Logit Research Articles

A novel log odds of positive lymph nodes–based nomogram for predicting overall survival in patients with colorectal signet ring cell carcinoma: a SEER population-based study

PurposeConsidering the poor prognosis and high lymph node (LN) involvement rate of colorectal signet ring cell carcinoma (SRCC), this study aimed to construct a prognostic nomogram to predict overall survival (OS) with satisfactory accuracy and utility, based on LN status indicators with superior predictability.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we obtained cases of colorectal SRCC patients and employed univariate and multivariate Cox analyses to determine independent prognostic factors. Kaplan–Meier curves were utilized to visualize survival differences among these factors. Receiver operating characteristic curves were generated to assess predictive performances of models incorporating various LN status indicators. A novel nomogram, containing optimal LN status indicators and other prognostic factors, was developed to predict OS, whose discriminatory ability and accuracy were evaluated using calibration curves and decision curve analysis.ResultsA total of 1663 SRCC patients were screened from SEER database. Older patients and those with grades III–IV, tumor sizes > 39 mm, T3/T4 stage, N1/N2 stage, M1 stage, and higher log odds of positive lymph nodes (LODDS) values exhibited poorer prognoses. Age, grade, tumor size, TNM stage, and LODDS were independent prognostic factors. The model containing N stage and LODDS outperformed the one relying solely on N stage as LN status indicator, resulting in a validated nomogram for accurately predicting OS in SRCC patients.ConclusionThe integration of LODDS, N stage, and other risk factors into a nomogram offered precise OS predictions, enhancing therapeutic decision-making and tailored follow-up management for colorectal SRCC patients.

Sex, racial, ethnic, and geographical disparities in major adverse cardiovascular outcome of glucagon-like peptide-1 receptor agonists among patients with and without diabetes mellitus: A meta-analysis of placebo-controlled randomized controlled trials

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been pivotal in the management of type 2 diabetes mellitus (T2DM) and in the reduction of major adverse cardiovascular events (MACE). Notably, large cardiovascular outcomes trials (CVOTs) demonstrate significant disparities in inclusion, based on sex, race, ethnicity, and geographical regions. We examined the impact of GLP-1RA on MACE in patients with or without T2DM, based on sex, race, ethnicity, and geography. A literature search for placebo controlled randomized controlled trials on GLP-1RA treatment was conducted. Thorough data extraction and quality assessment were carried out, focusing on key outcome, and ensuring a robust statistical analysis using a random effects model to calculate log odds ratio (OR) with 95% confidence intervals (CIs). A total of 8 CVOTs comprising 71,616 patients were included. Compared with placebo, GLP-1RAs significantly reduced MACE in both sexes (females: logOR -0.19, (95% CI, -0.28 to -0.10), p < 0.01) versus (males: logOR -0.17, (95% CI, -0.23 to -0.10), p < 0.01), (p interaction NS), and among Asians (logOR -34 (95% CI, -0.53 to -0.15), p < 0.01), and Whites (logOR -17 (95% CI, -0.25 to -0.09), p < 0.01), with no difference in MACE among Blacks and Hispanics. Odds of MACE were also reduced in Asia (logOR -31 (95% CI, -0.50 to -0.11), p < 0.01), and Europe (logOR -27 (95% CI, -0.40 to -0.13), p < 0.01), but there was no statistical difference in MACE in North America and Latin America. Significant reductions in MACE with GLP-1RA treatment were demonstrated between both sexes and across certain ethnicities and certain geographical regions.

