Weighted Regression Model Research Articles

Influence of Urban Flooding on the Spatial Equity of Access to Emergency Medical Services Among Nursing Homes in Shanghai

With the rapid aging of the population and increasing demand for elderly care services, ensuring equitable access to emergency medical service (EMS) for nursing homes has become a critical public health challenge. As the first Chinese city to experience an aging society, Shanghai faces compounding pressures from rapid urbanization and recurrent urban flooding, both of which exacerbate disparities in healthcare accessibility. This study investigates the spatial equity of EMS access among nursing homes in Shanghai, with a particular focus on the impacts of urban flooding. Using ordinary least squares and geographically weighted regression models, the study reveals that EMS accessibility is relatively equitable under normal conditions but deteriorates significantly during flood events, particularly in suburban and low-lying areas. The findings show that flood-induced disruptions to road networks disproportionately impact nursing homes in peripheral districts, widening accessibility gaps. Additionally, the study identifies that factors such as road density, emergency center distribution, and flood inundation depth play critical roles in shaping spatial equity. The results underscore the need for strategic interventions to enhance healthcare resilience, including optimized facility allocation and flood-resistant infrastructure. Policymakers should adopt integrated planning approaches to ensure equitable EMS access for vulnerable elderly populations during emergencies.

Neighborhoods Renewal Implications for the Post-Pandemic Era: A Study of COVID-19 Infections in Central Shanghai, China

Since the onset of the Coronavirus Disease 2019 (COVID-19), urban older neighborhoods have faced increased vulnerability, prompting research into neighborhood renewal and resilience. However, research on COVID-19 infection and influencing factors in China's older neighborhoods remains relatively scarce. This study analyzed COVID-19 infections in central Shanghai to identify neighborhood-level factors affecting transmission. Using principal component analysis (PCA) and person correlation coefficients (PCC) to process the data, we established multiple linear regression (MLR) and geographically weighted regression models (GWR). To explore nonlinear relationships, we incorporated the random forest method (RF). Results indicated that older neighborhoods had higher infection rates compared to newer ones. Socioeconomic and built environment factors significantly influenced infection rates. Specifically, higher population density, road network density, and the number of subway stations were positively correlated with increased infection rates. RF analysis revealed a complex, nonlinear relationship between the number of high-income residents and infection rates. This study integrates built environment, socioeconomic, and population characteristics factors using multiple modeling approaches to better understand their impact on infection rates. It also introduces research on mainland Chinese cities as case studies, offering valuable insights for updating older urban neighborhoods to enhance community resilience. However, the study did not fully consider the impact of policies at the time, and its findings are primarily applicable to older neighborhoods in cities similar to Shanghai. Future research should examine the effectiveness of various intervention policies, the long-term effects of neighborhood renewal on community resilience, and the applicability of these findings to other urban environments.

Correction: A scale-adaptive estimation for mixed geographically and temporally weighted regression models

Correction: A scale-adaptive estimation for mixed geographically and temporally weighted regression models

Study on Driving Mechanism of Geological Environment Carrying Capacity

The carrying capacity of the geological environment to human activities is limited, and the maximum limit of this carrying capacity is the geological environment carrying capacity. It is necessary to scientifically understand the concept of geological environment carrying capacity from two dimensions: the geological environmental conditions (the carrier) and human activities (the load). To date, research on geological environment carrying capacity has focused on the establishment of evaluation methods and indicator systems, and the research seldom deals with the driving mechanism of geological environment carrying capacity. This paper evaluates the background carrying capacity and the carrying state of the geological environment in the Dongning-Suifenhe region by using the hierarchical comprehensive index method. On this basis, the geological weighted regression model is used to study the driving mechanism of geological environment carrying capacity. The background carrying capacity of the geological environment, primary sector, and population are identified as the three driving factors. All of them have positive driving effects on the geological environment carrying state.

Indirect treatment comparison of lanadelumab and a C1-esterase inhibitor in pediatric patients with hereditary angioedema.

