Health Care Access and Use Among Children & Adolescents with History of Parental Incarceration — United States, 2019

Background: Children and adolescents exposed to parental incarceration (PI) have suboptimal health care access, utilization, and outcomes in adulthood. However, very little is known about health care access and use during childhood itself for children and adolescents exposed to PI. Significance: The United States incarcerates more people and at a higher rate than any other country in the world, with stark spillover impacts on the lives of over 5 million children who have had an incarcerated parent. Objective: Using the nationally representative 2019 National Health Interview Survey (NHIS) Child Sample, we examined the relationship between PI and key measures of health care utilization and access. Design: We conducted a survey-weighted cross-sectional analysis of in-home interviews conducted with the guardians of 7,405 children 2-17 years old. Respondents were asked about outcomes across the 12 months preceding the interview, including poor preventive care access (lack of usual source of care, well visit, or routine dental cleaning), unmet health care needs due to cost (delayed or forgone dental, medical, or mental health care), and health care use (urgent care use, emergency department use, or hospitalization). First, we used χ2 tests for bivariate associations between PI exposure and each outcome. Then, we estimated marginal effects from multivariable logistic regressions modeling the associations between PI and each outcome, with adjustment for age, sex, race/ethnicity, parental education, family structure, rurality, income, insurance, and disability. We multiplied these marginal effects by weighted sample sizes to generate population-wide estimates. Results: Of 7,405 individuals, 467 (weighted 6.2% [95% CI 5.5-6.9]) were exposed to PI. In bivariate analyses, children exposed to PI had significantly worse access to a usual source of care; greater rates of delayed or forgone dental, medical, and mental health care due to cost; and higher likelihood of emergency department use and hospitalization (p\u3c0.05). In adjusted analyses, exposure to PI was associated with a predicted increase of ~2.1 million children lacking a usual source of care, ~2.2 million forgoing needed dental care, ~1.1 million delaying needed mental health care, and ~795,000 forgoing needed mental health care. Conclusion: Exposure to PI is associated with worse access to a usual source of care and unmet dental and mental health care needs due to cost, impacting millions of US children and adolescents. This nationally representative study extends and updates existing literature about PI and suboptimal access to care in adulthood by demonstrating that these trends start within childhood itself.https://digitalcommons.unmc.edu/chri_forum/1053/thumbnail.jp

Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Diving into the vertical dimension of elasmobranch movement ecology

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements