OBJECTIVE: To explore the effects of occupational stress and resilience on insomnia among Chinese nurses during the COVID-19 pandemic.
METHODS: A quantitative description study. The data were collected via a cross-sectional survey. A total of 725 front-line nurses at three tertiary hospitals in western China were included from December 2022 to January 2023. The Connor-Davidson Resilience Scale, Job Content Questionnaire, and Athens Insomnia Scale were used to collect data from a self-reported online questionnaire.
METHODS: The outcome variable was insomnia, and structural equation modelling was used to assess the associations among resilience, occupational stress and insomnia.
RESULTS: The prevalence of insomnia among the participants was 58.76%. The structural equation model showed that resilience had a negative direct effect on insomnia and occupational stress, and occupational stress had a positive direct effect on insomnia. Involvement in COVID-19-related work has a positive effect on insomnia through occupational stress. In contrast, higher education levels improved insomnia through increased resilience.
CONCLUSIONS: A significantly higher prevalence of insomnia has been observed among Chinese nurses during the COVID-19 pandemic. Our study suggests that better resilience may improve insomnia by relieving occupational stress, and implementing measures to promote resilience is essential to reduce occupational stress in nurses and improve their sleep quality.

Millions of families with children in the U.S. struggle to afford adequate housing. Housing cost burden places families at risk for homelessness, and prevention efforts are hindered by limited understanding of insecure housing experiences at the margins. The present study investigated variation in housing insecurity experiences in a sample of mothers, as well as which risk profiles were most strongly associated with subsequent homelessness. Latent class analysis identified four distinct subgroups of housing insecurity: \"Stable,\" \"Unstable,\" \"Rent-Focused,\" and \"Strategic Bill-Paying.\" Classes differed on whether they made rent or utility payments on time, experienced utility shutoffs, or were evicted. Mothers who missed rent payments were significantly more likely to experience subsequent homelessness, whereas those who prioritized rent were more likely to have their utilities shut off but remain housed. Policy efforts should emphasize increased wages, rent control, changes to zoning laws and tax codes to prioritize affordable housing, and benefits that help mothers maintain their incomes such as comprehensive healthcare, paid maternity leave, and subsidized childcare.

OBJECTIVE: Persons living with dementia are a heterogeneous population with complex needs whose healthcare use varies widely. This study aimed to identify the healthcare use profiles in a cohort of persons with incident dementia, and to describe their characteristics.
METHODS: This is a retrospective cohort study of health administrative data in Quebec (Canada). The study population included persons who: (i) had an incident dementia diagnosis between 1 April 2015 and 31 March 2016; (ii) were aged ≥65 years and living in the community at the time of diagnosis. We carried out a latent class analysis to identify subgroups of healthcare users. The final number of groups was chosen based on clinical interpretation and statistical indicators.
RESULTS: The study cohort consisted of 15 584 individuals with incident dementia. Four profiles of healthcare users were identified: (i) Low Users (36.4%), composed of individuals with minimal healthcare use and fewer comorbidities; (ii) Ambulatory Care-Centric Users (27.5%), mainly composed of men with the highest probability of visiting cognition specialists; (iii) High Acute Hospital Users (23.6%), comprised of individuals mainly diagnosed during hospitalization, with higher comorbidities and mortality rate; and (iv) Long-Term Care Destined Users (12.5%), who showed the highest proportion of antipsychotics prescriptions and delayed hospitalization discharge.
CONCLUSIONS: We identified four distinct subgroups of healthcare users within a population of persons living with dementia, providing a valuable context for the development of interventions tailored to specific needs within this diverse population. Geriatr Gerontol Int 2024; ••: ••-••.

UNASSIGNED: The aim of this study was to classify distinct subgroups of adolescents based on the severity levels of their mobile phone addiction and to investigate how these groups differed in terms of their psychosocial characteristics. We surveyed a total of 2,230 adolescents using three different questionnaires to assess the severity of their mobile phone addiction, stress, anxiety, depression, psychological resilience, and personality. Latent class analysis was employed to identify the subgroups, and we utilized Receiver Operating Characteristic (ROC) curves and multinomial logistic regression for statistical analysis. All data analyses were conducted using SPSS 26.0 and Mplus 8.5.
UNASSIGNED: We classified the subjects into subgroups based on their mobile phone addiction severity, and the results revealed a clear pattern with a three-class model based on the likelihood level of mobile phone addiction (p < 0.05). We examined common trends in psychosocial traits such as age, grade at school, parental education level, anxiety levels, and resilience. ROC analysis of sensitivity versus 1-specificity for various mobile phone addiction index (MPAI) scores yielded an area under the curve (AUC) of 0.893 (95% CI, 0.879 to 0.905, p < 0.001). We also determined diagnostic value indices for potential cutoff points ranging from 8 to 40. The optimal cutoff value for MPAI was found to be >14, which corresponded to the maximum Youden index (Youden index = 0.751).
UNASSIGNED: The latent classification process in this research confirmed the existence of three distinct mobile phone user groups. We also examined the psychosocial characteristics that varied in relation to the severity levels of addiction.
UNASSIGNED: This study provides valuable insights into the categorization of adolescents based on the severity of mobile phone addiction and sheds light on the psychosocial characteristics associated with different addiction levels. These findings are expected to enhance our understanding of mobile phone addiction traits and stimulate further research in this area.

