Differential Privacy and the Accuracy of County-Level Net Migration Estimates

AbstractEach decade since the 1950s, demographers have generated high-quality net migration estimates by age, sex, and race for US counties using decennial census data as starting and ending populations. The estimates have been downloaded tens of thousands of times and widely used for planning, diverse applications, and research. Census 2020 should allow the series to extend through the 2010–2020 decade. The accuracy of new estimates, however, could be challenged by differentially private (DP) disclosure avoidance techniques in Census 2020 data products. This research brief estimates the impact of DP implementation on the accuracy of county-level net migration estimates. Using differentially private Census 2010 demonstration data, we construct a hypothetical set of DP migration estimates for 2000–2010 and compare them to published estimates, using common accuracy metrics and spatial analysis. Findings show that based on demonstration data released in 2020, net migration estimates by five-year age groups would only be accurate enough for use in about half of counties. Inaccuracies are larger in counties with populations less than 50,000, among age groups 65 and over, and among Hispanics. These problems are not fully resolved by grouping into broader age groups. Moreover, errors tend to cluster spatially in some regions of the country. Ultimately, the ability to generate accurate net migration estimates at the same level of detail as in the past will depend on the Census Bureau’s allocation of the privacy loss budget.

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Changes in Census Data Will Affect Our Understanding of Infant Health

Socius Sociological Research for a Dynamic World ◽

10.1177/23780231211023642 ◽

2021 ◽

Vol 7 ◽

pp. 237802312110236

Author(s):

Alexis R. Santos-Lozada

Keyword(s):

Infant Mortality ◽

Infant Health ◽

Census Data ◽

Differential Privacy ◽

The United States ◽

Mortality Rates ◽

Census Bureau ◽

Health Statistics ◽

Health Dynamics ◽

The Impact

Descriptions of the effect of the implementation of a new disclosure avoidance system (DAS), which relies on differential privacy, emphasize the impact of our understanding of contemporary social and health dynamics. However, focusing on overall population may obscure important changes in subpopulation indicators such as age-specific rates resulting from this implementation. The author provides a visualization that compares infant mortality rates calculated using 2009–2011 county-level average death counts and denominators derived from the traditional and proposed DASs. Death counts come from the National Center for Health Statistics and denominators come from the first U.S. Census Bureau demonstration products. These visualizations indicate that infant mortality rates produced using the proposed DAS are different from those produced using the traditional methods, with higher variation observed for nonmetropolitan counties and areas with smaller populations. These findings suggest that the proposed DAS will hinder our ability to understand contemporary health dynamics in the United States.

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The level of detail for 4D BIM modeling

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1209/1/012002 ◽

2021 ◽

Vol 1209 (1) ◽

pp. 012002

Author(s):

Y Nechyporchuk ◽

R Baskova

Keyword(s):

Level Of Detail ◽

The Past ◽

Levels Of Detail ◽

The Impact ◽

Different Levels

Abstract 4D modeling has been actively developing over the past decade along with the progress of BIM implementation. 4D model can provide enhanced early decisions about the space-temporal criticality of work elements. This models is a collection of graphical and scheduling information about an object. These inputs can have different levels of detail (LOD). In creating and using BIM projects, the LOD of datasets is an important aspect. However, to date there is limited research thoroughly investigating the issue of LOD within 4D models. The article provides an overview of studies related to the level of detail for 4D models, and also describes the impact of LOD on the final 4D model.

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Living Alone: Its Place in Household Formation and Change

Sociological Research Online ◽

10.5153/sro.971 ◽

2004 ◽

Vol 9 (3) ◽

pp. 42-54 ◽

Cited By ~ 27

Author(s):

Joan Chandler ◽

Malcolm Williams ◽

Moira Maconachie ◽

Tracey Collett ◽

Brian Dodgeon

Keyword(s):

Census Data ◽

Living Arrangement ◽

Age Groups ◽

Significant Rise ◽

Living Alone ◽

Life Styles ◽

Single Person ◽

Working Age ◽

The Stability ◽

The Impact

In recent decades there has been a significant rise in the numbers of people who live alone and it was predicted that by 2002 that a third of all households will be single-person households. The predicted increase has occurred with indications of continued growth in this type of living arrangement. Furthermore, although living alone remains common among older age groups, the largest growth has been within younger populations. This demographic trend has attracted speculation about the numbers of people who will experience solo living, the stability of living alone in people's biography, and the impact of gender differences in the likelihood and stability of living alone. To answers these questions, this paper uses longitudinally linked Census data from England and Wales to explore the household origins and household destinations of working age people who live alone. This longitudinal data derives from the 1971, 1981 and 1991 Censuses. The data from this analysis confirms other research demonstrating the increasingly numbers of non-retired people who live alone. Furthermore it demonstrates that once a person lives alone, they are more likely to continue to live in that household arrangement than any other and that the tendency to live alone and to continue to live alone is more likely amongst younger cohorts of people. It also demonstrates that the largest increase in living alone in amongst men, but that once women live alone they are more likely to continue to live alone. These findings have an important bearing on current debates about ‘individualisation’, the contemporary experience of family life, life course trajectories and the emergent life styles of younger populations.