Aim: To compare the efficacy and safety of lanadelumab versus other approved long-term prophylaxis (LTP) treatments in patients with pediatric hereditary angioedema (HAE) aged <12years. Materials & methods: A systematic literature review was conducted to identify studies of LTP in patients with HAE aged <12years. Two studies met the inclusion criteria in an indirect treatment comparison of efficacy and safety data in pediatric HAE patients. These were for lanadelumab (SPRING, NCT04070326) and intravenous-C1-esterase inhibitor (C1-INH[IV], NCT02052141). A propensity score analysis used individual patient-level data from both studies in a logistic regression model to estimate inverse probability weights. To avoid convergence issues and an underpowered analysis due to the small sample size (n=29), the base case was defined as Poisson regression analyses on monthly attack rate adjusting for one covariate (baseline attack rate). Model selection among unadjusted, adjusted and weighted regression models was conducted through the Akaike and Bayesian Information Criteria. Results: Lanadelumab 150mg every 2weeks (Q2W) reduced the monthly HAE attack rate by 82.1% versus C1-INH(IV) 1000 IU twice weekly (every 3-4days [BIW]; rate ratio [RR], 0.1792 [95% CI: 0.0296-1.0853]) and by 88.9% versus C1-INH(IV) 500 IU BIW (RR: 0.1107 [95% CI: 0.0234-0.5239]). Treatment with lanadelumab Q2W reduced the risk of total adverse events by 56.2% versus C1-INH(IV) 1000 IU BIW (RR:0.4377 [95% CI: 0.1536-1.2469]) and by 66.0% versus C1-INH(IV) 500 IU BIW (RR: 0.3401 [95% CI: 0.1234-0.9371]). Conclusion: This exploratory analysis suggested a trend toward greater efficacy and fewer adverse events with lanadelumab 150mg Q2W compared with C1-INH(IV) BIW 1000 IU and 500 IU in pediatric patients with HAE. Future studies could potentially assess larger samples over longer periods of time for the long-term preventative efficacy, safety and tolerability of lanadelumab and C1-INH(IV).

Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities

The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. Extreme gradient boosting (XGBoost) multiscale geographically weighted regression models(MGWR) and SHAP theory from game theory were employed to deeply investigate the disparities in relevance, spatial heterogeneity, and multiscale fluctuations of carbon emissions and air pollution. The main results are summarized as follows: (1) Between 2015 and 2018, CO2 emissions exhibited significant fluctuations, while SO2 and PM2.5 concentrations decreased markedly. (2) The XGBoost-SHAP model identified seven key driving factors, demonstrating high precision, the SHAP model is used to explain the model and reveal the influence of driving factors on carbon emissions. (3) The concentrations of CO2, SO2, and PM2.5 were positively correlated, the influence of each factor exhibited significant spatiotemporal differences, with varying directions of fluctuation across different regions. Thus, the symbiotic relationship between carbon emissions and air pollutants can inform decision-making for regional planning and sustainable urban development.

Association between composite dietary antioxidant index and hyperlipidemia in adults based on the NHANES

Hyperlipidemia, a major risk factor for cardiovascular diseases, can potentially be alleviated by dietary antioxidants. This study explored the relationship between hyperlipidemia and the Composite Dietary Antioxidant Index (CDAI), which measures antioxidant intake from carotenoids, selenium, zinc, and vitamins A, C, and E. Analyzing data from 27,493 participants aged 20 and older from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2018, we used weighted regression models to assess this association while adjusting for multiple covariates. Restricted cubic splines were also applied to check for any non-linear relationships. Findings revealed that higher CDAI scores significantly correlated with lower hyperlipidemia prevalence. Specifically, each unit increase in log-transformed CDAI was linked to a 14% decrease in hyperlipidemia risk (OR = 0.86, 95% CI: 0.77–0.95). Participants in the top CDAI quartile had a 15% lower hyperlipidemia prevalence compared to those in the bottom quartile (OR = 0.85, 95% CI: 0.77–0.95). No significant non-linear relationship was observed (p = 0.822). In conclusion, increased antioxidant intake, as measured by CDAI, is associated with reduced hyperlipidemia, underscoring the role of antioxidants in its management.

