Multinomial Logistic Models Research Articles

Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program.

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis

Health and disease are fundamentally influenced by microbial communities and their genes (the microbiome). An in-depth analysis of microbiome structure that enables the classification of individuals based on their health can be crucial in enhancing diagnostics and treatment strategies to improve the overall well-being of an individual. In this paper, we present a novel semi-supervised methodology known as Randomized Feature Selection based Latent Dirichlet Allocation (RFSLDA) to study the impact of the gut microbiome on a subject’s health status. Since the data in our study consists of fuzzy health labels, which are self-reported, traditional supervised learning approaches may not be suitable. As a first step, based on the similarity between documents in text analysis and gut-microbiome data, we employ Latent Dirichlet Allocation (LDA), a topic modeling approach which uses microbiome counts as features to group subjects into relatively homogeneous clusters, without invoking any knowledge of observed health status (labels) of subjects. We then leverage information from the observed health status of subjects to associate these clusters with the most similar health status making it a semi-supervised approach. Finally, a feature selection technique is incorporated into the model to improve the overall classification performance. The proposed method provides a semi-supervised topic modelling approach that can help handle the high dimensionality of the microbiome data in association studies. Our experiments reveal that our semi-supervised classification algorithm is effective and efficient in terms of high classification accuracy compared to popular supervised learning approaches like SVM and multinomial logistic model. The RFSLDA framework is attractive because it (i) enhances clustering accuracy by identifying key bacteria types as indicators of health status, (ii) identifies key bacteria types within each group based on estimates of the proportion of bacteria types within the groups, and (iii) computes a measure of within-group similarity to identify highly similar subjects in terms of their health status.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Aims/hypothesisThis study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al.MethodsWe used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed.ResultsSubgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%.Conclusions/interpretationData-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.Graphical

Investigation of Factors Influencing Infant Mortality at Greater Accra Regional Hospital, Ghana.

Annually, 5.4 million children under five face mortality, with 2.5 million deaths in the first month, 1.6 million between one and eleven months, and 1.3 million aged one to four. Despite global strides, sub-Saharan Africa, including Ghana, grapples with persistent high child mortality. This study employs statistical methods to pinpoint factors driving under-five mortality in the Greater Accra Regional Hospital. The data was acquired from Greater Accra Regional Hospital, Ghana, spanning January to December 2020. The data comprised all under-five deaths recorded in the hospital in 2020. The statistical tools employed were the chi-square test of association and the multinomial logistic regression model. In 2020, there were 238 cases of under-five mortality recorded in the hospital, with males constituting the majority (55%). About 85% of these cases occurred within the first month of birth, primarily attributed to respiratory distress, prematurity, and sepsis. Notably, meconium aspiration was the least common among grouped diagnoses. The test of association and multinomial logistic model emphasised the child's age, birth type, and weight at birth as significant factors influencing child mortality. Conversely, attributes like sex, marital status, and mother's age displayed no notable association with the diagnosis of death. The study on child mortality at the Greater Accra Regional Hospital unveils key factors shaping child health outcomes, emphasising the role of age, birth type, and weight. While specific demographics show no significant association, identified predictors are vital for targeted interventions. Proposed strategies encompass education programs, improved care, birthing practices, and data-driven policies.

Clinical factors associated with patterns of endocrine therapy adherence in premenopausal breast cancer patients

