Impact of traffic, poverty and facility ownership on travel time to emergency care in Nairobi, Kenya

Background: In many low and middle-income countries (LMICs), timely access to emergency healthcare services is limited. In urban settings, traffic can have a significant impact on travel time, leading to life-threatening delays for time-sensitive injuries and medical emergencies. In this study, we examined travel times to hospitals in Nairobi, Kenya, one of the largest and most congested cities in the developing world. Methods: We used a network approach to estimate average minimum travel times to different types of hospitals (e.g. ownership and level of care) in Nairobi under both congested and uncongested traffic conditions. We also examined the correlation between travel time and socioeconomic status. Results: We estimate the average minimum travel time during uncongested traffic conditions to any level 4 health facility (primary hospitals) or above in Nairobi to be 4.5 min (IQR 2.5–6.1). Traffic added an average of 9.0 min (a 200% increase). In uncongested conditions, we estimate an average travel time of 7.9 min (IQR 5.1–10.4) to level 5 facilities (secondary hospitals) and 11.6 min (IQR 8.5–14.2) to Kenyatta National Hospital, the only level 6 facility (tertiary hospital) in the country. Traffic congestion added an average of 13.1 and 16.0 min (166% and 138% increase) to travel times to level 5 and level 6 facilities, respectively. For individuals living below the poverty line, we estimate that preferential use of public or faith-based facilities could increase travel time by as much as 65%. Conclusion: Average travel times to health facilities capable of providing emergency care in Nairobi are quite low, but traffic congestion double or triple estimated travel times. Furthermore, we estimate significant disparities in timely access to care for those individuals living under the poverty line who preferentially seek care in public or faith-based facilities

Reinforcement learning in ophthalmology: potential applications and challenges to implementation

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them

Quantification and Predictors of OCT-Based Macular Curvature and Dome-Shaped Configuration: Results From the UK Biobank

PURPOSE. To investigate macular curvature, including the evaluation of potential associations and the dome-shaped macular configuration, given the increasing myopia prevalence and expected associated macular malformations. METHODS. The study included a total of 65, 440 subjects with a mean age (± SD) of 57.3 ± 8.11 years with spectral-domain optical coherence tomography (OCT) data from a unique contemporary resource for the study of health and disease that recruited more than half a million people in the United Kingdom (UK Biobank). A deep learning model was used to segment the retinal pigment epithelium. The macular curvature of the OCT scans was calculated by polynomial fit and evaluated. Further, associations with demographic, functional, ocular, and infancy factors were examined. RESULTS. The overall macular curvature values followed a Gaussian distribution with high inter-eye agreement. Although all of the investigated parameters, except maternal smoking, were associated with the curvature in a multilinear analysis, ethnicity and refractive error consistently revealed the most significant effect. The prevalence of a macular dome-shaped configuration was 4.8% overall, most commonly in Chinese subjects as well as hypermetropic eyes. An increasing frequency up to 22.0% was found toward high refractive error. Subretinal fluid was rarely found in these eyes. CONCLUSIONS. Macular curvature revealed associations with demographic, functional, ocular, and infancy factors, as well as increasing prevalence of a dome-shaped macular configuration in high refractive error including high myopia and hypermetropia. These findings imply different pathophysiologic processes that lead to macular development and might open new fields to future myopia and macula research

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records

Citation: Baughman DM, Su GL, Tsui I, Lee CS, Lee AY. Validation of the total visual acuity extraction algorithm (TOVA) for automated extraction of visual acuity data from free text, unstructured clinical records. Trans Vis Sci Tech. 2017;6(2):2, doi: 10.1167/tvst.6.2.2 Copyright 2017 The Authors Purpose: With increasing volumes of electronic health record data, algorithm-driven extraction may aid manual extraction. Visual acuity often is extracted manually in vision research. The total visual acuity extraction algorithm (TOVA) is presented and validated for automated extraction of visual acuity from free text, unstructured clinical notes. Methods: Consecutive inpatient ophthalmology notes over an 8-year period from the University of Washington healthcare system in Seattle, WA were used for validation of TOVA. The total visual acuity extraction algorithm applied natural language processing to recognize Snellen visual acuity in free text notes and assign laterality. The best corrected measurement was determined for each eye and converted to logMAR. The algorithm was validated against manual extraction of a subset of notes. Conclusions: The total visual acuity extraction algorithm is a novel tool for extraction of visual acuity from free text, unstructured clinical notes and provides an open source method of data extraction. Translational Relevance: Automated visual acuity extraction through natural language processing can be a valuable tool for data extraction from free text ophthalmology notes

Generating a 4-photon Tetrahedron State: Towards Simultaneous Super-sensitivity to Non-commuting Rotations

It is often thought that the super-sensitivity of a quantum state to an observable comes at the cost of a decreased sensitivity to other non-commuting observables. For example, a squeezed state squeezed in position quadrature is super-sensitive to position displacements, but very insensitive to momentum displacements. This misconception was cleared with the introduction of the compass state, a quantum state equally super-sensitive to displacements in position and momentum. When looking at quantum states used to measure spin rotations, N00N states are known to be more advantageous than classical methods as long as they are aligned to the rotation axis. When considering the estimation of a rotation with unknown direction and amplitude, a certain class of states stands out with interesting properties. These states are equally sensitive to rotations around any axis, are second-order unpolarized, and can possess the rotational properties of platonic solids in particular dimensions. Importantly, these states are optimal for simultaneously estimating the three parameters describing a rotation. In the asymptotic limit, estimating all d parameters describing a transformation simultaneously rather than sequentially can lead to a reduction of the appropriately-weighted sum of the measured parameters' variances by a factor of d. We report the experimental creation and characterization of the lowest-dimensional such state, which we call the "tetrahedron state" due to its tetrahedral symmetry. This tetrahedron state is created in the symmetric subspace of four optical photons' polarization in a single spatial and temporal mode, which behaves as a spin-2 particle. While imperfections due to the hardware limit the performance of our method, we argue that better technology can improve our method to the point of outperforming any other existing strategy in per-photon comparisons.Comment: 13 pages, 6 figure