The Rising Relative Presence of Whites in Majority Non-White Cities

After several decades of Whites fleeing large metropolitan areas, they are now increasingly gentrifying urban neighborhoods and communities. This analysis uses data from the 2000 decennial census and the 2012 and 2017 American Community Survey to assess the growing presence of Whites in U.S. cities. The analysis examines the extent to which Whites have experienced an increase in their percentage share of the populations of 212 majority non-White communities with 50,000 or more inhabitants over two time periods (2000 to 2008–2012 and 2008–2012 to 2013–2017). The results show that 39 communities have experienced an expanding relative presence of Whites in one or both periods. Whites generally are growing at a faster pace than Blacks and Latinos in these communities and there are large socioeconomic gaps favoring Whites. The article concludes with a discussion of the policy implications of the findings.

Impact of Differential Privacy and Census Tract Data Source (Decennial Census Versus American Community Survey) for Monitoring Health Inequities

Objectives. To investigate how census tract (CT) estimates of mortality rates and inequities are affected by (1) differential privacy (DP), whereby the public decennial census (DC) data are injected with statistical “noise” to protect individual privacy, and (2) uncertainty arising from the small number of different persons surveyed each year in a given CT for the American Community Survey (ACS). Methods. We compared estimates of the 2008–2012 average annual premature mortality rate (death before age 65 years) in Massachusetts using CT data from the 2010 DC, 2010 DC with DP, and 2008–2012 ACS 5-year estimate data. Results. For these 3 denominator sources, the age-standardized premature mortality rates (per 100 000) for the total population respectively equaled 166.4 (95% confidence interval [CI] = 162.2, 170.6), 166.4 (95% CI = 162.2, 170.6), and 166.3 (95% CI = 162.1, 170.5), and inequities in the range from best to worst quintile for CT racialized economic segregation were from 103.4 to 260.1, 102.9 to 258.7, and 102.8 to 262.4. Similarity of results across CT denominator sources held for analyses stratified by gender and race/ethnicity. Conclusions. Estimates of health inequities at the CT level may not be affected by use of 2020 DP data and uncertainty in the ACS data. (Am J Public Health. Published online ahead of print December 22, 2020: e1–e4. https://doi.org/10.2105/AJPH.2020.305989 )

Seeking the ‘Truth’ from ‘Enumerating’ Numbers: Leprosy in Census and Public Health Reports of Colonial Bengal: 1890s–1940s

Figures are required to execute the policies in a way in which the state prepares the statistics of ‘progresses’. This is a form of ‘truth’ that is unquestionable and absolute to the administration. But often, the numbers are constructed, exaggerated and even invented, hiding the grim reality for the sake of upholding the achievements of a welfare state. Census has been one of the ‘scientific tools’ since the nineteenth century, by which the state retains its supervision over the population and preserves the information for future reference. Such process of institutionalising ‘truism’ would be considered as an ultimate solution of the problems that the enumerators encounter with the ‘infirm’. This article seeks to explore how far the enumerations had paved the way for resolving the issues regarding the ‘lepers’ and ‘deformity’ in colonial Bengal since 1891 when the decennial census was taken immediately after the death of Father Damien. The article also intends to problematise the notion on ‘diseased bodies’, that is, ‘lepers’ that the Raj tried to marginalise through census figures to protect the healthy populace from probable degeneration. Thus, it studies the leprosy and ‘lepers’ both by analysing the census and public health reports within the context of Empire and colonial Bengal.

Intercensal and Postcensal Estimation of Population Size for Small Geographic Areas in the United States

IntroductionPopulation estimation techniques are often used to provide updated data for a current year. However,estimates for small geographic units, such as census tracts in the United States, are typically notavailable. Yet there are growing demands from local policy making, program planning and evaluationpractitioners for such data because small area population estimates are more useful than those forlarger geographic areas. ObjectivesTo estimate the population sizes at the census block level by subgroups (age, sex, and race/ethnicity)so that the population data can be aggregated up to any target small geographic areas. MethodsWe estimated the population sizes by subgroups at the census block level using an intercensal ap-proach for years between 2000 and 2010 and a postcensal approach for the years following the2010 decennial census (2011-2017). Then we aggregated the data to the county level (intercensalapproach) and incorporated place level (postcensal approach) and compared our estimates to corre-sponding US Census Bureau (the Census) estimates. ResultsOverall, our intercensal estimates were close to the Census’ population estimates at the countylevel for the years 2000-2010; yet there were substantive errors in counties where population sizesexperienced sudden changes. Our postcensal estimates were also close to the Census’ populationestimates at the incorporated place level for years closer to the 2010 decennial census. ConclusionsThe approaches presented here can be used to estimate population sizes for any small geographicareas based on census blocks. The advantages and disadvantages of their application in public healthpractice should be considered.

Editor's Corner

Howdy everyone! It is 2020 and Census Day is April 1, 2020. There are many changes with the Census this year. Census 2020 will be the first census that people can respond to online. Sometimes people are not sure where to be counted (for example people in shelters and college students) and the Census Bureau has been working on this issue since 2015 (https://www.census.gov/programs-surveys/decennial-census/2020-census/about/residence-rule.html). For more information check out the 2020 Census Operational Plan—https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-management/planning-docs/operational-plan.html.

Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff

Background: The 2020 US Census will use a novel approach to disclosure avoidance to protect respondents’ data, called TopDown. This TopDown algorithm was applied to the 2018 end-to-end (E2E) test of the decennial census. The computer code used for this test as well as accompanying exposition has recently been released publicly by the Census Bureau. Methods: We used the available code and data to better understand the error introduced by the E2E disclosure avoidance system when Census Bureau applied it to 1940 census data and we developed an empirical measure of privacy loss to compare the error and privacy of the new approach to that of a (non-differentially private) simple-random-sampling approach to protecting privacy. Results: We found that the empirical privacy loss of TopDown is substantially smaller than the theoretical guarantee for all privacy loss budgets we examined. When run on the 1940 census data, TopDown with a privacy budget of 1.0 was similar in error and privacy loss to that of a simple random sample of 50% of the US population. When run with a privacy budget of 4.0, it was similar in error and privacy loss of a 90% sample. Conclusions: This work fits into the beginning of a discussion on how to best balance privacy and accuracy in decennial census data collection, and there is a need for continued discussion.