IntroductionPatients with hormone receptor positive breast cancer are recommended at least five years of adjuvant endocrine therapy, but adherence to this treatment is often suboptimal. We investigated longitudinal trends in adjuvant endocrine therapy (AET) adherence among premenopausal breast cancer patients and identified clinical characteristics, including baseline comorbidities and non-cancer chronic medication use, associated with AET adherence.MethodsWe included stage I–III premenopausal breast cancer patients diagnosed during 2002–2011 and registered in the Danish Breast Cancer Group clinical database who initiated AET. We used group-based trajectory modeling to describe AET adherence patterns. We also linked patients to Danish population-based registries and fit multinomial logistic models to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) associating clinical characteristics with AET adherence patterns.ResultsWe identified three adherence patterns among 4,353 women—high adherers (57%), slow decliners (36%), and rapid decliners (6.9%). Women with stage I disease (vs. stage II; OR: 1.9, 95% CI 1.5, 2.5), without chemotherapy (vs. chemotherapy; OR: 4.3, 95% CI 3.0, 6.1), with prevalent comorbid disease (Charlson Comorbidity Index score ≥ 1 vs. 0; OR: 1.6, 95% CI 1.1, 2.3), and with a history of chronic non-cancer medication use (vs. none; OR: 1.3, 95% CI 1.0, 1.8) were more likely to be rapid decliners compared with high adherers.ConclusionsWomen with stage I cancer, no chemotherapy, higher comorbidity burden, and history of chronic non-cancer medication use were less likely to adhere to AET. Taking steps to promote adherence in these groups of women may reduce their risk of recurrence.

Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events.

Do Recessions and Bear Markets Occur Concurrently across Countries? A Multinomial Logistic Approach

Abstract We introduce a novel multinomial logistic model for detecting and forecasting concurrent recessions and bear markets across multiple countries. Our framework leverages cross-country panel features and provides additional information for robust analysis. Through a comprehensive simulation study, we demonstrate the computational efficiency and accuracy of our model, even when handling multiple binary indicators. Applying our framework to empirical data from the United States, the UK, and Euro Area, we find that the multinomial logistic model produces superior medium-term forecasting of concurrent recession and bear market events across countries compared to multiple independent single logistic models. Additionally, our counterfactual analysis reveals that specific events, such as a recession and bear market in the United States, along with the tightening of financial conditions and a negative interest rate spread in the United States, increase the probability of concurrent and individual recession and bear market occurrences in the UK and Euro Area.

Eating Disorder Symptomatology Among Young Adult Cigarette and E-Cigarette Users

Objective: Tobacco use is elevated among individuals with eating disorders (EDs). Yet, further research is needed to understand associations between cigarette and e-cigarette use patterns and ED symptomatology. To gain a more comprehensive understanding of tobacco use and EDs, this study characterized ED symptomatology and tobacco use patterns, including exclusive cigarette use, e-cigarette use, dual use, and nonuse. Method: Young adults aged 18-24 years who self-reported exclusive cigarette, e-cigarette, dual, or nonuse (N = 2500) were recruited via Lucid, an online survey management company. Participants completed questionnaires assessing body dissatisfaction, global ED psychopathology, binge eating and self-induced vomiting frequency, and demographics. ED diagnostic groups included: anorexia nervosa (AN), bulimia spectrum eating disorders (BSED), atypical AN, and night eating syndrome (NES). Results: Multinomial logistic models revealed those with AN were more likely to be dual users, those with atypical AN were more likely to be exclusive e-cigarette users, and participants with a BSED or NES were more likely to be exclusive e-cigarette or dual users, compared to those without an ED. General linear models suggested body dissatisfaction and global ED psychopathology were higher among exclusive e-cigarette and dual users, while binge eating and self-induced vomiting frequencies were greater among all tobacco use groups compared to nonusers. Discussion: Our findings suggest young adults with ED symptomatology were more likely to be users of e-cigarettes exclusively or dual users. It will be necessary to examine how these associations manifest using longitudinal and clinical populations in future research.

Epistasis in neurotransmitter receptors linked to posttraumatic stress disorder and major depressive disorder comorbidity in traumatized Chinese.

