Dataset Information

Latent class analysis of the Epidemic-Pandemic Impacts Inventory on mental health outcomes in Siyan Clinical patients.

ABSTRACT:

Background and aims

The COVID-19 pandemic has made an outsized negative impact on mental health worldwide. However, research indicates that this impact was not uniform. This study aimed to determine how mental health patients experienced the COVID-19 pandemic to characterize mental health disparities and identify underlying factors.

Methods

We used the Epidemic-Pandemic Impacts Inventory (EPII) and latent class analysis to determine the impacts of epidemics and pandemics across several life domains in 245 survey respondents, all of whom were mental health patients at Siyan Clinical. Respondents were predominately White (84.5%) and female (76.3%), with the majority being diagnosed with anxiety or mood disorders (76.3%).

Results

In the work life domain, respondents in the higher-impact class were more likely to be employed and/or working in healthcare. In both the home life and emotional/physical health and infection domain, respondents with mood disorders, substance use disorders, or children under 18 living at home were more likely to be in the higher-impact class. In the home life and positive change domains, respondents that were married were more likely to be in the higher-impact class, indicating that this group experiences more impacts from the pandemic, both positive and negative. Finally, some groups stood out as having fewer impacts from the pandemic: respondents that were male, over age 55, White, and/or have anxiety disorders were more likely to experience fewer impacts from the pandemic in the work life and home life domains.

Conclusions

This study provides evidence that certain groups may experience greater or fewer impacts from the pandemic.

SUBMITTER: Shah A

PROVIDER: S-EPMC10119487 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Latent class analysis of the Epidemic-Pandemic Impacts Inventory on mental health outcomes in Siyan Clinical patients.

Shah Anish A Arstein-Kerslake Olivia O Darling Michele M Morgan Tiffany T Torea Aubree Vance AV Laines Helen H Joshi Bhargav B Pena Karina K Young James J

Health science reports 20230420 4

<h4>Background and aims</h4>The COVID-19 pandemic has made an outsized negative impact on mental health worldwide. However, research indicates that this impact was not uniform. This study aimed to determine how mental health patients experienced the COVID-19 pandemic to characterize mental health disparities and identify underlying factors.<h4>Methods</h4>We used the Epidemic-Pandemic Impacts Inventory (EPII) and latent class analysis to determine the impacts of epidemics and pandemics across se ...[more]

PMID: 37091358

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Project description:BackgroundRecent research has shown that the impacts of the COVID-19 pandemic and social isolation on people's mental health are quite extensive, but there are limited studies on the effects of the pandemic on patients with mental health disorders.ObjectiveThe objective of this study was to assess the negative impacts of the COVID-19 pandemic on individuals who have previously sought treatment for a mental health disorder.MethodsThis study uses the newly developed Epidemic-Pandemic Impacts Inventory (EPII) survey. This tool was designed to assess tangible impacts of epidemics and pandemics across personal and social life domains. From November 9, 2020, to February 18, 2021, a total of 245 adults recruited from a mental health clinic completed the consent form and responded to the survey link from the Siyan Clinical Corporation and Siyan Clinical Research practices located in Santa Rosa, California, USA.ResultsWe found that the least affected age group included individuals aged 75 years or older. This was followed closely by the 65- to 75-year-old age group. People with children under the age of 18 years also reported both more negative indicators associated with the pandemic and more positive indicators compared to those without children at home. Gender queer, nonconforming, and transgender individuals may also be at higher risk for more negative impacts associated with the pandemic. When respondents were assessed with regard to their mental health diagnosis, no differences were noted. Substance use also increased during the pandemic.ConclusionsIn conclusion, the data collected here may serve as foundational research in the prevention, care, and treatment of mental health disorders during pandemics such as COVID-19. Populations such as those with previously diagnosed mental health disorders are particularly at risk for negative effects of pandemic-related stressors such as social isolation, especially if they have children in the household, are part of a younger age group, or have substance use disorder. Gender may also be a factor. Further, the EPII survey may prove to be a useful tool in understanding these effects. Overall, these data may be a critical step toward understanding the effects of the COVID-19 pandemic on populations with a mental health diagnosis, which may aid mental health practitioners in understanding the consequences of pandemics on their patients' overall well-being.Trial registrationClinicalTrials.gov NCT04568135; https://clinicaltrials.gov/ct2/show/NCT04568135.

