Hierarchical Generalized Linear Models Research Articles

Group Size: An Active Ingredient of School-Based Language Therapy.

This study explored the relation between therapy group size and language outcomes for children receiving school-based language therapy through an implementation science lens. Data for the current study were gathered as part of the Speech-Language Therapy Experiences in Public Schools study. Participants included 273 English-speaking kindergarten through second-grade children with language impairment receiving business-as-usual therapy on the caseloads of 75 speech-language pathologists. Data were collected over an academic year, including weekly therapy logs, speech-language pathology questionnaires, and pre- and post-language measures. Descriptive analysis revealed that children primarily experienced small-group therapy sessions (two to four children); however, there was considerable variability in group size. Hierarchical general linear modeling indicated that caseload size did not explain group size variability. However, the number of student cancellations was positively associated with receipt of large-group sessions. Notably, a significant negative association was found between receipt of large-group sessions (i.e., five to 10 children) and language outcomes. Children who received more than the average number of sessions in large groups (i.e., more than 5% of total sessions) experienced 0.18 SD less language gain over the academic year compared to the mean (0.54 SD). No other group size configurations (i.e., individual, small group, and extra large) yielded significant associations with language outcomes. The findings suggest that young children receiving language-based therapy in large groups make substantially fewer language gains over an academic year. These results have considerable implications for educational policy and clinical practice, which are discussed through an implementation science frame.

Parent-offspring resemblance for educational attainment reduces with increased social class in a global sample: evidence for the compensatory advantage hypothesis.

The degree to which (self-reported) social class predicts parent-offspring resemblance for educational attainment (EA) is examined in a globally representative dataset of 69,116 individuals sourced from 56 countries. A hierarchical general linear model is used to predict participant EA with the two-way interaction between class and parental EA, after controlling for regional effects, the main effects of age, class, parental EA, and interactions among these. Social class-by-parental EA interaction negatively predicts participant EA (semipartial r = -0.04, 95% CI = -0.05 to -0.03), meaning that among those who report belonging to a "higher" social class, the degree of parent-offspring resemblance for EA is reduced, contrary to the Scarr-Rowe hypothesis, which holds that genetic influences on cognitive ability and related phenotypes (captured here in part by parent-offspring resemblance) should be greater among those from higher socioeconomic status (SES) backgrounds. These results replicate using a quantile regression model, where it was found that among those with lower social class ordinals, the strength of the parent-participant EA association is significantly stronger relative to those in the highest ordinal. No significant sex differences are present. These findings are consistent with the compensatory advantage hypothesis, which predicts decreased heritability of EA and related phenotypes among affluent families, as increased access to educational resources should enhance opportunities for cognitive growth in a way that compensates for intrinsic disadvantages.

Bullying nas escolas públicas e privadas: os efeitos de gênero, raça e nível socioeconômico

Abstract This article explores the effects of social markers of gender, race and socioeconomic status (SES) on bullying among 9th-grade students in Brazil. The aim is to investigate whether students from privileged social groups within the social hierarchy (i.e., men, white individuals, and those of higher SES) exhibit higher rates of bullying perpetration, and conversely, whether students from disadvantaged social groups (women, black individuals, and those of lower SES) are more likely to be victims of these acts. We analyzed data from the 2015 National Survey of School Health (PeNSE) through Hierarchical Generalized Linear Models (HGLM) adjusted separately for public and private schools and for different types of student involvement with bullying: as victim, aggressor, or victim-aggressor. The results indicate that, only as to SES, we can state that bullying reproduces the status hierarchy found in society. In both public and private schools, students with a higher SES are more likely to bully someone, while students with a lower SES are more likely to be bullied. As for gender, it is observed that male students are more prone to be involved in bullying than female students, whether as a victim, aggressor, or victim-aggressor. Differentials are more significant in private schools than in public schools. As for race, it is observed that acting like a bully permeates racial groups in multiple ways.