Effects of autonomous driving on residential location choice behavior: A travel-based multitasking perspective

Fully autonomous vehicles (AVs) allow users to engage in multitasking behavior while traveling, potentially inducing longer travel because multitasking in AVs would generate a positive utility. Eventually, this may further induce residential relocation, as positive utility virtually reduces the value of time. Such influence may vary depending on whether the AV is used individually or with others (i.e., ride-sharing), as well as the type and amount of multitasking activities carried out in the vehicle. This study examines the influence of the type of AV (ride-sharing or individually used) and the type and amount of in-vehicle multitasking activities on residential location choice behavior through a pivoted stated preference survey. Residential location choice behavior is represented by a panel binary mixed logit model. The model estimation results indicate that the willingness to pay for monthly rent to shorten commuting time is significantly lower when individually used AVs are introduced, compared to non-AVs (i.e., existing automobiles) and ride-shared AVs. Hence, further urban sprawl could occur if individually used AVs become prevalent. Such negative impacts on urban form, however, would be substantially small when AV is introduced under the ride-sharing scheme. It was also found that individuals who can engage in more multitasking behavior in an AV will accept longer travel regardless of the type of AV (ride-sharing or individually used), while individuals who can hardly perform in-car activities tend to resist additional commuting travel time. Moreover, the impact of automated driving at the city scale was examined by running simulations of residential choice in Hiroshima City as a case study. The results suggest that multitasking behaviors in AVs would have modest impacts on urban structure.

DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation

BackgroundMicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations.ResultsIn the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancing positive and negative samples with k-means clustering, and constructing training samples. Then, deep autoencoder was used to extract low-dimensional feature from two kinds of feature representations for miRNAs and diseases, namely, original association information-based and similarity information-based. Next, we combined the resulting features for each miRNA-disease pair and used a logistic regression (LR) classifier to infer all unknown miRNA-disease interactions. Under five and tenfold cross-validation (CV) frameworks, DAE-CFR not only outperformed six popular algorithms and nine classifiers, but also demonstrated superior performance on an additional dataset. Furthermore, case studies on three diseases (myocardial infarction, hypertension and stroke) confirmed the validity of DAE-CFR in practice.ConclusionsDAE-CFR achieved outstanding performance in predicting miRNA-disease associations and can provide evidence to inform biological experiments and clinical therapy.

Abstract 2386: Meta-analysis of efficacy and safety in early-phase clinical trials for refractory colorectal cancer

Abstract Introduction: Despite a decade of metastatic colorectal cancer (mCRC) therapeutic innovations, a comprehensive synthesis of patient outcome and risk-benefit assessment of phase 1/2 trials, extending beyond single-center studies, is missing. The aim of this meta-analysis is to assess efficacy, safety, and trends over time for phase 1 and 2 mCRC trials by examining clinical benefit rate (CBR), overall response rate (ORR), grade 3 or higher adverse events (AE), and discontinuation due to AE. Methods: The protocol and search were preregistered on PROSPERO. We searched PubMed and Embase for publications of phase 1/2 trials between 2010-2022. Reports were screened for inclusion criteria, followed by data-extraction by two researchers. Trials reporting on non-standard therapies for treatment-refractory mCRC or trials reporting specific mCRC efficacy data were included. CBR was defined as the proportion of patients with complete/partial response or stable disease ≥14 weeks. We used logit transformations for pooled effect analysis via a random-effects model, assigning study weights by the inverse variance method. Subgroup analyses were performed for publication year, preselection on molecular traits, and drug class: antiangiogenics, chemotherapy, immunomodulator, targeted therapy, or vaccine. Data are presented with 95% confidence intervals (CI); reported differences had non-overlapping CIs. Results: The search strategy yielded 4175 unique reports, from which we extracted data for 279 trials. Most trials were phase 1 (63%). ORR was limited to 6% [6-7] in both phase 1 and 2, CBR was higher in phase 2 than in phase 1 trials (36% vs 27%). Trials testing antiangiogenic drugs had the highest CBR (47%) and immunomodulators the highest ORR (8%). Trials with patients preselected on molecular traits had higher CBR compared to other trials (38% vs 28%). We observed an increase in efficacy, especially in phase 2 trials, with a CBR rising from 22% to 42% between trials published in 2010-2012 and 2019-2021. Compared with 2010-2012, trials in 2019-2021 were more often testing immunomodulators (23% vs 4%), performed on molecular preselected populations (38% vs 4%), and including younger patients (median age &amp;lt;60 in 66% vs 44%). The occurrence of grade 3+ AE was 33% [28-38] in both phase 1 and 2. AE led to discontinuation in 12% of phase 1 trial patients, and 7% of phase 2 patients. Grade 3+ AE occurred in 4% of vaccine trial patients, in contrast to 41% in trials on targeted therapies. Occurrence of grade 3+ AE did not change over time. Conclusion: We found limited ORR and CBR in early phase trials conducted in 2010-2021 for mCRC patients, while noting severe AEs in many patients. However, during this period, clinical benefit rate increased for phase 2 trials, possibly due to evolving study strategies that entail testing new treatment types in a younger, fitter, and stricter preselected study population based on molecular traits. Citation Format: Maarten A. Huismans, Lidwien P. Smabers, Niels N. van Nieuwenhuijzen, Monique C. Minnema, Miriam Koopman, Hugo J. Snippert, Anne M. May, Jeanine M. Roodhart. Meta-analysis of efficacy and safety in early-phase clinical trials for refractory colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2386.