Breaking down barriers: how board composition drives sustainability in GCC countries

This study empirically examines the impact of board composition on firm sustainability within Gulf Cooperation Council (GCC) countries from 2017 to 2021. Using a sample of 135 non-financial, publicly listed GCC firms (364 firm-year observations), this article investigates the effects of gender diversity, board size, independence, skills, and the presence of sustainability committees on sustainability outcomes. Employing advanced econometric techniques—including panel regression, weighted regression models, quantile regression, Tobit, and logit analyses—to ensure robustness and address potential endogeneity, the study provides evidence of a significant positive effect of board gender diversity on sustainability. In contrast, other board characteristics do not consistently influence sustainability performance. These findings emphasise the unique role of female directors in promoting sustainable governance in the GCC, suggesting that increasing gender diversity may be a more effective strategy for corporate sustainability than focusing on other board factors. This article extends agency and resource dependence theories in the GCC context, offering practical insights for policymakers and managers in emerging markets. It highlights the importance of diverse boards in promoting sustainability and calls on policymakers to implement regulations encouraging board diversity for long-term corporate sustainability in the region—a call to action for a sustainable future.

Drivers of forest productivity in two regions of the United States: Relative impacts of management and environmental variables.

Climate, environmental conditions, and management strategies are key factors affecting forest net ecosystem production (NEP). However, little is known about the relationship between management approaches and regional to continental-scale forest productivity. In this study, we utilized forests of the U.S. Southeast (SEUS) and Pacific Northwest (PNW), two ecologically and socio-politically distinct regions, to answer the question: Does management exert a stronger influence on NEP than environmental factors? We estimated Geographically Weighted Regression models of NEP derived from the Ecosystem Demography Model as a function of soil, topography, climate, and forest management practices for the period 2000-2015 using 383 and 407 10×10km2 landscapes in SEUS and PNW, respectively. Results showed that forest management practices were important in predicting NEP only in mountainous northeastern areas of the SEUS; in the PNW, NEP had a more complex relationship with management and was positively related to ecological, preservation, and passive management. Management, topography, and soil were more strongly correlated with NEP in the PNW than in the SEUS, in which 81%, 83%, and 83% of PNW locations showed significant relationships with at least one management, topography, or soil variable, repectively. In contrast, seasonal precipitation and temperature were stronger predictors of NEP in the SEUS than other drivers, with 99% and 84% of the locations influenced by at least one seasonal temperature or precipitation variable, respectively. The findings of this study may provide a valuable framework for forest management - climate change strategies that could be extended across regional scales.

Genetic Test Utilization and Cost among Families of Children Evaluated for Genetic Conditions: An Analysis of USA Commercial Claims Data.

Healthcare payers in the USA increasingly cover genetic testing, including exome sequencing (ES), for pediatric indications. Analysis of claims data enables understanding of utilization and costs in real-world settings. The objective of this study was to describe genetic test utilization, diagnostic outcomes, and costs for children who received ES as well as for those who received less comprehensive forms of genetic testing, along with their families. We analyzed linked family claims data for commercially insured members of a large regional health plan. The sample included children younger than 18 years of age who had at least 1 year of continuous plan enrollment and at least one claim for genetic testing from 2016 to 2022, as well as their family members. We compared outcomes for children who ever had a claim for ES (ES cohort) with those for children who had claims for only less comprehensive genetic testing (other genetic testing (OGT) cohort). We evaluated the frequency of ICD-10 codes indicating genetic diagnoses, health care utilization, and out-of-pocket costs in relation to the timing of the index genetic test using t-tests and inverse-probability-of-treatment weighted regression models to control for observable clinical and demographic characteristics associated with type of testing received. Our sample included 182 children (mean comorbidity index 4.78) in the ES cohort and 1789 children in the OGT cohort (3.63; p<0.001). ES led to an average of 1.44 (95% confidence interval [CI] 0.67-2.20) more new genetic diagnostic codes after testing than OGT. A larger proportion of the proband's family members had subsequent genetic testing in the ES cohort (mean 33.3%) than in the OGT cohort (0.5%; p<0.001), but no differences in the number of new genetic diagnoses in family members were observed. Out-of-pocket costs for genetic testing did not differ between the two cohorts stratified by clinical severity. In our sample of commercially insured pediatric patients, claims for ES were less frequent and occurred among children with more clinical complexity than those for less comprehensive genetic testing. Children in the ES cohort had a higher number of new genetic diagnoses post-testing than those in the OGT cohort with no significant differences in out-of-pocket cost of testing to families. Our findings suggest that ES is being reimbursed for children who may be difficult to diagnose.

