Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study

Background: Road traffic noise is a serious public health problem globally as it has adverse psychosocial and physiologic effects (i.e., sleep). Since previous studies mainly focused on individual levels, we aim to examine associations between road traffic noise and sleep deprivation on a large scale; namely, the US at county level. Methods: Information from a large-scale sleep survey and national traffic noise map, both obtained from Government's open data, were utilized and processed with GIS techniques. To examine the associations between traffic noise and sleep deprivation, we used a hierarchical Bayesian spatial modelling framework to simultaneously adjust for multiple socioeconomic factors while accounting for spatial correlation. Findings: With 62.90% of people not getting enough sleep, a 10 dBA increase in average sound-pressure level (SPL) or SPL of the relatively noisy area in a county, was associated with a 49% (OR: 1.49; 95% CrIs:1.19–1.86) or 8% (1.08; 1.00–1.16) increase in the odds of a person in a particular county not getting enough sleep. A 10% increase in noise exposure area or population ratio was associated with a 3% (1.03; 1.01–1.06) or 4% (1.04; 1.02–1.06) increase in the odds of a person within a county not getting enough sleep. Interpretation: Traffic noise can contribute to variations in sleep deprivation among counties. This study suggests that policymakers could set up different noise-management strategies for relatively quiet and noisy areas (i.e., different limiting SPLs) and incorporate geo-spatial noise indicators, such as exposure population or area ratio. Furthermore, urban planners should consider urban sprawl patterns differently

A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data

We developed a statistical model to improve the detection of glaucomatous visual field (VF) progression as defined by the consensus of expert clinicians

Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil.

BackgroundPneumococcal conjugate vaccines (PCVs) are being used worldwide. A key question is whether the impact of PCVs on pneumonia is similar in low- and high-income populations. However, most low-income countries, where the burden of disease is greatest, lack reliable data that can be used to evaluate the impact. Data from middle-income countries that have both low- and high-income subpopulations can provide a proxy measure for the impact of the vaccine in low-income countries.MethodsWe evaluated the impact of PCV10 on hospitalizations for all-cause pneumonia in Brazil, a middle-income country with localities that span a broad range of human development index (HDI) levels. We used complementary time series and spatiotemporal methods (synthetic controls and hierarchical Bayesian spatial regression) to test whether the decline in pneumonia hospitalizations associated with vaccine introduction varied across the socioeconomic spectrum.ResultsWe found that the declines in all-cause pneumonia hospitalizations in children and young and middle-aged adults did not vary substantially across low and high HDI subpopulations. Moreover, the estimated declines seen in infants and young adults were associated with higher levels of uptake of the vaccine at a local level.ConclusionsThese results suggest that PCVs have an important impact on hospitalizations for all-cause pneumonia in both low- and high-income populations

Bayesian Spatial Design of Optimal Deep Tubewell Locations in Matlab, Bangladesh

We introduce a method for statistically identifying the optimal locations of deep tubewells (dtws) to be installed in Matlab, Bangladesh. Dtw installations serve to mitigate exposure to naturally occurring arsenic found at groundwater depths less than 200 meters, a serious environmental health threat for the population of Bangladesh. We introduce an objective function, which incorporates both arsenic level and nearest town population size, to identify optimal locations for dtw placement. Assuming complete knowledge of the arsenic surface, we then demonstrate how minimizing the objective function over a domain favors dtws placed in areas with high arsenic values and close to largely populated regions. Given only a partial realization of the arsenic surface over a domain, we use a Bayesian spatial statistical model to predict the full arsenic surface and estimate the optimal dtw locations. The uncertainty associated with these estimated locations is correctly characterized as well. The new method is applied to a dataset from a village in Matlab and the estimated optimal locations are analyzed along with their respective 95% credible regions

Combining Spectral Domain Optical Coherence Tomography Structural Parameters for the Diagnosis of Glaucoma With Early Visual Field Loss

To create a multivariable predictive model for glaucoma with early visual field loss using a combination of spectral-domain optical coherence tomography (SD-OCT) parameters, and to compare the results with single variable models