/or the presence of albuminuria. Generalized additive models were used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs), adjusting for sociodemographic and lifestyle factors. Flood exposure significantly increased the odds of CKD at a one-year lag (OR 1.87, 95 % CI 1.67-2.11), and this effect persisted up to seven years post-event (OR 1.12, 95 % CI 1.00-1.25). Albuminuria showed the strongest association with flood exposure at the one-year lag (OR 2.25, 95 % CI 1.98-2.56), subsequently declining, whereas reduced eGFR (<60 mL/min/1.73 m2) became significantly associated from three years onward, peaking at five years post-flood (OR 2.20, 95 % CI 1.75-2.75). Subgroup analyses indicated that rural residents, lower-income individuals, and females were particularly vulnerable within the first year following flooding. These findings suggest that flood exposure has long-term adverse impacts on CKD, particularly among certain vulnerable populations. Further research is needed to explore the underlying mechanisms of this relationship.

Introduction:This study aimed to evaluate the guideline concordance of chronic kidney disease (CKD) testing among high-risk patients in a city in Northwest China and identify key factors influencing testing practices. Methods:A retrospective cohort study was conducted using electronic health records data across Weinan city. The study included 202,847 adult patients diagnosed with diabetes and/or hypertension, excluding those with known CKD at baseline. Considering albuminuria test is not available in Weinan city, guideline-concordant CKD testing in this study was defined as conducting annual tests for both estimated glomerular filtration rate (eGFR) and proteinuria throughout the follow-up period, with the endpoint being the incidence of CKD or mortality. A Cox regression model was used to identify key factors influencing CKD testing practices. Results:The study population had 19.3% diagnosed with diabetes only, 71.8% with hypertension only, and 8.9% with both conditions. Throughout the follow-up period, only 0.70% of participants underwent annual tests for both eGFR and proteinuria as recommended by guidelines, while 3.44% had at least one test for both eGFR and proteinuria. Better adherence to CKD testing guidelines was associated with presence of diabetes, male gender, younger age, higher educational attainment, nonsmoking status, urban healthcare insurance, residence in urban areas, and engagement in light physical work. Conclusion:Routine CKD testing in Northwest Chinese with diabetes and hypertension remains uncommon, despite guideline recommendations. Given that diabetes and hypertension are leading causes of CKD in China, these findings emphasize the urgent need for strategies to improve kidney health management among high-risk populations.

Background: Long-term exposure to fine particulate matter brought by dust storms (dust PM2.5) poses a significant risk to children's health, particularly those in low- and middle-income countries (LMICs). To quantify the impact of dust PM2.5 on children, current research focuses on acute respiratory infection (ARI) as a key health outcome, given its significant contribution to child mortality. However, the relationship used to evaluate the disease burden is mainly based on the total PM2.5 concentration, neglecting the specific effect of dust PM2.5 distinct from other PM2.5.This study aimed to develop a dust-specific exposure-response function (ERF) of ARI in children <5 years of age (U5-ARI) for future risk assessments. Method: We combined population data derived from the Demographic and Health Survey covering 53 LMICs, with environmental data, including the gridded concentration of dust PM2.5. ARI in children <5 years of age (U5-ARI) was the outcome of interest, which was defined by a standard questionnaire-based method. The dust PM2.5 exposure was derived from the integration of two well-recognized datasets, and matched to each participant at the community level. We analyzed the linear association between the annual average dust PM2.5 concentration and the odds of U5-ARI with logistic regression and fixed effects after adjusting for multiple covariates. We also used the spline method to develop a dust-specific ERF. Based on the function, we estimated the burden of dust-associated U5-ARI across 100 LMICs and compared it with the results from two well-established functions of total PM2.5 mass. Results: The analysis of 1,223,118 children showed that a 10 mu g/m(3) increase in dust PM2.5 was associated with a 7.43% (95% confidence interval [CI]: 4.77-10.15%) increase in the odds of U5-ARI. The spline model indicated that the risk of U5-ARI increased monotonically and linearly with dust PM2.5 concentration with no evident effect threshold. In 2017, based on the dust-specific ERF, across the 100 LMICs, the number of dust-associated U5-ARI was estimated to be 159,000 (95% CI: 153,000-165,000), which was consistently higher than the estimates from ERFs based on total PM2.5 mass (142,000 [95% CI: 97,000-181,000] or 114,000 [95% CI: 80,000-153,000]). The long-term dust PM2.5 exposure contributed to 12-13% of all the children affected by U5-ARI between 2000 and 2017. The geographic hotspots were the arid and populous areas of South Asia and Africa. Conclusion: This study provides critical insight into the association between long-term exposure to dust PM2.5 and the health of children in LMICs, highlighting the need for specific ERFs to distinguish the adverse effects of different PM2.5 components. Personal protection during sand dust storms can be an effective intervention to safeguard the respiratory health of children.