Supporting an Invisible Workforce: The Case for the Creation of the Home Healthcare Workers Support Act.

This Article seeks to synthesize academic research on home healthcare workers during the COVID-19 pandemic to understand how their workplace challenges were magnified. Crisis communication researchers note that a crisis provides both opportunities and threats for growth. This Article argues that many of the issues that have impacted home healthcare workers have always been present, but the pandemic allows policy makers the ability to see them clearly because the pandemic brought a spotlight to the issues that home healthcare workers face on a daily basis. Consequently, the author employed a narrative analysis of the literature concerning home healthcare workers during the pandemic to understand how the pandemic exacerbated structural issues that led to an increase in mental health difficulties for this population. By understanding how the pandemic exacerbated mental health issues, policy makers can craft solutions that can withstand the next public health crisis which will undoubtedly impact the most disenfranchised.

Belonging through a communicative lens in dementia caregiving: qualitative study of autobiographical caregivers narratives.

This qualitative study examined 23 autobiographical narratives to understand how dementia caregivers discursively construct a sense of belonging through In this study, belonging is understood as an individual feeling valued by a group that is communicatively enacted by both the individual and members of the group. This exploratory study revealed two characteristics of belonging as a communicative concept: (a) two-way street and (b) multi-faceted. Findings suggest that belonging is a more nuanced concept than previously thought.

A computational algorithm for personalized medicine in schizophrenia.

Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine.

Results of Florida's Amyotrophic Lateral Sclerosis Surveillance Project, 2009-2011.

OBJECTIVE: Studies to determine the incidence and prevalence of amyotrophic lateral sclerosis (ALS) in defined geographic areas in the USA are needed. The Florida Department of Health received funding from the federal Agency for Toxic Substances and Disease Registry to implement a state-wide ALS Surveillance Project. The objectives of the project were to describe the demographic characteristics of ALS cases and to calculate the incidence and prevalence of ALS in Florida. SETTING/PARTICIPANTS: All neurologists were asked to submit case reports for persons with ALS diagnosed and/or under their care during 1 January 2009 through 31 December 2011. A medical record verification form and an electromyogram (EMG) report were requested for a sample of cases and reviewed by an independent consulting neurologist to confirm ALS diagnosis. Death data were used to aid with case report collection. PRIMARY AND SECONDARY OUTCOME MEASURES: Demographics, relevant history and clinical characteristics, El Escorial classifications, time from symptom onset to diagnosis, crude annual incidence rates and 2009 period prevalence are presented. RESULTS: The 1450 reported ALS cases were more likely to be older, male, white and non-Hispanic. Slightly more than 4% of cases were reported as also having dementia, and 4.8% were reported to have an immediate family member diagnosed with ALS. Incidence rates ranged from 1.7 to 1.9 per 100,000 person-years during the project period and the 2009 period prevalence was 4.0 per 100,000 persons. CONCLUSIONS: Project findings are generally consistent with findings of population-based studies in Europe, as well as geographically limited studies in the USA. Our findings add to the growing body of epidemiological literature about ALS in the USA. Future epidemiological studies in the USA should focus on identifying cases from minority groups and those that may have limited access to healthcare, and should consider conducting capture-recapture analysis to assess case ascertainment.