Research on the Evolution Game of The Digital Transformation of Cold Chain Logistics Enterprises Under the Government's Participation

Whether cold chain logistics enterprises choose to carry out digital transformation is a process of evolutionary game. By analyzing the evolution and stability strategy in the process of digital transformation of cold chain logistics enterprises under the participation of the government, and using matlab for simulation, we discuss the influence of each parameter on the decision-making of cold chain logistics enterprises. The results show that the cost of digital transformation of cold chain logistics enterprises will hinder their transformation, and the increase coefficient of enterprise income, the increase of government subsidies and the reduction of tax rate will all promote the implementation of digital transformation of cold chain logistics enterprises. Finally, corresponding countermeasures are put forward to accelerate the digital transformation of cold chain logistics enterprises, so as to lay a solid foundation for the further economic development of the whole country.

Construction and validation of log odds of positive lymph nodes (LODDS)-based nomograms for predicting overall survival and cancer-specific survival in ovarian clear cell carcinoma patients.

Ovarian clear cell carcinoma (OCCC) is one of the special histologic subtypes of ovarian cancer. This study aimed to construct and validate log odds of positive lymph nodes (LODDS)-based nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Patients who underwent surgical treatment between 2010 and 2016 were extracted from the Surveillance Epidemiology and End Results (SEER) database and the data of OCCC patients from the First Affiliated Hospital of Dalian Medical University were used as the external validation group to test the validity of the prognostic model. The best-fitting models were selected by stepwise Cox regression analysis. Survival probability was calculated by the Kaplan-Meier method, and the differences in survival time between subgroups were compared using the log-rank test. Each nomogram's performance was assessed by the calibration plots, decision curve analysis (DCA), and receiver operating characteristics (ROC) curves. T stage, distant metastasis, marital status, and LODDS were identified as significant risk factors for OS. A model with four risk factors (age, T stage, stage, and LODDS value) was obtained for CSS. Nomograms were constructed by incorporating the prognostic factors to predict 1-, 3- and 5-year OS and CSS for OCCC patients, respectively. The area under the curve (AUC) range of our nomogram model for OS and CSS prediction ranged from 0.738-0.771 and 0.769-0.794, respectively, in the training cohort. The performance of this model was verified in the internal and external validation cohorts. Calibration plots illustrated nomograms have good prognostic reliability. Predictive nomograms were constructed and validated to evaluate the OS and CSS of OCCC patients. These nomograms may provide valuable prognostic information and guide postoperative personalized care in OCCC.

The Effect of Food and Financial Remittances on Household Food Security in Northern Nigeria

Remittances from migrants are an important livelihood strategy to mitigate the impact of adverse economic conditions, particularly in developing countries and rural areas. This study examines how migration and remittances affect household food security by analyzing data collected from 180 farmers in northern Nigeria in 2018. The descriptive results show that households use remittances mainly for health care and education. Remittances are most commonly provided in the form of finances and food. Binary logit regression results show that food remittances have a significant positive impact on food security status. Financial remittances did not have a statistically significant impact on food security. As the number of migrants in a household increases, the probability that the household is food secure decreases. The propensity scores matching result also showed that migrant households receiving food remittances were more food secure. In light of these findings, we emphasized the need to include both financial and food remittances in studies to understand the effect on food security.

Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4+T cells in multiple donors.

Atherosclerosis is a major pathological condition that underlies many cardiovascular diseases (CVDs). Its etiology involves breach of tolerance to self, leading to clonal expansion of autoreactive apolipoprotein B (APOB)-reactive CD4+T cells that correlates with clinical CVD. The T-cell receptor (TCR) sequences that mediate activation of APOB-specific CD4+T cells are unknown. In a previous study, we had profiled the hypervariable complementarity determining region 3 (CDR3) of CD4+T cells that respond to six immunodominant APOB epitopes in most donors. Here, we comprehensively analyze this dataset of 149,065 APOB-reactive and 199,211 non-reactive control CDR3s from six human leukocyte antigen-typed donors. We identified 672 highly expanded (frequency threshold > 1.39E-03) clones that were significantly enriched in the APOB-reactive group as compared to the controls (log10 odds ratio ≥1, Fisher's test p < 0.01). Analysis of 114,755 naïve, 91,001 central memory (TCM) and 29,839 effector memory (TEM) CDR3 sequences from the same donors revealed that APOB+ clones can be traced to the complex repertoire of unenriched blood T cells. The fraction of APOB+ clones that overlapped with memory CDR3s ranged from 2.2% to 46% (average 16.4%). This was significantly higher than their overlap with the naïve pool, which ranged from 0.7% to 2% (average 1.36%). CDR3 motif analysis with the machine learning-based in-silico tool, GLIPHs (grouping of lymphocyte interactions by paratope hotspots), identified 532 APOB+ motifs. Analysis of naïve and memory CDR3 sequences with GLIPH revealed that ~40% (209 of 532) of these APOB+ motifs were enriched in the memory pool. Network analysis with Cytoscape revealed extensive sharing of the memory-affiliated APOB+ motifs across multiple donors. We identified six motifs that were present in TCM and TEM CDR3 sequences from >80% of the donors and were highly enriched in the APOB-reactive TCR repertoire. The identified APOB-reactive expanded CD4+T cell clones and conserved motifs can be used to annotate and track human atherosclerosis-related autoreactive CD4+T cells and measure their clonal expansion.

Comparisons of various estimates of the statistic for quantifying between-study heterogeneity in meta-analysis.

Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The statistic has been a popular measure for quantifying heterogeneity, but its usage has been challenged from various perspectives in recent years. In particular, it should not be considered an absolute measure of heterogeneity, and it could be subject to large uncertainties. As such, when using to interpret the extent of heterogeneity, it is essential to account for its interval estimate. Various point and interval estimators exist for . This article summarizes these estimators. In addition, we performed a simulation study under different scenarios to investigate preferable point and interval estimates of . We found that the Sidik-Jonkman method gave precise point estimates for when the between-study variance was large, while in other cases, the DerSimonian-Laird method was suggested to estimate . When the effect measure was the mean difference or the standardized mean difference, the -profile method, the Biggerstaff-Jackson method, or the Jackson method was suggested to calculate the interval estimate for due to reasonable interval length and more reliable coverage probabilities than various alternatives. For the same reason, the Kulinskaya-Dollinger method was recommended to calculate the interval estimate for when the effect measure was the log odds ratio.

The impact of using near-infrared autofluorescence on parathyroid gland parameters and clinical outcomes during total thyroidectomy: a meta-analytic study of randomized controlled trials.

