The Forestry and Agriculture Sector Optimization Model with Greenhouse Gases (FASOMGHG) has historically relied on regional average costs of land conversion to simulate land use change across cropland, pasture, rangeland, and forestry. This assumption limits the accuracy of the land conversion estimates by not recognizing spatial heterogeneity in land quality and conversion costs. Using data from Nielsen et al. (2014), we obtained the afforestation cost per county, then estimated nonparametric regional marginal cost functions for land converting land to forestry. These afforestation costs were then incorporated into FASOMGHG. Three different assumptions for land moving into the forest sector were run; constant average conversion cost, static rising marginal costs and dynamic rising marginal cost, in order to assess the implications of alternative land conversion cost assumptions on key outcomes, such as projected forest area and cropland use, carbon sequestration, and forest product output.
We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term ("loading up" after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
Food products containing cannabis extract (edibles) have emerged as a popular and lucrative facet of the legalized market for both recreational and medicinal cannabis. The many formulations of cannabis extracts used in edibles present a unique regulatory challenge for policy makers. Though edibles are often considered a safe, discreet, and effective means of attaining the therapeutic and/or intoxicating effects of cannabis without exposure to the potentially harmful risks of cannabis smoking, little research has evaluated how ingestion differs from other methods of cannabis administration in terms of therapeutic efficacy, subjective effects, and safety. The most prominent difference between ingestion and inhalation of cannabis extracts is the delayed onset of drug effect with ingestion. Consumers often do not understand this aspect of edible use and may consume a greater than intended amount of drug before the drug has taken effect, often resulting in profoundly adverse effects. Written for the educated layperson and for policy makers, this paper explores the current state of research regarding edibles, highlighting the promises and challenges that edibles present to both users and policy makers, and describes the approaches that four states in which recreational cannabis use is legal have taken regarding regulating edibles.
Plotting growth curves is a powerful graphical approach used in exploratory data analysis for continuous longitudinal data. However, plotted growth curves for multiple participants rapidly become uninterpretable with categorical data. Categorical data define specific states (e.g., being single, married, divorced), and these states do not necessarily need to represent any hierarchical order. Thus, a trajectory becomes a sequence of states rather than a continuum. We introduce a horizontal line plot that uses shade or color to differentiate between states on a categorical longitudinal variable for multiple participants. With appropriate sorting, stacking the horizontal lines that represent each participant can reveal important patterns such as the shape of, or heterogeneity in, the trajectories. We illustrate the plotting techniques for large sample sizes, observed groups, the exploration of unobserved latent classes, large numbers of time points such as are found with intensive longitudinal designs or multivariate time series data, individually varying times observation, unique numbers of observations, and missing data. We used the R package longCatEDA to create the illustrations. Illustrative data include both simulated data and alcohol consumption data in adult schizophrenics from the Clinical Antipsychotic Trials of Intervention Effectiveness.
In 2011, the prevalence of prescription drug abuse exceeded that of any other illicit drug except marijuana. Consequently, efforts to curtail abuse of new medications should begin during the drug development process, where abuse liability can be identified and addressed before a candidate medication has widespread use. The first step in this process is scheduling with the Drug Enforcement Agency so that legal access is appropriately restricted, dependent upon levels of abuse risk and medical benefit. To facilitate scheduling, the Food and Drug Administration (FDA) has published guidance for industry that describes assessment of abuse liability. The purpose of this paper is to review methods that may be used to satisfy the FDA's regulatory requirements for animal behavioral and dependence pharmacology. Methods include psychomotor activity, self-administration (an animal model of the rewarding effects of a drug), drug discrimination (an animal model of the subjective effects of a drug), and evaluation of tolerance and dependence. Data from tests conducted at RTI with known drugs of abuse illustrate typical results, and demonstrate that RTI is capable of performing these tests. While using preclinical data to predict abuse liability is an imperfect process, it has substantial predictive validity. The ultimate goal is to increase consumer safety through appropriate scheduling of new medications.
