Introduction Housing stability is a key health determinant and there is a need for early screening for instability with existing electronic health record (EHR) data to improve health outcomes. We aim to establish recorded address changes as a screening variable for housing instability and homelessness and to attempt to define the threshold of high churn. Methods Our study is a single-center cross-sectional study of EHR data (2018-2024) conducted at a US academic center with eleven sites across Chicago. We include patients 18 years or older with at least three hospital encounters over three different years. We define address churn as the number of address changes recorded in the EHR corrected to three-year intervals. We compare demographic and clinical characteristics of individuals with varying address churn with the student T-test to look at distribution of address churn for patients with and without record of homelessness, ANOVA to evaluate the distribution of ages for different levels of churn, and the chi-square test to evaluate for association between churn and clinical diagnoses. We perform multivariable logistic regression to measure the association between people with a record of homelessness and address changes. Results The study includes 1,068,311 patients with 756,222 having zero address changes, 156,911 having one address change, 137,491 with two address changes, 9,558 with three address changes, and 8,129 with four or more address changes. People with no record of homelessness in the EHR have mean address changes of 0.6 (SD 0.7) whereas people with record of homelessness have mean address changes of 1.8 (SD 1.3). Diagnostic profiles of the varying address change groups show increased prevalence of psychiatric diagnoses (65.2% in the 4 or more-address change group) compared to lower address change (27.7% in the 0-address change group). Address churn is significantly associated with homelessness with an odds ratio (OR) of 1.44 (95% CI =  [1.42-1.47], P

Univariate and multivariate logistic regression odd ratio and 95% CI.

This dataset represents an archived record of annual California sea otter surveys from 1985-2014. Survey procedures involve counting animals during the "spring survey" -- generally beginning in late April or early May and usually ending in late May early June but may extend into early July, depending on weather conditions. Annual surveys are organized by survey year and within each year, three shapefiles are included: census summary of southern sea otter, extra limit counts of southern sea otter, and range extent of southern sea otter. The surveys, conducted cooperatively by scientists of the U.S. Geological Survey, California Department of Fish and Wildlife, U.S. Fish and Wildlife Service and Monterey Bay Aquarium with the help of experienced volunteers, cover about 375 miles of California coast, from Half Moon Bay south to Santa Barbara. The information gathered may be used by federal and state wildlife agencies in making decisions about the management of this threatened marine mammal. These data, in conjunction with findings from several more in-depth studies, may also provide the necessary information to assess female reproductive rates and changes in reproductive success of the California sea otter population through time. For more information on annual California sea otter surveys, including most current surveys and associated data and corresponding USGS Data Series reports, go to: https://www.sciencebase.gov/catalog/item/5601b6dae4b03bc34f5445ec The GIS shapefile "Census summary of southern sea otter" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring range-wide census. This census has been undertaken each year using consistent methodology involving both ground-based and aerial-based counts. This range-wide census provides the primary basis for gauging population trends by State and Federal management agencies. This shapefile includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California. Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al. 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California. The GIS shapefile "Extra limit counts of southern sea otters" is a point layer representing the locations of sea otter sightings that fall outside the officially recognized range of the southern sea otter in mainland California. These data were collected during the spring range-wide census. Sea otter distribution in California (the mainland range) is considered to comprise a band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits as defined above. However, a few individual sea otters (almost always males) can frequently be found outside this officially recognized range, and these "extra-limital" animals are also counted during the census. The GIS shapefile "Range extent of southern sea otters" is a simple polyline representing the geographic distribution of the southern sea otter in mainland California, based on data collected during the spring range-wide census. The spring 2011 survey was incomplete due to weather conditions and there were no “extra-limital” sightings of otters during the spring 1992 survey, hence no data or shapefile for “Extra limit counts 1992.” For ease of access, an additional CSV file of the census summary from 1985-2014 is provided: "AnnualCaliforniaSeaOtter_Census_summary_1985_2014.csv" References: Tinker, M. T., Doak, D. F., Estes, J. A., Hatfield, B. B., Staedler, M. M. and Bodkin, J. L. (2006), INCORPORATING DIVERSE DATA AND REALISTIC COMPLEXITY INTO DEMOGRAPHIC ESTIMATION PROCEDURES FOR SEA OTTERS. Ecological Applications, 16: 2293–2312, https://doi.org/10.1890/1051-0761(2006)016[2293:IDDARC]2.0.CO;2 Tinker, M. T. , D. P. Costa , J. A. Estes , and N. Wieringa . 2007. Individual dietary specialization and dive behaviour in the California sea otter: using archival time–depth data to detect alternative foraging strategies. Deep Sea Research II 54: 330–342, https://doi.org/10.1016/j.dsr2.2006.11.012

This map shows the change in particulate matter 2.5 (PM 2.5) air quality data for the US between 2010 and 2016 based on NASA SEDAC gridded data. The color indicates better or worse air quality, and the size of the symbol indicates population growth.This map shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county.