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) comorbidity occurs through exposure to trauma with genetic susceptibility. Neuropeptide-Y (NPY) and dopamine are neurotransmitters associated with anxiety and stress-related psychiatry through receptors. We attempted to explore the genetic association between two neurotransmitter receptor systems and the PTSD-MDD comorbidity. Four groups were identified using latent profile analysis (LPA) to examine the patterns of PTSD and MDD comorbidity among survivors exposed to earthquake-related trauma: low symptoms, predominantly depression, predominantly PTSD, and PTSD-MDD comorbidity. NPY2R (rs4425326), NPY5R (rs11724320), DRD2 (rs1079597), and DRD3 (rs6280) were genotyped from 1,140 Chinese participants exposed to earthquake-related trauma. Main, gene-environment interaction (G × E), and gene-gene interaction (G × G) effects for low symptoms, predominantly depression, and predominantly PTSD were tested using a multinomial logistic model with PTSD-MDD comorbidity as a reference. The results demonstrated that compared to PTSD-MDD comorbidity, epistasis (G × G) NPY2R-DRD2 (rs4425326 × rs1079597) affects low symptoms (β = -0.66, OR = 0.52 [95% CI: 0.32-0.84], p = 0.008, pperm = 0.008) and predominantly PTSD (β = -0.56, OR = 0.57 [95% CI: 0.34-0.97], p = 0.037, pperm = 0.039), while NPY2R-DRD3 (rs4425326 × rs6280) impacts low symptoms (β = 0.82, OR = 2.27 [95% CI: 1.26-4.10], p = 0.006, pperm = 0.005) and predominantly depression (β = 1.08, R = 2.95 [95% CI: 1.55-5.62], p = 0.001, pperm = 0.001). The two G × G effects are independent. NPY and dopamine receptor genes are related to the genetic etiology of PTSD-MDD comorbidity, whose specific mechanisms can be studied at multiple levels.

Vulnerability to functional decline is associated with noncardiovascular cause of 90-day readmission in hospitalized patients with heart failure.

Hospital readmission is common among patients with heart failure. Vulnerability to decline in physical function may increase the risk of noncardiovascular readmission for these patients, but the association between vulnerability and the cause of unplanned readmission is poorly understood, inhibiting the development of effective interventions. We examined the association of vulnerability with the cause of readmission (cardiovascular vs. noncardiovascular) among hospitalized patients with acute decompensated heart failure. This prospective longitudinal study is part of the Vanderbilt Inpatient Cohort Study. The primary outcome was the cause of unplanned readmission (cardiovascular vs. noncardiovascular). The primary independent variable was vulnerability, measured using the Vulnerable Elders Survey (VES-13). Among 804 hospitalized patients with acute decompensated heart failure, 315 (39.2%) experienced an unplanned readmission within 90 days of discharge. In a multinomial logistic model with no readmission as the reference category, higher vulnerability was associated with readmission for noncardiovascular causes (relative risk ratio [RRR] = 1.36, 95% confidence interval [CI]: 1.06-1.75) in the first 90 days after discharge. The VES-13 score was not associated with readmission for cardiovascular causes (RRR = 0.94, 95% CI: 0.75-1.17). Vulnerability to functional decline predicted noncardiovascular readmission risk among hospitalized patients with heart failure. The VES-13 is a brief, validated, and freely available tool that should be considered in planning care transitions. Additional work is needed to examine the efficacy of interventions to monitor and mitigate noncardiovascular concerns among vulnerable patients with heart failure being discharged from the hospital.

Predictors of Frailty Transitions in Mexican Older Adults.

