Showing papers by "RAND Corporation published in 2022"
TL;DR: This paper found that high mistrust of the vaccine itself (e.g., concerns about harm and side effects), or weak subjective norms for vaccination in one's close social network, was a significant predictor of not wanting to get vaccinated.
Abstract: National data indicate low intentions for COVID-19 vaccination among a substantial minority of Black Americans, and disproportionately lower vaccination rates among Black Americans than White Americans.A total of 207 of the 318 Black participants (65%) in the RAND American Life Panel, a nationally representative internet panel, were surveyed about COVID-19 vaccine intentions in November-December 2020. Participants' census tracts were geocoded using the Centers for Disease Control and Prevention's Social Vulnerability Index.Overall, 35% agreed or strongly agreed that they would not get a COVID-19 vaccine, 40% agreed or strongly agreed that they would get vaccinated, and 25% reported "don't know." Significant multivariable predictors of not wanting to get vaccinated included high mistrust of the vaccine itself (e.g., concerns about harm and side effects), OR (95% CI) = 2.2 (1.2-3.9), p = .007, and weak subjective norms for vaccination in one's close social network, OR (95% CI) = 0.6 (0.4-0.7), p < .001. Residence in an area of higher socioeconomic vulnerability was a marginally significant predictor, OR (95% CI) = 3.1 (0.9-11.0), p = .08.High mistrust around COVID-19 vaccines may lower vaccine confidence. Social network members' attitudes can be influential in encouraging vaccination. Public health communications could use transparent and clear messaging on safety and efficacy, and acknowledge historical and ongoing discrimination and racism as understandable reasons for low confidence in COVID-19 vaccines. Future research is needed to consider vaccine access challenges in tandem with mistrust as contributing to low vaccination rates across health conditions.
40 citations
TL;DR: In this paper , the authors examined trajectories of alcohol use and alcohol problems over a 9-month period during the COVID-19 pandemic, the extent to which these trajectories are predicted by social stress and drinking motives, and whether results differ for women and men.
Abstract: Increased alcohol use coinciding with onset of the COVID-19 pandemic, particularly among women, has been documented among U.S. adults. This study examines trajectories of alcohol use and alcohol problems over a 9-month period during the pandemic, the extent to which these trajectories are predicted by social stress and drinking motives, and whether results differ for women and men.Data come from three online surveys of a nationally representative sample of U.S. adults ages 30-80 conducted in May-July 2020, October-November 2020, and March 2021. The analytic sample consists of N = 1118 who initially reported any past year alcohol use. The early-COVID survey assessed demographics, social stressors, and drinking motives. All three surveys assessed average drinks per day in past month and drinking-related problems.Alcohol use declined for men, but remained stable for women. Alcohol problems increased for both sexes, especially for men. Level of alcohol use was associated with loneliness and social demands for men, and drinking motives for both sexes, with changes in use related to loneliness and social demands for men. Level of alcohol problems was associated with loneliness for women and drinking motives for both sexes, with changes in problems related to drinking motives for women. Interactions of social stress with drinking motives were not found.Sex differences in alcohol use and alcohol problems during the pandemic-as well as their associations with indicators of social stress and drinking motives-highlight the importance of tailoring prevention and treatment efforts for men and women.
17 citations
TL;DR: The emerging science on potential long-term or chronic effects of HAB toxins with a particular emphasis on microcystins, especially in vulnerable populations such as those with pre-existing liver or gastrointestinal disease, is summarized in this article .
Abstract: Freshwater harmful algal blooms (HABs) are increasing in number and severity worldwide. These HABs are chiefly composed of one or more species of cyanobacteria, also known as blue-green algae, such as Microcystis and Anabaena. Numerous HAB cyanobacterial species produce toxins (e.g., microcystin and anatoxin-collectively referred to as HAB toxins) that disrupt ecosystems, impact water and air quality, and deter recreation because they are harmful to both human and animal health. Exposure to these toxins can occur through ingestion, inhalation, or skin contact. Acute health effects of HAB toxins have been well documented and include symptoms such as nausea, vomiting, abdominal pain and diarrhea, headache, fever, and skin rashes. While these adverse effects typically increase with amount, duration, and frequency of exposure, susceptibility to HAB toxins may also be increased by the presence of comorbidities. The emerging science on potential long-term or chronic effects of HAB toxins with a particular emphasis on microcystins, especially in vulnerable populations such as those with pre-existing liver or gastrointestinal disease, is summarized herein. This review suggests additional research is needed to define at-risk populations who may be helped by preventative measures. Furthermore, studies are required to develop a mechanistic understanding of chronic, low-dose exposure to HAB toxins so that appropriate preventative, diagnostic, and therapeutic strategies can be created in a targeted fashion.
