Composite Index Research Articles

A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts.

Association between composite dietary antioxidant index and fatty liver index among US adults

BackgroundThe potential beneficial health effects of dietary antioxidants have been reported. However, the association of a composite dietary antioxidant index (CDAI) with fatty liver index (FLI) remains unclear. This study aims to assess whether CDAI (including its components) is associated with FLI among US adults.MethodsThis population-based cross-sectional study used data on US adults from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 cycles. Weighted generalized linear regression models were used to analyze the association between CDAI (including vitamin A, C, E, zinc, selenium, and carotenoids) and FLI, which was calculated by using body mass index (BMI), waist circumference and levels of γ-glutamyl transferase and triglycerides.ResultsWeighted generalized linear regression models showed an inverse association between CDAI and FLI in the total population (β, −0.40; 95% CI, −0.59, −0.21), in women (β, −0.56; 95% CI, −0.94, −0.18), and in men (β, −0.32; 95% CI, −0.54, −0.10) after adjusting for various confounders. The restricted cubic splines showed the negative linear dose–response associations between CDAI and FLI (all P non_linear &amp;gt;0.05). The dietary selenium intake in women has an inverse U-shaped relationship with FLI, with an inflection point value of 110 μg. In model 3, intake of dietary antioxidants Vitamins A, C, E, and carotenoids were significantly negatively associated with FLI in female but only were vitamins A and E negatively associated with FLI in male. In subgroup analysis, CDAI showed a significantly negative relation to FLI among those aged 60 years or older (β, −0.57; 95% CI, −0.81, −0.33), among those who engaged in active physical activity (β, −0.46; 95% CI, −0.63, −0.29), among those without metabolic syndrome (β, −0.43; 95% CI, −0.62, −0.24), and those without hyperuricemia (β, −0.43; 95% CI, −0.60, −0.26). Additionally, CDAI was significantly negatively associated with male FLI, regardless of whether they had diabetes or not.ConclusionIn conclusion, our results indicate that higher CDAI may be associated with a lower FLI.

A rapid method for measuring the rock brittleness index: Rapid characterization of rock brittleness based on LIBS technology

The brittle index is a crucial indicator in the assessment of rockbursts. The mineral brittle index (MBI) is widely utilized due to its simplicity and accessibility. However, the lengthy and inefficient mineral composition testing cycle presents a significant challenge. A novel approach using laser-induced breakdown spectroscopy (LIBS) to rapidly convert spectral elements into minerals and measure the rock brittleness index was introduced in this paper. The laser spectra of metamorphic sandstone and granite were measured by LIBS, and some rock elements were tested by X-ray fluorescence (XRF) to construct a spectral-elemental model. Furthermore, the mineral composition of the rocks was determined by X-ray diffraction (XRD). Eight major elements, Si, Al, K, Ca, Na, Fe, Mg, and Ti, were selected as independent variables. Element-mineral correlation analysis was performed using centered log-ratio transformation (CLR) processing. A random forest regression (RF-R) element-mineral transformation model was established for rapid conversion of spectral-element-mineral brittle index. Finally, mechanical testing of rock embrittlement in the DJ Tunnel was conducted to verify the efficacy of MBI in characterizing rock embrittlement in the DJ Tunnel. The results demonstrate that predicted values of mineral compositions are in good agreement with experimental values and that predicted values of brittleness indices are also in good agreement with experimental values. The rapid and effective application of LIBS in determining rock mineral composition and rock brittleness index has been realised, which is of great significance for further realising the rapid assessment of rock brittleness and rockburst prediction at engineering sites.