The added benefit of using near-infrared autofluorescence (NIRAF) during total thyroidectomy (TT) remains controversial. This study investigated whether or not NIRAF results in improved patient outcomes postoperatively. We analyzed 1711 TT patients, reported in nine randomized controlled trials, following a systematic search of five databases. NIRAF was compared to the standard of care (naked eye with/without white light). Outcomes included parathyroid gland (PG) and calcium parameters and other clinical outcomes. For dichotomous outcomes, the log odds ratio (logOR) was calculated, and for continuous outcomes, the crude mean difference (MD) was measured. Sensitivity analysis was performed when heterogeneity was significant. The revised Cochrane risk of bias tool was used to assess the methodological quality. Compared to the standard of care, the use of NIRAF was associated with a significant reduction in postoperative hypoparathyroidism [logOR=-0.31; 95% CI: -0.57: -0.05], inadvertent PG removal [logOR=-0.93; 95% CI: -1.60: -0.26], and postoperative hypocalcemia [logOR=-0.43mmol/L; 95% CI: -0.77: -0.09]. It was also associated with significantly higher postoperative PTH levels [MD=4.78pg/mL; 95% CI: 2.13: 7.43], PG identification rate [logOR=1.02; 95% CI: 0.31: 1.72], postoperative serum calcium [MD=0.05; 95% CI: 0.00: 0.09], and operative time [MD=9.38 minutes; 95% CI: 6.68: 12.09]. No difference was seen regarding PG autotransplantation, length of hospital stay, and hospitalization due to hypocalcemia. Seven trials had low risk and the remainder had some concerns. NIRAF is superior to the naked eye in identifying all four parathyroid glands during total thyroidectomy. The reduced risk of postoperative hypoparathyroidism and hypocalcemia reflected this preservation value. However, it was not associated with a change in the length of hospital stay. Although rare, the readmission rate due to hypocalcemia was similar across both methods.

A Deep Vision Sensing-Based Smart Control Method for Apple-Picking Robots Under the Context of Agricultural E-Commerce

The growing development of the agricultural e-commerce industry is promoting the Digital transformation of agricultural production and logistics. Aiming at the problem of labor shortage in apple picking, this paper proposes a performance control method for robot apple picking based on a nonlinear wide convolutional neural network (NLWCNN) and multi-objective cooperative distance transformation algorithm (MOCDTA). This method utilizes deep learning algorithms and visual sensing technology to enable robots to accurately recognize apples and perform autonomous picking operations based on their maturity and position. We have designed a machine learning-based algorithm that enables robots to accurately recognize and distinguish apples of different maturity levels through training on a large amount of sample data. At the same time, we also combined the motion control algorithm of the robot to enable efficient picking operations based on the position and maturity information of the apples. The experimental results indicate that the NLWCNN method can significantly improve the efficiency and accuracy of apple picking, ensure the safety and traceability of agricultural products, increase consumer trust, and promote agricultural product transactions.

Determinan Perilaku Belanja Online di Kota Jambi

Online shopping is a trend that has spread throughout Indonesia, especially during Covid 19. This trend continues to grow today. As the capital of Jambi Province, Jambi City is an area that has the most complete internet facilities compared to other areas in Jambi Province. The availability of internet facilities and other suggestions means that online shopping in this area is quite large. This research aims to assess consumer behavior in online shopping in Jambi City; and Factors that influence online shopping in Jambi City. The analysis model used is descriptive analysis and binary logit regression model. The research results show that the average frequency of online shopping in Jambi City is 1-2 times a month. 40.2% of respondents visited 2 online sites. The e-commerce application that respondents use most for shopping is Shopee. Cheap and easy to get goods is the first reason to shop online. The more online shopping sites that are visited, the public's perception is that online shopping saves time and buying shoes/bags and accessories online are factors that have a positive and significant influence on people's online shopping in Jambi City.

Assessing non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity

Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection). Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across non-motorists) in the dependency structure. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and socio-demographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity for the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by crash location type.

