An integrated dual process simulation model of alcohol use behaviours in individuals, with application to US population-level consumption, 1984-2012.

INTRODUCTION: The Theory of Planned Behaviour (TPB) describes how attitudes, norms and perceived behavioural control guide health behaviour, including alcohol consumption. Dual Process Theories (DPT) suggest that alongside these reasoned pathways, behaviour is influenced by automatic processes that are determined by the frequency of engagement in the health behaviour in the past. We present a computational model integrating TPB and DPT to determine drinking decisions for simulated individuals. We explore whether this model can reproduce historical patterns in US population alcohol use and simulate a hypothetical scenario, "Dry January", to demonstrate the utility of the model for appraising the impact of policy interventions on population alcohol use. METHOD: Constructs from the TPB pathway were computed using equations from an existing individual-level dynamic simulation model of alcohol use. The DPT pathway was initialised by simulating individuals' past drinking using data from a large US survey. Individuals in the model were from a US population microsimulation that accounts for births, deaths and migration (1984-2015). On each modelled day, for each individual, we calculated standard drinks consumed using the TPB or DPT pathway. In each year we computed total population alcohol use prevalence, frequency and quantity. The model was calibrated to alcohol use data from the Behavioral Risk Factor Surveillance System (1984-2004). RESULTS: The model was a good fit to prevalence and frequency but a poorer fit to quantity of alcohol consumption, particularly in males. Simulating Dry January in each year led to a small to moderate reduction in annual population drinking. CONCLUSION: This study provides further evidence, at the whole population level, that a combination of reasoned and implicit processes are important for alcohol use. Alcohol misuse interventions should target both processes. The integrated TPB-DPT simulation model is a useful tool for estimating changes in alcohol consumption following hypothetical population interventions.

Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems.

The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.

A software architecture for mechanism-based social systems modelling in agent-based simulation models.

This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

The Normative Underpinnings of Population-Level Alcohol Use: An Individual-Level Simulation Model.

Background. By defining what is "normal," appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood. Aims. To test the ability of social norms mechanisms to predict changes in population-level drinking patterns. Method. An individual-level model was developed to simulate dynamic normative mechanisms and behavioral rules underlying drinking behavior over time. The model encompassed descriptive and injunctive drinking norms and their impact on frequency and quantity of alcohol use. A microsynthesis initialized in 1979 was used as a demographically representative synthetic U.S. population. Three experiments were performed in order to test the modelled normative mechanisms. Results. Overall, the experiments showed limited influence of normative interventions on population-level alcohol use. An increase in the desire to drink led to the most meaningful changes in the population's drinking behavior. The findings of the experiments underline the importance of autonomy, that is, the degree to which an individual is susceptible to normative influence. Conclusion. The model was able to predict theoretically plausible changes in drinking patterns at the population level through the impact of social mechanisms. Future applications of the model could be used to plan norms interventions pertaining to alcohol use as well as other health behaviors.

Introducing CASCADEPOP: an open-source sociodemographic simulation platform for us health policy appraisal.

Largescale individual-level and agent-based models are gaining importance in health policy appraisal and evaluation. Such models require the accurate depiction of the jurisdiction's population over extended time periods to enable modeling of the development of non-communicable diseases under consideration of historical, sociodemographic developments. We developed CASCADEPOP to provide a readily available sociodemographic micro-synthesis and microsimulation platform for US populations. The micro-synthesis method used iterative proportional fitting to integrate data from the US Census, the American Community Survey, the Panel Study of Income Dynamics, Multiple Cause of Death Files, and several national surveys to produce a synthetic population aged 12 to 80 years on 01/01/1980 for five states (California, Minnesota, New York, Tennessee, and Texas) and the US. Characteristics include individuals' age, sex, race/ethnicity, marital/employment/parental status, education, income and patterns of alcohol use as an exemplar health behavior. The microsimulation simulates individuals' sociodemographic life trajectories over 35 years to 31/12/2015 accounting for population developments including births, deaths, and migration. Results comparing the 1980 micro-synthesis against observed data shows a successful depiction of state and US population characteristics and of drinking. Comparing the microsimulation over 30 years with Census data also showed the successful simulation of sociodemographic developments. The CASCADEPOP platform enables modelling of health behaviors across individuals' life courses and at a population level. As it contains a large number of relevant sociodemographic characteristics it can be further developed by researchers to build US agent-based models and microsimulations to examine health behaviors, interventions, and policies.

System Dynamics to Investigate Opioid Use and Chiropractic Care for Chronic Musculoskeletal Pain.

