Including The Group Quarters Population In The Us Synthesized Population Database

In 2005, RTI International researchers developed methods to generate synthesized population data on US households for the US Synthesized Population Database. These data are used in agent-based modeling, which simulates large-scale social networks to test how changes in the behaviors of individuals affect the overall network. Group quarters are residences where individuals live in close proximity and interact frequently. Although the Synthesized Population Database represents the population living in households, data for the nation’s group quarters residents are not easily quantified because of US Census Bureau reporting methods designed to protect individuals’ privacy Including group quarters population data can be an important factor in agent-based modeling because the number of residents and the frequency of their interactions are variables that directly affect modeling results. Particularly with infectious disease modeling, the increased frequency of agent interaction may increase the probability of infectious disease transmission between individuals and the probability of disease outbreaks This report reviews our methods to synthesize data on group quarters residents to match US Census Bureau data. Our goal in developing the Group Quarters Population Database was to enable its use with RTI’s US Synthesized Population Database in the Modeling of Infectious Diseases Agent Study.

In 2005, RTI International researchers developed methods to generate synthesized population data on US households for the US Synthesized Population Database. These data are used in agent-based modeling, which simulates large-scale social networks to test how changes in the behaviors of individuals affect the overall network. Group quarters are residences where individuals live in close proximity and interact frequently. Although the Synthesized Population Database represents the population living in households, data for the nation’s group quarters residents are not easily quantified because of US Census Bureau reporting methods designed to protect individuals’ privacy. Including group quarters population data can be an important factor in agent-based modeling because the number of residents and the frequency of their interactions are variables that directly affect modeling results. Particularly with infectious disease modeling, the increased frequency of agent interaction may increase the probability of infectious disease transmission between individuals and the probability of disease outbreaks. This report reviews our methods to synthesize data on group quarters residents to match US Census Bureau data. Our goal in developing the Group Quarters Population Database was to enable its use with RTI’s US Synthesized Population Database in the Modeling of Infectious Diseases Agent Study.

Following several years of testing and evaluation, the American Community Survey (ACS) was launched in 2005 as a replacement for the census "long form," used to collect detailed social, economic, and housing data from a sample of the U.S. population as part of the decennial census. During the first year of the ACS implementation, the Census Bureau collected data only from households. In 2006 a sample of group quarters (GQs)-such as correctional facilities, nursing homes, and college dorms-was added to more closely mirror the design of the census long-form sample. The design of the ACS relies on monthly samples that are cumulated to produce multiyear estimates based on 1, 3, and 5 years of data. The data published by the Census Bureau for a geographic area depend on the area's size. The multiyear averaging approach enables the Census Bureau to produce estimates that are intended to be robust enough to release for small areas, such as the smallest governmental units and census block groups. However, the sparseness of the GQ representation in the monthly samples affects the quality of the estimates in many small areas that have large GQ populations relative to the total population. The Census Bureau asked the National Research Council to review and evaluate the statistical methods used for measuring the GQ population. This book presents recommendations addressing improvements in the sample design, sample allocation, weighting, and estimation procedures to assist the Census Bureau's work in the very near term, while further research is conducted to address the underlying question of the relative importance and costs of the GQ data collection in the context of the overall ACS design.

Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses. RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants. For this database, RTI developed the methods for • generating synthesized households and persons • assigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into account • generating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes). In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models. Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.

In the early 1990s, the Census Bureau proposed a program of continuous measurement as a possible alternative to the gathering of detailed social, economic, and housing data from a sample of the U.S. population as part of the decennial census. The American Community Survey (ACS) became a reality in 2005, and has included group quarters (GQ)-such places as correctional facilities for adults, student housing, nursing facilities, inpatient hospice facilities, and military barracks-since 2006, primarily to more closely replicate the design and data products of the census long-form sample. The decision to include group quarters in the ACS enables the Census Bureau to provide a comprehensive benchmark of the total U.S. population (not just those living in households). However, the fact that the ACS must rely on a sample of what is a small and very diverse population, combined with limited funding available for survey operations, makes the ACS GQ sampling, data collection, weighting, and estimation procedures more complex and the estimates more susceptible to problems stemming from these limitations. The concerns are magnified in small areas, particularly in terms of detrimental effects on the total population estimates produced for small areas. Small Populations, Large Effects provides an in-depth review of the statistical methodology for measuring the GQ population in the ACS. This report addresses difficulties associated with measuring the GQ population and the rationale for including GQs in the ACS. Considering user needs for ACS data and of operational feasibility and compatibility with the treatment of the household population in the ACS, the report recommends alternatives to the survey design and other methodological features that can make the ACS more useful for users of small-area data.

In the early 1990s, the Census Bureau proposed a program of continuous measurement as a possible alternative to the gathering of detailed social, economic, and housing data from a sample of the U.S. population as part of the decennial census. The American Community Survey (ACS) became a reality in 2005, and has included group quarters (GQ)-such places as correctional facilities for adults, student housing, nursing facilities, inpatient hospice facilities, and military barracks-since 2006, primarily to more closely replicate the design and data products of the census long-form sample. The decision to include group quarters in the ACS enables the Census Bureau to provide a comprehensive benchmark of the total U.S. population (not just those living in households). However, the fact that the ACS must rely on a sample of what is a small and very diverse population, combined with limited funding available for survey operations, makes the ACS GQ sampling, data collection, weighting, and estimation procedures more complex and the estimates more susceptible to problems stemming from these limitations. The concerns are magnified in small areas, particularly in terms of detrimental effects on the total population estimates produced for small areas. Small Populations, Large Effects provides an in-depth review of the statistical methodology for measuring the GQ population in the ACS. This report addresses difficulties associated with measuring the GQ population and the rationale for including GQs in the ACS. Considering user needs for ACS data and of operational feasibility and compatibility with the treatment of the household population in the ACS, the report recommends alternatives to the survey design and other methodological features that can make the ACS more useful for users of small-area data.