Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Trajectory of medical expenditure and regional disparities in hypertensive patients in South Korea.

The aim of this study is to understand how different regions influence the management and financial burden of hypertension, and to identify regional disparities in hypertension management and medical expenditure. The study utilized data from the Korean Health Panel Survey conducted between 2014 and 2018, focusing on individuals with hypertension. Medical expenditures were classified into three trajectory groups: "Persistent Low," "Expenditure Increasing," and "Persistent High" over a five-year period using trajectory analysis. Inverse Probability Weighting (IPW) analysis was then employed to identify the association between regions and medical expenditure trajectories. The results indicate that individuals residing in metropolitan cities (Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan) and rural areas were more likely to belong to the "Expenditure Increasing" group compared to the "Persistent Low Expenditure" group (OR = 1.07; 95% CI; p < 0.001), as opposed to those in the capital city (Seoul) (OR = 1.07; 95% CI; p < 0.001). Additionally, residents of rural areas were more likely to be in the "High Expenditure" group compared to the "Persistent Low Expenditure" group than those residing in the capital city (OR = 1.05; 95% CI; p = 0.001). These findings suggest that individuals in rural areas may be receiving relatively inadequate management for hypertension, leading to higher medical expenditures compared to those in the capital region. These disparities signify health inequality and highlight the need for policy efforts to address regional imbalances in social structures and healthcare resource distribution to ensure equitable chronic disease management across different regions.

Survival loss linked to guideline-based indications for degenerative mitral regurgitation surgery.

AIMS: Operating on patients with severe degenerative mitral regurgitation (DMR) is based on ACC/AHA or ESC/EACTS-guidelines. Doubts persist on best surgical indications and their potential association with postoperative survival loss. We sought to investigate whether guideline-based indications lead to late postoperative survival loss in DMR-patients. METHODS AND RESULTS: : We analyzed outcome of 2833 patients from the MIDA-registry undergoing surgical correction of DMR. Patients were stratified by surgical indications: Class-I-trigger (symptoms, left ventricular end-systolic diameter≥40mm, or left ventricular ejection fraction<60%, n=1677), isolated-Class-IIa-trigger (atrial fibrillation [AF], pulmonary hypertension [PH], or left atrial diameter≥55mm, n=568), or no-trigger (n=588). Postoperative survival was compared after matching for clinical differences. Restricted-mean-survival time (RMST) was analyzed. During a median 8.5-year follow-up, 603 deaths occurred. Long-term postoperative survival was lower with Class-I-trigger than in Class-IIa-trigger and no-trigger (71.4±1.9%, 84.3±2.3%, 88.9±1.9% at 10 years, p<0.001). Having at least one Class-I-criterion led to excess mortality (p<0.001), while several Class-I-criteria conferred additional death-risk (HR:1.53, 95%CI:1.42-1.66). Isolated-Class-IIa-triggers conferred an excess mortality risk versus those without (HR:1.46, 95%CI:1.00-2.13, p=0.05). Among these patients, isolated-PH led to decreased postoperative-survival versus those without (83.7%±2.8% vs. 89.3%±1.6%, p=0.011), with the same pattern observed for AF (81.8%±5.0% vs. 88.3%±1.5%, p=0.023). According to RMST-analysis, compare to those operated on without triggers, operating on Class-I-trigger patients led to 9.4-month survival-loss (p<0.001) and operating on isolated-Class-IIa-trigger patients displayed 4.9-month survival loss (p=0.001) after 10-years. CONCLUSIONS: : Waiting for the onset of Class-I or isolated-Class-IIa-triggers before operating on DMR patients is associated with postoperative survival loss. These data encourage an early surgical-strategy.

Inverse probability weighting for self-selection bias correction in the investigation of social inequality in mortality.

BACKGROUND: Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage. METHODS: The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009-11 (ages 49-63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population. RESULTS: Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]). CONCLUSIONS: Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy.

Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.

Optimization-Based Stable Balancing Weights Versus Propensity Score Weighting for Samples With High Covariate Imbalance.