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COVID-19 scenarios for comparing the effectiveness of age-specific vaccination regimes, exemplified for the city of Aschaffenburg (Germany)

10.1101/2021.08.31.21262915 ◽

2021 ◽

Author(s):

Tobias Krebs ◽

Holger von Jouanne-Diedrich ◽

Michael J Moeckel

Keyword(s):

Local Population ◽

Age Groups ◽

The Elderly ◽

Strong Increase ◽

Published Data ◽

Model Parameters ◽

Agent Based ◽

The Past ◽

The Impact ◽

The City

Purpose of this report: The purpose of this rapid communication is to illustrate the effectiveness of different vaccination regimes for controlling the number of severe and critical COVID-19 cases in the city of Aschaffenburg, Germany. Our results show that, despite numerous vaccinations in the past, further vaccinations are necessary to immunize the population and to keep the number of severe and critical cases low in the coming months. Considering that not all people can or want to receive vaccination, we compare different age-specific vaccination approaches. Applied Methods: We use the agent-based epidemiological simulator Covasim for discussing the impact of different vaccination strategies. We calibrate it to reproduce the historical course of the COVID-19 pandemic in the city of Aschaffenburg, Germany; for this, we model and integrate numerous public health interventions imposed on the local population. As for some of the political actions rigorous quantification is currently not available, we fit those unknown (free) model parameters to published data on the measured epidemiological dynamics. Then we calculate the state of immunization of the population, gained through infections and vaccinations, at any time in the past, including models for time-dependent immunity decay that have been made available in Covasim. Finally, we define and compare scenar-ios of different vaccination regimes, especially with regard to vaccinating adolescents and providing booster vaccinations to the elderly. Key message: Without further vaccinations, we expect a strong increase in severe and critical cases. In order to restrict their growth our simulations suggest that in all considered cases vaccinations of unvaccinated people is more effective than booster vaccinations for already fully vaccinated people. This applies even to vaccinations of young people who are not themselves at high risk of developing severe or critical illness. We attribute this observation to the fact that immunization of adolescents indirectly protects vulnerable age groups by preventing the spread of the virus more ef-fectively than further immunizing other age groups. This indicates that with the pandemic ongoing, strategies focussed on minimizing individual health risks by vaccinations may no longer coincide with those needed to minimize the num-ber of severe and critical cases.

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The Impact of Early or Late Lockdowns on the Spread of COVID-19 in US Counties

10.1101/2021.03.19.21253997 ◽

2021 ◽

Author(s):

Xiaolin Huang ◽

Xiaojian Shao ◽

Li Xing ◽

Yushan Hu ◽

Don Sin ◽

...

Keyword(s):

Inflection Point ◽

Family Income ◽

Census Data ◽

Principal Component ◽

The United States ◽

County Level ◽

Functional Principal Component ◽

Case Count ◽

Us Census ◽

The Impact

Background: COVID-19 is a highly transmissible infectious disease that has infected over 122 million individuals worldwide. To combat this pandemic, governments around the world have imposed lockdowns. However, the impact of these lockdowns on the rates of COVID-19 transmission in communities is not well known. Here, we used COVID-19 case counts from 3,000+ counties in the United States (US) to determine the relationship between lockdown as well as other county factors and the rate of COVID-19 spread in these communities. Methods: We merged county-specific COVID-19 case counts with US census data and the date of lockdown for each of the counties. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate scores that described the trajectory of COVID-19 spread across the counties. We used machine learning methods to identify important factors in the county including the date of lockdown that significantly influenced the FPC scores. Findings: We found that the first FPC score accounted for up to 92.81% of the variations in the absolute rates of COVID-19 as well as the topology of COVID-19 spread over time at a county level. The relation between incidence of COVID-19 and time at a county level demonstrated a hockey-stick appearance with an inflection point approximately 7 days prior to the county reporting at least 5 new cases of COVID-19; beyond this inflection point, there was an exponential increase in incidence. Among the risk factors, lockdown and total population were the two most significant features of the county that influenced the rate of COVID-19 infection, while the median family income, median age and within-county move also substantially affect COVID spread. Interpretation: Lockdowns are an effective way of controlling the COVID-19 spread in communities. However, significant delays in lockdown cause a dramatic increase in the case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities.