Towards designing a comprehensive composite index for social vulnerability to natural hazards in the big data era: potential challenges and partial solutions

AbstractThis study provides a practical definition and framework to measure social vulnerability to natural hazards, addressing gaps in the literature after three decades of Susan Cutter's Place-Based Model. The current social vulnerability index, designed based on available data such as census data, is limited in capturing all aspects of social vulnerability and spatial inequalities. This research explored and proposed a new theoretical perspective and methodological framework for designing a comprehensive index for social vulnerability to natural disasters using emerging big data, which is practical and feasible and can be applied to social vulnerability studies in general and social vulnerability to natural hazards in particular. This research first defines constructs of social vulnerability to natural hazards, including (1) socioeconomic status or conditions, (2) physical infrastructure or accessibility to facilities and services, (3) ecological-environmental conditions, (4) access to security facilities and crime rate, (5) technological inequalities, (6) health conditions of the citizens, and (7) susceptibility. Then, it proposes potential solutions for developing a comprehensive composite index under ideal conditions without (big) data limitations, using the US and the UK as case examples. These solutions are not just theoretical but also practical and feasible, instilling confidence in their implementation. This research offers valuable insights for researchers and policymakers in diverse sectors, supporting the design of effective disaster risk reduction strategies and intervention programs.

Study of cellulose nanocrystals -enhanced natural gas foam performance and profile control & oil displacement mechanism

ABSTRACT To address the poor performance of conventional natural gas foam flooding in the tight conglomerate reservoirs of the Mahu region in Xinjiang, a high-activity green material, cellulose nanocrystals (CNC), was introduced to improve foam performance based on a conventional AOS+SDS foam system. However, the impact of CNC on the performance of the conventional foam system and the synergistic effects between CNC and surfactants are not yet clear. Therefore, this study evaluates the improvement effects of CNC on foam performance from both macroscopic and microscopic perspectives by analyzing the foam composite index and the synergistic effects between CNC and surfactants. The study also investigates the factors affecting foam performance and uses a planar visualization model to evaluate the enhanced gas foam flooding effects from a microscopic mechanism perspective. The results indicate that CNC increases the foam composite index by 26,850 mL·min on a macroscopic level. Microscopically, it makes the foam more compact, thickens the foam liquid film by 0.1 mm, improves the wettability reversal by 10%, and reduces the oil-water interfacial tension by 0.26 mN/m. After mixing 1 vol% crude oil, the foam volume and half-life decrease by 12% and 4%, respectively. At reservoir temperature and pressure, the foam volume decreases by 12.7% and 10.5%, but the half-life increases by 4 hours and 3.7 hours, respectively. At formation water mineralization conditions, the foam volume and half-life decrease by 6.6% and 1.7 hours, respectively. In summary, it still has good performance under reservoir conditions. The visualization model demonstrates from a microscopic perspective that the foam system has good gas shutoff capability. After foam flooding, the gas sweep efficiency increases by 54.12%, and the foam flooding and subsequent gas flooding improve the crude oil recovery by 20.32%.

Reproductive Health Status in India: Comparison of NFHS-IV and NFHS-V Findings

Background This study investigates the disparities in reproductive health and the underlying causes in various Indian states. Based on a large-scale population-based, nationally representative data set, this study aims to track the changes between National Family Health Survey (NFHS)-IV and NFHS-V in the status of reproductive health and examine the factors contributing to it in various regions of India for women aged 15–49 years in 29 states (NFHS-IV and V). Methods According to claims, in India’s states’ reproductive health status is gauged by the reproductive health index (RHI). The total fertility rate, infant mortality rate, childbirth order, mother-and-child delivery care and female educational attainment rate are the five variables that make up this composite index. Results In NFHS-IV, India’s RHI score was 61.5, and in NFHS-V, it was 67.8. Although there has been progress in RHI, it has not been at the level that was anticipated. Eleven NFHS-IV states and eight NFHS-V states have index values below India’s RHI threshold. Conclusion To move the situation forward, desires for using contraception should be given more weight. The level of educational achievement of women should also be raised, as should their passionate participation in economic activity.