16 citations
TL;DR: In this paper, the authors compare 17 widely-used dense-gas dispersion models using observations from the Jack Rabbit II (JR II) chlorine release experiments, which were led by the U.S. Department of Homeland Security Science & Technology (DHS S&T) Chemical Security Analysis Center (CSAC) and a collaborative team of inter-agency partners, and conducted at the Army Dugway Proving Ground in 2015 and 2016.
14 citations
TL;DR: For example, this paper found that vaccination rates are lower among young adults with recent experiences of homelessness than those in the general US population, indicating a need for direct outreach that includes both offering the vaccine and addressing misconceptions about its safety to increase vaccination rates in this population.
11 citations
TL;DR: In this paper, a longitudinal cohort of Black residents living in two racially isolated Pittsburgh neighborhoods was found to experience behavioral responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) conditions (e.g., closures of businesses, schools, government offices).
11 citations
01 Apr 2022
TL;DR: In this article , the authors investigated left ventricular remodeling, mechanics, systolic and diastolic function, combined with clinical characteristics and heart-failure treatment in association to death or heart-transplant (DoT) in pediatric idiopathic, genetic or familial dilated cardiomyopathy (DCM), using interpretable machine-learning.
Abstract: We investigated left ventricular (LV) remodeling, mechanics, systolic and diastolic function, combined with clinical characteristics and heart-failure treatment in association to death or heart-transplant (DoT) in pediatric idiopathic, genetic or familial dilated cardiomyopathy (DCM), using interpretable machine-learning.Echocardiographic and clinical data from pediatric DCM and healthy controls were retrospectively analyzed. Machine-learning included whole cardiac-cycle regional longitudinal strain, aortic, mitral and pulmonary vein Doppler velocity traces, age and body surface area. We used unsupervised multiple kernel learning for data dimensionality reduction, positioning patients based on complex conglomerate information similarity. Subsequently, k-means identified groups with similar phenotypes. The proportion experiencing DoT was evaluated. Pheno-grouping identified 5 clinically distinct groups that were associated with differing proportions of DoT. All healthy controls clustered in groups 1 to 2, while all, but one, DCM subjects, clustered in groups 3 to 5; internally validating the algorithm. Cluster-5 comprised the oldest, most medicated patients, with combined systolic and diastolic heart-failure and highest proportion of DoT. Cluster-4 included the youngest patients characterized by severe LV remodeling and systolic dysfunction, but mild diastolic dysfunction and the second-highest proportion of DoT. Cluster-3 comprised young patients with moderate remodeling and systolic dysfunction, preserved apical strain, pronounced diastolic dysfunction and lowest proportion of DoT.Interpretable machine-learning, using full cardiac-cycle systolic and diastolic data, mechanics and clinical parameters, can potentially identify pediatric DCM patients at high-risk for DoT, and delineate mechanisms associated with risk. This may facilitate more precise prognostication and treatment of pediatric DCM.
8 citations
TL;DR: This paper examined the associations among family functioning, subjective and actigraphy-measured sleep, mental health (depressive and anxiety symptoms), and cultural identity in a sample of urban American Indian/Alaska Native (AI/AN) youth.
7 citations
TL;DR: In this paper , the authors identify high-risk initial prescriptions, defined as >7 days supply, average daily MME >90, or concurrent with benzodiazepines and estimated three multivariable logistic regression models to assess the association between policies and outcomes controlling for patient, prescriber, and county characteristics.