Project description:BackgroundThe mental health of the UK population declined at the onset of the COVID-19 pandemic. Convenience sample surveys indicate that recovery began soon after. Using a probability sample, we tracked mental health during the pandemic to characterise mental health trajectories and identify predictors of deterioration.MethodsThis study was a secondary analysis of five waves of the UK Household Longitudinal Study (a large, national, probability-based survey that has been collecting data continuously since January, 2009) from late April to early October, 2020 and pre-pandemic data taken from 2018-19. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12). We used latent class mixed models to identify discrete mental health trajectories and fixed-effects regression to identify predictors of change in mental health.FindingsMental health was assessed in 19 763 adults (≥16 years; 11 477 [58·1%] women and 8287 [41·9%] men; 3453 [17·5%] participants from minority ethnic groups). Mean population mental health deteriorated with the onset of the pandemic and did not begin improving until July, 2020. Latent class analysis identified five distinct mental health trajectories up to October 2020. Most individuals in the population had either consistently good (7437 [39·3%] participants) or consistently very good (7623 [37·5%] participants) mental health across the first 6 months of the pandemic. A recovering group (1727 [12·0%] participants) showed worsened mental health during the initial shock of the pandemic and then returned to around pre-pandemic levels of mental health by October, 2020. The two remaining groups were characterised by poor mental health throughout the observation period; for one group, (523 [4·1%] participants) there was an initial worsening in mental health that was sustained with highly elevated scores. The other group (1011 [7·0%] participants) had little initial acute deterioration in their mental health, but reported a steady and sustained decline in mental health over time. These last two groups were more likely to have pre-existing mental or physical ill-health, to live in deprived neighbourhoods, and be of Asian, Black or mixed ethnicity. Infection with SARS-CoV-2, local lockdown, and financial difficulties all predicted a subsequent deterioration in mental health.InterpretationBetween April and October 2020, the mental health of most UK adults remained resilient or returned to pre-pandemic levels. Around one in nine individuals had deteriorating or consistently poor mental health. People living in areas affected by lockdown, struggling financially, with pre-existing conditions, or infection with SARS-CoV-2 might benefit most from early intervention.FundingNone.

Project description:ObjectivesResearch about the mental health of children in Francophone West Africa is scarce. This paper examines the relationships between adverse childhood experiences, including exposure to violence and exploitation, and mental health outcomes among children living in ultra-poverty in rural Burkina Faso.MethodsThis paper utilizes baseline data collected from 360 children ages 10-15 and 360 of their mothers recruited from twelve impoverished villages in the Nord Region of Burkina, located near the Sahel Desert and affected by extreme food insecurity. We used a Latent Class Analysis to identify underlying patterns of maltreatment. Further, the relationships between latent classes and mental health outcomes were tested using mixed effected regression models adjusted for clustering within villages.ResultsAbout 15% of the children in the study scored above the clinical cut-off for depression, 17.8% for posttraumatic stress disorder (PTSD), and 6.4% for low self-esteem. The study identified five distinct sub-groups (or classes) of children based on their exposure to adverse childhood experiences. Children with the highest exposure to violence at home, at work and in the community (Abused and Exploited class) and children not attending school and working for other households, often away from their families (External Laborer class), demonstrated highest symptoms of depression and trauma. Despite living in adverse conditions and working to assist families, the study also identified a class of children who were not exposed to any violence at home or at work (Healthy and Non-abused class). Children in this class demonstrated significantly higher self-esteem (b = 0.92, SE = 0.45, p<0.05) and lower symptoms of trauma (b = -3.90, SE = 1.52, p<0.05).ConclusionsThis study offers insight into the psychological well-being of children in the context of ultra-poverty in Burkina Faso and associated context-specific adverse childhood experiences. Identifying specific sub-groups of children with increased exposure to life stressors has implications for program developers. Study findings indicate a further need to explore the mental health consequences of traumatic experiences within the context of ultra-poverty and to develop integrated economic and psychosocial interventions that prevent or mitigate childhood adversities linked with the family-level poverty and violence in the family.

