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In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers' decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions. [ABSTRACT FROM AUTHOR]

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020., (© 2024. The Author(s).)

The invasive Asian longhorned tick Haemaphysalis longicornis that vectors and transmits several animal pathogens is significantly expanding in the United States. Recent studies report that these ticks also harbor human pathogens including Borrelia burgdorferi sensu lato, Babesia microti, and Anaplasma phagocytophilum. Therefore, studies that address the interactions of these ticks with human pathogens are important. In this study, we report the characterization of H. longicornis organic anion-transporting polypeptides (OATPs) in interactions of these ticks with A. phagocytophilum. Using OATP-signature sequence, we identified six OATPs in the H. longicornis genome. Bioinformatic analysis revealed that H. longicornis OATPs are closer to other tick orthologs rather than to mammalian counterparts. Quantitative real-time PCR analysis revealed that OATPs are highly expressed in immature stages when compared to mature stages of these ticks. In addition, we noted that the presence of A. phagocytophilum upregulates a specific OATP in these ticks. We also noted that exogenous treatment of H. longicornis with xanthurenic acid, a tryptophan metabolite, influenced OATP expression in these ticks. Immunoblotting analysis revealed that antibody generated against Ixodes scapularis OATP cross-reacted with H. longicornis OATP. Furthermore, treatment of H. longicornis with OATP antibody impaired colonization of A. phagocytophilum in these ticks. These results not only provide evidence that the OATP-tryptophan pathway is important for A. phagocytophilum survival in H. longicornis ticks but also indicate OATP as a promising candidate for the development of a universal anti-tick vaccine to target this bacterium and perhaps other rickettsial pathogens of medical importance. [ABSTRACT FROM AUTHOR]

Homozygous familial hypercholesterolemia (HoFH) is an underdiagnosed and undertreated ultra-rare disease. We utilized claims data from the Komodo Healthcare Map database to develop a machine-learning model to identify potential HoFH patients. We tokenized patients enrolled in MyRARE (patient support program for those prescribed evinacumab-dgnb in the United States) and linked them with their Komodo claims. A true positive HoFH cohort (n = 331) was formed by including patients from MyRARE and patients with prescriptions for evinacumab-dgnb or lomitapide. The negative cohort (n = 1423) comprised patients with or at risk for cardiovascular disease. We divided the cohort into an 80% training and 20% testing set. Overall, 10,616 candidate features were investigated; 87 were selected due to clinical relevance and importance on prediction performance. Different machine-learning algorithms were explored, with fast interpretable greedy-tree sums selected as the final machine-learning tool. This selection was based on its satisfactory performance and its easily interpretable nature. The model identified four useful features and yielded precision (positive predicted value) of 0.98, recall (sensitivity) of 0.88, area under the receiver operating characteristic curve of 0.98, and accuracy of 0.97. The model performed well in identifying HoFH patients in the testing set, providing a useful tool to facilitate HoFH screening and diagnosis via healthcare claims data. [ABSTRACT FROM AUTHOR]

There is debate over whether Asian American students face additional barriers, relative to white students, when applying to selective colleges. Here we present the results from analyzing 685,709 applications submitted over five application cycles to 11 highly selective colleges (the "Ivy-11"). We estimate that Asian American applicants had 28% lower odds of ultimately attending an Ivy-11 school than white applicants with similar academic and extracurricular qualifications. The gap was particularly pronounced for students of South Asian descent (49% lower odds). Given the high yield rates and competitive financial aid policies of the schools we consider, the disparity in attendance rates is likely driven, at least in part, by admissions decisions. In particular, we offer evidence that this pattern stems from two factors. First, many selective colleges give preference to the children of alumni in admissions. We find that white applicants were substantially more likely to have such legacy status than Asian applicants. Second, we identify geographic disparities potentially reflective of admissions policies that disadvantage students from certain regions of the United States. We hope these results inform discussions on equity in higher education. [ABSTRACT FROM AUTHOR]

Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network—of differentially expressed genes, transcription factors, and protein neighbors of transcription factors—that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life. [ABSTRACT FROM AUTHOR]

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future., (© 2024. The Author(s).)

While residential segregation is a persistent attribute of metropolitan areas, recent studies find segregation levels fluctuate throughout the day, reaching their lowest levels during daytime hours. This paper shows hourly variations in Black-White segregation from Monday through Sunday for the top 49 most populated metropolitan areas with Global Positioning System (GPS) data collected from mobile phones from October 2018. I find that segregation levels are higher on average over weekends compared to that of weekdays. I use models to identify the characteristics of neighborhoods with higher levels of segregation on weekends, which include all demographic variables and nearly a third of 35 sectors of businesses and organizations, such as retail, personal care, and religious organizations. I also find more than a third of the sectors are associated with higher levels of segregation during business hours on weekdays, including academic institutions, health care, manufacturing, and financial institutions. Findings from this paper display the significance in the distinction between weekdays and weekends with where people spend their time and how this relates to racial segregation. Specifically, Black people, on average, stay in their home census tracts and visit non-White neighborhoods for organizational resources more so than White people. Significant patterns of associations between racial segregation and the majority of businesses demonstrate the salience of race for more industries than previously understood., (© 2024. The Author(s).)