Association between serum uric acid, hyperuricemia and low muscle mass in middle-aged and elderly adults: A national health and nutrition examination study.

Recent research suggests that uric acid, as a metabolite with antioxidant properties, may affect muscle function and health. However, the association between serum uric acid (SUA) and low muscle mass remains relatively obscure. This study focuses on the association between SUA and low muscle mass in a middle-aged and elderly population in the United States. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), a total of 12,106 patients aged ≥45 years, possessing complete analytical data, were incorporated. Low muscle mass in our study is defined as indices below 0.789 for males and 0.512 for females, according to the FNIH Biomarkers Consortium. Gender stratified analyses were conducted employing a multivariate weighted logistic regression model. When examining serum uric acid (SUA) levels, the SUA dataset was stratified into deciles, and odds ratios (ORs) were calculated across distinct subgroups of males and females. A restricted cubic spline (RCS) method was employed to investigate the potential nonlinear association between SUA levels and low muscle mass. A series of subgroup analyses stratified by demographic variables and clinical experience were conducted. A total of 2,185 participants (18.05%) were identified with low muscle mass, comprising 1,121 males and 1,064 females. Females with low muscle mass had higher SUA levels and an increased incidence of hyperuricemia compared to those without low muscle mass. In females, a fully adjusted multivariable weighted regression model revealed a positive association between hyperuricemia and low muscle mass (OR, 1.43; 95% CI, 1.06 to 1.92; P = 0.021). No significant association was observed in males. Additionally, RCS curves indicated a J-shaped relationship between increasing SUA levels and the risk of low muscle mass in females, and an inverse J-shaped relationship in males. This study reveals a significant positive correlation between hyperuricemia and the risk of low muscle mass in middle-aged and older women in the United States, whereas the relationship between SUA levels and low muscle mass did not attain statistical significance. In the male cohort, neither SUA levels nor hyperuricemia demonstrated a significant association with low muscle mass.

Dual antiplatelet therapy after minor strokes or high-risk TIA: Evidence from the Austrian stroke registry.

Three large, randomized trials demonstrated the benefit of short-term dual antiplatelet therapy (DAPT) versus monotherapy after non-cardioembolic minor stroke or high-risk transient ischemic attack (TIA). The aim of this study was to evaluate effects of DAPT versus monotherapy on functional outcomes and safety in a real-life setting. Patients with minor stroke (NIHSS <4) or high-risk TIA (ABCD2 score ≥4) of non-cardioembolic origin without major vessel occlusion or revascularization therapy (thrombolysis or thrombectomy) treated between 2018 and 2023 were analyzed based on a prospective nationwide stroke unit registry. Data on risk factors, stroke etiology, admission stroke severity (NIHSS), functional status at 3 months (mRS), and mortality were extracted. Excellent functional outcome (mRS 0-1) at 3 months, early neurological deterioration (END), symptomatic intracranial hemorrhage (SICH) and major extracranial bleeds were defined as study endpoints and adjusted for covariates using inverse probability of treatment weighted regression models. Two Thousand Two Hundred Fifty-four of 8546 patients with non-cardioembolic minor stroke or high-risk TIA received DAPT. Patients treated with DAPT had significantly more risk factors and comorbidities compared to those treated with monotherapy. After robust statistical adjustment, DAPT was significantly associated with lower occurrence of END (OR 0.50 95% CI 0.35-0.72), increased odds of excellent outcome at 3 months (aOR 1.59; 95% CI 1.20-2.09) and equivalent frequencies of SICH (aOR 1.19, 95% CI 0.30-4.73) or major extracranial bleeding (aOR 0.84; 95% CI 0.16-4.56). DAPT in non-cardioembolic minor stroke or high-risk TIA in a real-life setting appears to be safe and associated with improved functional outcome.

Assessing microbial risks of Escherichia coli: A spatial and temporal study of virulence and resistance genes in surface water in resource-limited regions.