BACKGROUND: Single parent families are at higher risk of re-report to Child Protective Services (CPS) than two-parent families. Yet, how single-family homes differ in risk from two-parent families remains under researched.
OBJECTIVE: To identify heterogenous patterns of child and caregiver factors among CPS-involved families and the subsequent risk for CPS re-report based on child and family characteristics (i.e., sociodemographic information, family structure, and risk indicators).
METHODS: Data were from the 2017 National Child Abuse and Neglect Data System Child File (N = 249,026).
METHODS: We conducted latent class analysis (LCA) to identify discrete patterns (i.e., classes) based on child and caregiver risk indicators (e.g., substance use, behavioral health). We then used logistic regression to examine family structure and other family characteristics and CPS indicators predicted CPS re-report for each class.
RESULTS: Results yielded five distinct classes: 1) Financial Stressors (25 % of the sample); 2) Caregiver Substance Use (16 %); 3) Complex Household Stressors (3 %); 4) Child Disabilities (4 %); and 5) Minimal Household Stressors (53 %). Family structure was significantly associated with CPS re-reports for Classes 1, 2, and 5. For Class 1, single father families had increased odds of CPS re-report compared to other family structures. For Classes 2 and 5, single father families\' odds of CPS re-reports were greater than those of married families, but lower than single mother families.
CONCLUSIONS: Children growing up in single father families have different likelihoods of repeat CPS involvement compared to those in single mother and married families. Financial stressors and parental substance use within single father families should be addressed.

BACKGROUND: Lifestyle has become a crucial modulator in the management of diabetes and is intimately linked with the development and exacerbation of comorbid depression. The study aimed to analyze lifestyle patterns and their impact on depression in individuals with diabetes and to explore the role of the Dietary Inflammatory Index (DII) in the relationship between lifestyle patterns and depression.
METHODS: Data was attained from the National Health and Nutrition Examination Survey (NHANES) between 2009 and 2020. A latent class analysis (LCA) was performed on 3,009 diabetic adults based on lifestyle behaviors. A generalised linear model (GLM) was employed to analyse the effects of different lifestyle patterns on depression. The mediation effect model was utilised to examine the relationship between lifestyle patterns, DII and Patient Health Questionnaire-9 (PHQ-9) scores.
RESULTS: The cohort was divided through LCA into unhealthy lifestyle (44.53%), unhealthy but non-alcohol use (48.06%) and healthy but smoking (7.41%) groups of lifestyle behaviors, the unhealthy but non-alcohol use group was identified as a risk factor for depression (OR = 1.379, 95%CI = 1.095 ~ 1.735, P = 0.006). The DII partially mediated the relationship between the unhealthy but non-alcohol use group and PHQ-9, and fully mediated the relationship between the healthy but smoking group and PHQ-9, with effect coefficients of - 0.018 (95%CI: -0.044 ~ - 0.001) and - 0.035 (95%CI: -0.083 ~ - 0.001).
CONCLUSIONS: Lifestyle patterns significantly influence the occurrence of depression among diabetes patients. The dietary inflammation plays a varying mediating role between different lifestyle patterns and depression. Restricting pro-inflammatory diets or encouraging anti-inflammatory diets, combined with the promotion of healthy lifestyle practices, may be an effective method for preventing and alleviating symptoms of depression among patients with diabetes.

BACKGROUND: Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes.
METHODS: Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership.
RESULTS: A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77).
CONCLUSIONS: Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals\' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals\' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model\'s performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals\' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.

OBJECTIVE: To identify subgroups of patients with distinct chemotherapy-induced vomiting (CIV) profiles; determine how these subgroups differ on several demographic, clinical, and symptom characteristics; and evaluate factors associated with chemotherapy-induced nausea and CIV profiles.
UNASSIGNED: Adult patients (N = 1,338) receiving cancer chemotherapy.
UNASSIGNED: Data were collected on demographic, clinical, and symptom characteristics. Differences among subgroups of patients with distinct CIV profiles were evaluated using parametric and nonparametric tests.
RESULTS: Three CIV profiles (None, Decreasing, and Increasing) were identified. Compared with the None class, Decreasing and Increasing classes were more likely to have lower household income and a higher comorbidity burden, as well as to report higher rates of dry mouth, nausea, diarrhea, depression, anxiety, sleep disturbance, morning fatigue, and pain interference.
CONCLUSIONS: Clinicians need to assess common and distinct risk factors for CIV and chemotherapy-induced nausea.

UNASSIGNED: Internet hospitals, online health communities, and other digital health APPs have brought many changes to people\'s lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users.
UNASSIGNED: We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources.
UNASSIGNED: Information quality (β = 0.31, p < 0.05), service quality (β = 0.19, p < 0.05), platform reputation (β = 0.34, p < 0.05), and emotional support (β = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (β = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (β = 0.103, t = 3.377, p < 0.001).
UNASSIGNED: The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users\' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.