Latent classification model for censored longitudinal binary outcome.

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.

Mood Tracker: A Randomized Controlled Trial of a Self-Monitoring Intervention for Emotional Distress After Traumatic Brain Injury.

OBJECTIVE: Persons with traumatic brain injury (TBI) frequently experience emotional distress (ED) manifested in anxiety and depression. However, they may not access mental health services due to external (eg, access, transportation, and cost) or internal (eg, stigma and discomfort with traditional counseling) barriers. Based on substantial literature indicating that self-monitoring can ameliorate several health conditions, we conducted a randomized, parallel group, wait-list control (WLC) trial of a self-monitoring intervention to decrease ED after TBI. SETTING: Community in the southwestern United States. PARTICIPANTS: Persons with medically documented complicated mild, moderate, and severe TBI. DESIGN: About 127 participants were randomized in blocks of 6 to an active treatment (AT) group, wherein they completed multiple assessments of ED each week over a 6-week period via a smartphone app, or a WLC group in a parallel group, controlled trial. Participants received weekly support calls to promote self-monitoring of ED using ecological momentary assessment. MAIN MEASURES: ED (Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7), Satisfaction with Life Scale, and Participation Assessment with Recombined Tools-Objective. RESULTS: Analysis of the primary study outcome at 6 weeks after initiation of treatment for the AT group did not demonstrate that self-monitoring was effective in decreasing ED as compared to the WLC group. Brief support calls made weekly to promote compliance with self-monitoring were effective in achieving the target number of self-assessments. About 80% of support calls lasted less than 5 minutes. Greater ED was associated with lower life satisfaction and lower participation indicating the importance of addressing ED in persons with TBI. CONCLUSION: Additional work is needed to develop nontraditional interventions to circumvent barriers that prevent persons with TBI from accessing care for ED. Brief support calls may be an effective, low-cost intervention to improve compliance with self-monitoring or self-management interventions.

Characterizing Health Literacy and Its Correlates Among Individuals With Traumatic Brain Injury (TBI): A TBI Model Systems Study.

OBJECTIVE: To characterize health literacy among individuals with traumatic brain injury (TBI) at least a year postinjury and to explore its relationship to sociodemographic variables, injury severity, and cognition. SETTING: Community following discharge from inpatient rehabilitation. PARTICIPANTS: In total, 205 individuals with complicated mild to severe TBI who completed follow-up as part of a national longitudinal study of TBI and completed a web-based health literacy measure. DESIGN: Multicenter, cross-sectional, observational study. MAIN MEASURES: Health Literacy Assessment Using Talking Touchscreen Technology. RESULTS: Thirty-one percent of the sample demonstrated marginal/inadequate health literacy; 69% demonstrated adequate health literacy. A higher proportion of non-Hispanic White adults had adequate health literacy than non-Hispanic Black and Hispanic adults. Individuals with greater than a high school education were more likely to have adequate health literacy than those with a high school education or less. Better executive functioning performance was related to adequate health literacy. Better episodic memory performance was related to adequate health literacy, but only for those with complicated mild to moderate injury. CONCLUSIONS: A substantial proportion of individuals with TBI have marginal/inadequate health literacy, which may impact their understanding, appreciation, and use of health-related information and recommendations. While low health literacy may be preexisting, directly related to TBI, or a combination of both, it should be screened and considered by professionals when communicating with persons with TBI. Healthcare providers should tailor their communication approaches and presentation of health information, particularly for those with low health literacy.

Baseline characteristics of SARS-CoV-2 vaccine non-responders in a large population-based sample.

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.