Abstract: Multiple state policies, such as prescription drug monitoring programs (PDMPs) and duration limits, have been implemented to decrease high-risk opioid prescribing. Studies demonstrate that many policies decrease certain opioid prescribing behaviors, but few examine their intended effects on the targeted high-risk prescribing practices, nor disentangle the effects of concurrent state or federal policies likely to influence those practices. Forty-one million initial prescriptions for new opioid episodes from 2007 to 2018 were identified using national pharmacy claims. We identified high-risk initial prescriptions, defined as >7 days’ supply, average daily MME >90, or concurrent with benzodiazepines and estimated three multivariable logistic regression models to assess the association between policies and outcomes controlling for patient, prescriber, and county characteristics. Initial prescriptions for >7 days declined from 23.8% in 2007 to 14.9% in 2018, associated with mandatory and interoperable PDMPs and prescription duration limits but not other policies examined. Initial prescriptions with daily MME > 90 declined from 13.2% to 1.9%, associated with pain management clinic laws but not consistently with other policies. Initial prescriptions concurrent with benzodiazepines declined only modestly from 6.9% to 6.5%, associated with pain management clinic laws but not other policies examined. The opioid policy environment has changed rapidly with a range of different policies being implemented addressing high-risk prescribing. PDMP laws mandating prescriber use and pain clinic laws both appear efficacious but decrease different types of high-risk opioid prescribing. New policies should be considered in light of the prevalence of the problem being addressed.
6 citations
01 Jan 2022
TL;DR: In the mammary alveolus, milk ejection is both a neural and endocrinologic process, whereby suckling stimulates sensory nerve endings in the areola and nipple, which activates the afferent neural reflexes leading to secretion and release of prolactin and oxytocin this paper .
Abstract: Lactation represents the completion of the reproductive cycle and occurs as one of the major stages of mammary gland development: embryogenesis; mammogenesis; lactogenesis, or secretory differentiation (stage 1 lactogenesis) and secretory activation (stage II lactogenesis); lactation (or stage III lactogenesis), or full milk secretion; and involution. Hormones play a central role in mammary gland development and lactation (estrogen and progesterone, prolactin, insulin, hydrocortisone, human placental lactogen, human growth hormone, oxytocin). Milk ejection is both a neural and endocrinologic process, whereby suckling stimulates sensory nerve endings in the areola and nipple, which activates the afferent neural reflexes leading to secretion and release of prolactin and oxytocin. Lactation changes the mother’s metabolism greatly, redistributing the blood supply and increasing the demand for nutrients. Milk synthesis and secretion in the mammary alveolus include four major transcellular pathways and one paracellular pathway: exocytosis of milk protein and lactose; milk-fat secretion via the milk-fat globule; secretion of ions and water across the apical membrane; pinocytosis/exocytosis of immunoglobulins; and the paracellular pathway for plasma components and leukocytes.
6 citations
01 Jan 2022
TL;DR: Human milk is a highly complex composite liquid of nutrients for infant growth, consisting primarily of fat, carbohydrates, and proteins, as well as minerals, vitamins, and other nutrients as mentioned in this paper .
Abstract: Human milk is a highly complex composite liquid of nutrients for infant growth, consisting primarily of fat, carbohydrates, and proteins, as well as minerals, vitamins, and other nutrients. The delicate balance of nutrients and the dynamic lactation process make human milk the only food substance during life that is adequate as the sole source of nutrition for a period of time in an infant's life. The biochemistry of human milk changes throughout the stages of breastfeeding and as a function of infant needs and demands for growth and development. There remains a tremendous amount to be learned about the interaction of macronutrients, micronutrients and bioactive factors in human milk as the optimal nutrition for a human infant.
TL;DR: In this article , the authors examined the role of sleep problems in contributing to cognitive function and clinically adjudicated cognitive impairment in a predominantly African Americans (AAs) sample and found that higher sleep efficiency and less wakefulness after sleep onset (WASO) were associated with greater attention, executive function, and visuospatial ability.
Abstract: Sleep problems may contribute to the disproportionate burden of Alzheimer's disease and related dementias (ADRD) among African Americans (AAs).To examine the role of sleep problems in contributing to cognitive function and clinically adjudicated cognitive impairment in a predominantly AA sample.This study (n = 216, 78.8% female; mean age = 67.7 years) examined associations between 1) the level (i.e., measured in 2018) and 2) change over time (from 2013 to 2018; n = 168) in actigraphy-assessed sleep with domain-specific cognitive function and clinically adjudicated cognitive impairment (2018) in a community-dwelling, predominantly AA (96.9%) sample. A comprehensive cognitive battery assessed global cognitive function (3MS) and domain-specific cognitive function (attention, visuo-spatial ability, language, delayed recall, immediate recall, and executive function) in 2018. Sleep was measured in 2013 and 2018 via actigraphy.Higher sleep efficiency and less wakefulness after sleep onset (WASO; measured in 2018) were associated with greater attention, executive function, and visuospatial ability. Increases in sleep efficiency between 2013 and 2018 were associated with better executive function, language, immediate recall, and visuospatial ability, whereas increases in WASO (2013-2018) were associated with poorer attention, executive function, and visuospatial ability. Level or change in sleep duration were not associated with domain-specific cognitive function, nor were any sleep measures associated with clinically adjudicated cognitive impairment.In a predominantly AA sample of older adults, both the level and change (i.e., worsening) of sleep efficiency and WASO were associated with poorer cognitive function. Improving sleep health may support ADRD prevention and reduce health disparities.
