scholarly journals Detecting “real” population changes with American Community Survey data: The implicit assumption of treating between-year differences as “trends”

2014 ◽  
Vol 4 (2) ◽  
pp. 494 ◽  
Author(s):  
Carlos Siordia
Keyword(s):  
Population Change ◽  
The United States ◽  
Community Survey ◽  
Population Characteristics ◽  
American Community Survey ◽  
Implicit Assumption ◽  
Point Estimates ◽  
Real Population ◽  
The Us ◽  
Real Change

<p><strong>Abstract</strong></p><p><strong>BACKGROUND:  </strong>The American Community Survey (ACS) in the United States (US) collects detailed demographic information on the US population. Pressures to use year-to-year population estimates to analyze “trends” (i.e., between-year differences on the characteristics of interest) have motivated the need to explore how single- or multi-year estimates can be used to investigate changes in US population over time. <strong>OBJECTIVE: </strong>The specific aim of this manuscript is to provide empirical evidence that between-year differences in population characteristics have difference levels of uncertainty around point-estimates. <strong>METHODS:</strong> Six ACS Public Use Microdata Sample (PUMS) single year files from 2005 through 2010 are used to empirically show the heterogeneity of uncertainty in “between-year differences” on level of education, for a birth cohort born between 1960 and 1970 of non-Latino-whites and Mexican Latinos/as. <strong>RESULTS: </strong>The data show the precision of the education estimate decreases as the specificity of the population increases. For example, Mexican’s 99% confidence intervals have wider and more time-varying bandwidths than non-Latino-whites. <strong>CONCLUSIONS: </strong>Inferring meaningful population change requires the challengeable assumption that between-year differences are not the product of data artifacts. Harvesting reputable ACS data demands further research before between-year differences can be treated as “real change.”    </p><p> </p>

scholarly journals Precision of Disability Estimates for Southeast Asians in the American Community Survey 2008-2010 Microdata

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Carlos Siordia ◽  
Vi Donna Le
Keyword(s):  
Independent Living ◽  
The United States ◽  
Southeast Asian ◽  
Community Survey ◽  
American Community Survey ◽  
Southeast Asians ◽  
Margin Of Error ◽  
The Us ◽  
Long Form ◽  
Hearing Difficulties

Detailed social data about the United States (US) population was collected as part of the US decennial Census up until 2000. Since then, the American Community Survey (ACS) has replaced the long form previously administered in decennial years. The ACS uses a sample rather than the entire US population and therefore, only estimates can be created from the data. This investigation computes disability estimates, standard error, margin of error, and a more comprehensive “range of uncertainty” measure for non-Latino-whites (NLW) and four Southeast Asian groups. Findings reveal that disability estimates for Southeast Asians have a much higher degree of imprecision than for NLW. Within Southeast Asian groups, Vietnamese have the highest level of certainty, followed by the Hmong. Cambodians and Laotians disability estimates contain high levels of uncertainty. Difficulties with self-care and vision contain the highest level of uncertainty relative to ambulatory, cognitive, independent living, and hearing difficulties.

Use of a dynamic disease progression model to estimate prevalence and prognosis for patients with urothelial carcinoma (UC) in the United States (US).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19144-e19144
Author(s):  
Sarah Fleming ◽  
Kirk Solo ◽  
Xuehua Ke ◽  
Waleed Shalaby ◽  
Arlene O. Siefker-Radtke
Keyword(s):  
The United States ◽  
Disease Stage ◽  
Clinical State ◽  
Progression Model ◽  
Point Estimates ◽  
The Us ◽  
Disease Progression Model ◽  
Estimate Prevalence ◽  
The Impact ◽  
Incidence Prevalence

e19144 Background: Quantifying the distribution and prognosis of patients with UC as a function of disease stage may allow the impact of existing and novel therapies to be assessed in the real-world setting. We present a dynamic progression model that estimates the incidence, prevalence, and mortality of UC clinical state (CS) in the US. Methods: This UC dynamic progression model used US estimates of UC incidence and distribution of stage at diagnosis from the National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) database to establish new patients. Progression and mortality for each CS were based on published clinical trials and OPTUM claims data. The simulation started in 1990, introducing incident patient cohorts allocated across initial CSs (Table). Historical therapy distributions were applied for each year as additional incident UC cohorts were introduced into the model. This proprietary model built to annual point prevalence dynamically through historical incidence, progression and mortality. Results: Based on the progression model, point estimates of prevalence, incidence and annual mortality hazard are provided by stage of disease (Table). For all stages of UC, the model estimated a prevalence of 719,387 patients in 2019. For stage II/III and metastatic UC (mUC) disease, the model estimated that 5,205 and 12,499 patients will die in 2019, respectively. This combines to 17,704 which closely aligns with the SEER estimate of 17,670. Conclusions: This dynamic UC progression model provides estimates for incidence, prevalence, and mortality of UC by clinical state at diagnosis. Incorporating associated claims and clinical data with this model could estimate the benefits of newer therapies as they become available. [Table: see text]

scholarly journals Potential Data Sources for a New Study of Social Mobility in the United States

2014 ◽  
Vol 657 (1) ◽  
pp. 208-246
Author(s):  
John Robert Warren
Keyword(s):  
United States ◽  
Social Mobility ◽  
Population Survey ◽  
Data Access ◽  
The United States ◽  
Community Survey ◽  
Data Sources ◽  
American Community Survey ◽  
Costs And Benefits ◽  
Trade Offs

In this article I define the main criteria that ought to be considered in evaluating the costs and benefits of various data resources that might be used for a new study of social and economic mobility in the United States. These criteria include population definition and coverage, sample size, topical coverage, temporal issues, spatial issues, sustainability, financial expense, and privacy and data access. I use these criteria to evaluate the strengths and weakness of several possible data resources for a new study of mobility, including existing smaller-scale surveys, the Current Population Survey, the American Community Survey, linked administrative data, and a new stand-alone survey. No option is perfect, and all involve trade-offs. I conclude by recommending five possible designs that are particularly strong on the criteria listed above.

scholarly journals Age, disability, and household composition of nurses and physicians who are not in the labor force

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247967
Author(s):  
Dan P. Ly
Keyword(s):  
Labor Force ◽  
The United States ◽  
Community Survey ◽  
Household Composition ◽  
The Us ◽  
Nurses And Physicians ◽  
Nationally Representative ◽  
Female Nurses ◽  
Us Census ◽  
At Home

While several areas in the United States have asked nurses and physicians who are not in the labor force to return to help with the COVID-19 pandemic, little is known about the characteristics of these clinicians that may present barriers to returning. We studied age, disability, and household composition of clinicians not in the workforce using the American Community Survey from 2014 to 2018, a nationally-representative survey of US households administered by the US Census. Overall, we found that, for nurses and physicians not in the labor force, over three-quarters were 55 and over and about 15 percent had a disability. For female nurses and physicians not in the labor force, over half of those ages 20–54 had a child under 15 at home and over half of those ages 65+ had another adult 65 and over at home. These characteristics may present challenges and risks to returning.

scholarly journals A New Infrastructure for Monitoring Social Mobility in the United States

2014 ◽  
Vol 657 (1) ◽  
pp. 63-82 ◽  
Author(s):  
David B. Grusky ◽  
Timothy M. Smeeding ◽  
C. Matthew Snipp
Keyword(s):  
Social Science ◽  
Labor Markets ◽  
Social Mobility ◽  
Science Inquiry ◽  
The United States ◽  
Great Accuracy ◽  
Community Survey ◽  
Evidence Based ◽  
American Community Survey ◽  
New Infrastructure

The country’s capacity to monitor trends in social mobility has languished since the last major survey on U.S. social mobility was fielded in 1973. It is accordingly difficult to evaluate recent concerns that social mobility may be declining or to develop mobility policy that is adequately informed by evidence. This article presents a new initiative, dubbed the American Opportunity Study (AOS), that would allow the country to monitor social mobility efficiently and with great accuracy. The AOS entails developing the country’s capacity to link records across decennial censuses, the American Community Survey, and administrative sources. If an AOS of this sort were assembled, it would open up new fields of social science inquiry; increase opportunities for evidence-based policy on poverty, mobility, child development, and labor markets; and otherwise constitute a new social science resource with much reach and impact.

Zero Undocumented Population Growth Is Here to Stay and Immigration Reform Would Preserve and Extend These Gains

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Robert Warren
Keyword(s):  
United States ◽  
Population Growth ◽  
Homeland Security ◽  
Large Scale ◽  
Population Change ◽  
The United States ◽  
The Us ◽  
Southern Border ◽  
Zero Population Growth ◽  
Legal Immigration

This report demonstrates that a broad and sustained reduction in undocumented immigration to the United States occurred in the 2008 to 2015 period. First, the report shows that, contrary to conventional wisdom, the Great Recession had little, if any, role in the transformation to zero population growth. The population stopped growing because of increased scrutiny of air travel after 9/11, a decade and a half of accelerating efforts to reduce illegal entries across the southern border, long-term increases in the numbers leaving the population each year, and improved economic and demographic conditions in Mexico. These conditions are likely to continue for the foreseeable future. It is time to recognize that the era of large-scale undocumented population growth has ended, and that there is a need to reform the US legal immigration system to preserve and extend these gains (Kerwin and Warren 2017, 319-21). Major findings of the report include:   The recession did not reduce arrivals or accelerate departures from the undocumented population; it essentially had very little impact on population change.[1]Population growth was lower in 2008 to 2015 than in 2000 to 2008 for all major sending areas and for 13 of the top 15 countries of origin.[2]Population growth was lower in 2008 to 2015 than in 2000 to 2008 in all of the top 15 states. In 10 of the 15 top states, growth changed to decline.Nearly twice as many left[3] the undocumented population from Mexico than arrived in the 2008 to 2015 period — 1.7 million left the population and 900,000 arrived.Almost twice as many overstays as persons who entered without inspection (EWIs) “arrived” (joined the undocumented population) from 2008 to 2015 — 2.0 million overstays compared to 1.1 million EWIs.Overstays leave the undocumented population at higher rates than EWIs: about 1.9 million, or 40 percent, of overstays that lived in the United States in 2008 had left the undocumented population by 2015, compared to 1.6 million, or 24 percent, of EWIs.The rate of overstays (65% of the newly undocumented), compared to EWIs, is more dramatic than the numbers indicate since estimates of the undocumented count Central American asylum seekers that cross the US southern border as EWIs.[1] The term “population” in this paper refers to the undocumented population, both persons who have stayed in the United States beyond the period of their temporary admission (“overstays”) and those who entered without inspection (EWIs).[2] In this paper, the terms “2000 to 2008 period” and “2008 to 2015 period” are not overlapping; they are used for ease of presentation. Estimates for the two time periods are based on data for 2000, 2008, and 2015. Technically, the earlier period is for 2000 through 2007 (eight years), and the latter period is for 2008 through 2014 (seven years).[3] Undocumented residents can leave the population in four ways: emigrate voluntarily, adjust to lawful status, be removed by the Department of Homeland Security (DHS), or (a relatively small number) die.

scholarly journals Social Distancing Has Merely Stabilized COVID-19 in the US

2020 ◽  
Author(s):  
Aaron B. Wagner ◽  
Elaine L. Hill ◽  
Sean E. Ryan ◽  
Ziteng Sun ◽  
Grace Deng ◽  
...  
Keyword(s):  
United States ◽  
Reproduction Number ◽  
The United States ◽  
Policy Implications ◽  
Significant Evidence ◽  
Social Distancing ◽  
Point Estimates ◽  
The Us ◽  
The World

AbstractSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.

scholarly journals Effects of Measurement Error in the American Community Survey on Statistical Analysis: An Example using County and Census Tract Mortality Rates.

2019 ◽  
Author(s):  
Corey Sparks ◽  
Lloyd B. Potter
Keyword(s):  
Bayesian Model ◽  
The United States ◽  
Mortality Rates ◽  
Community Survey ◽  
Standard Errors ◽  
American Community Survey ◽  
Regression Analyses ◽  
Census Tracts ◽  
Indicator Data ◽  
Naive Model

The American Community Survey (ACS) summary file data provide rolling 5-year estimates of demographic and socioeconomic indicator data for small geographiesthroughout the United States. These estimates are commonly used as indicators forregression models to measure conditions in communities. The Margins of Error (MOE) inthe ACS estimates for small geographic areas can often be very large, and without takingthem into account, regression analyses using them can be mis-specified, leading to bias inregression coefficients and model standard errors. This paper directly comparesmeasurement error model specifications to naive model specifications for a mortalityoutcome in Texas Census tracts using Bayesian model specializations. The results showthat there is bias in the naive regression model results. We urge users of the ACSsummary file data to be aware of such bias as it can potentially impact interpretation ofmodel results and hypothesis tests.

scholarly journals Abortion Access in Ohio’s Changing Legislative Context, 2010–2018

2020 ◽  
Vol 110 (8) ◽  
pp. 1228-1234
Author(s):  
Alison H. Norris ◽  
Payal Chakraborty ◽  
Kaiting Lang ◽  
Robert B. Hood ◽  
Sarah R. Hayford ◽  
...  
Keyword(s):  
Public Health ◽  
Data Warehouse ◽  
First Trimester ◽  
The United States ◽  
Vital Statistics ◽  
Health Data ◽  
Community Survey ◽  
American Community Survey ◽  
Public Health Data ◽  
Abortion Access

Objectives. To examine abortion utilization in Ohio from 2010 to 2018, a period when more than 15 abortion-related laws became effective. Methods. We evaluated changes in abortion rates and ratios examining gestation, geographic distribution, and abortion method in Ohio from 2010 to 2018. We used data from Ohio’s Office of Vital Statistics, the Centers for Disease Control and Prevention’s Abortion Surveillance Reports, the American Community Survey, and Ohio’s Public Health Data Warehouse. Results. During 2010 through 2018, abortion rates declined similarly in Ohio, the Midwest, and the United States. In Ohio, the proportion of early first trimester abortions decreased; the proportion of abortions increased in nearly every later gestation category. Abortion ratios decreased sharply in most rural counties. When clinics closed, abortion ratios dropped in nearby counties. Conclusions. More Ohioans had abortions later in the first trimester, compared with national patterns, suggesting delays to care. Steeper decreases in abortion ratios in rural versus urban counties suggest geographic inequity in abortion access. Public Health Implications. Policies restricting abortion access in Ohio co-occur with delays to care and increasing geographic inequities. Restrictive policies do not improve reproductive health.

You Are Where You Are From

2015 ◽  
Vol 5 (3) ◽  
pp. 86-89
Author(s):  
Robin Mejia
Keyword(s):  
United States ◽  
The United States ◽  
Community Survey ◽  
American Community Survey ◽  
United States Census ◽  
Using Data ◽  
The Way

Using data from the United States Census 2013 American Community Survey, Robin Mejia looks at the way geography affects a person’s health, wealth, education, and prospects in life.

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