Frailty is a dynamic state in older adults. Current evidence, mostly in high-income countries, found that improving frailty is more likely in mild states (prefrailty). We aimed to determine the probability of frailty transitions and their predictors. Participants were adults aged 50 years or over from the Study on Global Ageing and Adult Health in Mexico during 4 waves (2009, 2014, 2017, and 2021). We defined frailty with the frailty phenotype and we used multinomial logistic models to estimate the probabilities of frailty transitions and determine their predictors. For the 3 analyzed periods (2009-2014, 2014-2017, and 2017-2021), transition probabilities from frail to robust were higher for the younger age group (50-59 years) at 0.20, 0.26, and 0.20, and lower for the older age group (≥80 years), 0.03, 0.08 and 0.04. Transitioning from prefrail to robust had probabilities of 0.38, 0.37, and 0.35, for the younger age group, and 0.09, 0.18, and 0.10, for the older age group. The probabilities of transitioning to frail and to death were lower for the younger age group and for the robust at baseline; but higher for the older age group and for the frail at baseline. We identified age, disability, and diabetes as the most significant predictors of frailty transitions. These findings show that frailty has a dynamic nature and that a significant proportion of prefrail and frail individuals can recover to a robust or prefrail state. They also emphasize that prefrailty should be the focus of interventions.

Determinants of Household Energy Use for Cooking in Tanzania

Abstract This article aims to examine determinants that influence the choice of energy fuel for cooking among households in Tanzania mainland. The study employed descriptive and inferential statistics to analyse data from the 2017/18 Tanzania Household Budget Survey. Regarding the main source of energy used for cooking descriptive statistics revealed that the majority (66.5%) of the households use firewood for cooking and 24.6% use charcoal while only 8.8% use other (non-biomass fuels). This indicates that the majority (91.1%) relies on traditional fuel(biomass) for Cooking in Tanzania. Based on Inferential statistics, the chi-square test found that all independent variables except Marital status and income variables were statistically significant predictors of the study dependent variable in Tanzania at a 5 per cent significant level. On the other hand, multinomial logistic model results showed that households with few members, younger heads, male-headed, not employed heads, heads having a higher level of education, located in urban and married heads were less likely to use firewood and charcoal as the main fuel type for cooking than the other types of energy such as electricity, kerosene, industrial gas, coal, generator, and solar. The researcher recommends the use of these findings in formulating appropriate policies to improve access to affordable modern energy to lessen the environmental and health impact of biomass energy use. Keywords: Households Traditional energy, Modern Energy fuel, Multinomial Logistic Model

Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility

As urban density increases, it becomes increasingly common for multiple functions to coexist within the same space, intensifying the complexity of human activity dynamics. However, traditional urban zoning, which relies on the spatial distribution of urban functions and human activities, focuses on the spatial heterogeneity of urban space and fails to capture the temporal dynamics of urban space usage. This paper aims to investigate the daily rhythm of urban space usage and illustrate how the distribution and combination of urban functions affect the daily usage rhythm. Taking Beijing in China as a case, we first identified the daily rhythm of urban space usage with the k-means algorithm and zoned urban space accordingly. Subsequently, multinomial logistic (MNL) models were employed to elucidate how the distribution and combination of urban functions influenced these daily usage patterns. Furthermore, a validation study in typical zones was conducted. The results revealed the existence of a distinct daily rhythm in urban space usage, resulting in the classification of urban space into seven distinct zones: high equilibrium, low equilibrium, diurnal, nocturnal, morning, evening, and noon-type zones. Also, we found that this daily usage rhythm is closely intertwined with the distribution and coexistence of urban functions. Our findings could provide valuable insights for the enhancement of various intricate aspects within urban decision-making processes, including urban planning, transportation management, and more, at a fine-grained scale.

Mitigating overinvestment in Japanese zombie firms: the role of foreign ownership and earnings quality

PurposeThis study investigates the relationship between foreign ownership, earnings quality and overinvestment in Japanese zombie firms.Design/methodology/approachThe study makes use of data from Japanese firms listed on the first section of the Tokyo Stock Exchange from 2009 to 2019. The study employs logistic and multinomial logistic models to test whether the overinvestment behavior of zombie firms is mitigated by foreign shareholdings and earnings quality.FindingsThe results show that (1) zombie firms tend to overinvest; (2) an increase in foreign ownership mitigates the overinvestment of zombie firms and (3) the mitigation of zombie firms' overinvestment by foreign ownership is stronger with higher earnings quality.Originality/valueThis study extends the discussion of earnings quality and investment efficiency to the zombie firm setting. Previous studies in accounting suggest that high earnings quality enhances firms' investment efficiency. The findings suggest that both a change in ownership structure and high-quality accounting information are necessary to mitigate the inefficiency of zombie firms.

