Browsing by Subject "Mental Health"
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Item A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses(Springer Nature, 2022-02-04) Tan, Zhengqi; de la Torre, Jimmy; Ma, Wenchao; Huh, David; Larimer, Mary E.; Mun, Eun-YoungIn research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.Item Autism: Association Between Autism and Parenting Stress(2009-12-01) Banini, Simon D.; Bae, SejongAutism is a developmental disorder, whose etiology is still an active research. Objectives of this study are to identify: risk factors of parenting stress; statistical tools for analysis and interpretations of data to ascertain and reinforce risk factors; and recommendations to mitigate parenting stress of autistic children. Data: National Survey of Children with Special Care Needs (NS-CSHCN), 2005-06. Analysis was performed on NS-CSHCN (n=40,723). Among parents with autistic children (n=2,123), the highest adjusted odds of stress were reported associated with primary language (OR= 9.44), insurance (OR=0.34), and Respite care (OR=3.71). Parents with non-autistic children (n=38,133) was the reference population with 467 missing values. Recommendations: Re-evaluation to improve provider language services especially for CSHCN; Family centered public health delivery rather than patient-provider interactive approach.Item Bodyweight Changes During COVID-19 for Patients Diagnosed with Depression: A Retrospective Cohort Study(2022-05) Arellano Villanueva, Elias; Fulda, Kimberly; Franks, Susan; Schranz, DamonBackground: The COVID-19 pandemic led to an unprecedented lockdown of millions of Americans from the spring of 2020 to the fall of 2020 This lockdown exacerbated the mental and physical health status of millions of individuals worldwide. Studies done on the impact of COVID-19 on mental health and body weight have been important to our understanding of the effects of the pandemic. However, these studies on depression and BMI change have not identified a possible direction of the causality of the relationship between depression and body weight as affected by lockdown measures during a pandemic. Therefore, this study examined whether a diagnosis of depression is associated with changes in BMI during the COVID-19 pandemic for adults (aged ≥ 18 years). Methods: A retrospective cohort study design using EHR data from a family medicine university clinic was utilized. Adults > 18 years who visited the clinic within a 6-month period prior to lockdown and at least once in the 6-month post-lockdown period were included. Diagnosis of depression, BMI, and potential confounding variables were obtained from EHR. Mann-Whitney U was used to compare the median change in BMI between depressed and non-depressed patients Simple linear regression was used to identify the relationship between diagnosis of depression and BMI change. Multiple linear regression was used to control for age, sex, race/ethnicity, medications, and chronic conditions; and to predict age effects in BMI change while stratified by diagnosis of depression and no diagnosis of depression. Results: Statistical analysis showed that there was a significant difference in BMI changes (p=<0.001) between the group diagnosed with depression and the group with no depression. Similarly, a diagnosis of depression significantly predicted BMI changes (p = >0.001]). This significance was maintained even while including confounding variables in the model (p=0.009). Further statistical analysis showed that age between 31 and 50 significantly predicted BMI changes in those patients with no depression while controlling for confounding variables (p = 0.027). Conclusion: This study demonstrated that individuals with depression had significant changes in BMI during the COVID-19 pandemic and age predicted these changes in middle-aged adults (30-50 years old). The significance of this finding places importance in identifying and following up with individuals with a depression diagnosis given the effects on their BMI in extended isolation periods. Future studies could investigate other variables that might impact BMI change to influence the directionality of this relationship. Providing insight into this relationship could enable providers to inform patients that might be at risk for these types of changes over extended periods of isolation, and hopefully result in positive patient health outcomes.Item Improvement in mental health following total hip arthroplasty: the role of pain and function(BioMed Central Ltd., 2019-06-29) Nguyen, Uyen-Sa D. T.; Perneger, Thomas; Franklin, Patricia D.; Barea, Christophe; Hoffmeyer, Pierre; Lubbeke, AnneBACKGROUND: Mental health has been shown to improve after total hip arthroplasty (THA). Little is known about the role of pain and function in this context. We assessed whether change in mental health was associated with improvement in pain and function 1 year post-surgery. METHODS: This prospective study included patients enrolled in a THA registry from 2010 to 2014. We examined the mental component score (MCS) before and 1 year post-surgery, and 1-year change, in association with Western Ontario McMaster Universities (WOMAC) pain and function scores. All scores were normalized, ranging from 0 to 100 (larger score indicating better outcome). Analyses were adjusted for potential confounders. RESULTS: Our study included 610 participants, of which 53% were women. Descriptive statistics are as follows: the average (SD) for age (years) was 68.5 (11.8), and for BMI was 26.9 (4.9). In addition, the MCS average (SD) at baseline was 44.7 (11.2), and at 1-year after THA was 47.5 (10.5). The average change from baseline to 1-year post-THA in MCS was 2.8 (95% CI: 1.9, 3.6), for an effect size of 0.26. As for the WOMAC pain score, the average change from baseline to 1-year post-THA was 44.2 (95%CI: 42.4, 46.0), for an effect size of 2.5. The equivalent change in WOMAC function was 38.1 (95% CI: 36.2, 40.0), for an effect size of 2.0. Results from multivariable analysis controlling for covariates showed that an improvement of 10 points in the 1-year change in pain score resulted in a 0.78 point (95%: CI 0.40, 1.26) increase in the 1-year change in MCS, whereas a 10-point improvement in the 1-year change in function was associated with a 0.94 point (95% CI: 0.56, 1.32) increase. CONCLUSIONS: Mental health significantly improved from baseline to 1-year post-THA. Greater improvement in pain and function was associated with greater improvement in mental health 1 year post-THA.Item Key Signaling Pathways in Aging and Potential Interventions for Healthy Aging(MDPI, 2021-03-16) Yu, Mengdi; Zhang, Hongxia; Wang, Brian; Zhang, Yinuo; Zheng, Xiaoying; Shao, Bei; Zhuge, Qichuan; Jin, KunlinAging is a fundamental biological process accompanied by a general decline in tissue function. Indeed, as the lifespan increases, age-related dysfunction, such as cognitive impairment or dementia, will become a growing public health issue. Aging is also a great risk factor for many age-related diseases. Nowadays, people want not only to live longer but also healthier. Therefore, there is a critical need in understanding the underlying cellular and molecular mechanisms regulating aging that will allow us to modify the aging process for healthy aging and alleviate age-related disease. Here, we reviewed the recent breakthroughs in the mechanistic understanding of biological aging, focusing on the adenosine monophosphate-activated kinase (AMPK), Sirtuin 1 (SIRT1) and mammalian target of rapamycin (mTOR) pathways, which are currently considered critical for aging. We also discussed how these proteins and pathways may potentially interact with each other to regulate aging. We further described how the knowledge of these pathways may lead to new interventions for antiaging and against age-related disease.Item Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations(MDPI, 2023-01-09) Ikram, Mohammad; Shaikh, Nazneen F.; Vishwanatha, Jamboor K.; Sambamoorthi, UshaDuring the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income >/= $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.