TL;DR: This article examined the effects of the four-day school week on achievement across 12 states and found statistically significant negative effects on math and English/language arts achievement for 4-day week districts with low, middle, and high levels of time in school.
09 Jun 2022
TL;DR: In this paper , the authors used path analysis to assess whether low job control predicted shorter breastfeeding among working mothers and may contribute to racial disparities in BF, and they found that working mothers with lower job control breastfed less.
Abstract: In Brief Background Low job control may predict shorter breastfeeding (BF) among working mothers and may contribute to racial disparities in BF. Methods We used demographic, employment, and health data for n = 631 observations from the Panel Study of Income Dynamics. Job control scores came from a job-exposure matrix. Using path analysis, we assessed whether job control predicted BF and mediated Black-White BF differences. We controlled for education, working hours, marital status, and low birthweight. Results Lower job control predicted decreased odds of BF for at least 6 months (odds ratio, 0.61; 95% confidence interval, 0.31–0.90; reference, no BF). Low job control explained 31% of the Black-White difference for both shorter-term and longer-term BF. Conclusions Low job contributes to shorter BF and to BF disparities by race. Intervening to enhance job control could improve BF. Job control refers to decision-making discretion and learning opportunities at work. This study found that working mothers with lower job control breastfed less. Black mothers were exposed to lower job control than White mothers, and job control explained part of the Black-White breastfeeding disparity.
TL;DR: In this article, the authors examined patterns and correlates of CBD product use and co-use with marijuana in a sample of young adults and found that more frequent CBD use was associated with more frequent and heavier marijuana use but was not associated with marijuana use-related problems.
TL;DR: In this article , the authors examined associations of body composition at birth and body composition trajectories from birth to early childhood with hepatic fat in early childhood using multivariable-adjusted linear regression.
14 Feb 2022
TL;DR: In this article , the authors examine the content of the recommendations given to those providers aimed at improving provider-patient interactions, characterize these recommendations, and examine their actionability, concluding that most of the most common recommendations mapped to behavioral aspects of provider communication.
Abstract: Health care organizations track patient experience data, identify areas of improvement, monitor provider performance, and assist providers in improving their interactions with patients. Some practices use one-on-one provider counseling ("shadow coaching") to identify and modify provider behaviors. A recent evaluation of a large shadow coaching program found statistically significant improvements in coached providers' patient experience scores immediately after being coached. This study aimed to examine the content of the recommendations given to those providers aimed at improving provider-patient interactions, characterize these recommendations, and examine their actionability.Providers at a large, urban federally qualified health center were selected for coaching based on Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) patient experience scores (92 of 320 providers), shadowed by a trained peer coach for a half to full day and received recommendations on how to improve interactions with their patients. We coded 1082 recommendations found in the 92 coaching reports.Reports contained an average of 12 recommendations. About half encouraged consistency of existing behaviors and half encouraged new behaviors. Most recommendations related to behaviors of the provider rather than support staff and targeted actions within the examination room rather than other spaces (eg, waiting room). The most common recommendations mapped to behavioral aspects of provider communication. Most recommendations targeted verbal rather than nonverbal communication behaviors. Most recommendations were actionable (ie, specific, descriptive), with recommendations that encouraged new behaviors being more actionable than those that encouraged existing actions.Patient experience surveys are effective at identifying where improvement is needed but are not always informative enough to instruct providers on how to modify and improve their interactions with patients. Analyzing the feedback given to coached providers as part of an effective shadow-coaching program provides details about implementation on shadow-coaching feedback. Recommendations to providers aimed at improving their interactions with patients need to not only suggest the exact behaviors defined within patient experience survey items but also include recommended behaviors indirectly associated with those measured behaviors. Attention needs to be paid to supplementing patient experience data with explicit, tangible, and descriptive (ie, actionable) recommendations associated with the targeted, measured behaviors. Research is needed to understand how recommendations are put into practice by providers and what motivates and supports them to sustain changed behaviors.
