Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness.

BACKGROUND: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role. METHODS: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. RESULTS: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. CONCLUSION: In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.

Association of systemic lupus erythematosus autoantibody diversity with breast cancer protection.

BACKGROUND: Epidemiologic data suggest that patients with systemic lupus erythematosus (SLE) have a lower risk of breast cancer than women in the general population. In light of mechanistic studies suggesting that anti-DNA antibodies have anti-cancer effects, we sought to examine breast cancer risk in autoantibody strata in a well-characterized SLE cohort. METHODS: SLE patients without a cancer diagnosis prior to entry in the Hopkins Lupus Cohort were studied (N = 2431). Overall and site-specific cancer incidence was calculated in racial strata and compared with the US Surveillance, Epidemiology and End Results (SEER) registry. Breast cancer incidence was further examined in autoantibody subsets. Patients were considered positive for an autoantibody if they were ever positive for a specificity during their disease course. RESULTS: Patients with SLE had a 37% lower risk of breast cancer (SIR 0.63, 95% CI 0.39-0.95). The risk of HPV-associated cancers (SIR 4.39, 95% CI 2.87-6.44) and thyroid cancer (SIR 2.27, 95% CI 1.04-4.30) was increased. Cancer risk varied by race, with breast cancer protection occurring in non-African Americans (SIR 0.29, 95% CI 0.11-0.63) and the increased risk of HPV-associated cancers occurring in African Americans (SIR 7.23, 95% CI 4.35-11.3). Breast cancer risk was decreased in patients ever positive for anti-dsDNA (SIR 0.55, 95% CI 0.29-0.96), anti-La (SIR 0.00, 95% CI 0.00-0.78), and lupus anticoagulant (SIR 0.37, 95% CI 0.10-0.94). Patients who were positive for fewer (0-2) SLE autoantibodies did not have a lower risk of breast cancer (SIR 0.84, 95% CI 0.47-1.39), but patients with 3+ autoantibodies had a 59% decreased risk (SIR 0.41, 95% CI 0.16-0.84). CONCLUSIONS: Positivity for multiple SLE autoantibodies was associated with a lower risk of breast cancer, supporting the hypothesis that a highly diversified immune response may exert an anti-cancer effect against some cancers. Validation of racial differences in cancer risk in SLE is required to determine whether cancer screening strategies should be targeted to racial subgroups.

Autoantibodies and scleroderma phenotype define subgroups at high-risk and low-risk for cancer.

OBJECTIVES: Recent studies demonstrate autoantibodies are powerful tools to interrogate molecular events linking cancer and the development of autoimmunity in scleroderma. Investigating cancer risk in these biologically relevant subsets may provide an opportunity to develop personalised cancer screening guidelines. In this study, we examined cancer risk in distinct serologic and phenotypic scleroderma subsets and compared estimates with the general population. METHODS: Patients in the Johns Hopkins Scleroderma Center observational cohort were studied. Overall and site-specific cancer incidence was calculated in distinct autoantibody and scleroderma phenotypic subsets, and compared with the Surveillance, Epidemiology and End Results registry, a representative sample of the US population. RESULTS: 2383 patients with scleroderma contributing 37 686 person-years were studied. 205 patients (8.6%) had a diagnosis of cancer. Within 3 years of scleroderma onset, cancer risk was increased in patients with RNA polymerase III autoantibodies (antipol; standardised incidence ratio (SIR) 2.84, 95% CI 1.89 to 4.10) and those lacking centromere, topoisomerase-1 and pol antibodies (SIR 1.83, 95% CI 1.10 to 2.86). Among antipol-positive patients, cancer-specific risk may vary by scleroderma subtype; those with diffuse scleroderma had an increased breast cancer risk, whereas those with limited scleroderma had high lung cancer risk. In contrast, patients with anticentromere antibodies had a lower risk of cancer during follow-up (SIR 0.59, 95% CI 0.44 to 0.76). CONCLUSIONS: Autoantibody specificity and disease subtype are biologically meaningful filters that may inform cancer risk stratification in patients with scleroderma. Future research testing the value of targeted cancer screening strategies in patients with scleroderma is needed.

Modeling the Impact of School-Based Universal Depression Screening on Additional Service Capacity Needs: A System Dynamics Approach.

Although it is widely known that the occurrence of depression increases over the course of adolescence, symptoms of mood disorders frequently go undetected. While schools are viable settings for conducting universal screening to systematically identify students in need of services for common health conditions, particularly those that adversely affect school performance, few school districts routinely screen their students for depression. Among the most commonly referenced barriers are concerns that the number of students identified may exceed schools' service delivery capacities, but few studies have evaluated this concern systematically. System dynamics (SD) modeling may prove a useful approach for answering questions of this sort. The goal of the current paper is therefore to demonstrate how SD modeling can be applied to inform implementation decisions in communities. In our demonstration, we used SD modeling to estimate the additional service demand generated by universal depression screening in a typical high school. We then simulated the effects of implementing "compensatory approaches" designed to address anticipated increases in service need through (1) the allocation of additional staff time and (2) improvements in the effectiveness of mental health interventions. Results support the ability of screening to facilitate more rapid entry into services and suggest that improving the effectiveness of mental health services for students with depression via the implementation of an evidence-based treatment protocol may have a limited impact on overall recovery rates and service availability. In our example, the SD approach proved useful in informing systems' decision-making about the adoption of a new school mental health service.