Delineating urban growth boundaries by coupling urban interactions and ecological conservation

Urban growth boundaries (UGBs) are efficient tools for controlling disorderly urban expansion/sprawl and ensuring sustainable urban development. Previous studies have delineated UGBs from the perspective of ecological conservation or efficiency improvement and have widely applied them to spatial planning. However, few studies have investigated the role of urban interactions and connections in delineating UGBs, especially in rapidly developing urban agglomerations. Our study proposed a novel framework for UGBs delineation in urban agglomerations by coupling urban interactions and ecological conservation, with two innovative points. First, interurban interactions and connections were quantified to elucidate the transition rules of cellular automata (CA), and ecological conservation was configured as a constraint of the CA model. Second, the hierarchical generalized linear model (HGLM)-CA model was used to simulate the urban land pattern in 2035, and the dilation-erosion algorithm realized the final UGBs. The proposed framework was applied to UGBs delineation in the Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration, a rapidly developing urban agglomeration in China. Furthermore, a comparison among multi-scenario UGBs shows that urban interactions and connections can accelerate the clustering of urban development, and the delineated UGBs are more practical. Our study highlighted the importance of urban interactions for UGBs delineation in urban agglomerations and evaluated a case of UGBs delineation to validate our appeal.

Evaluating the Efficiency of Restricted Pseudo Likelihood Estimation in Balanced and Unbalanced Clustered Binary Data Models

Clustered binary data analysis is a common task in various fields, such as social sciences and epidemiology. Restricted Pseudo Likelihood Estimation (PLE) is a widely used approach for analyzing clustered binary data, providing flexibility in handling complex dependencies within clusters. This study aims to evaluate the efficiency of Restricted Pseudo Likelihood Estimation in balanced and unbalanced clustered binary data models. Using simulated data, we compare the performance of PLE in balanced and unbalanced clustered binary data scenarios. We consider various factors such as the number of clusters, cluster sizes, and intra-cluster correlation. The preferred class of models for clustered binary data is the Hierarchical Generalized Linear Model (HGLM). This article compares the performance of a restricted pseudo-likelihood estimation method of the Hierarchical Generalized Linear Model (HGLM) with equal and unequal cluster sizes. Through comprehensive simulation experiments, we assess the accuracy and precision of PLE estimates in terms of parameter estimation, standard errors, and hypothesis testing. Our findings provide insights into the efficiency of Restricted Pseudo Likelihood Estimation (RPLE) in balanced and unbalanced clustered binary data models. The results highlight the advantages and limitations of PLE in different scenarios, aiding researchers in selecting appropriate modeling approaches for their specific data characteristics. The results can guide researchers in making informed decisions regarding the selection and application of PLE in their own studies, ultimately enhancing the validity and reliability of statistical analyses in the presence of clustered binary data.

Psychological Processes, Not Physiological Parameters, Are Most Important Contributors to Symptom Severity in Patients With Refractory Heartburn/Regurgitation Symptoms

Physiological and psychological factors have been found to influence esophageal symptom reporting. We aimed to evaluate which of these factors are associated with 3 reflux symptom severity outcomes (ie, Total Reflux, Heartburn, and Sleep Disturbance) through a traditional statistical and a complementary machine-learning approach. Consecutive adult patients with refractory heartburn/regurgitation symptoms underwent standard 24-hour pH-impedance monitoring and completed questionnaires assessing past and current gastrointestinal and psychological health. In the traditional statistical approach, hierarchical general linear models assessed relationships of psychological and physiological variables (eg, total number of reflux episodes) with reflux severity scores. Mediation analyses further assessed pathways between relevant variables. In the machine-learning approach, all psychological and physiological variables were entered into 11 different models and cross-validated model performance was compared among the different models to select the best model. Three hundred ninety-three participants (mean [SD] age, 48.5 [14.1] years; 60% were female) were included. General psychological functioning emerged as an important variable in the traditional statistical approach, as it was significantly associated with all 3 outcomes and mediated the relationship between childhood trauma and both Total Reflux and Heartburn Severity. In the machine-learning analyses, general psychological variables (eg, depressive symptoms) were most important for Total Reflux and Sleep Disturbance outcomes, and symptom-specific variables, like visceral anxiety, were more influential for Heartburn Severity. Physiological variables were not significant contributors to reflux symptom severity outcomes in our sample across reflux classifications and statistical methodology. Psychological processes, both general and symptom-specific, should be considered as another important factor within the multifactorial processes that impact reflux symptom severity reporting across the reflux spectrum.