Environmental microbial pollution can potentially cause illnesses and deaths among human populations. Environmental factors such as geomorphology, land use, human activities, and seasonality could influence pathogen dissemination. This study performed a space-temporal analysis of Escherichia coli concentrations, virulence (eaeA, bfpA, stx2, aatA, and st), and resistance (tetA, blaTEM, blaOXA, Intl-1, Intl-2, sul-1, and sul-2) genes in surface water within the Virilla sub-watershed, Costa Rica. The research employs microbiological, geostatistical, and quantitative microbial risk assessment (QMRA) techniques to evaluate the health risks associated with diarrheagenic E. coli. Samples collected monthly over a year were analyzed for fecal coliforms, E. coli, Enterococcus faecalis, physicochemical parameters, and the presence of virulence and resistance genes. The geographically weighted regression model identified significant associations between the virulence and resistance genes presence and environmental factors influence such as land use, slope, altitude, and seasonal changes. The results show that urban and grazing land use significantly influence the presence of resistance genes, while human fecal pollution is associated with higher concentrations of virulence genes. Seasonal variations showed that wet seasons contributed to increased gene presence (number of positive samples) and diversity (variety of virulence and resistance genes) compared with dry season. The QMRA results indicated that the total health disease burden was higher than the benchmark for drinking water recommended by WHO (1.00×10-6 DALY - Disability-Adjusted Life Years - per person per year) by between one and four magnitudes orders, particularly in regions with urban and grazing land use, human fecal pollution class, and lower altitudes. This study highlights the relevance of integrating environmental and microbial data to enhance water quality management and public health strategies in resource-limited regions. It underscores the need for targeted interventions, such as improved wastewater treatment and establishing buffer zones, to mitigate the risks associated with microbial pollution in surface waters.

A Study on Spatial Differentiation of Urban Land Prices Based on Geographically Weighted Regression Models

Urban residential land price is related to the basic life of residents as well as the healthy and stable development of society. In this paper, the residential land price in QZ city was selected as the research object, and the residential land price data of 107 sample points in the built-up area were collected for the 10 years from 2013 to 2022, and the Exploratory Spatial Data Analysis (ESDA) method and Geostatistical Kriging Spatial Interpolation (GSI) method were combined to investigate the law of spatial heterogeneity of urban land price. Finally, general linear regression model and geographically weighted regression model are constructed to investigate the influencing factors of urban land price spatial differentiation by selecting suitable influencing factors. The sample data of residential land price in the study area basically conforms to normal distribution, and the land price shows a U-shape in the latitudinal direction, and in the horizontal direction, the land price shows a trend of decreasing from the city center to the periphery, and a “mono-core” spatial structure of decreasing from the center to the periphery. There are large regional differences in the contribution of plot ratio, distance from main roads, distance from hospitals and distance from schools to residential land price, among which the marginal effect of plot ratio on residential land price reaches 122.5385yuan/m², which reflects the importance of plot ratio to residential land price. The study provides a decision-making basis for government targeted land planning, urban infrastructure investment and real estate development.

Synergistic effects of pollution reduction and carbon mitigation from socioeconomic factors, land use and urban innovation: a case study of Wuhan metropolitan area

Achieving synergistic effects in pollution reduction and carbon mitigation is a major national strategy for China. Given the common origins and processes of air pollutants and greenhouse gases, this study constructs a theoretical framework for the study of the synergistic effects of air pollution and carbon emissions. Based on the coupling coordination degree model and the geographically and temporally weighted regression model, it identifies significant factors influencing the synergistic effects of air pollution and carbon emissions and their varying mechanisms of action. Results are as follows: 1) The spatial and temporal trends of PM2.5 pollution and carbon emissions in the Wuhan metropolitan area exhibit homogeneity. The coupling coordination degree between air pollution and carbon emissions shows an initial increase followed by a decrease over time and a spatial pattern of “local clustering of areas with medium–high-level coupling coordination”. 2) Twelve factors significantly impact the synergistic effects of air pollution and carbon emissions at the county level in the Wuhan metropolitan area: number of inversion days, precipitation, temperature, vegetation coverage, number of green patents, total population, regional GDP, per capita regional GDP, proportion of secondary industry, total nighttime light, energy consumption efficiency and built-up area. 3) The impact intensity of these factors on the synergistic effects of air pollution and carbon emissions varies not only over time but also across different regions within the same year. Regions with strong impact forces shift over time. This manuscript provides a solid foundation for theoretical research on and practical strategies for advancing differentiated pollution reduction and carbon mitigation coordination.

Post-COVID Condition Risk Factors and Symptom Clusters and Associations with Return to Pre-COVID Health-Results from a 2021 Multi-State Survey.

Little is known about how symptoms or symptom clusters of Post-COVID Conditions (PCC) impact an individual's return to pre-COVID health. We used four state-level COVID-19 case reporting systems and patient-reported survey data to identify patients with PCC and associations with an individual's return to pre-COVID health after laboratory-confirmed SARS-CoV-2 infection. Participants had a positive SARS-CoV-2 test between March-December 2020. Weighted regression models were used to 1) estimate prevalence of PCC; 2) identify risk factors associated with developing PCC; and 3) examine associations between PCC symptom clusters and return to pre-COVID health. Factor analysis was used to statistically identify post-COVID symptom clusters. Prevalence of PCC in this population-based sample was 29·9% for persons with SARS-CoV-2 infection, during the pre-delta variant period (March-December 2020); 77·2% of persons experiencing PCC had not returned to pre-COVID health within 8-60 weeks after infection. Female sex, acute COVID-19 illness severity, and number of pre-existing comorbidities were significant risk factors associated with PCC. Myalgic encephalomyelitis/chronic fatigue syndrome-like symptoms, upper-respiratory symptoms, and gastrointestinal symptoms were significantly associated with not returning to pre-COVID health. Understanding PCC symptom clustering may provide insight into pathophysiology, severity of PCC, and management for patients who have not returned to their usual state of health after SARS-CoV-2 infection. Tracking PCC can help measure the impact of COVID-19 vaccination and acute COVID-19-specific treatments on reducing PCC in the US.

Environmental justice issues in drinking water contaminant exposure in a European context.

Previous studies have documented ethnic and sociodemographic disparities in exposure to drinking water (DW) contaminants. A majority were conducted in the U.S., with fewer studies conducted in other regions. This research aims to assess available evidence regarding environmental justice (EJ) issues in Europe, identify contaminants and potential drivers. A Scoping Review was conducted, exploring the existing European studies from 1990 to 2022. The review encompasses types of DW contaminants studied in relation to EJ, research designs, and potential drivers contributing to inequalities in exposure to specific contaminants. In addition, a case study was conducted in Ille-et-Vilaine, France, focusing on contaminants identified in the review and using a national monitoring database. Inequalities in contaminants' exposure were assessed using a composite deprivation index, FDep, at the census tract level (IRIS) applied in multilevel models and geographically weighted regression models, accounting for the rural-urban heterogeneity. Results show a limited number of primary studies focusing on EJ and DW contaminants exposure in Europe (n=16). Various chemical contaminants such as nitrates, trihalomethanes (THMs), heavy metals, fluoride and pesticides have been assessed. Case study findings suggest some association between FDep and contaminants, with a different level of correlation depending on the contaminant. THMs show a negative correlation with deprivation, while lead displays a positive correlation related to the FDep. Disparities in exposure were also found according to the spatial scale of analysis. In rural areas, higher deprivation levels were associated with higher levels of nitrate (OR: 1.47; 95%CI: 1.02, 2.15) and lower level of fluoride (OR: 0.16; 95%CI: 0.07, 0.30) or THMs (OR: 0.73; 95%CI: 0.55, 0.98) in tap water. This study emphasizes the need for comprehensive research on EJ and DW contaminants exposure on a larger scale. Understanding complex interactions between contaminant distribution, socioeconomic factors, and exposure is essential for addressing EJ in drinking water.