Association between sensitivity to thyroid hormones and non-high-density lipoprotein cholesterol levels in patients with type 2 diabetes mellitus

BACKGROUND Dyslipidemia and type 2 diabetes mellitus (T2DM) are chronic conditions with substantial public health implications. Effective management of lipid metabolism in patients with T2DM is critical. However, there has been insufficient attention given to the relationship between thyroid hormone sensitivity and dyslipidemia in the T2DM population, particularly concerning non-high-density lipoprotein cholesterol (non-HDL-C). AIM To clarify the association between thyroid hormone sensitivity and dyslipidemia in patients with T2DM. METHODS In this cross-sectional study, thyroid hormone sensitivity indices, the thyroid feedback quantile-based index (TFQI), the thyroid-stimulating hormone index (TSHI), the thyrotrophic T4 resistance index (TT4RI), and the free triiodothyronine (FT3)/free thyroxine (FT4) ratio were calculated. Logistic regression analysis was performed to determine the associations between those composite indices and non-HDL-C levels. Random forest variable importance and Shapley Additive Explanations (SHAP) summary plots were used to identify the strength and direction of the association between hyper-non-HDL-C and its major predictor. RESULTS Among the 994 participants, 389 (39.13%) had high non-HDL-C levels. Logistic regression analysis revealed that the risk of hyper-non-HDL-C was positively correlated with the TFQI (OR: 1.584; 95%CI: 1.088-2.304; P = 0.016), TSHI (OR: 1.238; 95%CI: 1.034-1.482; P = 0.02), and TT4RI (OR: 1.075; 95%CI: 1.006-1.149; P = 0.032) but was not significantly correlated with the FT3/FT4 ratio. The relationships between composite indices of the thyroid system and non-HDL-C levels differed according to sex. An increased risk of hyper-non-HDL-C was associated with elevated TSHI levels in men (OR: 1.331; 95%CI: 1.003-1.766; P = 0.048) but elevated TFQI levels in women (OR: 2.337; 95%CI: 1.4-3.901; P = 0.001). Among the analyzed variables, the average SHAP values were highest for TSHI, followed by TT4RI. CONCLUSION Impaired sensitivity to thyroid hormones was associated with high non-HDL-C levels in patients with T2DM.

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

Evaluating Sustainability Practices in Viticulture Through a Comprehensive Study of Environmental, Social, and Economic Factors: A Case from Batman Province, Türkiye

This study aims to assess the sustainability level of viticulture farms in Batman province and identify the factors that hinder their sustainability. The data used for the study were collected in the 2021 production year through surveys of 94 farms. A composite sustainability index was developed to measure the sustainability level of viticultural activities. Positive or endogenous weighting methods, such as Principal Component Analysis (PCA), were used to reduce the number of sustainability indicators and determine their weights. The index values for economic, environmental, and social sustainability were found to be 0.40, 0.44, and 0.53, respectively. The overall sustainability index value for the farms was calculated as 0.43. The study also highlighted that 46.81% of the farms are at risk in terms of sustainability, with larger farms showing better sustainability performance compared to smaller ones.

Determinants JCI of the Indonesian Stock Exchange (Comparison of IHSG Projection of OLS Method and ARIMA and GARCH Methods for the Period 2015-2023)

This study aims to examine the effect of interest rate, exchange rate, inflation, gross domestic product and LQ-45 index variables on the movement of the Composite Stock Price Index using the ordinary least square method. Comparison in predicting the movement of the stock price index through three models, and then compared with the projection results with OLS, ARIMA and GARCH methods. This research was conducted using monthly data on JCI, interest rates, exchange rates, inflation, gross domestic product and the LQ-45 index for the period May 2015 - December 2023. From the results it can be concluded that interest rates have a significant negative effect on JCI, exchange rates have a significant negative effect on JCI, inflation has a significant negative effect on the value of JCI, gross domestic product has a significant negative effect on JCI and the LQ-45 index has a positive effect on JCI. The results of the comparison of JCI projections with 3 models showed that the OLS model offers more accurate results and has the lowest MAPE value in predicting stock price movements.

Evaluation and pollution analysis of water environment of state-controlled river in Zaozhuang City from 2016 to 2022