Spatial variations in the associations of mental distress with sleep insufficiency in the United States: a county-level spatial analysis

ABSTRACT In this research, we conducted hierarchical multiple regression and complex sample general linear model (CSGLM) to expand knowledge on factors contributing to mental distress, particularly from a geographic perspective. Based on the Getis-Ord G* hot-spot analysis, geographic distribution of both FMD and insufficient sleep showed several contiguous hotspots in southeast regions. Moreover, in the hierarchical regression, even after accounting for potential covariates and multicollinearity, a significant association between FMD and insufficient sleep was found, explaining that mental distress increases with increasing insufficient sleep (R 2 = 0.835). In the CSGLM, a R 2 value of 0.782 indicated that the CSGLM procedure provided concrete evidence that FMD was significantly related to sleep insufficiency even after taking complex sample designs and weighting adjustments in the BRFSS into account. This geographic association between FMD and insufficient sleep based on cross-county study has not been reported before in the literature. These findings suggest a need for further investigation on geographic disparity on mental distress and insufficient sleep and have novel implications in our understanding of the etiology of mental distress.

Hierarchical generalized linear models, correlation and a posteriori ratemaking

Insurance pricing is the premium set by the insurance companies. Hierarchical generalized linear models (HGLM) particularly those dealing with count data and their application to insurance pricing are investigated. In the context of car insurance a posteriori ratemaking, the Poisson-gamma HGLM and the negative binomial-beta HGLM are compared. It is shown that contrary to the HGLM Poisson-gamma, the negative binomial-beta HGLM fits the correlation between successive claim numbers of a given insured, which generates a significant difference of the resulting a posteriori premiums. Simulations and an application based on a real portfolio of car insurance are carried out to support the theoretical results.

Associations among Health Status, Occupation, and Occupational Injuries or Diseases: A Multi-Level Analysis.

The present study used a hierarchical generalized linear model to explore the effects of physical and mental health and occupational categories on occupational injuries and diseases. The data were obtained from the Registry for Beneficiaries of the 2002-2013 National Health Insurance Research Database. The benefit categories involved adults with occupational injuries and diseases. Six major occupational categories and 28 subcategories were used. The main analysis methods were binary logistic regression (BLR) and hierarchical generalized linear model (HGLM). After adjustment for relevant factors, the three major occupation subcategories most likely to develop occupational injuries and diseases were Subcategory 12 "employees with fixed employers" of Category 1 "civil servants, employees in public or private schools, laborers, and self-employed workers"; Subcategory 2 "employees in private organizations" of Category 1; and "sangha and religionists" of Category 6 "other citizens." Conditions such as mental disorders and obesity increased the risk of occupational injuries and diseases. A portion of the occupational categories had a higher risk of occupational injuries and diseases. Physical and mental health issues were significantly correlated with occupational injuries and diseases. To the authors' knowledge, this is the first study to use HGLM to analyze differences in occupational categories in Taiwan.

Abstract C087: Neighborhood influences on chronic hepatitis B monitoring behaviors among Asian Americans residing in Philadelphia county

Abstract The incidence of liver and intrahepatic bile duct cancer for Asian Americans is almost 2 times greater in comparison to Non-Hispanic white women and men. A major factor that contributes to liver cancer is hepatitis b (HBV) infection, which disproportionally affects Asian Americans, who represent 7% of the total US population but makes up around 58% of HBV-linked hepatocellular carcinoma cases. The purpose of this study was to examine the relationship between the neighborhood environment, including neighborhood walkability, Asianicity, and neighborhood disadvantage on HBV monitoring related behavior such as doctors' visits and HBV blood testing among Asian HBV patients. A total of 160 study participants were recruited from March 2019 to March 2020 through combined recruitment approaches, including through medical record review by authorized staff from collaborating health clinics, pharmacies, or community health centers and community-based organizations including racial/ethnic based educational and community centers, religion-based organizations, and senior centers. Study measures included baseline assessment information including sociodemographics, HBV infection history, HBV monitoring behavior, and knowledge about HBV prevention, diagnosis and treatment. Neighborhood level measures included neighborhood disadvantage, Asianicity, and neighborhood walkability using census and online measures. Hierarchical generalized linear models (HGLM) were used to account for individual nested within neighborhoods for predicting factors associated with doctor’s visits and blood test. Greater neighborhood disadvantage (OR=0.97, 95% CI=0.95-0.99, p=0.0041) and Asianicity (OR=0.97, 95% CI=0.95-0.98, p=0.0003), and lower neighborhood walkability (OR=1.06, 95% CI=1.02-1.09, p=0.0039) were associated with a lower likelihood of doctor visits for CHB. Greater Asianicity (OR=0.97, 95% CI=0.96-0.99, p=0.0044) was associated with a lower likelihood of HBV blood testing in the past 6 months. Evident neighborhood environment effects suggest the need to incorporate these factors into interventions to enhance HBV self-monitoring behaviors and slow disease progression of chronic HBV. Citation Format: Aisha Bhimla, Wenyue Lu, Lin Zhu, Yin Tan, Di Zhu, Jade Truehart, Ming-Chin Yeh, Minhhuyen Nguyen, Grace X. Ma. Neighborhood influences on chronic hepatitis B monitoring behaviors among Asian Americans residing in Philadelphia county [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C087.

