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Background There is evidence of increased risk of cognitive disability due to short sleep duration and adverse Social Determinants of Health (SDoH). To determine whether spatial associations (correlation between spatially distributed variables within a given geographic area) exist between neighborhoods with short sleep duration and cognitive disability across the United States (US) after adjusting for other factors. We conducted a spatial analysis using a spatial lag model at the neighborhood-level with the census tract as unit-of-analysis within each state in the US. We aggregated our results nationally using a weighted analysis to adjust for the number of census tracts per state. This study used Centers for Disease Control and Prevention (CDC) data on short sleep duration, cognitive disability and other health factors. We used 2021–2022 neighborhood-level data from the CDC and US Census Bureau adjusting for social determinants of health (SDoH) and demographics, excluding Florida due to inconsistencies in data availability. Our exposure variable was self-reported short sleep defined by the CDC (“sleep less than 7 hours per 24 hour period”). Our outcome was self-reported cognitive disability defined by the CDC (“difficulty concentrating, remembering, or making decision”). We adjusted for other factors including ‘health outcomes’, ‘preventive practices’, and the CDC’s Social Vulnerability Index. Results The spatial analysis revealed a significant association between short sleep duration and an increased risk of cognitive disability across the US (estimate range [0.29; 1.27], p  < 0.005) after adjustment. Notably, six Western states (New Mexico, Alaska, Arizona, Nevada, Idaho, and Oregon) were at increased risk of cognitive disability due to short sleep duration and this pattern was significant ( p  = 0.007). Conclusions Our study highlights the importance of short sleep duration as a significant predictor of cognitive disability across the US after adjusting for other confounders. The association between short sleep and cognitive disability was especially strong in the Western region of the US providing a deeper understanding of how geographic context and local factors can shape health outcomes.

Background To determine whether objective markers of sleepiness can be collected passively using voice data to detect sleepiness in individuals undergoing testing in situations where sleepiness is not the focal point of assessment. We assessed verbal reaction time (VRT) as a vocalic marker of subjective sleepiness in middle aged and older adults with history of insomnia and benzodiazepine-receptor-agonist (BZRA) use. Methods Adults aged ≥55 without a diagnosis of dementia were recruited from a BZRA deprescribing clinical trial and enrolled in the present study that tested the feasibility of cognitive testing using out-of-office, self-directed mobile apps. Participants’ working/episodic memory were assessed through recorded verbal responses to Verbal Paired Associates (VPA) tests, and ecological momentary assessments (EMA) of self-reported sleepiness (1[not at all] to 4[more prominent]). Using a generalized additive model, we examined the association between VRT during VPA testing and self-reported sleepiness, adjusting for demographic, test parameters, caffeine intake, cognition, mood, and BZRA-use ( p ≤0.05 was considered significant). A stratified k-fold cross-validation/random forest (SKCV/RF) was performed to classify sleepiness levels, adjusting for other variables. Results We analyzed 1,513 observations from 16 patients. VRT was operationalized as the time duration between recording start time and first speech epoch. Longer VRTs were positively associated with greater EMA sleepiness ( p ≤0.05). The SKCV/RF model yielded a mean F1-score of 0.80 ± 0.08 across folds. Conclusions Longer VRTs correlated with greater self-reported sleepiness, indicating that voice data can be used as a marker of sleepiness in patients undergoing cognitive testing in out-of-office settings.

Citation: Te TT, Phan TT. Racial and ethnic disparities in pediatric obstructive sleep apnea: insights from a large study on adolescents in southern California. J Clin Sleep Med. 2024;20(10):1567–1568.

Study Objectives The objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities. Methods Cases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)–based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities. Results In total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity. Conclusions Our study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management.

Computed tomography is particularly sensitive for osseous structures and is the modality of choice for most odontogenic infections.2 Fluid collection and tissue biopsies should be examined for evidence of acute or chronic inflammation and infection. A spreading infection is more serious than localized swelling of gingival tissue, because the infection could spread outward from the root of the tooth throughout the bone and periosteum.3 Failure to recognize a systemic infection can lead to chronic infections or life-threatening complications, such as airway obstruction, mediastinitis, necrotizing fasciitis, cavernous sinus thrombosis, cerebral abscess, orbital abscess, and sepsis.4 Severe complications require intravenous antibiotics, incision and drainage, and probable removal of the source of infection.3 A localized infection is usually not urgent and can be managed in the outpatient setting. Pyogenic granulomas are erythematous, nonpainful, and smooth, and bleed easily when touched.8 SUMMARY TABLE Condition Characteristics Dental abscess Moderate to severe pain, swelling, sensitivity to hot and cold; may have history of a recent dental procedure Langerhans cell histiocytosis Bone and skin are most commonly affected; oral involvement is possible, most often manifesting as an intraoral mass, gingivitis, mucosal ulcers, and loose teeth; most common in children one to three years of age Mucocele Multiple episodes of swelling with periodic rupture, nontender, often appears inside the lower lip, most common in young children Pyogenic granuloma Rapidly growing, erythematous, nonpainful, smooth lesions that bleed easily Address correspondence to Nguyet-Cam Lam, MD, FAAFP, at Nguyet-Cam.Lam@sluhn.org.

CITATION Te T. You lack the season of all natures, sleep. J Clin Sleep Med . 2022;18(2):343–344.