Food insecurity: Discrepancy within Australian couple households

AbstractFood security remains a global public health priority but there may be bias in the prevalence of household food insecurity, depending upon who answers the questions. Using a cross‐section from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, we analysed components of food insecurity reported separately by both partners in 718 households, allowing examination of discrepancies among couples. We modelled discrepancy in reports of food insecurity on 8 items using binary logit models for each question and a binomial regression for the number of questions on which there was discrepancy. Among couples who differed on an item, (conditional) discrepancy rates ranged from 71.75% to 85.86%. Females were more likely than males to report food insecurity on most items. Key characteristics associated with discrepancy were female's and male's age, female's education, being born in Australia, renting and male's employment. Male's age and birthplace were associated with discrepancy on more items while male's higher education and being a homeowner were associated with discrepancy on fewer items. Among couples who differed in responses, females were more likely to report food insecurity if they were older or had a disability, if their male partner was younger or had no disability, or if either person had more education than high school. The prevalence of food insecurity among couple households may be underestimated by 7.4 percentage points due to discrepancies in reporting. Discrepancies can reduce the accuracy of measures of food insecurity, impeding the targeting of responses, and lead to a less efficient allocation of resources to combat food insecurity.

Sex-Specific Association of Alcohol Use Disorder With Suicide Mortality

Despite individual studies suggesting that sex differences exist in the association between alcohol use disorder (AUD) and suicide, most existing systematic reviews and meta-analyses have reported associations across the sexes. To estimate the sex-specific association between AUD and suicide mortality. Embase, MEDLINE (including MEDLINE In-Process), PsycINFO, PubMed, and Web of Science were searched from database inception to April 27, 2022. Inclusion criteria consisted of the following: (1) original, quantitative study, (2) inclusion of a measure of association and its corresponding measure of variability (or sufficient data to calculate these [eg, 95% CI]), and (3) results stratified by sex. Data extraction was completed by one reviewer and then cross-checked by a second reviewer. Risk of bias was assessed by study design. Categorical random-effects meta-analyses were conducted to obtain sex-specific pooled estimates of the association between AUD and suicide mortality risk. Methodological moderators (ie, study design and comparator group) were assessed using sex-stratified meta-regressions. The association between AUD and suicide mortality. A total of 16 347 unique records were identified in the systematic search; 24 studies were ultimately included for 37 870 699 participants (59.7% male and 40.3% female) (23 risk estimates for male and 17 for female participants). Participants ranged in age from 15 years to 65 years or older. Sex-specific meta-regression models indicated that study design (ie, longitudinal vs cross-sectional study design) affected the observed association between AUD and suicide mortality for both male participants (log odds ratio, 0.68 [95% CI, 0.08-1.28]; P = .03) and female participants (log odds ratio, 1.41 [95% CI, 0.57-2.24]; P < .001). For males and females, among longitudinal studies, the pooled odds ratios were 2.68 (95% CI, 1.86-3.87; I2 = 99% [n = 14]) and 2.39 (95% CI, 1.50-3.81; I2 = 90% [n = 11]), respectively. This systematic review and meta-analysis yielded substantive evidence that AUD was associated with suicide mortality and that the association was similar across the sexes. The findings underscore the importance of identifying and treating AUD as part of a comprehensive suicide prevention strategy.

Early Warning Systems for World Energy Crises

Different severe energy crisis episodes have occurred in the world in the last five decades. Energy crises lead to the deterioration of international relations, economic crises, changes in monetary systems, and social problems in countries. This paper aims to show the essential determinants of energy crises by developing a binary logit model that estimates the predictive ability of thirteen indicators in a sample that covers the period from January 1973 to December 2022. The empirical results show that the energy crises are mainly due to energy supply–demand imbalances (petroleum stocks, fossil energy production–consumption imbalances, and changes in energy imports by countries), energy investments (oil and natural gas drilling activities), economic and financial disruptions (inflation, dollar indices, and indices of global real economic activity) and geopolitical risks. Additionally, the model is capable of accurately predicting world energy crisis events with a 99% probability.