PURPOSE: To compare the performance (covariate balance, effective sample size [ESS]) of stable balancing weights (SBW) versus propensity score weighting (PSW). Two applied cases were used to compare performance: (Case 1) extreme imbalance in baseline covariates between groups and (Case 2) substantial discrepancy in sample size between groups. METHODS: Using the Premier Healthcare Database, we selected patients who (Case 1) underwent a surgical procedure with one of two different bipolar forceps between January 2000 and June 2020, or (Case 2) a neurological procedure using one of two different nonabsorbable surgical sutures between January 2000 and March 2020. Average treatment effects on the treated (ATT) weights were generated based on selected covariates. SBW was implemented using two techniques: (1) "grid search" to find weights of minimum variance at the lowest target absolute standardized mean difference (SMD); (2) finding weights of minimum variance at prespecified SMD tolerance. PSW and SBW methods were compared on postweighting SMDs, the number of imbalanced covariates, and ESS of the ATT-weighted control group. RESULTS: In both studies, improved covariate balance was achieved with both SBW techniques. All methods suffered from postweighting ESS that was lower than the unweighted control group's original sample size; however, SBW methods achieved higher ESS for the control groups. Sensitivity analyses using SBW to apply variable-specific SMD thresholds increased ESS, outperforming PSW. CONCLUSIONS: In this applied example, the optimization-based SBW method provided ample flexibility with respect to prespecification of covariate balance goals and resulted in better postweighting covariate balance and larger ESS as compared with PSW.

Reassessing the Impact of Coffee Consumption on Liver Disease: Insights from a Large-Scale Cohort Study with IPTW Adjustment.

Coffee consumption is globally widespread and has become a lifestyle habit. This study investigated coffee consumption and liver-related survival in a large cohort of 455,870 individuals with UK biobank, categorized into without steatosis, metabolic dysfunction-associated steatotic liver disease (MASLD), and MASLD and increased alcohol intake (MetALD). Inverse probability of treatment weighting (IPTW) adjusted for confounding variables was used, followed by Kaplan-Meier analysis. Moderate coffee consumption (1-2 cups per day) was associated with lower all-cause mortality across the entire cohort, without the steatosis, MASLD (p < 0.0001), and MetALD cohorts (p = 0.0047 for pre-IPTW, p = 0.027 for post-IPTW). Before IPTW adjustment, consuming one or more cups of coffee per day appeared to significantly reduce liver-related mortality in the overall (p = 0.015) and MASLD cohorts (p = 0.011). However, post-IPTW application, no significant differences in liver-related mortality were observed between the coffee intake groups (p = 0.778, 0.319, 0.564, 0.238 for each group). While increased coffee consumption initially seemed to reduce liver-related mortality, after IPTW adjustment, only all-cause mortality significantly decreased (p < 0.0001 and p = 0.027). These findings suggest that previous studies might have overestimated the favorable effect of coffee intake on chronic liver disease due to confounding factors.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method.

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Application of information from external data to correct for collider bias in a Covid-19 hospitalised cohort.

BACKGROUND: Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. METHODS: The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 - a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. RESULTS: Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity - differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. CONCLUSIONS: In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.

Risk and survival of patients with non-small cell lung cancer and pre-existing autoimmune disorders receiving immune checkpoint blockade therapy: Survival analysis with inverse probability weighting from a nationwide, multi-institutional, retrospective study (NEJ047).

BACKGROUND: The risk and survival of patients with non-small cell lung cancer (NSCLC) with pre-existing autoimmune disorders (AIDs) receiving immune checkpoint blockade (ICB) therapy have not been clearly established. PATIENTS AND METHODS: This multi-institutional, retrospective cohort study was conducted in collaboration with 20 centers in Japan. RESULTS: In total, 229 patients with advanced or recurrent NSCLC and pre-existing AID, with or without ICB treatment from January 2010-February 2020, were included and analyzed. Among 69 patients who received ICB, 2 received two lines of ICBs with a total of 71 ICB treatments; 57 (80.3 %) and 14 (19.7 %) patients received ICB monotherapy and combination therapy, respectively. AID flares were observed in 18 patients (25.4 %, 95 % confidence interval [CI], 15.8-37.1 %) receiving ICB. AID exacerbations were more likely when NSCLC was diagnosed less than 1 year after the AID diagnosis (odds ratio 5.26 [95 % CI, 1.40-21.61]; P = 0.016). Immune-related adverse events were observed in 32 patients (45.1 %, 95 % CI, 33.2-57.3 %); 17 had grade 3 or higher. The safety profile of combination immunotherapy was not significantly different from that of the monotherapy. After inverse probability weighting, the use of ICB prolonged survival (hazard ratio 0.43 [95 % CI, 0.26-0.70]; P = 0.0006). CONCLUSIONS: These findings revealed a novel risk factor for AID flares following ICB treatment, that is the diagnosis of NSCLC within 1 year of AID diagnosis, and showed that ICBs may improve survival in this population. These results support the utilization of ICB in patients with NSCLC and pre-existing AID.