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At the End of the Day, Are Screens Good or Bad for You? A Critical Review of Randomized Controlled Studies

10.31234/osf.io/6mysa ◽

2018 ◽

Author(s):

Noa Albelda ◽

Carrah Simkins ◽

Dalith Tal-Shir ◽

Nava Levit-Binnun

Keyword(s):

Critical Review ◽

Well Being ◽

High Quality ◽

Comprehensive Review ◽

Randomized Controlled ◽

The Past ◽

Randomized Controlled Studies ◽

And Behavior ◽

The Impact ◽

Screen Use

In recent years, screen-based technologies have changed the way we communicate, study and consume goods, entertainment and information. Thus, our environment has changed profoundly, and it is reasonable to assume that children today have a different developmental environment compared to the past, making it crucial to understand the impact of our technology-rich environment on development as well as on physical and psychological well-being. The goal of the present paper is to provide a comprehensive review of randomized controlled studies assessing effects of screen use on physiology and behavior, in order to give as wide as possible a picture of screen use and its various effects. We conclude with a discussion focusing on the ability to reach “bottom lines” regarding the down- and upsides of screen-use. We also highlight and discuss the major areas in which knowledge about the effects of screens is lacking, requiring further high-quality and well-controlled studies.

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US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis

BMJ Open ◽

10.1136/bmjopen-2020-039886 ◽

2020 ◽

Vol 10 (9) ◽

pp. e039886 ◽

Cited By ~ 3

Author(s):

Taylor Chin ◽

Rebecca Kahn ◽

Ruoran Li ◽

Jarvis T Chen ◽

Nancy Krieger ◽

...

Keyword(s):

Severe Disease ◽

Health Resources ◽

County Level ◽

Community Characteristics ◽

Cross Sectional ◽

Online Tool ◽

Community Factors ◽

Individual Household ◽

Us Counties ◽

The Impact

ObjectivesTo illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond.DesignWe identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined ‘high’ risk counties as those above the 75th percentile. This threshold can be changed using the online tool.SettingUS counties.ParticipantsAnalyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings.ResultsOur findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour.ConclusionFederal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.

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The impact of income inequality on rental affordability: An empirical study in large American metropolitan areas

Urban Studies ◽

10.1177/0042098017710380 ◽

2017 ◽

Vol 55 (10) ◽

pp. 2106-2122 ◽

Cited By ~ 6

Author(s):

Hongwei Dong

Keyword(s):

Income Inequality ◽

Low Income ◽

Gini Coefficient ◽

Metropolitan Areas ◽

County Level ◽

Longitudinal Analyses ◽

Cross Sectional ◽

The Past ◽

Percentage Points ◽

The Impact

American metropolitan areas have experienced rising income inequality and worsening rental affordability in the past few decades. Has the rise of inequality caused worsening rental affordability? This study conducts both cross-sectional and longitudinal analyses to examine the impact of income inequality on rental affordability for low-income tenant households at the county level in America’s largest 100 metropolitan areas. The cross-sectional analyses reveal that, everything else equal, an increase of Gini coefficient by 0.1 in a county was associated with 2.2 and 4.4 percentage points more severely rent-burdened low-income households in 2000 and 2008–2012, respectively. The longitudinal analyses confirm that rising income inequality caused worsening rental affordability for low-income tenant households in large American metropolitan areas between 2000 and 2008–2012. On average, counties that experienced a 0.1 greater increase in Gini coefficient from 2000 to 2008–2012 saw faster growth of severely rent-burdened low-income tenant households by 2.9 percentage points.

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Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure

Public Health Reports ◽

10.1177/0033354921999407 ◽

2021 ◽

pp. 003335492199940

Author(s):

Anne M. Roubal ◽

Elizabeth A. Pollock ◽

Keith P. Gennuso ◽

Courtney K. Blomme ◽

Marjory L. Givens

Keyword(s):

Public Health ◽

Life Expectancy ◽

Child Poverty ◽

Age Groups ◽

Data Availability ◽

County Level ◽

Health Measures ◽

Us Counties ◽

Communication Needs ◽

Length Of Life

Introduction Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. Methods We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). Results Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 ( r = −0.91) and PAAM ( r = −0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. Practice Implications Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

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The Economic Impact of the Wine Industry on Hotels and Restaurants: Evidence from Washington State

Journal of Wine Economics ◽

10.1017/s1931436100001437 ◽

2010 ◽

Vol 5 (1) ◽

pp. 164-183 ◽

Cited By ~ 1

Author(s):

Karl Storchmann

Keyword(s):

Economic Impact ◽

Jel Classification ◽

Washington State ◽

Tourism Industry ◽

Wine Industry ◽

County Level ◽

High Quality ◽

Panel Model ◽

The Impact ◽

The U.S

AbstractWashington State enjoys an extraordinarily fast growing wine industry and is now the second largest wine producing state in the U.S. This paper examines the impact of this growth on the revenue of hotels and restaurants. Employing a dynamic quarterly panel model at the county level we show that the regional reputation as high quality wine county, as expressed by critical wine points in the national wine press, has a significant effect on the tourism industry. For Walla Walla, the most prominent wine county in the state, less than 17% of all restaurant and approximately 40% of all hotel revenue is tied to the wine cluster (2007). However, regional reputation is not long-living and needs constant replenishment. (JEL Classification: R11, R15, Q19)

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