Real-time momentary mood as a predictor of college students' prescription drug misuse in daily life: Direct links and the moderating role of background mental health.

The present study addressed calls for research to identify real-time predictors of prescription drug misuse (Schepis et al., 2020) by testing young adults' momentary reports of their negative mood and positive mood as predictors of event-level misuse in daily life. We implemented a 28-day ecological momentary assessment (EMA) procedure that collected individuals' mood and other contextual experiences in moments preceding prescription drug misuse. Consistent with models of problematic substance use as a means to reduce negativity (Khantzian, 1997), results from hierarchical generalized linear modeling (HGLM) indicated within-person links between higher than usual negative mood and greater likelihood of prescription misuse in daily life. Contrary to the hypothesis, misuse was also more likely when preceded by elevated positive mood. We found consistent support for the hypothesized between-person effects, with prescription misuse in daily life associated with higher average levels of negative mood, and lower average levels of positive mood, across the reporting period. We further predicted that individuals reporting greater levels of social anxiety, depression, and externalizing symptoms would evidence stronger links between their momentary negative mood and prescription misuse. Partial support for this moderation hypothesis was found, with the positive within-person link between negative mood and prescription misuse significantly stronger among individuals higher (vs. lower) on social anxiety and depression. Results provide support for intricate connections between young adults' momentary mood, mental health symptoms, and prescription drug misuse. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The role of cross-species relative brain size variation and time since domestication in explaining human empathy towards domesticated mammals

Although multiple publications have addressed the nexus between individual differences and attitudes towards nonhuman animals, fewer studies have discussed the rise of human-animal interactions, in particular with domesticated mammals, through an evolutionary lens. Two evolutionary hypotheses, the Symbiotic and Neurocognitive hypothesis, provide complementary predictions regarding the evolution of these cognitive and emotional mechanisms. According to the Symbiotic hypothesis, the longer the time humans have had to interact symbiotically with domesticated the animals, the greater their empathy towards them. Alternatively, the Neurocognitive hypothesis argues that human empathy directed at animals increases in response to the neural and cognitive complexity of nonhuman animals. To test these hypotheses, the present study reported questionnaire data from 322 Southwestern university students. These psychometric instruments measured the participants' levels of cognitive empathy (the attribution of mental states to animals), emotional empathy (emotional concern about animals), and harm avoidance (reluctance to mistreat animals) when rating 13 domesticated mammals. The species' times since domestication and relative brain sizes were then used as predictors of cognitive empathy, emotional empathy, and harm avoidance. The results of Linear Mixed and Hierarchical General Linear Cascade Models found empirical support for the Symbiotic and Neurocognitive hypotheses.

An adjusted scale binomial Beta H-Likelihood estimation method for unbalanced clustered

In practice, clustered binary responses are very prevalent, where binary data is naturally grouped by sampling techniques. Clusters are often unequal in size in some areas of studies, such as medicine, education and others. The most suitable models for binary data clusters of unbalanced sizes are the Hierarchical Generalized Linear Model (HGLM), where the random term over-dispersion counts; and it is k known as clustered binary data. Current techniques for estimating parameters in (HGLM) are many, but these techniques do not allow over dispersion to be distinct from cluster to cluster. Where clustered binary data resulted in over-variation, that reasonable to conclude the unequal size of clustered binary data may have been distinct variations for distinct clusters. By ignoring the chance of shifting over variability between clusters, test statistics may be inflated in the Type I error rates. In this paper, the binomial beta (BB) (HGLM) method has been altered to account for distinct variations across separate clusters. In order to explore whether the Adjusted Scale Binomial Beta (ASBB) method is more suitable than the (BB) technique for dealing with over-dispersion for unequal cluster binary data models, the author was used simulation, the adjusted method was compared to the original "existing" technique in terms of, Type I error rate, estimator standard errors and power. (ASBB) h-likelihood “adjusted” method was comparable to BB "existing" technique, as it has a less standard error and the Type I error was acceptable. Moreover, Type I error inflated in “exist method” (BB) h-likelihood.

Albatross analytics a hands-on into practice: statistical and data science application

Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model effects, including Hierarchical Generalized Linear Models (HGLMs), Double Hierarchical Generalized Linear Models (DHGLMs), Multivariate Double Hierarchical Generalized Linear Models (MDHGLMs), Survival Analysis, Frailty Models, Support Vector Machines (SVMs), and Hierarchical Likelihood Structural Equation Models (HSEMs). We provide 94 types of dataset examples.

Correlation Study of Community Social Environment and Self-Rated Health of Middle-Aged and Older Adults

To analyze the impact of the social environment of a community on the self-rated health of middle-aged and older adults living in the community, and to provide a basis for building favorable community social environment that promotes the health of middle-aged and older adults. Using the community and individual survey data from China Health and Retirement Longitudinal Study (CHARLS) done in 2011, 7822 middle-aged and older adults living in 397 communities were selected as the subjects of the study. Community social environment under study included community socioeconomic status, community social security and community services. Self-rated health was measured with a five-level score. A three-level hierarchical generalized linear model (HGLM) was used to analyze the impact of community social environment on the self-rated health of middle-aged and older adults. The proportion of subjects who reported self-rated poor health was 28.8%. According to the results of the three-level HGLM, the self-rated health of middle-aged and older adults showed differences at both the city and the community levels, and the variance reduction ratio reached 35.71% at the community level. The self-rated health status of residents in the communities with medium and high net per capita income was better than that in the communities with low net per capita income ( OR=0.84, P<0.05; OR=0.70, P<0.05). The self-rated health status of residents in the communities with secondary and higher education levels was better than that in the communities with low education level ( OR=0.78, P<0.05; OR=0.73, P<0.05). The self-rated health of middle-aged and older adults in the communities with unemployment subsidies was much better ( OR=0.77, P<0.05). The self-rated health status of middle-aged and older adults with chronic diseases was worse ( OR=4.46, P<0.05). Middle-aged and older adults living in cities had better self-rated health status ( OR=0.67, P<0.05). Community socioeconomic status and community unemployment subsidy are positively correlated with the self-rated health of middle-aged and older adults living in the community. It is necessary to pay special attention to the health status of middle-aged and older adults in communities of low socioeconomic status, improve unemployment insurance policies, and promote the health of middle-aged and older adults.

Examining the Relationships Between Social Determinants of Health and Health-Related Quality of Life Among Individuals Living With HIV

PurposeThe current study examined the relationships between health-related quality of life (HRQOL) and social determinants of health (SDH), which are the individual and structural factors that contribute to health outcomes.MethodA hierarchical general linear model was used to assess the sequential effect of the SDH on the scores of the Short Form-12 among people living with HIV.ResultsResults show that many psychosocial, medical, demographic, financial, and vocational factors are significant predictors of HRQOL.ConclusionsGaining a deeper understanding of the impact of SDH on HRQOL is critical for rehabilitation counselors to develop effective assessments and interventions.

Effects of Compliance With the Early Management Bundle (SEP-1) on Mortality Changes Among Medicare Beneficiaries With Sepsis: A Propensity Score Matched Cohort Study

US hospitals have reported compliance with the SEP-1 quality measure to Medicare since 2015. Finding an association between compliance and outcomes is essential to gauge measure effectiveness. What is the association between compliance with SEP-1 and 30-day mortality among Medicare beneficiaries? Studying patient-level data reported to Medicare by 3,241 hospitals from October 1, 2015, to March 31, 2017, we used propensity score matching and a hierarchical general linear model (HGLM) to estimate the treatment effects associated with compliance with SEP-1. Compliance was defined as completion of all qualifying SEP-1 elements including lactate measurements, blood culture collection, broad-spectrum antibiotic administration, 30mL/kg crystalloid fluid administration, application of vasopressors, and patient reassessment. The primary outcome was a change in 30-day mortality. Secondary outcomes included changes in length of stay. We completed two matches to evaluate population-level treatment effects. In standard match, 122,870 patients whose care was compliant were matched with the same number whose care was noncompliant. Compliance was associated with a reduction in 30-day mortality (21.81%vs27.48%, respectively), yielding an absolute risk reduction (ARR) of 5.67%(95%CI, 5.33-6.00; P< .001). In stringent match, 107,016 patients whose care was compliant were matched with the same number whose care was noncompliant. Compliance was associated with a reduction in 30-day mortality (22.22%vs26.28%, respectively), yielding an ARR of 4.06%(95%CI, 3.70-4.41; P< .001). At the subject level, our HGLM found compliance associated with lower 30-day risk-adjusted mortality (adjusted conditional OR, 0.829; 95%CI, 0.812-0.846; P< .001). Multiple elements correlated with lower mortality. Median length of stay was shorter among cases whose care was compliant (5 vs6days; interquartile range, 3-9 vs4-10, respectively; P< .001). Compliance with SEP-1 was associated with lower 30-day mortality. Rendering SEP-1 compliant care may reduce the incidence of avoidable deaths.

