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BackgroundThe United States Food and Drug Administration authorized COVID-19 vaccines for children ages 5–11 years in October 2021 during the Omicron predominant period. Parental vaccine hesitancy was prevalent during this time, resulting in low childhood COVID-19 vaccine uptake. Most studies exploring factors influencing parental vaccine hesitancy have focused on racial and ethnic minorities and lower socioeconomic populations; however, there is little knowledge of the drive drivers of vaccine hesitancy among White parents with higher education and socioeconomic statuses.MethodsWe conducted semi-structured interviews with a sample of 15 White mothers of children ages 5–11 years in Atlanta, GA, between October–December 2021. Thematic analysis was performed using NVivo 12.ResultsMothers were college-educated, homeowners, and fully vaccinated against COVID-19. Key findings included decreased pediatrician’s recommendations for COVID-19 vaccines, reliance on information from specialized doctors and scientists, distrust in public health authorities, high risk-perception of COVID-19 vaccines, and low risk-perception of COVID-19 disease. Factors related to vaccine acceptance were altruism and practicality.ConclusionThis study adds to the sparse literature on reasons for vaccine hesitancy among White mothers of children ages 5–11 years with higher educational and socioeconomic status. Improving vaccine uptake among this group is critical for protecting the health of their children and other vulnerable populations. Tailored vaccine messaging and intervention are warranted to address their unique attitudes, beliefs, and behaviors. An enhanced understanding of the factors influencing subpopulations of parents can help vaccine policymakers and healthcare providers improve efforts to reduce vaccine hesitancy, particularly for new vaccines.

BackgroundAdverse Childhood Experiences (ACEs) have been associated with long-term physical and mental health conditions, toxic stress levels, developing unstable interpersonal relationships, and substance use disorders due to unresolved childhood adversities.AimsThis study assessed the perspectives of mental health providers (MHPs) regarding their adult patients’ coping with ACEs during COVID-19 in Houston, Texas. Specifically, we explored how individuals with ACEs are coping with the increased stresses of the pandemic, how MHPs may provide therapeutic support for individuals with ACEs during this pandemic, pandemic-related challenges of accessing and utilizing mental health services for individuals with ACEs, and the awareness and treatment of ACEs among MHPs.MethodsTen in-depth semi-structured virtual interviews were conducted with licensed MHPs from November 2021 to April 2022 in Houston, Texas. Interviews were coded and analyzed for emerging themes through an inductive open coding approach to discover insights regarding coping with ACEs during COVID-19.ResultsFour key themes experienced by individuals with ACEs emerged from the MHP interviews: (1) Maladaptive emotional dissonance and coping outlets during the pandemic, (2) Difficulties with social connectedness and significance of social support, (3) Heightened daily life stressors and coping with the ongoing disruption of the pandemic, and (4) Changing interactions with the mental health system. Themes from this study highlighted that resilience, seeking treatment, and strong social support can help develop healthy coping strategies among individuals with ACEs.ConclusionThis study may help inform best clinical practices to develop interventions and policies regarding ACEs such as a resilience-promotion approach that targets all the socio-ecological levels. In addition, findings highlight the synergy of psychotherapeutic and pharmacological management via tele-health modalities, in helping individuals with ACEs continue receiving the care they deserve and need during a persistent pandemic and an uncertain future.

Public health impacts can be achieved when evidence-based interventions are implemented to those most in need. Too often implementation never or slowly occurs. The community-wide campaign intervention Tu Salud ¡Si Cuenta! has evidence of improving health outcomes related to chronic disease among low-income, Latinos. Using the RE-AIM Framework, this study examined if the scaled-up version of the intervention is associated with improvements in hypertension and obesity in 12 locations. Each element of the RE-AIM framework was examined. For “Effectiveness,” we examined outcomes overall and by implementing location. We used linear and logistic regression to assess if exposure in the intervention was associated with improvement in hypertension and weight loss. Participants were stratified into “low exposure” (2–3 outreach visits) vs. “high exposure” (4–5 outreach visits). Based on the RE-AIM Framework, the intervention “reached” its intended population of low-income Latinos, demonstrated “effectiveness” in improving hypertension and obesity, was “adopted” at a high level in all but one site, was “implemented” with fidelity to the intervention model with moderate success across locations, and showed high “maintenance” over time. For effectiveness specifically, we found that out of 5,019 participants, 2,508 (50%) had a baseline hypertensive blood pressure (BP) reading. Of the 2,508, 1,245 (49.9%) recovered to normal blood pressure or pre-hypertension stage by last follow-up. After adjusting for baseline BP and potential confounders in multivariable linear regression models, the high exposure group had significantly more reduction in systolic BP (adjusted mean difference in % change = −0.96; p = 0.002) and diastolic BP (adjusted mean difference in % change = −1.61; p < 0.0001) compared to the low exposure group. After controlling for baseline weight and other confounders, the high exposure group had significantly greater decrease in weight compared to the low exposure group (adjusted mean difference in % change = −1.28; p < 0.0001). Results from the multivariable logistic regression models indicated that compared to the low exposure group the high exposure group was more likely to achieve a clinically significant minimum 5% weight loss [adjusted odds ratio (OR) = 2.97; p < 0.0001). This study contributes evidence that a Community-Wide Campaign model holds promise for addressing hypertension and obesity among low-income Latinos.