Association of serum Klotho with the severity and mortality among adults with cardiovascular-kidney-metabolic syndrome

BackgroundCardiovascular-kidney-metabolic (CKM) syndrome is characterized as a systemic disease resulting from the pathophysiological interplay among metabolic risk factors, chronic kidney disease (CKD), and cardiovascular disease (CVD). The Klotho protein may serve as a novel biomarker. However, the utility of serum Klotho levels as an indicator of severity and mortality risk in CKM syndrome remains uncertain.MethodsThis study involved 9,871 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2016. Serum Klotho levels were measured using an enzyme-linked immunosorbent assay kit. The optimal cutoff value was established through the maximum Youden’s index. Multivariable weighted regression models were employed to calculate the odds ratio and hazard ratio, along with the 95% confidence interval, to evaluate the association between serum Klotho levels and the severity of CKM syndrome, as well as all-cause and cardiovascular mortality. Additionally, the receiver operating characteristic curve and restricted cubic spline curves were utilized to assess predictive efficacy and to explore nonlinear relationships.ResultsAfter adjusting for potential confounding factors, a non-linear relationship was seen between the Klotho protein, and CKM syndrome. In the multivariable, piecewise logistic regression, when the Serum klotho was less than 801, the risk of CKM syndrome decreased with the increase in Serum klotho (OR = 0.82, 95%CI 0.70, 0.96; p < 0.001). Furthermore, we observed the association when the Serum klotho was greater than 801 (OR = 0.94, 95%CI 0.89, 0.99; p = 0.035). The relationship between serum Klotho levels and all-cause mortality was U-shaped, while the relationship with cardiovascular mortality was L-shaped. Specifically, low serum Klotho levels were associated with an increase in all-cause mortality by 21% and cardiovascular mortality by 76% among patients with CKM syndrome. Furthermore, serum Klotho levels demonstrated excellent predictive efficacy for both the severity and mortality associated with CKM syndrome.ConclusionsThis study indicates that low serum Klotho levels serve as reliable indicators of both the severity of CKM syndrome and the associated risk of mortality.

On the calibration of multiscale geographically and temporally weighted regression models

Spatiotemporal analysis and modeling have long been key foci of geographical information science. In a recent paper, Wu et al. expanded the local spatial modeling technique of multiscale geographically weighted regression (MGWR) to incorporate a temporal component. This new model is named multiscale geographically and temporally weighted regression (MGTWR). Despite the utility of expanding MGWR to incorporate a temporal weighting function in addition to the existing spatial one, the approach developed by Wu et al. has two limitations: the bandwidth selection algorithm cannot guarantee an optimal result and no formulation for the effective number of parameters (ENPs) in the model is given. To address these issues, this paper describes a procedure to derive an optimal temporal and an optimal spatial bandwidth for MGTWR and also develops a formula for the ENPs in the model. The former ensures the reliability of the model outputs while the latter is essential for making reliable inferences from the local parameter estimates generated in the calibration of models by MGTWR. These advances in the MGTWR framework are demonstrated through applications to simulated and real-world data.

Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Heilongjiang Province in the Past 20 years

PM2.5 is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM2.5 remote sensing data from 2000 to 2021, the Theil-Sen Median trend analysis, Mann-Kendall significant inspection, and spatial auto correlation were used to analyze the characteristics of space-time changes in PM2.5 concentration, and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM2.5 spatial differences. The results showed that： ① The average PM2.5 value of Heilongjiang Province was between 22.01 and 41 μg·m-3 from 2000 to 2021. From 2008 to 2015, the average PM2.5 value was higher than the secondary concentration limit （35 μg·m-3） of the "Environmental Air Quality Standard." The turning point of the PM2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM2.5 pollution. The PM2.5 concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin, Daqing City, and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM2.5 spatial differences. The remaining key driver factors were in turn： high-end, population density, average annual wind speed, land use, night lights, annual years precipitation, slope, annual relative humidity, and NDVI. Interactive detection showed that the interpretation of PM2.5 points after interaction was higher than a single factor after interaction, indicating that affecting PM2.5 spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM2.5 had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM2.5 pollution and NDVI and land use played an inhibitory effect on PM2.5 pollution. PM2.5 was significantly different from the action role of various influencing factors and the average annual temperature, annual average wind speed, and NDVI impact scale were the smallest, with a variable bandwidth of 43； population density and land use impact scale were the largest, with a variable bandwidth of 140.