The quality of water environment is closely linked to the survival and development of human beings. Thanks to the Water Pollution Prevention and Control Action Plan, the quality of the water environment has improved significantly. However, challenges remain in the surface water environment due to human activities in the basin. We analyze the surface water quality of the six state-controlled rivers in Zaozhuang City, which are key tributaries to the Nansi Lake and the water storage and distribution area of the South-to-North Water Diversion East Project. Physical-chemical parameters and metal pollutants were detected from 2016 to 2022. The Various indices were used for evaluating the surface water environmental quality and pollution issue. Although there were significant inter-year differences in each river, the results of Water quality index and Nemerow composite index analysis indicated that the water quality of the six rivers has continuously improved over the years. Nevertheless, as this research reports, such as considerable annual fluctuations in pollution factors, obvious pollution characteristics in river basins, and difficulty in controlling non-point source pollution, which still restrict the further improvement of the water environment. Each river presents various water environment problems due to differences in industrial structures within its basin. Through the analysis of production and lifestyle in each basin, the main pollution sources of each river were evaluated and determined. Based on the type and characteristics of pollution sources, control measures and optimization strategies are proposed for the improvement of the water environment.

Water–Ecological Health Assessment Considering Water Supply–Demand Balance and Water Supply Security: A Case Study in Xinjiang

Water scarcity and ecological degradation in arid zones present significant challenges to regional ecological health. Despite this, integrating the water supply–demand balance and water supply security (SEC) into ecological health assessments—particularly through composite indicators—remains underexplored in arid regions. In this study, we assessed the ecological health changes in Xinjiang by utilizing multivariate remote sensing data, focusing on the balance between water supply and demand, the degree of SEC, and ecosystem resilience (ER). Our results indicate that while water supply and demand remained relatively stable in northern Xinjiang between 2000 and 2020, the conflict between supply and demand intensified in the southern and eastern agricultural regions. SEC evaluations revealed that 73.3% of the region experienced varying degrees of decline over the 20-year period. Additionally, ER assessments showed that 7.12% of the region exhibited a significant decline, with 78.6% experiencing overall reductions in ecological health. The indicators’ response to drought demonstrated that improvements in ecological health during wet conditions were less pronounced than declines during droughts. This study underscores the necessity of prioritizing areas with lower ecological health in future water allocation strategies to optimize water resource utilization.

PREDICTION INDONESIA COMPOSITE INDEX USING INTEGRATION DECOMPOSITION- NEURAL NETWORK ENSEMBLE DURING VUCA ERA

The Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) era causes turmoil in the capital markets, stocks, commodities, etc. The impact is a decline in the Composite Stock Price Index (IHSG) in 2020. Therefore, future data is needed to inform investors and business people when making portfolio decisions. This paper develops a decomposition and Neural Network (NN) integration model to predict ICI during the VUCA era. The results are presented empirically to show the model's effectiveness in reducing prediction errors. First, the actual data is converted into three components; second, with the Neural Network Ensemble (NNE) approach where the initial step of decomposition results is trained using artificial NN with architecture, training data, and topology to produce individual networks; The output is selected using Principal Component Analysis (PCA) and becomes input to the ensemble model, then combined using a simple average and weighted average. The empirical results from ICI predictions illustrate: (1) decomposition has the potential to overcome data that is characterized by high volatility; (2) NNE is able to reduce errors (MSE≤0.100e-4, MAE≤0.01) compared to individual networks (MSE=0.0024 MAE=0.0376); (3) ensemble combinations using weighted averages (MSE≤3.00e-5,MAE≤0.002) are superior to simple averages (MSE≤5.00e-5,MAE≤0.01); (4) the integration carried out shows effectiveness in predicting ICI and provides better prediction results.

A Comparative Analysis of Serum Irisin Levels and Body Composition Indices in Women With and Without Polycystic Ovary Syndrome (PCOS)

A Comparative Analysis of Serum Irisin Levels and Body Composition Indices in Women With and Without Polycystic Ovary Syndrome (PCOS)

How Can Digital Villages Improve Basic Public Services Delivery in Rural Areas? Evidence from 1840 Counties in China

Digital transformation is spreading from urban to rural areas, and the construction of digital villages has become a key growth point for rural sustainable development globally. Digital villages improve the level of basic public services delivery in rural areas via the penetration of digital technology. However, few studies have empirically investigated the theoretical mechanisms underlying the impact of digital villages on various aspects of rural basic public services. To address these gaps, this study investigates the impact mechanisms of digital villages on rural basic public services delivery in terms of accessibility, equity, agility, holistic nature and participation. Using 1840 counties in China as the research sample, this study applies the entropy method to extract a composite index of basic public services and performs correlation, regression, and heterogeneity analyses to examine the effects of digital villages on basic public services delivery. Empirical analysis results show that the construction of digital villages is positively associated with the level of basic public services in rural areas. Meanwhile, heterogeneity analysis results confirms that this relationship is mainly observed in eastern counties but not observed in central and western counties. These findings provide a basis for using digital inclusion to improve basic public services delivery in rural areas and achieve balanced development across regions.

Developing a New Method to Rapidly Map Eucalyptus Distribution in Subtropical Regions Using Sentinel-2 Imagery

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (CIEC) and for eucalyptus–broadleaf forest separation (CIEB), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for CIEC and 1.29 for CIEB) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions.

The relationship between oxidative balance score and circadian syndrome: evidence from the NHANES 2005-2018.

The oxidative balance score (OBS) is a composite indicator that evaluates the balance between pro-oxidants and antioxidants in one's diet and lifestyle. However, the relationship between OBS and circadian syndrome (CircS) has remained unexplored. This investigation aimed to determine a correlation between OBS and CircS. This population-based study examined 7,202 participants from the 2005 to 2018 National Health and Nutrition Examination Survey (NHANES), 1,433 of whom had CircS. We utilized weighted multivariate logistic regression, trend tests, subgroup analysis, and interaction tests to evaluate the correlation between OBS (total OBS, dietary OBS, and lifestyle OBS) and CircS. Restricted cubic splines (RCS) models and threshold effect analysis were used to explore nonlinear relationships. Multivariate logistic regression analysis indicated that the protective factor for CircS was a high OBS level (total OBS: Odds ratio (OR) = 0.95, 95% Confidence interval (CI): 0.93-0.97; dietary OBS: OR = 0.98, 95% CI: 0.96-1.00; lifestyle OBS: OR = 0.65, 95% CI: 0.61-0.69). Compared to the quartile 1 group, OBS (total OBS, dietary OBS, and lifestyle OBS) was negatively and statistically significantly associated with the risk of developing Circs in the quartile 4 group (total OBS: OR = 0.47, 95% CI: 0.32-0.70; dietary OBS: OR = 0.69, 95% CI: 0.48-0.99; lifestyle OBS: OR = 0.07, 95% CI: 0.04-0.11). According to subgroup analysis and interaction tests, there was an interaction effect between the association of lifestyle OBS and CircS in terms of education level (p for interaction = 0.01). Furthermore, we observed a nonlinear negative relationship between lifestyle OBS and CircS prevalence, with inflection points at 6 (p for nonlinearity = 0.002). The results showed a substantial negative connection between OBS and CircS. Encouraging foods filled with antioxidants and antioxidant-rich lifestyles may reduce the risk of CircS.

Association between Dietary Antioxidants and Atherosclerotic Cardiovascular Disease in South Korea: Insights from a Comprehensive Cross-Sectional Analysis

Background/Objectives: The multifactorial nature of atherosclerotic cardiovascular disease (ASCVD) implicates genetic, environmental, and dietary habits. Antioxidants found in foods have garnered attention for their potential role in mitigating ASCVD risk by combating oxidative stress. This study seeks to confirm the findings of previous research through a large-scale cross-sectional analysis performed in a unique population with Korea National Health and Nutrition Examination Survey data to explore the association between the composite dietary antioxidant index (CDAI) and ASCVD prevalence among middle- and old-aged individuals in South Korea. Methods: This study includes data from 2016 to 2021. The CDAI was calculated based on nutrition intake, including zinc, beta-carotene, vitamin A, vitamin C, vitamin E, and docosahexaenoic acid. This cross-sectional analysis explored the relationship between the CDAI and ASCVD after adjusting for relevant covariates. Logistic regression models were employed, and subgroup analyses by sex were conducted to discern sex-specific effects. Results: A total of 19,818 individuals were analyzed, with 7.0% of them diagnosed with ASCVD. CDAI distribution and antioxidant analyses revealed higher CDAI levels in non-ASCVD individuals. Standardized antioxidant values increased across CDAI quartiles. Initially, a significant association (odds ratio [95% confidence interval]: 0.96 [0.94–0.99]) was found between the CDAI and ASCVD, which was attenuated after adjusting for covariates (1.0 [0.98–1.02]). Subgroup analyses by sex showed nuanced associations, with the CDAI potentially reducing the risk of ASCVD in men (0.71 [0.53–0.94]) while increasing it in women (1.4 [1.01–1.95]). Conclusions: This study provides valuable insights into the association between dietary antioxidant intake and the risk of ASCVD, highlighting sex-specific differences.