Estimating parameters accurately in groundwater models for aquifers is challenging because the models are non-explicit solutions of complex partial differential equations. Modern research methods, such as Monte Carlo methods and metaheuristic algorithms, for searching an efficient design to estimate model parameters require hundreds, if not thousands of model calls, making the computational cost prohibitive. One method to circumvent the problem and gain valuable insight on the behavior of groundwater is to first apply a Galerkin method and convert the system of partial differential equations governing the flow to a discrete problem and then use a Proper Orthogonal Decomposition to project the high-dimensional model space of the original groundwater model to create a reduced groundwater model with much lower dimensions. The reduced model can be solved several orders of magnitude faster than the full model and able to provide an accurate estimate of the full model. The task is still challenging because the optimization problem is non-convex, non-differentiable and there are continuous variables and integer-valued variables to optimize. Following convention, heuristic algorithms and a combination is used search to find efficient designs for the reduced groundwater model using various optimality criteria. The main goals are to introduce new design criteria and the concept of design efficiency for experimental design research in hydrology. The two criteria have good utility but interestingly, do not seem to have been implemented in hydrology. In addition, design efficiency is introduced. Design efficiency is a method to assess how robust a design is under a change of criteria. The latter is an important issue because the design criterion may be subjectively selected and it is well known that an optimal design can perform poorly under another criterion. It is thus desirable that the implemented design has relatively high efficiencies under a few criteria. As applications, two heuristic algorithms are used to find optimal designs for a small synthetic aquifer design problem and a design problem for a large-scale groundwater model and assess their robustness properties to other optimality criteria. The results show the proof of concept is workable for finding a more informed and efficient model-based design for a water resource study.

Abstract  Highly nonlinear advection–dispersion-reaction equations govern numerous transport phenomena. Robust, accurate, and efficient algorithms to solve these equations hold the key to the success of applying numerical models to field problems. This paper presents the development and verification of a computational algorithm to approximate the highly nonlinear transport equations of reactive chemical transport and multiphase flow. The algorithm was developed based on the Lagrangian-Eulerian decoupling method with an adaptive ZOOMing and Peak/valley Capture (LEZOOMPC) scheme. It consists of both backward and forward node tracking, rough element determination, peak/valley capturing, and adaptive local grid refinement. A second-order tracking was implemented to accurately and efficiently track all fictitious particles. Shanks’ method was introduced to deal with slowly converging case. The accuracy and efficiency of the algorithm were verified with the Burger equation for a variety of cases. The robustness of the algorithm to achieve convergent solutions was demonstrated by highly nonlinear reactive contaminant transport and multiphase flow problems. [ABSTRACT FROM AUTHOR]

In recent years, considerable progress has been made in the development of fast network flow algorithms aiming at solving linear programming problems with network substructures. Recognizing the fact that the basic structure of a water-supply system is a network, network algorithms have been used for system operation and management. However, to date, most algorithms are designed to solve transshipment problems in a pure network setting with total demand being equal to total supply. The non–network-type constraints and variables are precluded from the network models. Consequently, network models are used to perform optimization only for the network portion of the water-supply system under certain overall operational guidelines. In this study, an algorithm, EMNET, is introduced for solving the regional-water-supply-system optimization that corresponds to a generalized network problem with additional non–network-type constraints and non–network-type variables. The multiperiod, multiobjective optimization model for the regional water-supply system of the Metropolitan Water District of Southern California is used for case study. The results show that, depending on the network substructure in the model, EMNET is 11–117 times faster than standard linear programming codes, such as MINOS. [ABSTRACT FROM AUTHOR]

Kreweras considered the problem of counting noncrossing partitions of the set $\{1,2,\cdots,n\}$, whose elements are arranged into a cycle in its natural order, into $p$ parts of given sizes $n_1,n_2,\cdots,n_p$ (but not specifying which part gets which size). He gave a beautiful and surprising result whose proof resorts to a recurrence relation. In this paper we give a direct, entirely bijective, proof starting from the same initial idea as Kreweras' proof. [ABSTRACT FROM AUTHOR]