Recognizing a need for rigorous, experimental research to support the efforts of workplaces and policymakers in improving the health and wellbeing of employees and their families, the National Institutes of Health and the Centers for Disease Control and Prevention formed the Work, Family & Health Network (WFHN). The WFHN is implementing an innovative multisite study with a rigorous experimental design (adaptive randomization, control groups), comprehensive multilevel measures, a novel and theoretically based intervention targeting the psychosocial work environment, and translational activities. This paper describes challenges and benefits of designing a multilevel and transdisciplinary research network that includes an effectiveness study to assess intervention effects on employees, families, and managers; a daily diary study to examine effects on family functioning and daily stress; a process study to understand intervention implementation; and translational research to understand and inform diffusion of innovation. Challenges were both conceptual and logistical, spanning all aspects of study design and implementation. In dealing with these challenges, however, the WFHN developed innovative, transdisciplinary, multi-method approaches to conducting workplace research that will benefit both the research and business communities.
The pervasive and potentially severe economic, social, and public health consequences of infectious disease in farmed animals require that plans be in place for a rapid response. Increasingly, agent-based models are being used to analyze the spread of animal-borne infectious disease outbreaks and derive policy alternatives to control future outbreaks. Although the locations, types, and sizes of animal farms are essential model inputs, no public domain nationwide geospatial database of actual farm locations and characteristics currently exists in the United States. This report describes a novel method to develop a synthetic dataset that replicates the spatial distribution of poultry farms, as well as the type and number of birds raised on them. It combines county-aggregated poultry farm counts, land use/land cover, transportation, business, and topographic data to generate locations in the conterminous United States where poultry farms are likely to be found. Simulation approaches used to evaluate the accuracy of this method when compared to that of a random placement alternative found this method to be superior. The results suggest the viability of adapting this method to simulate other livestock farms of interest to infectious disease researchers.
Gathering and communicating knowledge are important aspects of the scientific endeavor. Yet presentation of data in public forums such as scientific meetings and publications makes it available not only to scientists, but also to others who may have different ideas about how to use research findings. A recent example of this type of hijacking is the introduction of synthetic cannabinoids that are sprayed on herbal products and subsequently smoked for their marijuana-like intoxicating properties. Originally developed for the legitimate research purpose of furthering understanding of the cannabinoid system, these synthetic cannabinoids are being abused worldwide, creating issues for regulatory and law enforcement agencies that are struggling to keep up with the growing number of compounds of various structural motifs. Basic and clinical scientists need to provide advice now to facilitate decision-making about the health threats posed by this emerging problem.
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.
Increasingly, researchers are turning to computational models to understand the interplay of important variables on systems' behaviors. Although researchers may develop models that meet the needs of their investigation, application limitations-such as nonintuitive user interface features and data input specifications-may limit the sharing of these tools with other research groups. By removing these barriers, other research groups that perform related work can leverage these work products to expedite their own investigations. The use of software engineering practices can enable managed application production and shared research artifacts among multiple research groups by promoting consistent models, reducing redundant effort, encouraging rigorous peer review, and facilitating research collaborations that are supported by a common toolset. This report discusses three established software engineering practices- the iterative software development process, object-oriented methodology, and Unified Modeling Language-and the applicability of these practices to computational model development. Our efforts to modify the MIDAS TranStat application to make it more user-friendly are presented as an example of how computational models that are based on research and developed using software engineering practices can benefit a broader audience of researchers.
Communicable-disease transmission models are useful for the testing of prevention and intervention strategies. Agent-based models (ABMs) represent a new and important class of the many types of disease transmission models in use. Agent-based disease models benefit from their ability to assign disease transmission probabilities based on characteristics shared by individual agents. These shared characteristics allow ABMs to apply transmission probabilities when agents come together in geographic space. Modeling these types of social interactions requires data, and the results of the model largely depend on the quality of these input data. We initially generated a synthetic population for the United States, in support of the Models of Infectious Disease Agent Study. Subsequently, we created shared characteristics to use in ABMs. The specific goals for this task were to assign the appropriately aged populations to schools, workplaces, and public transit. Each goal presented its own challenges and problems; therefore, we used different techniques to create each type of shared characteristic. These shared characteristics have allowed disease models to more realistically predict the spread of disease, both spatially and temporally.
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.
