Background: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. Methods: Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. Results: Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p = 0.021). An increase of 10,000 people per km 2 is associated with a 2.4% (95% CI 0.6 to 4.2%, p = 0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p < 0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p < 0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p < 0.001) increase in positivity rate. The percentage of Hispanic (p = 0.718), Asian (p = 0.966), or Other (p = 0.588) populations were not statistically significant factors. Conclusions: Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.

Changes in the gravitational vector by postural changes or weightlessness induce fluid shifts impacting ocular hemodynamics and regional pressures. This investigation explores the impact of changes in direction of the gravitational vector on intraocular pressure (IOP), mean arterial pressure at eyelevel (MAPeye), and ocular perfusion pressure (OPP), which is critical for ocular health. Thirteen subjects underwent 360° of tilt (including both prone and supine positions) at 15º increments. At each angle, steady-state IOP and MAPeye were measured and OPP calculated as MAPeye-IOP. Experimental data were compared to a 6-compartment lumped parameter model of the eye. Mean IOP, MAPeye, and OPP significantly increased from 0º supine to 90º head down tilt (HDT) by 20.7±1.7 mmHg (ᵅD; < 0.001), 38.5±4.1 mmHg (ᵅD; < 0.001), and 17.4±3.2 mmHg (ᵅD; <0.001), respectively. Head up tilt (HUT) significantly decreased OPP by 16.5±2.5 mmHg (ᵅD; < 0.001). IOP was significantly higher in prone vs. supine position for much of the tilt range. Our study indicates that OPP is highly gravitationally dependent. Specifically, data show that MAPeye is more gravitationally dependent than IOP, thus causing OPP to increase during HDT and to decrease during HUT. Additionally, IOP was elevated in prone position compared to supine position due to the additional hydrostatic column between the base of the rostral globe to the mid-caudal plane, supporting the notion that hydrostatic forces play an important role in ocular hemodynamics. Changes in OPP as a function of changes in gravitational stress and/or weightlessness may play a role in the pathogenesis of spaceflight-associated neuro-ocular syndrome.

Background:New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the number of detected COVID-19 cases.

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