Determinants of Small-Scale Farmers’ Participation in Social Capital Networks to Enhance Adoption of Climate Change Adaptation Strategies in OR Tambo District, South Africa

Globally, climate change remains one of the most pressing challenges, and it is also an obstacle to the fundamental achievement of the Sustainable Development Goals. The purpose of the study was to examine the determinants of small-scale farmers’ participation in social capital networks to enhance adoption of climate change adaptation strategies. Multistage and purposive sampling were used to carry out the study. A cross-sectional research design was used to carry out the study and structured questionnaires were used as a data collection tool. The data collected were analyzed using descriptive statistics, the Binary Logit model, and the Ordered Logit model. The findings of the study show that gender, household size, age, marital status, education, and employment status affect a farmer’s participation in social capital networks. The results also show that household size, employment status, and income level affect the extent of participation in social networks. As the paper is the first to look at the determinants of the participation of farmers in social capital networks in Eastern Cape, the results are of paramount importance to policy formulators in order to formulate policies that will encourage farmers to join localized farmer-based social capital networks to adopt climate change adaption measures.

Surprising Gendered Age Differences in Rural Malawians' Early COVID-19 Pandemic Prevention Efforts.

We assess how age, the presence of mature adults aged 45+ years, and recent deaths in rural households are associated with coronavirus disease 2019 (COVID-19) preventative actions and the likelihood of getting vaccinated against the virus in Malawi during early stages of the pandemic. We draw upon data from 2,187 rural Malawians who participated in a 2020 COVID-19 Phone Survey. We estimate the log odds of engaging in "low-cost" and "high-cost" COVID-19 preventative actions based on age, gender, household composition, and recent household deaths. Low-cost prevention efforts were washing hands with soap and water frequently, avoiding close contact with people when going out, and avoiding shaking hands. High-cost actions included staying at home and decreasing time spent close to people not living in their household. We also estimate the chances of acquiring the COVID-19 vaccine in early stages of its availability. Mature women (45+ years) in general and younger men (<45 years)-living with at least one mature adult in the household-were less likely than others to comply with low-cost actions. Mature men were more likely than younger men (<45 years) to take on high-cost actions. To some extent, individuals who experienced a recent family death were more likely to engage in high-cost COVID-19 preventative actions as well as getting vaccinated. Gendered age differences in preventing the transmission of COVID-19 offer hints of larger social norms affecting protective efforts. The analyses also inform future COVID-19 public health outreach efforts in Malawi and other rural SSA contexts.

Does the Cognitive Change Index Predict Future Cognitive and Clinical Decline? Longitudinal Analysis in a Demographically Diverse Cohort.

The Cognitive Change Index (CCI) is a widely-used measure of self-perceived cognitive ability and change. Unfortunately, it is unclear if the CCI predicts future cognitive and clinical decline. We evaluated baseline CCI to predict transition from normal cognition to cognitive impairment in nondemented older adults and in predementia groups including, subjective cognitive decline, motoric cognitive risk syndrome, and mild cognitive impairment. Different versions of the CCI were assessed to uncover any differential risk sensitivity. We also examined the effect of ethnicity/race on CCI. Einstein Aging Study participants (N = 322, Mage = 77.57±4.96, % female=67.1, Meducation = 15.06±3.54, % non-Hispanic white = 46.3) completed an expanded 40-item CCI version (CCI-40) and neuropsychological evaluation (including Clinical Dementia Rating Scale [CDR], Montreal Cognitive Assessment, and Craft Story) at baseline and annual follow-up (Mfollow-up=3.4 years). CCI-40 includes the original 20 items (CCI-20) and the first 12 memory items (CCI-12). Linear mixed effects models (LME) and generalized LME assessed the association of CCI total scores at baseline with rate of decline in neuropsychological tests and CDR. In the overall sample and across predementia groups, the CCI was associated with rate of change in log odds on CDR, with higher CCI at baseline predicting faster increase in the odds of being impaired on CDR. The predictive validity of the CCI broadly held across versions (CCI-12, 20, 40) and ethnic/racial groups (non-Hispanic black and white). Self-perception of cognitive change on the CCI is a useful marker of dementia risk in demographically/clinically diverse nondemented samples. All CCI versions successfully predicted decline.