An optimized machine learning model for identifying socio-economic, demographic and health-related variables associated with low vaccination levels that vary across ZIP codes in California.

There is an urgent need for an in-depth and systematic assessment of a wide range of predictive factors related to populations most at risk for delaying and refusing COVID-19 vaccination as cases of the disease surge across the United States. Many studies have assessed a limited number of general sociodemographic and health-related factors related to low vaccination rates. Machine learning methods were used to assess the association of 151 social and health-related risk factors derived from the American Community Survey 2019 and the Centers for Disease Control and Prevention (CDC) BRFSS with the response variables of vaccination rates and unvaccinated counts in 1,555 ZIP Codes in California. The performance of various analytical models was evaluated according to their ability to regress between predictive variables and vaccination levels. Machine learning modeling identified the Gradient Boosting Regressor (GBR) as the predictive model with a higher percentage of the explained variance than the variance identified through linear and generalized regression models. A set of 20 variables explained 72.90% of the variability of unvaccinated counts among ZIP Codes in California. ZIP Codes were shown to be a more meaningful geo-local unit of analysis than county-level assessments. Modeling vaccination rates was not as effective as modeling unvaccinated counts. The public health utility of this model provides for the analysis of state and local conditions related to COVID-19 vaccination use and future public health problems and pandemics.

Exploring the combined effects of sleep apnea and APOE-e4 on biomarkers of Alzheimer's disease.

Objective: We determined the interactive associations of apolipoprotein e4 (APOE-e4), and obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease and examined for racial/ethnic differences of this association. Methods: We used data from the National Alzheimer's Coordinating Center Uniform Dataset (NACC UDS). All participants undergo annual observations, including demographic survey, battery of neuropsychological tests, blood draw (with genotyping), and a clinical evaluation with medical and cognitive/dementia status assessment, while a subset of participants have cerebrospinal fluid (CSF) biomarkers and neuroimaging data. Biomarkers of AD were characterized as the presence of abnormally low amyloid in CSF, via validated Aß42 cut off protocols, and total segmented hippocampal volume, and volume of white matter hyper intensities (WMH). While clinical markers (to preview cognitive relationships) were characterized via the Montreal Cognitive Assessment (MOCA). Results: Biomarker and clinical marker data were derived from 1,387 participants at baseline (mean age = 69.73 ± 8.32; 58.6% female; 13.7% Black/African American), 18.4% of the sample had sleep apnea, and 37.9% were APOE-e4 carriers. Our results confirmed previous reports that OSA and APOE-e4 were independently associated with AD through abnormal levels of amyloid (F (1,306) = 4.27; p = 0.040; F (1,285) = 60.88; p < 0.000, respectively), WMH volume (F (1,306) = 4.27; p = 0.040; F (1,285) = 60.88; p < 0.000, respectively), and MOCA scores (F (1,306) = 4.27; p = 0.040; F (1,285) = 60.88; p < 0.000, respectively). No significant interaction between OSA and APOE-e4 relative to amyloid emerged, however, race stratified analyses indicated the interaction of OSA and APOE-e4 and was significantly associated with WMH and hippocampal volume in Black/African American, but not white participants. Conclusion: OSA and APOE-e4 are interactively associated with WHM in Black/African Americans. This interaction may partially explicate increased levels of risk in this population.

Opioids cause dissociated states of consciousness in C57BL/6J mice.

The electroencephalogram (EEG) provides an objective, neural correlate of consciousness. Opioid receptors modulate mammalian neuronal excitability, and this fact was used to characterize how opioids administered to mice alter EEG power and states of consciousness. The present study tested the hypothesis that antinociceptive doses of fentanyl, morphine, or buprenorphine differentially alter the EEG and states of sleep and wakefulness in adult, male C57BL/6J mice. Mice were anesthetized and implanted with telemeters that enabled wireless recordings of cortical EEG and electromyogram (EMG). After surgical recovery, EEG and EMG were used to objectively score states of consciousness as wakefulness, rapid eye movement (REM) sleep, or non-REM (NREM) sleep. Measures of EEG power (dB) were quantified as δ (0.5-4 Hz), θ (4-8 Hz), α (8-13 Hz), σ (12-15 Hz), ß (13-30 Hz), and γ (30-60 Hz). Compared with saline (control), fentanyl and morphine decreased NREM sleep, morphine eliminated REM sleep, and buprenorphine eliminated NREM sleep and REM sleep. Opioids significantly and differentially disrupted the temporal organization of sleep/wake states, altered specific EEG frequency bands, and caused dissociated states of consciousness. The results are discussed relative to the fact that opioids, pain, and sleep modulate interacting states of consciousness.NEW & NOTEWORTHY This study discovered that antinociceptive doses of fentanyl, morphine, and buprenorphine significantly and differentially disrupt EEG-defined states of consciousness in C57BL/6J mice. These data are noteworthy because: 1) buprenorphine is commonly used in medication-assisted therapy for opioid addiction, and 2) there is evidence that disordered sleep can promote addiction relapse. The results contribute to community phenotyping efforts by making publicly available all descriptive and inferential statistics from this study (Supplemental Tables S1-S8).