Territorial resilience on the aftermaths of COVID‐19 crisis—An exploratory analysis on the role of innovation

This study examines the impact of different regional determinants on heterogeneous territorial resilience to the COVID‐19 crisis, highlighting the role of innovation, particularly the engagement of research and innovation (R&I) entities in networks with businesses. Two ordinary least squares (OLS) models were separately estimated for the resistance and the recovery phases, complemented by a multinomial logistic model, applied to test for a nonlinear relationship between R&I networks and territorial resilience. The results are robust concerning the positive influence on municipalities' resistance and recovery of having R&I entities collaborating in innovation consortia. Education, stability, and sectoral diversification of the local labor market are also essential for regions to be able to resist to the crisis but seem not important for recovery. Finally, international trade openness and pre‐crisis labor performance penalizes both resistance and recovery from downturns.

Food insecurity in Brazil by household arrangements and characteristics between 2004 and 2022.

Although food insecurity presents a decreasing trend worldwide, some regions recently observed an increase in hunger levels. Such was the case in Brazil between 2014 and 2018, during and after the great Brazilian recession, and between 2020 and 2021, during the COVID-19 pandemic. This paper describes the evolution of food insecurity in Brazil between 2004 and 2022 using Brazilian National Household Sample Survey (PNAD), Brazilian Household Budget Survey (POF) and Continuous PNAD. Households were classified in 20 types of arrangements, and the most vulnerable living arrangements between 2004 and 2018 were identified by multinomial logistic models. Overall, households headed by women (single blacks, whites or in couples) with or without children were the most prone to food insecurity. As for the evolution of food insecurity in Brazil between 2018 and 2022, logistic models were applied to estimate moderate and severe food insecurity levels among the 20 household types. Additionally, effects of the emergency aid and idiosyncrasies of the COVID-19 pandemic were estimated.

Rural/nonrural sleep duration patterns among adults in middle and older adulthood

ObjectivesTo document sleep duration differences between rural and nonrural adults in middle/older adulthood. MethodsData consisted of adults aged 50-80 from the 2014, 2016, 2018, and 2020 Behavioral Risk Factor Surveillance System (n = 669,978). Hierarchical multinomial logistic regression models were fit predicting self-reported short (ie, 6 or fewer hours per 24-hour period) and long (ie, 9 or more hours per 24-hour period) compared to normal-sleep duration (ie, 7-8hours per 24-hour period). ResultsRural adults aged 50+ had slightly but significantly lower levels of short sleep (30.0% vs. 30.8%), and slightly but significantly higher levels of long sleep (10.6% vs. 9.4%). In multinomial logistic models that accounted for demographics, rural adults had significantly higher levels of long sleep (OR 1.16, 95% CI 1.12-1.21). The long-sleep differences were explained by socioeconomic measures. ConclusionsSleep researchers should continue to investigate and conceptualize sleeping patterns and heterogeneity among rural adults.

Risk of obstructive sleep apnea among health workers: results of a screening in a large Italian University Hospital.

