As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are the often

East-Southeast Asia is currently one of the fastest urbanizing regions in the world, with countries such as China climbing from 20 to 50% urbanized in just a few decades. By 2050, these countries are projected to add 1 billion people, with 90% of that growth occurring in cities. This population shift parallels an equally astounding amount of built-up land expansion. However, spatially-and temporallydetailed information on regional-scale changes in urban land or population distribution do not exist; previous efforts have been either sample-based, focused on one country, or drawn conclusions from datasets with substantial temporal/spatial mismatch and variability in urban definitions. Using consistent methodology, satellite imagery and census data for >1000 agglomerations in the East-Southeast Asian region, we show that urban land increased >22% between 2000 and 2010 (from 155 000 to 189 000 km 2 ), an amount equivalent to the area of Taiwan, while urban populations climbed >31% (from 738 to 969 million). Although urban land expanded at unprecedented rates, urban populations grew more rapidly, resulting in increasing densities for the majority of urban agglomerations, including those in both more developed (Japan, South Korea) and industrializing nations (China, Vietnam, Indonesia). This result contrasts previous sample-based studies, which conclude that cities are universally declining in density. The patterns and rates of change uncovered by these datasets provide a unique record of the massive urban transition currently underway in East-Southeast Asia that is impacting local-regional climate, pollution levels, water quality/availability, arable land, as well as the livelihoods and vulnerability of populations in the region.

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.

Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo‐located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo‐located tweets in 1x1 km grid cells over a 2‐month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests‐based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media‐derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.

To identify the consequences of the coronavirus 2019 (COVID-19) pandemic for individuals with traumatic brain injury (TBI), with particular attention to unique effects for individuals with chronic disability. Design: Individuals with and without a history of TBI completed a web-based survey. Setting: Participants were recruited from the Vanderbilt Brain Injury Patient Registry in Nashville, TN, and completed the survey from their homes between May and June 2020, during social distancing related to the COVID-19 pandemic. Participants: Participants (NZ47) in the chronic phase of moderate-severe TBI (>6mo postinjury) and 51 noninjured comparison (NC) peers completed the survey. Interventions: Not applicable. Main Outcome Measures: Participants, or respondents, answered a mix of multiple choice and free text questions about how the COVID-19 pandemic has affected their work, education, medical care, social communication, sources of information and decision making, and mental and physical well-being. Individuals with TBI also answered questions about how TBI has affected their experiences of the pandemic. Results: As a group, respondents with TBI reported less pandemic-related behavior change (eg, daily habits, virtual social visits, and masking) than NC peers. Both NCs and respondents with TBI identified health care providers as trusted sources of public health information. One-third of individuals with TBI indicated that brain injury has made coping with the pandemic more difficult, and respondents identified mental health challenges and social isolation as key barriers. Conclusions: These results suggest that health care providers should look for ways to provide tailored education and reduce social isolation for individuals with disability during the ongoing COVID-19 pandemic. We discuss several direct suggestions from participant responses.

Background Open access (OA) publications have changed the paradigm of dissemination of scientific research. Their benefits to low-income countries underline their value; however, critics question exorbitant publication fees as well as their effect on the peer review process and research quality. Purpose This study reports on the prevalence of OA publishing in orthopaedic research and compares benchmark citation indices as well as evidence quality derived from OA journals with conventional subscription based orthopaedic journals. Methods All 63 orthopaedic journals listed in ISI's Web of Knowledge Journal Citation Report (JCR) were examined. Bibliometric data attributed to each journal for the year 2012 was acquired from the JCR. Studies that fulfilled the criteria of level I evidence were identified for each journal within PubMed. Individual journal websites were reviewed to identify their open access policy. A total of 38 (60.3 %) journals did not offer any form of OA publishing; however, 20 (31.7 %) hybrid journals were identified which offered authors the choice to publish their work as OA if a publication fee was paid. Only five (8 %) journals published all their articles as OA. There was variability amongst the different publication fees for OA articles. Journals that published OA articles did not differ from subscription based journals on the basis of 2012 impact factor, citation number, self citation proportion or the volume of level I evidence published (p>0.05).Conclusions OA journals are present in orthopaedic research, though in small numbers. Over a third of orthopaedic journals catalogued in the ISI Web of Knowledge JCR® are hybrid journals that provide authors with the opportunity to publish their articles as OA after a publication fee is paid. This study suggests equivalent importance and quality of articles between OA and subscription based orthopaedic journals based on bibliometric data and the volume of level I evidence produced. Orthopaedic researchers must recognize the potential benefits of OA publishing and its emerging presence within the field. Further examination and consensus is required in orthopaedic research to generate an OA system that is robustly regulated and maintains research quality.

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

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