Project description:BackgroundMental health problems and substance use co-morbidities during and after the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing mental health problems and potential sequela can inform mitigating strategies. We aimed to identify distinct groups of individuals (i.e., latent classes) based on patterns of self-reported mental health symptoms and investigate their associations with alcohol and cannabis use.MethodsWe used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health most likely linked to effects of the pandemic: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses.ResultsWe identified two distinct classes: (1) individuals with low scores on all three mental health indicators (no/low-symptoms) and (2) those reporting high scores across the three measures (high-symptoms). Between 73.9 and 77.1% of participants were in the no/low-symptoms class and 22.9-26.1% of participants were in the high-symptom class. We consistently found across all six waves that individuals at greater risk of being in the high-symptom class were more likely to report worrying about getting COVID-19 with adjusted odds ratios (aORs) between 1.72 (95%CI:1.17-2.51) and 3.51 (95%CI:2.20-5.60). Those aged 60 + were less likely to be in this group with aORs (95%CI) between 0.26 (0.15-0.44) and 0.48 (0.29-0.77) across waves. We also found some factors associated with class membership varied at different time points. Individuals in the high-symptom class were more likely to use cannabis at least once a week (aOR = 2.28, 95%CI:1.92-2.70), drink alcohol heavily (aOR = 1.71, 95%CI:1.49-1.96); and increase the use of cannabis (aOR = 3.50, 95%CI:2.80-4.37) and alcohol (aOR = 2.37, 95%CI:2.06-2.74) during the pandemic. Women in the high-symptom class had lower odds of drinking more alcohol during the pandemic than men.ConclusionsWe identified the determinants of experiencing high anxiety, depression, and loneliness symptoms and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities.

Project description:Background: Latent class analysis (LCA) can be used to identify subgroups of children with similar patterns of mental health symptoms and/or strengths. The method is becoming more commonly used in child mental health research, but there are reservations about the replicability, reliability, and validity of findings. Objective: A systematic literature review was conducted to investigate the extent to which LCA has been used to study population mental health in children, and whether replicable, reliable and valid findings have been demonstrated. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A search of literature, published between January 1998 and December 2017, was carried out using MEDLINE, EMBASE, PsycInfo, Scopus, ERIC, ASSIA, and Google Scholar. A total of 2,748 studies were initially identified, of which 23 were eligible for review. The review examined the methods which studies had used to choose the number of mental health classes, the classes that they found, and whether there was evidence for the validity and reliability of the classes. Results: Reviewed studies used LCA to investigate both disparate mental health symptoms, and those associated with specific disorders. The corpus of studies using similar indicators was small. Differences in the criteria used to select the final LCA model were found between studies. All studies found meaningful or useful subgroups, but there were differences in the extent to which the validity and reliability of classes were explicitly demonstrated. Conclusions : LCA is a useful tool for studying and classifying child mental health at the population level. Recommendations are made to improve the application and reporting of LCA and to increase confidence in findings in the future, including use of a range of indices and criteria when enumerating classes, clear reporting of methods for replicability, and making efforts to establish the validity and reliability of identified classes.

Dataset Information

Latent class analysis of the Epidemic-Pandemic Impacts Inventory on mental health outcomes in Siyan Clinical patients.

Background and aims

Methods

Results

Conclusions

Publications

Latent class analysis of the Epidemic-Pandemic Impacts Inventory on mental health outcomes in Siyan Clinical patients.

Similar Datasets

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