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates.

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

PA-MSHA improves prognosis of patients undergoing radical cystectomy: a retrospective cohort study using inverse probability of treatment weighting.

Objective: To observe the effect of Pseudomonas aeruginosa mannose-sensitive hemagglutinin (PA-MSHA) on the prognosis and the incidence of lymphatic leakage in patients undergoing radical cystectomy (RC). Method: A total of 129 patients who underwent RC in Lanzhou University Second Hospital from 2013 to 2022 were enrolled in this study. They were divided into 43 patients treated with PA-MSHA and 86 patients in the control group. Inverse probability of treatment weighting (IPTW) was applied to reduce potential selection bias. Kaplan-Meier method and Cox regression analysis were used to analyze the effect of PA-MSHA on the survival of patients and the incidence of postoperative lymphatic leakage. Results: The PA-MSHA group exhibited improved overall survival (OS) and cancer-specific survival (CSS) rates compared to the control group. The 3-year and 5-year overall survival (OS) rates for the PA-MSHA group were 69.1% and 53.2%, respectively, compared to 55.6% and 45.3% for the control group (Log-rank=3.218, P=0.072). The 3-year and 5-year cancer-specific survival (CSS) rates for the PA-MSHA group were 73.3% and 56.5%, respectively, compared to 58.0% and 47.3% for the control group (Log-rank=3.218, P=0.072). Additionally, the 3-year and 5-year progression-free survival (PFS) rates for the PA-MSHA group were 74.4% and 56.8%, respectively, compared to 57.1% and 52.2% for the control group (Log-rank=2.016, P=0.156). Multivariate Cox regression analysis indicates that lymph node metastasis and distant metastasis are poor prognostic factors for patients, while the use of PA-MSHA can improve patients' OS (HR: 0.547, 95%CI: 0.304-0.983, P=0.044), PFS (HR: 0.469, 95%CI: 0.229-0.959, P=0.038) and CSS (HR: 0.484, 95%CI: 0.257-0.908, P=0.024). The same trend was observed in the cohort After IPTW adjustment. Although there was no significant difference in the incidence of postoperative lymphatic leakage [18.6% (8/35) vs. 15.1% (84.9%), P=0.613] and pelvic drainage volume [470 (440) ml vs. 462.5 (430) ml, P=0.814] between PA-MSHA group and control group, PA-MSHA could shorten the median retention time of drainage tube (7.0 d vs 9.0 d) (P=0.021). Conclusion: PA-MSHA may improve radical cystectomy in patients with OS, PFS, and CSS, shorten the pelvic drainage tube retention time.

Reduction in the need for surgery and mortality after early administration of fibrinolytics following empyema drainage.

OBJECTIVES: Although intrapleural administration of fibrinolytics is an important treatment option for the management of empyema, the addition of fibrinolytics failed to reduce the need for surgery and mortality in previous randomized controlled trials. This study aimed to investigate the effect of administrating fibrinolytics in the early phase (within 3 days of chest tube insertion) of empyema compared with late administration or no administration. METHODS: We used the Japanese Diagnosis Procedure Combination inpatient database to identify patients aged ≥ 16 years who were hospitalized and underwent chest tube drainage for empyema. A 1:2 propensity score matching and stabilized inverse probability of treatment weighting were conducted. RESULTS: Among the 16,265 eligible patients, 3,082 and 13,183 patients were categorized into the early and control group, respectively. The proportion of patients who underwent surgery was significantly lower in the early fibrinolytics group than in the control group; the odds ratio (95% confidence interval) was 0.69 (0.54-0.88) in the propensity score matching (P = 0.003) and 0.64 (0.50-0.80) in the stabilized inverse probability of treatment weighting analysis (P < 0.001). All-cause 30-day in-hospital mortality, length of hospital stay, duration of chest tube drainage, and total hospitalization costs were also more favorable in the early fibrinolytic group. CONCLUSIONS: The early administration of fibrinolytics may reduce the need for surgery and death in adult patients with empyema.