OBJECTIVE: The purpose of this investigation was to create a system dynamics (SD) model, including published data and required assumptions, as a tool for future research identifying the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population. METHODS: We present an illustrative case description of how we evaluated the feasibility of conducting a large-scale clinical trial to assess the impact of chiropractic care in mitigating excessive opioid use in Canada. We applied SD modeling using current evidence and key assumptions where such evidence was lacking. Modeling outcomes were highlighted to determine which potential factors were necessary to account for compelling study designs. RESULTS: Results suggest that a future clinical study diverting patients with nonmalignant musculoskeletal pain early to the chiropractic stream of care could be most effective. System dynamics modeling also highlighted design challenges resulting from unresearched assumptions that needed to be proxied for model completion. Assumptions included changing rates in opioid-associated deaths and rates of success in treatment management of addicted patients. CONCLUSION: In this case, SD modeling identified current research gaps and strong contenders for appropriate follow-up questions in a clinical research domain, namely the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

PTSD in civilian populations after hospitalization following traumatic injury: A comprehensive review.

BACKGROUND: Injuries and their comorbidities affect victims far beyond their physical recovery period. Some study-measures show that more than half of patients hospitalized for a traumatic injury suffer from Acute Stress Disorder, alcohol dependence, and recurrent trauma. Overall, this literature review serves to review risk factors for PTSD, screening tools, follow-up strategies, and gaps in the literature for achieving feasible patient-centered interventions for the prevention of PTSD after a traumatic injury. DATA SOURCES: A literature review was performed from August 1, 2017 to March 19, 2018, from 3 Databases: PubMed, CINAHL and Cochrane, with keywords: "PTSD", "Post-traumatic Stress Disorder", "Civilians", "Traumatic", "Injury", "Follow-up", "Treatment", "Referral", "surgery", "surgical", "Intervention", and "Insured", "underinsured". CONCLUSIONS: Reported risk factors for PTSD were: prior psychiatric disorder, gunshots, and lack of social support. Most articles use the Posttraumatic Stress Disorder Checklist - Civilian version. Follow-up strategies mainly focus on multidisciplinary intervention protocols, including social workers, behavioral health specialists, and psychiatrists. Finally, gaps in the literature show the need for bilingual/bicultural patient-centered care for elderly, diverse ethnic backgrounds, and insured vs. uninsured patients.

Restrictive versus liberal red blood cell transfusion strategy after hip surgery: a decision model analysis of healthcare costs.

BACKGROUND: Red blood cell transfusion related to select surgical procedures accounts for approximately 2.8 million transfusions in the United States yearly and occurs commonly after hip fracture surgeries. Randomized controlled trials have demonstrated lack of clinical benefit with higher versus lower transfusion thresholds in postoperative hip fracture repair patients with cardiac disease or risk factors for cardiac disease. The economic implications of a higher versus lower hemoglobin (Hb) threshold have not yet been investigated. STUDY DESIGN AND METHODS: A decision tree analysis was constructed to estimate differences in healthcare costs and charges between a Hb transfusion threshold strategy of 8 g/dL versus 10 g/dL from the perspective of both Centers for Medicare and Medicaid Services (CMS) as well as hospitals. Secondary outcome measures included differences in transfusion-related adverse events. RESULTS: Among the 133,697 Medicare beneficiaries undergoing hip fracture repair in 2012, we estimated that 45,457 patients would be anemic and at risk for transfusion. CMS would save an estimated $11.3 million to $24.3 million in payments, while hospitals would reduce charges by an estimated $52.7 million to $93.6 million if the restrictive transfusion strategy were to be implemented nationally. Additionally, rates of transfusion-associated circulatory overload, transfusion-related acute lung injury, acute transfusion reactions, length of stay, and mortality would be reduced. CONCLUSIONS: This model suggests that the uniform adoption of a restrictive transfusion strategy among patients with cardiac disease and risk factors for cardiac disease undergoing hip fracture repair would result in significant reductions in clinically important outcomes with significant cost savings.

Assessing productive efficiency and operating scale of community blood centers.

BACKGROUND: In recent years demand for blood products has decreased, and as a result, the blood product marketplace has become much more competitive. Reducing inefficiency in the procurement and processing of blood products at blood centers can reduce costs while assuring that demand for blood products is met. STUDY DESIGN AND METHODS: This study uses data envelopment analysis to compare the productive efficiency of 65 community blood centers to determine to what extent efficiency can be improved, what cost savings and increases in platelet (PLT) production may be obtained by eliminating inefficiency, and what scales of operation are the most efficient from a budgetary and staffing standpoint. Data were collected from the 2012 to 2013 AABB Directory of Community Blood Centers and Hospital Blood Banks. RESULTS: The study found that 27 of 65 blood centers are efficient. The remaining 38 blood centers can reduce budget and staff levels and may be able to expand output. If inefficient centers were to eliminate all inefficiency, the total savings would be $671 million, approximately 20% of the aggregated budget ($3.45 billion) of all centers in the study. In addition, the centers would also see a 36% increase in PLT production. Inefficiency of some large blood centers stems from operating at too large a scale, while inefficiency of most small blood centers is scale independent. CONCLUSION: The results suggest that reducing inefficiency in blood procurement may be a good strategy to maximize competitiveness in the blood product marketplace. These findings further suggest that the trend of blood center consolidation may be ill advised from a cost containment perspective.