TL;DR: In this paper , Monte Carlo simulations were used to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state-level policy evaluations, showing that high relative bias arises when policies are enacted in rapid succession.
Abstract: Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models.
TL;DR: In this paper , hydrogen sulfide (H2S) has strong antioxidative actions and it shares several properties of ACR scavenger glutathione (GSH), therefore, tested whether H2S could be involved in ACR detoxification.
TL;DR: In this article , an observational cohort study of isolated Coronary artery bypass grafting patients in the Society of Thoracic Surgeons adult cardiac surgery database from July 1, 2017, to June 30, 2019 was performed to explore whether intraoperative RBC transfusion is associated with increased odds of postoperative HAI.
Abstract: BACKGROUND: Coronary artery bypass grafting (CABG) is the most common cardiac surgical procedure in the world and up to one-third of patients are transfused red blood cells (RBCs). RBC transfusion may increase the risk for health care-associated infection (HAI) after CABG, but previous studies have shown conflicting results and many did not establish exposure temporality. Our objective was to explore whether intraoperative RBC transfusion is associated with increased odds of postoperative HAI. We hypothesized that intraoperative RBC transfusion would be associated with increased odds of postoperative HAI. METHODS: We performed an observational cohort study of isolated CABG patients in the Society of Thoracic Surgeons adult cardiac surgery database from July 1, 2017, to June 30, 2019. The exposure was intraoperative RBC transfusion modeled as 0, 1, 2, 3, or 4+ units. The authors focused on intraoperative RBC transfusion as a risk factor, because it has a definite temporal relationship before postoperative HAI. The study’s primary outcome was a composite HAI variable that included sepsis, pneumonia, and surgical site infection (both deep and superficial). Mixed-effects modeling, which controlled for hospital as a clustering variable, was used to explore the relationship between intraoperative RBC transfusion and postoperative HAI. RESULTS: Among 362,954 CABG patients from 1076 hospitals included in our analysis, 59,578 patients (16.4%) received intraoperative RBCs and 116,186 (32.0%) received either intraoperative or postoperative RBCs. Risk-adjusted odds ratios for HAI in patients who received 1, 2, 3, and 4+ intraoperative RBCs were 1.11 (95% confidence interval [CI], 1.03–1.20; P = .005), 1.13 (95% CI, 1.05–1.21; P = .001), 1.15 (95% CI, 1.04–1.27; P = .008), and 1.14 (95% CI, 1.02–1.27; P = .02) compared to patients who received no RBCs. CONCLUSIONS: Intraoperative RBC transfusion is associated with a small increase in odds of HAI in CABG patients. Future studies should explore whether reductions in RBC transfusion can also reduce HAIs.
TL;DR: In this paper , the authors conducted semi-structured interviews with 20 adults receiving care from one fully virtual tele-OUD service who had received 3 to 5 weeks of treatment, using an inductive and deductive approach informed by the modified Unified Theory of Acceptance and Use of Technology model.
Abstract: Telemedicine for opioid use disorder (tele-OUD) has the potential to increase access to medications for OUD (MOUD). Fully virtual tele-OUD services, in which all care is provided via telemedicine, are increasingly common, yet few studies document the experiences of patients who use such services. Understanding patient perspectives is one of multiple considerations to inform the regulation and reimbursement of tele-OUD services.We conducted semi-structured interviews with 20 adults receiving care from one fully virtual tele-OUD service who had received 3 to 5 weeks of treatment. Analyses were conducted using an inductive and deductive approach informed by the modified Unified Theory of Acceptance and Use of Technology model.Over three quarters of patients with past experience receiving in-person MOUD treatment described tele-OUD as more advantageous with its key strength being more patient centered. Over three quarters of patients said they felt tele-OUD helped to ameliorate social barriers to seeking treatment, and nearly all said they appreciated the speed at which they were able to initiate MOUD treatment via tele-OUD. Surprisingly, the pandemic was not among the factors that influenced patient interest in tele-OUD.Patients engaged in one fully virtual tele-OUD service described unique advantages of tele-OUD. More research is needed to determine the appropriateness of tele-OUD for people in various stages of recovery, and data on long-term treatment outcomes are needed to inform decisions regarding the regulation and reimbursement of fully virtual and hybrid care models for OUD.
