A survey of protective cushion usage in individuals with spinal cord injury while traveling in a motor vehicle and on a commercial airliner.

OBJECTIVE: While there are specific recommendations for pressure relieving cushions when seated in a wheelchair, there is a paucity of information regarding prescribed wheelchair cushions for persons with spinal cord injury (SCI) when traveling and not in their wheelchair seat. A questionnaire was designed to ascertain if individuals with SCI who are primarily wheelchair users utilize a prescribed wheelchair cushion when traveling in a motor vehicle (MV) or on a commercial airliner, as not utilizing one may be a causative factor in developing pressure ulcers. DESIGN AND SETTING: Survey design in an outpatient SCI rehabilitation setting. PARTICIPANTS: Full-time wheelchair users, with chronic (>1 year) SCI. RESULTS: Forty-two participants completed the survey, with a mean age of 39 years old and time post-injury of 10.4 years. All subjects used a prescribed wheelchair cushion when seated in their wheelchair. Twenty-seven subjects reported transferring to a MV seat (59.5% of sample), with 25 (92.6%) reporting not using a prescribed wheelchair cushion when sitting directly on the MV seat. For subjects who traveled on an airplane (n = 23-54.8%), 19 (82.6%) reported that they do not sit on a prescribed specialty cushion. CONCLUSION: Persons with chronic SCI, who are primary wheelchair users, utilize prescribed wheelchair cushions when sitting in their wheelchair, but most do not utilize a prescribed wheelchair cushion when seated in a MV (if they transfer out of their chair) or on a airplane seat. Studies to determine the pressures over the bony prominences on their travel surfaces may need to be undertaken to see whether the pressures are appropriate, as they may be a source of skin breakdown.

A simulated 'sandbox' for exploring the modifiable areal unit problem in aggregation and disaggregation.

We present a spatial testbed of simulated boundary data based on a set of very high-resolution census-based areal units surrounding Guadalajara, Mexico. From these input areal units, we simulated 10 levels of spatial resolutions, ranging from levels with 5,515-52,388 units and 100 simulated zonal configurations for each level - totalling 1,000 simulated sets of areal units. These data facilitate interrogating various realizations of the data and the effects of the spatial coarseness and zonal configurations, the Modifiable Areal Unit Problem (MAUP), on applications such as model training, model prediction, disaggregation, and aggregation processes. Further, these data can facilitate the production of spatially explicit, non-parametric estimates of confidence intervals via bootstrapping. We provide a pre-processed version of these 1,000 simulated sets of areal units, meta- and summary data to assist in their use, and a code notebook with the means to alter and/or reproduce these data.

Measuring the contribution of built-settlement data to global population mapping.

Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.

Surfer's myelopathy: an atypical case presentation.

INTRODUCTION: Surfer's myelopathy (SM) is a rare nontraumatic spinal cord injury seen in beginner surfers and people participating in activities involving prolonged lumbar hyperextension. The majority of cases of SM have been reported in younger patients under 40 years of age, with initial magnetic resonance imaging (MRI) showing T2 signal abnormalities. We present a case of SM in a person over 40 years old whose initial MRI did not show the T2 signal abnormalities usually reported in SM. CASE PRESENTATION: A 43-year-old male in good physical condition went surfing for the first time and developed generalized back pain that progressed to include bilateral lower extremity pain with numbness and weakness. MRI within 11-12 h of symptom onset revealed no acute T2 signal abnormalities. At the time of initial presentation he had classification consistent with a T12 American Spinal Injury Association Impairment Scale (AIS) A and at rehabilitation discharge, 6 weeks later, he had classification of T12 AIS B. DISCUSSION: Not all cases of SM present similarly. As surfing is a popular sport, education on early identification of warning signs is crucial for instructors and trainees, as well as health care providers. Our case highlights the importance of a comprehensive history and physical examination in developing the diagnosis, especially in presentations that are not classic in nature.

Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night.

Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.

Comprehensive, technology-based, team approach for a patient with locked-in syndrome: A case report of improved function & quality of life.

One of the most severe types of stroke is locked-in syndrome (LIS) due to the loss of almost all voluntary motor functions and a high mortality rate. The majority of the literature regarding LIS is based on case reports that utilized multidisciplinary interventions focused on improving functional communication and respiratory care with minimal focus on motor retraining. These reports were neither dynamic nor multi-sensory, and the only technology utilized was in the form of augmentative communication. There are additional types of technology frequently used in the general stroke population that can address similar motor deficits that occur in the LIS population. This case report explains an interdisciplinary approach using motor and communication interventions that are multisensory, progressive, multi-modal, and technology- based. The length of stay was 153 days in acute rehabilitation, after which the patient returned home making significant gains in overall function. In this patient, the FIM changes in motor (+42), cognitive (+29) and total change score of (+71) surpassed what was determined to be a minimal clinically important difference. These results suggest that this treatment program and approach may be a key reason why this patient was able to achieve significant functional gains and report improved quality of life.

Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania.

Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets.

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

Examining the correlates and drivers of human population distributions across low- and middle-income countries.

Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low- and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, they are generally remarkably consistent, pointing to universal drivers of human population distribution. Here, we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low- and middle-income regions of the world.

Spatiotemporal patterns of population in mainland China, 1990 to 2010.

According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.