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Provides a 3,000-year history of the census, chronicling the practices of the ancient world through the Supreme Court rulings of today, examining how censuses have been used as tools of democracy, exclusion and mass surveillance.

Americans are fleeing unaffordable blue states – and they’re taking the electoral college with them.

Introduction: Crowding is associated with poor patient outcomes in emergency departments (ED). Measures of crowding are often complex and resource-intensive to score and use in real-time. We evaluated single easily obtained variables to establish the presence of crowding compared to more complex crowding scores. Methods: Serial observations of patient flow were recorded in a tertiary Canadian ED. Single variables were evaluated including total number of patients in the ED (census), in beds, in the waiting room, in the treatment area waiting to be assessed, and total inpatient admissions. These were compared with Crowding scores (NEDOCS, EDWIN, ICMED, three regional hospital modifications of NEDOCS) as predictors of crowding. Predictive validity was compared to the reference standard of physician perception of crowding, using receiver operator curve analysis. Results: 144 of 169 potential events were recorded over 2 weeks. Crowding was present in 63.9% of the events. ED census (total number of patients in the ED) was strongly correlated with crowding (AUC = 0.82 with 95% CI = 0.76 - 0.89) and its performance was similar to that of NEDOCS (AUC = 0.80 with 95% CI = 0.76 - 0.90) and a more complex local modification of NEDOCS, the S-SAT (AUC = 0.83, 95% CI = 0.74 - 0.89). Conclusion: The single indicator, ED census was as predictive for the presence of crowding as more complex crowding scores. A two-stage approach to crowding intervention is proposed that first identifies crowding with a real-time ED census statistic followed by investigation of precipitating and modifiable factors. Real time signalling may permit more standardized and effective approaches to manage ED flow.

Primer Censo Nacional, Nomenclatura ocupacional, Sumampa, Estructura demográfica Abstract The article analyzes the demographic structure of the Sumampa department, in southern Santiago del Estero, based on the First National Census of 1869, paying special attention to the nomenclature of occupations. The analysis reveals several paradoxes, including a feminized structure very similar to that of other departments in Santiago - despite the department's specialization in livestock -, the predominance ofdomestic textile production, and farmers in a context of traditional large estates. Keywords: First National Census, Occupational nomenclature, Sumampa, Demographic Structure Introducción Poco después de dejar el Brácho en el carruaje del Jenerał Taboada, la tarde del 4 de Febrero, acompañados por el Jenerał y el Coronei Condarco, encontramos un grupo de hombres y mujeres a caballo. Con el objeto de ir ordenando ese recorrido, este artículo propone un primer análisis en profundidad de las cédulas del censo nacional de 1869 para Sumampa,7 departamento que pensaremos en sus semejanzas y en diferencias con los restantes de la provincia y con otras regiones.

This paper studies the evolution of the population in the province of Castellón in the 16th and 17th centuries. It quantifies the levels of under- or overestimation of the censuses at the time and analyses demographic growth through the results obtained from the corrected censuses and the series of sacramental registries.

The report puts forth a thesis about the falsification of the national census in Pirin, Macedonia in December 1946. The results of the census formally announces a Macedonian ethnic face of this territory. The report bases justifying the falsity results of the census on many Bulgarian archival documents of this period. They show a great manipulation in the question of national identification of population in the Pirin Region, in which the majority de facto unambiguously regarded themselves as Bulgarians. The census was carried out under strong pressure of political factors: Soviet Union, Yugoslavia and Bulgarian communist authorities.

While speaking to reporters inside the Oval Office, President Trump defended the inclusion of a citizenship question in the upcoming census survey on July 1.

Introduction Racial/ethnic residential segregation may contribute to sleep disparities by concentrating poverty and impairing sleep among minoritized groups. Yet, due to racism, equitable access to sleep-promoting resources may not occur in integrated neighborhoods. Further, social/cultural factors within ethnic enclaves could be protective. Methods To investigate whether relationships between racial/ethnic residential segregation and sleep vary by census tract-level poverty among US adults, we linked nationally-representative National Health Interview Survey data (2011-2017) to 2012 and 2017 American Community Survey census tract-level data. We used the local Getis-Ord Gi* statistic to categorize residential segregation (high, medium, low [reference]), considering differences between racial/ethnic composition within a census tract to the surrounding county. Survey-weighted, Poisson regressions with robust variance estimation were stratified by race/ethnicity and approximate census tract-level poverty tertile (high [>16.9% of residents below poverty level], medium [>7.9%-16.9%], low [0%-7.9%]) to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for self-reported sleep health measures. Results Among 126,539 participants (mean age±SE=46±0.1 years), non-Hispanic (NH)-Black and Latino adults were most likely to report short sleep (41%) and non-restorative sleep (37%), respectively. Racial/ethnic residential segregation was most common among NH-Black (38%), followed by non-Mexican Latino (34%) and NH-Asian adults (34%), and was least prevalent among NH-White adults (17%) but was not associated with sleep among Mexican, non-Mexican Latino, or NH-Black adults. Among NH-Asian adults, high segregation was marginally associated with a higher prevalence of short sleep (< 7-hours vs. recommended [7-9 hours]) in low-poverty neighborhoods (PR=1.17 [95% CI:0.97-1.42]) but was associated with recommended sleep in medium-poverty neighborhoods (PRshort sleep=0.79 [0.63-0.99]). Among NH-White adults, high residential segregation was associated with more restorative sleep only in high-poverty (PR=1.13 [1.06-1.20]) and low-poverty (PR=1.03 [1.00-1.07]) neighborhoods. Conclusion Associations between racial/ethnic residential segregation and sleep health varied by neighborhood-level poverty among NH-Asian and NH-White adults. The lack of differences among most minoritized groups may be related to the close proximity of higher-income and lower-income neighborhoods due to structural racism. Future studies including spatial analyses are warranted. The findings/conclusions in this research are those of the authors and do not necessarily represent the views of the Research Data Center, National Center for Health Statistics, or CDC. Support (if any)