Dynamic model of nonmedical opioid use trajectories and potential policy interventions.

BACKGROUND: Nonmedical use of pharmaceutical opioid analgesics (POA) increased dramatically over the past two decades and remains a major health problem in the United States, contributing to over 16 000 accidental poisoning deaths in 2010. OBJECTIVES: To create a systems-oriented theory/model to explain the historical behaviors of interest, including the various populations of nonmedical opioid users and accidental overdose mortality within those populations. To use the model to explore policy interventions including tamper-resistant drug formulations and strategies for reducing diversion of opioid medicines. METHODS: A system dynamics model was constructed to represent the population of people who initiate nonmedical POA usage. The model incorporates use trajectories including development of use disorders, transitions from reliance on informal sharing to paying for drugs, transition from oral administration to tampering to facilitate non-oral routes of administration, and transition to heroin use by some users, as well as movement into and out of the population through quitting and mortality. Empirical support was drawn from national surveys (NSDUH, TEDS, MTF, and ARCOS) and published studies. RESULTS: The model was able to replicate the patterns seen in the historical data for each user population, and the associated overdose deaths. Policy analysis showed that both tamper-resistant formulations and interventions to reduce informal sharing could significantly reduce nonmedical user populations and overdose deaths in the long term, but the modeled effect sizes require additional empirical support. CONCLUSION: Creating a theory/model that can explain system behaviors at a systems level scale is feasible and facilitates thorough evaluation of policy interventions.

Key Data Gaps Regarding the Public Health Issues Associated with Opioid Analgesics.

Most pharmaceutical opioids are used to treat pain, and they have been demonstrated to be effective medications for many. Their abuse and misuse pose significant public health concerns in the USA. Research has provided much insight into the prevalence, scope, and drivers of opioid abuse, but a holistic understanding is limited by a lack of available data regarding key aspects of this public health problem. Twelve data gaps were revealed during the creation of a systems-level computer model of medical use, diversion, nonmedical use, and the adverse outcomes associated with opioid analgesics in the USA. Data specific to these gaps would enhance the validity and real-world applications of systems-level models of this public health problem and would increase understanding of the complex system in which use and abuse occur. This paper provides an overview of these gaps, argues for the importance of closing them, and provides specific recommendations for future data collection efforts.

Data Sources Regarding the Nonmedical Use of Pharmaceutical Opioids in the United States.

OBJECTIVE: Recent increases in the nonmedical use of pharmaceutical opioids and the adverse outcomes associated with them have stimulated a large amount of research and data collection on this public health problem. Systematic organization of the available data sources is needed to facilitate ongoing research, analysis, and evaluation. This work offers a systematic categorization of data sources regarding the nonmedical use of pharmaceutical opioids in the United States. METHODS: A list of keywords regarding the nonmedical use of pharmaceutical opioids was used to conduct systematic searches in PubMed®. Filtration of search results generated 92 peer-reviewed academic articles, published between January 1995 and April 2012, as well as a number of primary data sources. Lists of topics were developed independently by two researchers which were later compared and consolidated. All sources were then categorized according to their relevance to each of these topics and according to their capacity for geographical and longitudinal trend analysis. RESULTS: Tables cataloging data sources can be used to identify data relevant to specific topics in diversion, nonmedical use, and adverse outcomes associated with pharmaceutical opioids, and they illustrate global trends in data coverage, identifying several topics that have minimal data. A network diagram illustrates global trends in data coverage, showing variation among sources in the number of topics they cover, as well as variation among topics in the number of sources that cover them. CONCLUSIONS: The categorization of data sources is hoped to facilitate ongoing research, analysis, and evaluation of this public health problem by serving as a guide for researchers, policy makers, and others who seek data regarding the nonmedical use of pharmaceutical opioids in the United States.

Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: a report on initial efforts.

Three educational interventions were simulated in a system dynamics model of the medical use, trafficking, and nonmedical use of pharmaceutical opioids. The study relied on secondary data obtained in the literature for the period of 1995 to 2008 as well as expert panel recommendations regarding model parameters and structure. The behavior of the resulting systems-level model was tested for fit against reference behavior data. After the base model was tested, logic to represent three educational interventions was added and the impact of each intervention on simulated overdose deaths was evaluated over a 7-year evaluation period, 2008 to 2015. Principal findings were that a prescriber education intervention not only reduced total overdose deaths in the model but also reduced the total number of persons who receive opioid analgesic therapy, medical user education not only reduced overdose deaths among medical users but also resulted in increased deaths from nonmedical use, and a "popularity" intervention sharply reduced overdose deaths among nonmedical users while having no effect on medical use. System dynamics modeling shows promise for evaluating potential interventions to ameliorate the adverse outcomes associated with the complex system surrounding the use of opioid analgesics to treat pain.