[Estimation of soil moisture and organic matter content in saline alkali farmland by using CARS algorithm combined with covariates]. / 利用CARS算法联合协变量估算盐碱农田土壤水分和有机质含量.

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.

Child meal support program, food and nutrition insecurity, and health among Korean children.

BACKGROUND: Korea's child meal support program (CMSP) aims to reduce food and nutrition insecurity (FNI) and improve health among children from low-income households. AIM: We examined the impact of different types of CMSP on children's FNI and health in Korea, analyzing meal frequency and healthful food consumption (FNI), and general health and depression (health) among child meal card (CMC) and facility meal service (FMS) participants compared with nonparticipants. METHODS: The 2018 Comprehensive Survey on Korean Children data were analyzed. Precisely, 847 children from low-income households aged 9-17 were categorized into CMC (n = 331), FMS (n = 209), and income-eligible nonparticipants (n = 307). Propensity score-weighted generalized linear models assessed CMSP's impact on FNI and health. Stratified generalized linear models examined heterogeneity in FNI-health associations by CMSP status. RESULTS: CMC participants reported more frequent breakfast consumption (odds ratio [OR] = 0.662, p < 0.05) but poorer self-rated general health (OR = 1.890, p < 0.05); FMS participants were less likely to have three meals (OR = 1.814, p < 0.05), fruits and vegetables (OR = 2.194, p < 0.001), and protein-rich foods daily (OR = 1.695, p < 0.05) than nonparticipants. Health risks associated with healthful food consumption and meal frequency were more pronounced among CMC and FMS/nonparticipants, respectively. CONCLUSION: CMSP had a limited impact on reducing FNI and improving health among children from low-income households. CMC appeared more effective than FMS in alleviating FNI, notwithstanding potential health concerns. Food assistance programs should seek comprehensive enhancements in children's food and nutrition security and health.

Association of waist circumference and BMI with premature death in young and middle-aged population.

Introduction: Premature death is a global health indicator, significantly impacted by obesity, especially in young and middle-aged population. Both body mass index (BMI) and waist circumference (WC) assess obesity, with WC specifically indicating central obesity and showing a stronger relationship with mortality. However, despite known associations between BMI and premature death, as well as the well-recognized correlation between WC and adverse health outcomes, the specific relationship between WC and premature death remains unclear. Therefore, focusing on young and middle-aged individuals, this study aimed to reliably estimate independent and combined associations between WC, BMI and premature death, thereby providing causal evidence to support strategies for obesity management. Methods: This study involved 49,217 subjects aged 18-50 years in the United States from 1999 to 2018 National Health and Nutrition Examination Survey (NHANES). Independent and combined associations between WC and BMI with premature death across sex and age stratum were examined by Cox regression. Survey weighting and inverse probability weighting (IPW) were further considered to control selection and confounding bias. Robustness assessment has been conducted on both NHANES and China Health and Retirement Longitudinal Study (CHARLS) data. Results: A linear and positive relationship between WC and all-cause premature death was found in both males and females, with adjusted HRs of 1.019 (95%CI = 1.004-1.034) and 1.065 (95%CI = 1.039-1.091), respectively. Nonlinear relationships were found with respect to BMI and all-cause premature death. For females aged 36-50 with a BMI below 28.6 kg/m2, the risk of premature death decreased as BMI increased, indicated by adjusted HRs of 0.856 (95%CI = 0.790-0.927). Joint analysis showed among people living with obesity, a larger WC increased premature death risk (HR = 1.924, 95%CI = 1.444-2.564). Discussion: WC and BMI exhibited prominent associations with premature death in young and middle-aged population. Maintaining an appropriate WC and BMI bears significant implications for preventing premature death.