Obstructive sleep apnea (OSA) is a common respiratory sleep disorder, related to increased mortality, poor quality of life, and higher risk of work accidents and injuries. Studies on the risk of OSA (rOSA) among health workers (HW) are scant. The aims of this study were to investigate this issue in a large University Hospital and to assess the effectiveness of a screening program. The STOP-BANG questionnaire (SBQ) was sent via e-mail to the 5031 HW employed at the University Hospital of Verona. HW who completed the SBQ were classified at low, moderate, and high rOSA. HW at high rOSA were invited to undergo nocturnal polygraphy. The determinants of rOSA were studied by non-parametric Kruskal-Wallis test, Pearson's chi-squared, and multinomial logistic model. Of 5031 HW, 1564 (31.1%) completed the online questionnaire. Responders with low, moderate, and high rOSA were 72.7%, 13.7%, and 13.6%. Male gender, older age, and higher body mass index (BMI) were significant predictors of high rOSA, as expected. Physicians had the lowest probability of being in the high-risk category. Polygraphy was performed in 64 subjects. The positive predictive value of the self-administered SBQ was 68.8% (95%C.I. 55.9-79.8%) but raised to 96.9% (95%C.I. 89.2-99.6%) when re-administered by medical staff. SBQ showed its effectiveness as a screening tool in detecting undiagnosed OSA in HW. Systematic screening for OSA in work settings could allow early diagnosis and treatment, reducing short- and long-term health effects of OSA.

Impact of policy measures on smallholders’ livelihood resilience: Evidence from Hehuang Valley, Tibetan Plateau

Enhancing livelihood resilience is an effective approach for smallholders to cope with climate change in which governments play an important role. Nevertheless, few studies have investigated the impact of policy measures on smallholders’ livelihood resilience (SLR), especially for the case of vulnerable Tibetan Plateau. Based on a longitudinal survey of 321 households in the semiagricultural and semipastoral region of Hehuang Valley during 2017–2022, this study assesses the changes in SLR. By adopting the multinomial logistic model, the impact of policy measures on SLR was been further explored. The results show that SLR in the focal area increased from 0.100 in 2017 to 0.122 in 2022. Policy measures of low-interest loans, irrigation facilities, improved seed and information sharing play a significant positive role in enhancing SLR, whereas training on skills and subsidies have no significant influence on SLR. In addition, the coverage of irrigation facilities and the promotion of training on skills need to be improved. The results not only help local government understand the effectiveness of existing policy measures on SLR but also provide recommendations for improving SLR.

Association of Early Longitudinal Changes in the Neutrophil-to-Lymphocyte Ratio With Adverse Clinical Outcomes in Acute Ischemic Stroke Patients After Endovascular Treatment

Background and PurposeWe aim to elucidate the contribution of early dynamic changes in the neutrophil-to-lymphocyte ratio (NLR) to poor clinical outcomes in acute ischemic stroke (AIS) patients after endovascular treatment (EVT). MethodsAIS patients who underwent EVT were consecutively recruited from January 2019 to July 2022. Blood cell counts were sampled at admission and at following 24 hours after EVT. Clinical outcome measures included 3-month functional dependence (modified Rankin scale of 3-6), symptomatic intracranial hemorrhage (sICH), and mortality at seven days and 30 days. Multinomial logistic regressions were used to evaluate the association of changes in NLR with unfavorable outcomes. Results590 patients were included in the final analysis. The multinomial logistic model indicated that the increasing changes in NLR after EVT was an independent factor for poor outcomes; the adjusted odds ratio (aOR) was 1.06 (95% CI 1.03-1.10; P<0.001) at poor 3-month functional outcomes, 1.07 (95% CI 1.04-1.10; P<0.001) at sICH, 1.08 (95% CI 1.05-1.12; P<0.001) at mortality at seven days, and 1.04 (95% CI 1.02-1.07; P=0.001) at mortality at 30 days. Areas under the curve (AUC) of changes in NLR to discriminate adverse outcomes were 0.725, 0.687, 0.664, and 0.659. The optimal cut-off values were 5.77 (56.6 % sensitivity, 81.0% specificity), 6.92 (60.0% sensitivity, 77.0% specificity), 8.64 (51.0% sensitivity, 82.0% specificity), and 8.64 (48.7% sensitivity, 83.0% specificity), respectively. ConclusionsNLR in AIS patients increased remarkably independent of successful reperfusion. Elevated changes in NLR might predict malignant hemorrhagic transformation, adverse functional outcomes, and short-term mortality.