Planetary health in action: developing a heatwave vulnerability tool for primary care.

Heatwaves are becoming longer and more frequent. Despite the availability of open environmental data, little is operable and formatted for primary care use. Create a user-friendly online mapping tool to assess the vulnerability of communities to heatwaves for use by primary care practitioners. This study questioned what knowledge needed to be deployed, who needed to participate and how the knowledge should be shared. A participatory action-research project based on knowledge mobilization in France as part of the Green Data for Health Challenge. Knowledge was summarized on the factors most affecting heatwave vulnerability in a collaborative process, enabling a consensus on data variables and mobilised content for the online tool. Purposive sampling included primary care stakeholders with Regional Health Agencies (ARS), Public Health France, and data scientists. Nineteen participants participated in ten co-construction workshops, a brainstorming carousel strategy and five weekly co-design meetings between December 2022 and June 2023. The heatwave vulnerability variable was constructed using surface temperature, social deprivation, vegetation coverage, and presence of air conditioning equipment. Identified experts mobilized data on the national composite indicator at the communal level for heatwave morbidity. There is no standard platform for sharing environmental data in France. This co-creation study offers a new approach to incorporating environmental data on heatwaves into primary care consultations. We demonstrate the importance of knowledge mobilisation in primary care to bridge the research-practice gap. Integrating primary care records with environmental data may promote broader applications for planetary health research.

Composite endpoints in COVID-19 randomized controlled trials: a systematic review.

This study aimed to determine the prevalence of ordinal, binary, and numerical composite endpoints among coronavirus disease 2019 trials and the potential bias attributable to their use. We systematically reviewed the Cochrane COVID-19 Study Register to assess the prevalence, characteristics, and bias associated with using composite endpoints in coronavirus disease 2019 randomized clinical trials. We compared the effect measure (relative risk) of composite outcomes and that of its most critical component (i.e. death) by estimating the Bias Attributable to Composite Outcomes index [ln(relative risk for the composite outcome)/ln(relative risk for death)]. Composite endpoints accounted for 152 out of 417 primary endpoints in coronavirus disease 2019 randomized trials, being more frequent among studies published in high-impact journals. Ordinal endpoints were the most common (54% of all composites), followed by binary or time-to-event (34%), numerical (11%), and hierarchical (1%). Composites predominated among trials enrolling patients with severe disease when compared to trials with a mild or moderate case mix (odds ratio = 1.72). Adaptations of the seven-point World Health Organization scale occurred in 40% of the ordinal primary endpoints, which frequently underwent dichotomization for the statistical analyses. Mortality accounted for a median of 24% (interquartile range: 6%-48%) of all events when included in the composite. The median point estimate of the Bias Attributable to Composite Outcomes index was 0.3 (interquartile range: -0.1 to 0.7), being significantly lower than 1 in 5 of 24 comparisons. Composite endpoints were used in a significant proportion of coronavirus disease 2019 trials, especially those involving severely ill patients. This is likely due to the higher anticipated rates of competing events, such as death, in such studies. Ordinal composites were common but often not fully appreciated, reducing the potential gains in information and statistical efficiency. For studies with binary composites, death was the most frequent component, and, unexpectedly, composite outcome estimates were often closer to the null when compared to those for mortality death. Numerical composites were less common, and only two trials used hierarchical endpoints. These newer approaches may offer advantages over traditional binary and ordinal composites; however, their potential benefits warrant further scrutiny. Composite endpoints accounted for more than a third of coronavirus disease 2019 trials' primary endpoints; their use was more common among studies that included patients with severe disease and their point effect estimates tended to underestimate those for mortality.