TL;DR: In this paper, simulation models can assist in setting policy that anticipates dementia in individuals and families who provide caregiving, which has an impact on both the affected individual and family members who provided care.
Abstract: BackgroundDementia is a common disease that has an impact on both the affected individual and family members who provide caregiving. Simulation models can assist in setting policy that anticipates ...
TL;DR: In this article , the authors compared the contribution of first-name information to the accuracy of basic and more complex racial-and-ethnic imputations that incorporate surname information and found that first names significantly improved the performance of these imputations.
Abstract: Data on race-and-ethnicity that are needed to measure health equity are often limited or missing. The importance of first name and sex in predicting race-and-ethnicity is not well understood.The objective of this study was to compare the contribution of first-name information to the accuracy of basic and more complex racial-and-ethnic imputations that incorporate surname information.We imputed race-and-ethnicity in a sample of Medicare beneficiaries under 2 scenarios: (1) with only sparse predictors (name, address, sex) and (2) with a rich set (adding limited administrative race-and-ethnicity, demographics, and insurance).A total of 284,627 Medicare beneficiaries who completed the 2014 Medicare Consumer Assessment of Healthcare Providers and Systems survey and reported race-and-ethnicity were included.Hispanic, non-Hispanic Asian/Pacific Islander, and non-Hispanic White racial-and-ethnic imputations are more accurate for males than females under both sparse-predictor and rich-predictor scenarios; adding first-name information increases accuracy more for females than males. In contrast, imputations of non-Hispanic Black race-and-ethnicity are similarly accurate for females and males, and first names increase accuracy equally for each sex in both sparse-predictor and rich-predictor scenarios. For all 4 racial-and-ethnic groups, incorporating first-name information improves prediction accuracy more under the sparse-predictor scenario than under the rich-predictor scenario.First-name information contributes more to the accuracy of racial-and-ethnic imputations in a sparse-predictor scenario than in a rich-predictor scenario and generally narrows sex gaps in accuracy of imputations.
01 Jan 2022
TL;DR: The mammary gland undergoes significant changes throughout life due to mammogenesis, lactogenesis, involution, and cyclical hormonal changes of menstruation as mentioned in this paper , which is not fully developed at birth.
Abstract: The mammary gland is not fully developed at birth. It undergoes significant changes throughout life due to mammogenesis, lactogenesis, involution, and the cyclical hormonal changes of menstruation. The skin, subcutaneous tissue/supportive structures (blood vessels, lymphatics, nerves, connective tissue), and corpus mammae make up the anatomy of the breast.
TL;DR: In this paper , the authors developed a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which applied to the performance of health plans for different patient groups.
Abstract: Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred.The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups.Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality.All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015-2018.MA plans with higher overall performance tended to be less inconsistent in their performance (r=-0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (>0.8 SD) and investigated the reasons for inconsistency for 2 example plans.This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.
01 Jan 2022
TL;DR: In this article , a 3D convolutional neural network was trained to recognize dynamic hand gestures in real-time using a huge training set consisting of numerous clips of people performing specific gestures in varying lighting conditions to train the model.
Abstract: Since the 1970s, the field of gesture recognition and its applications has been at the centre of considerable research in human–computer interaction. Researchers have been able to construct strong models that can recognize gestures in real time thanks to recent advances in deep learning and computer vision, but they face hurdles when it comes to classifying gestures in variable lighting conditions. In this paper, we train a 3D convolutional neural network to recognize dynamic hand gestures in real time. Our focus is on ensuring that gesture recognition systems can perform well under varying light conditions. We use a huge training set consisting of numerous clips of people performing specific gestures in varying lighting conditions to train the model. We were able to attain an accuracy of 76.40% on the training set and 66.56% on the validation set with minimal pre-processing applied to the data set. The trained model was able to successfully recognize hand gestures recorded from a Webcam in real time. We were then able to use the model’s predictions to control video playback on the VLC media player such as increasing/decreasing volume and pausing video. These experimental results show the effectiveness and efficiency of the proposed framework to recognize gestures in both bright and dim lighting conditions.
01 Jan 2022
TL;DR: In this paper , the authors explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance, and show that focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models.
Abstract: We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be nonlinear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance. Our findings showcase that these two models should, by default, include higher-order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments.