Assessing Differential Effects of Somatic Amplification to Positive Affect in Midlife and Late Adulthood—A Regression Mixture Approach

2021 ◽  
pp. 009141502110665
Author(s):  
Minjung Kim ◽  
Menglin Xu ◽  
Junyeong Yang ◽  
Susan Talley ◽  
Jen D. Wong
Keyword(s):  
Positive Affect ◽  
Mixture Model ◽  
Latent Class ◽  
Model Building ◽  
The United States ◽  
Aging Research ◽  
Statistical Tool ◽  
Differential Effects ◽  
Somatic Amplification ◽  
Regression Mixture

This study aims to provide an empirical demonstration of a novel method, regression mixture model, by examining differential effects of somatic amplification to positive affect and identifying the predictors that contribute to the differential effects. Data derived from the second wave of Midlife in the United States. The analytic sample consisted of 1,766 adults aged from 33 to 84 years. Regression mixture models were fitted using Mplus 7.4, and a two-step model-building approach was adopted. Three latent groups were identified consisting of a maladaptive (32.1%), a vulnerable (62.5%), and a resilient (5.4%) group. Six covariates (i.e., age, education level, positive relations with others, purpose in life, depressive symptoms, and physical health) significantly predicted the latent class membership in the regression mixture model. The study demonstrated the regression mixture model to be a flexible and efficient statistical tool in assessing individual differences in response to adversity and identifying resilience factors, which contributes to aging research.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

Couple Religiosity and Well-Being Among Older Adults in the United States

2021 ◽  
pp. 089826432110421
Author(s):  
Laura Upenieks ◽  
Jeremy E. Uecker ◽  
Markus H. Schafer
Keyword(s):  
Social Life ◽  
Latent Class ◽  
Well Being ◽  
The United States ◽  
Older Couples ◽  
Logistic Regression Models ◽  
Ordered Logistic Regression ◽  
Individual Trait ◽  
The Us ◽  
Married Men

Objectives: This article evaluates whether couples’ religious similarity is consequential for the health of older married men and women. Alternatively, we examine whether women’s religiosity alone is health-protective to their husbands . Methods: Using dyadic data from the US National Social Life, Health, and Aging Project, a representative sample of 913 individuals ages 62–91 plus their marital partners, we perform latent-class analysis to separate older couples into classes based on religious characteristics. Ordered logistic regression models are then used to assess whether different combinations of religious (dis)similarity are associated with married men and women’s well-being. Results: We find that older women in highly religious, homogamous marriages report better mental and physical health relative to women in heterogamous and secular (non-religious) marriages. No significant associations were observed for men. Discussion: Our results emphasize that religiosity is not only an individual trait—dis/similarities within a couple have important implications for older women’s well-being.

Abstract 002: Assessing Profiles Of Cardiometabolic Risk Among South Asians Living In The United States Using A Syndemic Framework And Acculturation

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Francisco A Montiel Ishino ◽  
Katia M Canenguez ◽  
Jeffrey H Cohen ◽  
Belinda Needham ◽  
Namratha Kandula ◽  
...  
Keyword(s):  
Mental Health ◽  
Latent Class ◽  
Family Income ◽  
South Asians ◽  
Health Indicators ◽  
The United States ◽  
Sociocultural Factors ◽  
Cultural Traditions ◽  
Using Data ◽  
Religiosity And Spirituality

Background: South Asians (SA) are the second largest US immigrant group and have excess cardiometabolic (CM) disease. While acculturation is associated with increased CM risk among immigrants and refugees, the role of acculturation on SA CM risk is relatively unknown. CM disease presents as a syndemic or synergistic epidemic involving multiple disease clusters as well as the biological, social, and psychological interactions from the acculturative process to worsen morbidity within subgroups. Methods: We used latent class analysis to identify SA CM risk based on acculturation subgroups using data from adults aged 40-84 in the Mediators of Atherosclerosis in South Asians Living in America study (N=771). The distal outcome of CM risk was constructed using hypertension, type 2 diabetes, and body mass index. Proxies of acculturation included years lived in the US, English proficiency, cuisine eaten at home, cultural traditions, ethnicity of friends, social and neighborhood support, and experienced discrimination; as well as mental health indicators, which included depression, trait anxiety, anger, and positive and negative spiritual coping. Covariates included demographic characteristics, family income, education, study site, exercise, smoking, alcohol use, religiosity and spirituality. Results: Four CM risk profiles and acculturation subgroups were identified: 1) lowest risk [73.8%] were the most integrated with both SA and US culture; 2) intermediate-low risk [13.4%] had high mental health distress and discrimination and separated from SA and US culture; 3) intermediate-high risk [8.9%] were more assimilated with US culture; and 4) highest risk [3.9%] were more assimilated with US culture [Figure]. Conclusion: Our approach identified distinct nuanced profiles of syndemic CM risk to understand how acculturation and sociocultural factors cluster with health in US South Asians. Our syndemic framework will further understanding of CM risk among SA to best design tailored prevention and intervention programs.

Stylistic variation among mobile speakers: Using old and new regional variables to construct complex place identity

2018 ◽  
Vol 30 (2) ◽  
pp. 175-202 ◽  
Author(s):  
Jennifer Nycz
Keyword(s):  
United States ◽  
New York ◽  
New York City ◽  
Positive Affect ◽  
York City ◽  
The United States ◽  
Place Identity ◽  
Washington Dc ◽  
Stylistic Variation ◽  
Show Evidence

AbstractThis paper examines stylistic variation in the (oh), (o), (aw), and (ay) classes among native speakers of Canadian English living in or just outside either New York City or Washington, DC. Speakers show evidence of change toward US norms for all four vowels, though only (aw) shows consistent style shifting: prevoiceless (aw) is realized with higher nuclei when speakers express ambivalence about or distance from the United States, and lower nuclei when closeness to or positive affect about the United States is being conveyed. Canadians in New York also show topic- and stance-based shift in (oh): (oh)s are higher when expressing positive affect or closeness to New York City and lower when expressing negative affect or distance. These results suggest that mobile speakers continue to exploit the socioindexical links in their native dialect while learning and using new links in their adopted dialect—but only if those links are socially salient.

scholarly journals Volunteers’ Felt Respect and Its Associations With Volunteering Retention, Daily Affect, Well-being, and Mortality

2018 ◽  
Vol 75 (8) ◽  
pp. 1625-1636 ◽  
Author(s):  
Dwight C K Tse
Keyword(s):  
United States ◽  
Negative Affect ◽  
Positive Affect ◽  
Well Being ◽  
The United States ◽  
National Sample ◽  
Retention Rates ◽  
Mortality Data ◽  
Volunteer Work ◽  
Daily Affect

Abstract Objectives Volunteering is associated with improved physical and psychological well-being; volunteers feeling more respect for their work may have better well-being than their counterparts. Methods This study investigated the effects of felt respect for volunteer work on volunteering retention, daily affect, well-being (subjective, psychological, and social), and mortality. The study analyzed survey and mortality data from a national sample of 2,677 volunteers from the Midlife in the United States Study over a 20-year span. Daily affect data were obtained from a subsample of 1,032 volunteers. Results Compared to volunteers feeling less respect from others, those feeling more respect (a) were more likely to continue volunteering 10 and 20 years later, (b) had higher levels of daily positive affect and lower levels of daily negative affect, and (c) had higher levels of well-being over a 20-year period. The effect of felt respect on mortality was not statistically significant. Discussion Greater level of felt respect for volunteer work is positively related to volunteers’ retention rates, daily affective experience, and well-being.

scholarly journals An Exploration of Fractal Based Prognostic Model and Comparative Analysis for Second Wave of COVID-19 Diffusion

2021 ◽  
Author(s):  
Easwaramoorthy D. ◽  
Gowrisankar A. ◽  
Manimaran A. ◽  
Nandhini S. ◽  
Santo Banerjee ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Death Rate ◽  
Transmission Rate ◽  
The United States ◽  
Fractal Interpolation ◽  
Interpolation Function ◽  
Statistical Tool ◽  
Dimension Estimate ◽  
Fractal Interpolation Function ◽  
Second Wave

Abstract The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the dataset of the number of deaths per day and the number of infected cases per day of the most affected countries, The United States of America, Brazil, Russia, India, and The United Kingdom have been considered in first and second wave. The correlation fractal dimension has been estimated for the prescribed datasets of COVID-19 and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to picturize the transmission rate in the both times.

scholarly journals Does Money Matter? A Theory-Driven Growth Mixture Model to Explain Travel-Mode Choice with Experimental Data

Methodology ◽  
2006 ◽  
Vol 2 (3) ◽  
pp. 124-134 ◽  
Author(s):  
Eldad Davidov ◽  
Kajsa Yang-Hansen ◽  
Jan-Eric Gustafsson ◽  
Peter Schmidt ◽  
Sebastian Bamberg
Keyword(s):  
Mixture Model ◽  
Latent Class ◽  
Mode Choice ◽  
Growth Mixture Model ◽  
Travel Mode ◽  
High Importance ◽  
Growth Mixture ◽  
Travel Mode Choice ◽  
Changes Over Time ◽  
Over Time

In the present article we apply a growth mixture model using Mplus via STREAMS to delineate the mechanism underlying travel-mode choice. Three waves of an experimental field study conducted in Frankfurt Main, Germany, are applied for the statistical analysis. Five major questions are addressed: (1) whether the choice of public transport rather than the car changes over time; (2) whether a soft policy intervention to change travel mode choice has any effect on the travel-mode chosen; (3) whether one can identify different groups of people regarding the importance allocated to monetary and time considerations for the decision of which travel mode to use; (4) whether the different subgroups of people have different initial states and rates of change in their travel-model choices; (5) whether sociodemographic variables have an additional effect on the latent class variables and on the changes in travel-mode choice over time. We also found that choice of public transportation in our study is stable over time. Moreover, the intervention has an effect only on one of the classes. We identify four classes of individuals. One class allocates a low importance to both monetary and time considerations, the second allocates high importance to money and low importance to time, the third allocates high importance to both, and the fourth allocates a low importance to money and a high importance to time. We found no difference in the patterns of travel-mode changes over time in the four classes. We also found some additional effects of sociodemographic characteristics on the latent class variables and on behavior in the different classes. The model specification and the empirical findings are discussed in light of the theory of the allocation of time of Gary Becker.

scholarly journals Maternal Education, Changing Family Circumstances, and Children’s Skill Development in the United States and UK

2017 ◽  
Vol 674 (1) ◽  
pp. 59-84 ◽  
Author(s):  
Margot I. Jackson ◽  
Kathleen Kiernan ◽  
Sara McLanahan
Keyword(s):  
United States ◽  
Early Childhood ◽  
Cognitive Development ◽  
Maternal Education ◽  
Latent Class ◽  
Educational Inequality ◽  
Good Deal ◽  
Well Being ◽  
The United States ◽  
National Analysis

Maternal education influences families’ socioeconomic status. It is strongly associated with children’s cognitive development and a key predictor of other resources within the family that strongly predict children’s well-being: economic insecurity, family structure, and maternal depression. Most studies examine the effects of these variables in isolation at particular points in time, and very little research examines whether findings observed among children in the United States can be generalized to children of a similar age in other countries. We use latent class analysis and data from two nationally representative birth cohort studies that follow children from birth to age five to answer two questions: (1) How do children’s family circumstances evolve throughout early childhood? and (2) To what extent do these trajectories account for differences in children’s cognitive development? Cross-national analysis reveals a good deal of similarity between the United States and UK in patterns of family life during early childhood, and in the degree to which those patterns contribute to educational inequality.

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