No abstract

Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD). Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.

Background Survival from out-of-hospital cardiac arrest (OHCA) is generally poor and varies by geography. Variability in automated external defibrillator (AED) locations may be a contributing factor. To inform optimal placement of AEDs, we investigated AED access in a major US city relative to demographic and employment characteristics. Methods and Results This was a retrospective analysis of a Philadelphia AED registry (2,559 total AEDs). The 2010 US Census and the Local Employment Dynamics (LED) database by ZIP code was used. AED access was calculated as the weighted areal percentage of each ZIP code covered by a 400 meter radius around each AED. Of 47 ZIP codes, only 9%(4) were high AED service areas. In 26%(12) of ZIP codes, less than 35% of the area was covered by AED service areas. Higher AED access ZIP codes were more likely to have a moderately populated residential area (p=0.032), higher median household income (p=0.006), and higher paying jobs (p=008). Conclusions The locations of AEDs vary across specific ZIP codes; select residential and employment characteristics explain some variation. Further work on evaluating OHCA locations, AED use and availability, and OHCA outcomes could inform AED placement policies. Optimizing the placement of AEDs through this work may help to increase survival.

This study examined variation in geographic access to Board Certified Behavior Analysts for children with autism spectrum disorder. Between March and May 2019, we integrated public data from the U.S. Department of Education’s Civil Rights Data Collection, Behavior Analyst Certification Board’s certificant registry, and U.S. Census. The study sample included all U.S. counties and county equivalents in 48 states and D.C. ( N = 3108). Using geographic information systems software, we assigned Board Certified Behavior Analysts to counties based on their residence, allocated children via school districts to counties, and generated per capita autism spectrum disorder/Board Certified Behavior Analyst ratios. We calculated the Getis-Ord G* statistics for each county and each ratio and compared counties in high-ratio clusters with counties in low-ratio clusters by socioeconomic variables. More than half of all counties had no Board Certified Behavior Analysts. Counties in the highest accessibility category had ⩽17.1 children with autism spectrum disorder per Board Certified Behavior Analyst ( n = 770), while counties in the lowest accessibility category had ⩾137.1 children with autism spectrum disorder per Board Certified Behavior Analyst ( n = 12). In all, 55 of the 129 counties with the highest autism spectrum disorder prevalence had no Board Certified Behavior Analysts. Higher accessibility counties were wealthier and had smaller uninsured populations. To improve geographic access, we must identify factors driving unequal distribution that can inform provider recruitment and retention efforts in underserved areas. Lay abstract This study looked at whether access to Board Certified Behavior Analysts for children with autism spectrum disorder is different between U.S. counties. The study included all U.S. counties and county equivalents in 48 states and D.C. ( N = 3108). Between March and May 2019, we combined data from the U.S. Department of Education’s Civil Rights Data Collection, Behavior Analyst Certification Board’s certificant registry, and U.S. Census. We assigned Board Certified Behavior Analysts to counties based on their address, matched children in school districts to counties, and determined how many children with autism spectrum disorder there were in a county compared with how many Board Certified Behavior Analysts there were in a county. The results show uneven numbers of Board Certified Behavior Analysts between U.S. counties. More than half of all counties had no Board Certified Behavior Analysts. National maps illustrate clusters of high and low accessibility to Board Certified Behavior Analysts. To improve access to Board Certified Behavior Analysts in underserved areas, we must identify what contributes to the differences in access.

Malone, Couch, and Barrett argue that a broader analysis of fired coaches, including adding in partial season fires, considering a wider range of causes of firing, and analyzing rehiring makes the results reported by Madden ''disappear.'' We show that Malone et al. have analyzed inaccurate data. When the data used by Malone et al. are corrected and their speculations tested empirically, Madden's conclusion that analyses of all employment decisions involving head coaches between 1990 and 2002 are consistent with discrimination against African Americans is supported.