Differential Item Functioning Among English Language Learners on a Large-Scale Mathematics Assessment.

The current study research showed the nature and potential sources of the gaps in mathematics achievement between English language learners (ELLs) and non-English language learners (non-ELLs). The nature of achievement gap was examined using three DIF methodologies: including Mantel-Haenszel procedure, Rasch model, and Hierarchical Generalized Linear Model (HGLM). These were conducted at the item level in contrast to total test level. Results revealed that the three DIF approaches identified 10 common items. These 10 items demonstrated in favor of non-ELLs. Findings from this study will help educational researchers, administrators, and policymakers understand the nature of the achievement gap in mathematics at item level so that United States can be competitive in middle school mathematics education. This study also suggested that item writers and test developers should construct assessments where language is equally accessible for ELL students.

Determinants of racial and ethnic disparities in utilization of hospital-based care for asthma among Medi-Cal children in Los Angeles

Objective We sought to identify racial/ethnic patterns of health care utilization for asthma among asthmatic children (ages 0-18) and address unequal access to optimal asthma management as a determinant of asthma disparities. Methods We used children Medi-Cal (California’s Medicaid program) enrollees, including African American, Asian, Hispanic, and White children in Los Angeles and retrieved individual hospital utilization records of 69,118 asthmatic children (2013-2018). We applied Hierarchical Generalized Linear Models (HGMLs) to identify the patterns of health care utilization at the individual level, controlling for demographic and neighborhood characteristics. Results African American children show a higher ratio of ED to outpatient visits (OR = 1.32, 95% CI 1.08-1.62) and hospitalizations to outpatient visits (OR = 1.50, 95% CI 1.30-1.73). They also had a high ratio of ED visits (OR = 1.36, 95% CI 1.10-1.68) and hospitalizations (OR = 1.47, 95% CI 1.26-1.71) relative to PCP visits. A ratio of ED visits and hospitalizations decreased if a ratio of controller medications to total medications was greater than 0.5, but increased if children were male, under 11 years old, or living in low-income neighborhoods (Median household income < 25th percentile, $45,629) with high poverty rates (>20%). Conclusions African American male children from disadvantaged neighborhoods are at the highest risk for higher utilization of hospital-based care for asthma. Our findings also indicate a lower ratio of controller medications contributed to increases in ED visits and hospitalizations, suggesting suboptimal management of asthma and a lack of intervention treatment through medications among minority children.

Emotional well-being in COVID-19 mass quarantine: the role of personal response and life activity: a 14-day diary study in China

Objectives This study aims to explore quarantined individuals’ emotional well-being over time and how personal response and life activity predict emotional well-being and its change. Design/Methods Daily data were collected from 134 participants with 71 having 14 consecutive days’ data. Hierarchical linear modeling (HLM) and General Linear Model (GLM) were used to examine the primary tests. Results Overall, positive and negative emotions declined significantly during the surveyed period. Meanwhile, differences were observed in the level of positive, depressed, and negative emotions and/or patterns of change among different population categories. The personal response of worrying about work and life was positively related to depressed and negative emotions at baseline, but was negatively related to the development of both depressed and negative emotions over time. Among life activities, family stressor was a significant predictor for both depressed and negative emotions while social support predicted positive emotions. Moreover, health & hygiene activity was positively related to positive emotions at baseline. Conclusions The results provide scientific evidence for public health policymakers on quarantine policies and inform the general public about quarantine life. They highlight the importance of addressing the needs of vulnerable groups (parents with young children, divorcees, clinicians) during the pandemic, and demonstrate the benefits of promoting healthcare and hygiene activity, having a sense of worry and access to social support.