Trends in suicide attempts and suicide deaths before and during the COVID-19 pandemic in New Taipei City, Taiwan: an interrupted time-series analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global health crisis that may cause mental health problems and heighten suicide risk. We investigated the impact of the COVID-19 pandemic on trends in suicide attempts and suicide deaths in New Taipei City, Taiwan. METHODS: The current study used the official daily data on suicide attempts and deaths in New Taipei City, Taiwan (4 million inhabitants) between 2015 and 2020 from the Taiwan National Suicide Prevention Reporting System. Interrupted time-series (ITS) analyses with parameters corrected by the estimated autocorrelations were applied on weekly aggregated data to examine whether the suicide trends during the early COVID-19 pandemic (late January to July 2020) deviated from previous trends (January 2015 to late January 2020). The impact due to the suicide prevention policy change was also examined (since August 2020). RESULTS: ITS analyses revealed no significant increases in both mean and trend on weekly suicide deaths during the COVID-19 pandemic and after the policy change. In contrast, there was a significant increasing trend in weekly suicide attempts since the COVID-19 outbreak at the rate of 1.54 attempts per week (95% confidence interval 0.49-2.60; p = 0.004). Sex difference analysis revealed that, however, this increasing trend was observed only in females not in males. CONCLUSIONS: The COVID-19 pandemic has different impacts on suicides attempts and deaths during the early pandemic in New Taipei City, Taiwan. The COVID-19 outbreak drastically increased the trend of suicide attempts. In contrast, the number of suicide deaths had remained constant in the investigated periods.

Ethical Bonds Transformation in Bereaved Taiwanese Families: A Preliminary Study.

Ethical bonds transformation is a cultural phenomenon in Taiwanese bereaved families. When the death event occurs, the absent status of the deceased invokes spontaneous change in whole family to cope with the irreparable loss. In the present study, 283 bereaved individuals were recruited to develop the ethical bonds transformation scale. Exploratory factor analysis has generated two factors: ethical bonds and symbolic bonds. Partial correlation has shown that ethical bonds was positively correlated with post-grief growth and negatively correlated with most of the grief related symptoms, indicating that ethical bonds might be a protective factor in the family grief process.

Treatment Outcomes of Pulpectomy in Primary Maxillary Incisors Filled with ZOE and Metapex: A Two-year Retrospective Study.

OBJECTIVES: To address a literature gap by evaluating, in a larger set of samples, the clinical and radiographic outcomes of pulpectomy in primary maxillary incisors using ZOE and calcium hydroxide/iodoform paste. To also identify the predisposing factors for treatment outcomes. STUDY DESIGN: Medical charts of 124 patients (aged 16 to 60 months) and radiographs of their incisors (309 incisors) were reviewed (128 with ZOE and 181 with Metapex). All incisors were restored with composite resin crowns. RESULTS: The radiographic success rates for ZOE and Metapex were: 85.9% and 82.9% at the 12-month recall, and 69.2% and 64% at the 24-month recall, with no statistically significant difference between the two groups. Clinical failures occurred more frequently in the Metapex group (P = 0.006), as clinical signs of pain and soft tissue pathosis were found in 6.2% of the Metapex cases at 24 months but none in the ZOE group. Significant predisposing factors for radiographic success were type of tooth, degree of obturation at recalls, and preoperative root resorption. CONCLUSIONS: The radiographic success rates are comparable between the ZOE and Metapex groups. Clinical pathological manifestations such as pain and soft tissue pathosis are seen in the Metapex group at recalls, but none in the ZOE group. Predisposing factors such as type of incisor, preoperative root resorption, and extent of filling at recalls are associated with the radiographic success rate.

Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models.

The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data. Model selection is an important step in any statistical modeling. The task of model selection for MLCM amounts to the decision on the number of discrete latent components at both higher and lower levels and is more challenging than standard Latent Class Models. In this article, simulation studies were conducted to systematically examine the effects of sample sizes, clusters/classes distinctness, and the number of latent clusters and classes on the performance of various information criteria in recovering the true latent structure. Results of the simulation studies are summarized and presented. The final section presents the remarks and recommendations about the simultaneous decision regarding the number of latent classes and clusters when applying MLCMs to analyze empirical data.

Models with discrete latent variables for analysis of categorical data: a framework and a MATLAB MDLV toolbox.

Studies in the social and behavioral sciences often involve categorical data, such as ratings, and define latent constructs underlying the research issues as being discrete. In this article, models with discrete latent variables (MDLV) for the analysis of categorical data are grouped into four families, defined in terms of two dimensions (time and sampling) of the data structure. A MATLAB toolbox (referred to as the "MDLV toolbox") was developed for applying these models in practical studies. For each family of models, model representations and the statistical assumptions underlying the models are discussed. The functions of the toolbox are demonstrated by fitting these models to empirical data from the European Values Study. The purpose of this article is to offer a framework of discrete latent variable models for data analysis, and to develop the MDLV toolbox for use in estimating each model under this framework. With this accessible tool, the application of data modeling with discrete latent variables becomes feasible for a broad range of empirical studies.

Long-term work quality of patients with mild traumatic brain injury: The associations with postconcussion symptoms.

Return to work (RTW) has always been a determinant functional outcome in patients with mild traumatic brain injury (MTBI). However, the quality of long-term RTW was still unclear. This study thus aims to examine long-term work quality and to reveal its associating factors. A total of 110 patients with MTBI was prospectively recruited. Post-concussion symptoms (PCS) and RTW were evaluated by the Checklist of Post-Concussion Symptoms (CPCS) and Work Quality Index (WQI) respectively at one-week and long-term evaluation (M = 2.90 years, SD = 1.29) post-injury. Only 16% of patients can successfully RTW at one-week post-injury, while 69% of patients have retained their jobs at long-term evaluations. Importantly, 12% of patients had to work under the adverse impacts of PCS at one-week after MTBI, and long-term WQI was significantly associated with PCS at one-week post-injury. Almost 1/3 of patients still had unfavorable long-term work quality even though they could return to work. Thus, a careful evaluation of the early PCS endorsement and work quality for patients with MTBI is merited.

Long-Term Presentation of Postconcussion Symptoms and Associated Factors: Analysis of Latent Class Modeling.

OBJECTIVE: Postconcussion symptoms (PCS) are commonly reported by patients with mild traumatic brain injury (MTBI). Although PCS significantly recovered by 3-month postinjury, a number of patients still experienced persistent PCS for >1 year. As few researchers investigated long-term PCS endorsement, the present study thus aims to show the latent structure of long-term PCS and further uncover its associating factors. METHODS: In total, 110 patients with MTBI and 32 healthy participants were prospectively enrolled. PCS was evaluated at 2 weeks and long-term evaluations (mean = 2.90 years) after MTBI. In addition, cognitive functions, which include memory, executive function, and information processing, and emotional disturbances, which include depression, anxiety, and irritability, were also examined at 2-week postinjury. RESULTS: Patients reported significantly more PCS at 2-week postinjury than healthy participants did, but PCS significantly improved at long-term evaluations when comparing with PCS at acute stage after MTBI. Both of PCS at 2 weeks and long-term evaluations can be further subdivided into subgroups based on the severity of PCS, in which specific PCS (e.g., fatigue, loss of energy, insomnia, slowness of information processing, irritability, and blurred vision) can be well differentiated among subgroups at long-term evaluations. CONCLUSIONS: This study directly showed the characteristics of long-term PCS and associating factors. It further evidenced that specific physical, cognitive, and emotional symptoms might be determinant to identify the subgroups of patients with long-term PCS endorsement.

Recommendations on the Sample Sizes for Multilevel Latent Class Models.

A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria-model selection accuracy, parameter estimation bias, standard error bias, and coverage rate-as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.

An Empirical Comparison of Different Models of Active Aging in Canada: The International Mobility in Aging Study.

Purpose: Active aging is a concept that lacks consensus. The WHO defines it as a holistic concept that encompasses the overall health, participation, and security of older adults. Fernández-Ballesteros and colleagues propose a similar concept but omit security and include mood and cognitive function. To date, researchers attempting to validate conceptual models of active aging have obtained mixed results. The goal of this study was to examine the validity of existing models of active aging with epidemiological data from Canada. Methods: The WHO model of active aging and the psychological model of active aging developed by Fernández-Ballesteros and colleagues were tested with confirmatory factor analysis. The data used included 799 community-dwelling older adults between 65 and 74 years old, recruited from the patient lists of family physicians in Saint-Hyacinthe, Quebec and Kingston, Ontario. Results: Neither model could be validated in the sample of Canadian older adults. Although a concept of healthy aging can be modeled adequately, social participation and security did not fit a latent factor model. A simple binary index indicated that 27% of older adults in the sample did not meet the active aging criteria proposed by the WHO. Implications: Our results suggest that active aging might represent a human rights policy orientation rather than an empirical measurement tool to guide research among older adult populations. Binary indexes of active aging may serve to highlight what remains to be improved about the health, participation, and security of growing populations of older adults.

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models.

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In the present study, a simulation study was conducted to investigate the impact of ignoring the higher-level nesting structure. Three criteria-the model selection accuracy, the classification quality, and the parameter estimation accuracy-were used to evaluate the impact of ignoring the nested data structure. The results of the simulation study showed that ignoring higher-level nesting structure in an MLCM resulted in the poor performance of the Bayesian information criterion to recover the true latent structure, the inaccurate classification of individuals into latent classes, and the inflation of standard errors for parameter estimates, while the parameter estimates were not biased. This article concludes with remarks on ignoring the nested structure in nonparametric MLCMs, as well as recommendations for applied researchers when LCM is used for data collected from a multilevel nested structure.