Methods report (RTI Press)最新文献

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.

This paper evaluates the acceptability, communication mode and use of audio computer-assisted self-interview (A-CASI) among minority pregnant women receiving prenatal care in six Washington, DC sites. A total of 2,913 women were screened for demographic eligibility (18+ years old, <29 weeks gestation, Black/African-American or Hispanic) and risk (smoking, environmental tobacco smoke exposure, depression, intimate partner violence). Questions were displayed on touch screen laptop monitors and heard through earphones. The mean length of time to complete the screener was almost 6 minutes.A-CASI experience, which included difficulty in using the computer, acceptability (enjoyment), and preferred communication mode, was compared across sites, the eligibility and risk groups and a subset of 878 enrolled women for whom educational attainment and receipt of WIC (a proxy for income) were available. Respondents thought A-CASI was not difficult to use and liked using the computer. Black/African-American or Hispanic respondents enjoyed it significantly more than did respondents of other race/ethnicities. Respondents who were demographically eligible, Black/African-American or Hispanic, or with lower education levels listened to questions significantly more than did their counterparts. Mainly listening or listening and reading does not impact burden in terms of the length of time it took to complete the screener.The acceptance of A-CASI as a screening tool opens the door for more uses of this technology in health-related fields. The laptop computer and headphones provide privacy and mobility so the technology can be used to ask sensitive questions in almost any locale, including busy clinic settings.

By understanding the movement patterns of people, mathematical modelers can develop models that can better analyze and predict the spread of infectious diseases. People can come into close contact in their workplaces. This report describes methods to develop georeferenced commuting patterns that can be used to characterize the work-related movement of US populations and help agent-based modelers predict workplace contacts that result in disease transmission. We used a census data product called "Census Spatial Tabulation: Census Track of Work by Census Tract of Residence (STP64)" as the data source to develop commuting pattern data for agent-based synthesized populations databases and to develop map products to visualize commuting patterns in the United States. The three primary maps we developed show inbound, outbound, and net change levels of inbound versus outbound commuters by census tract for the year 2000. Net change counts of commuters are visualized as elevations. The results can be used to quantify and assign commuting patterns of synthesized populations among different census tracts.

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 personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating 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.