Scaling structural learning with NO-BEARS to infer causal transcriptome networks.

Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NO-TEARS algorithm with two improvements. First, we propose a new constraint and its fast approximation to reduce the computational cost of the NO-TEARS algorithm. Next, we introduce a polynomial regression loss to handle non-linearity in gene expressions. Our implementation utilizes modern GPU computation that can decrease the time of hours-long CPU computation to seconds. Using synthetic data, we demonstrate improved performance, both in processing time and accuracy, on inferring gene regulatory networks from gene expression data.

From Epidemiologic Knowledge to Improved Health: A Vision for Translational Epidemiology.

Epidemiology should aim to improve population health; however, no consensus exists regarding the activities and skills that should be prioritized to achieve this goal. We performed a scoping review of articles addressing the translation of epidemiologic knowledge into improved population health outcomes. We identified 5 themes in the translational epidemiology literature: foundations of epidemiologic thinking, evidence-based public health or medicine, epidemiologic education, implementation science, and community-engaged research (including literature on community-based participatory research). We then identified 5 priority areas for advancing translational epidemiology: 1) scientific engagement with public health; 2) public health communication; 3) epidemiologic education; 4) epidemiology and implementation; and 5) community involvement. Using these priority areas as a starting point, we developed a conceptual framework of translational epidemiology that emphasizes interconnectedness and feedback among epidemiology, foundational science, and public health stakeholders. We also identified 2-5 representative principles in each priority area that could serve as the basis for advancing a vision of translational epidemiology. We believe an emphasis on translational epidemiology can help the broader field to increase the efficiency of translating epidemiologic knowledge into improved health outcomes and to achieve its goal of improving population health.

Social cohesion and passive adaptation in relation to climate change and disease.

Climate change affects biophysical processes related to the transmission of many infectious diseases, with potentially adverse consequences for the health of communities. While our knowledge of biophysical associations between meteorological factors and disease is steadily improving, our understanding of the social processes that shape adaptation to environmental perturbations lags behind. Using computational modeling methods, we explore the ways in which social cohesion can affect adaptation of disease prevention strategies when communities are exposed to different environmental scenarios that influence transmission pathways for diseases such as diarrhea. We developed an agent-based model in which household agents can choose between two behavioral strategies that offer different levels of protection against environmentally mediated disease transmission. One behavioral strategy is initially set as more protective, leading households to adopt it widely, but its efficacy is sensitive to variable weather conditions and stressors such as floods or droughts that modify the disease transmission system. The efficacy of the second strategy is initially moderate relative to the first and is insensitive to environmental changes. We examined how social cohesion (defined as average number of household social network connections) influences health outcomes when households attempt to identify an optimal strategy by copying the behaviors of socially connected neighbors who seem to have adapted successfully in the past. Our simulation experiments suggest that high-cohesion communities are able to rapidly disseminate the initially optimal behavioral strategy compared to low-cohesion communities. This rapid and pervasive change, however, decreases behavioral diversity; i.e., once a high cohesion community settles on a strategy, most or all households adopt that behavior. Following environmental changes that reduce the efficacy of the initially optimal strategy, rendering it suboptimal relative to the alternative strategy, high-cohesion communities can fail to adapt. As a result, despite faring better early in the course of computational experiments, high-cohesion communities may ultimately experience worse outcomes. In the face of uncertainty in predicting future environmental stressors due to climate change, strategies to improve effective adaptation to optimal disease prevention strategies should balance between intervention efforts that promote protective behaviors based on current scientific understanding and the need to guard against the crystallization of inflexible norms. Developing generalizable models allows us to integrate a wide range of theories multiple datasets pertaining to the relationship between social mechanisms and adaptation, which can provide further understanding of future climate change impacts. Models such as the one we present can generate hypotheses about the mechanisms that underlie the dynamics of adaptation events and suggest specific points of measurement to assess the impact of these mechanisms. They can be incorporated as modules within predictive simulations for specific socio-ecological contexts.

Tuberculosis Incidence Among Populations at High Risk in California, Florida, New York, and Texas, 2011-2015.

OBJECTIVES: To illustrate the magnitude of between-state heterogeneities in tuberculosis (TB) incidence among US populations at high risk for TB that may help guide state-specific strategies for TB elimination. METHODS: We used data from the National Tuberculosis Surveillance System and other public sources from 2011 to 2015 to calculate TB incidence in every US state among people who were non-US-born, had diabetes, or were HIV-positive, homeless, or incarcerated. We then estimated the proportion of TB cases that reflected the difference between each state's reported risk factor-specific TB incidence and the lowest incidence achieved among 4 states (California, Florida, New York, Texas). We reported these differences for the 4 states and also calculated and aggregated across all 50 states to quantify the total percentage of TB cases nationally that reflected between-state differences in risk factor-specific TB incidence. RESULTS: On average, 24% of recent TB incidence among high-risk US populations reflected heterogeneity at the state level. The populations that accounted for the greatest percentage of heterogeneity-reflective cases were non-US-born individuals (51%) and patients with diabetes (24%). CONCLUSIONS: State-level differences in TB incidence among key populations provide clues for targeting state-level interventions.

Modeling the Effects of E-cigarettes on Smoking Behavior: Implications for Future Adult Smoking Prevalence.

BACKGROUND: Electronic cigarette (e-cigarette) use has increased rapidly in recent years. Given the unknown effects of e-cigarette use on cigarette smoking behaviors, e-cigarette regulation has become the subject of considerable controversy. In the absence of longitudinal data documenting the long-term effects of e-cigarette use on smoking behavior and population smoking outcomes, computational models can guide future empirical research and provide insights into the possible effects of e-cigarette use on smoking prevalence over time. METHODS: Agent-based model examining hypothetical scenarios of e-cigarette use by smoking status and e-cigarette effects on smoking initiation and smoking cessation. RESULTS: If e-cigarettes increase individual-level smoking cessation probabilities by 20%, the model estimates a 6% reduction in smoking prevalence by 2060 compared with baseline model (no effects) outcomes. In contrast, e-cigarette use prevalence among never smokers would have to rise dramatically from current estimates, with e-cigarettes increasing smoking initiation by more than 200% relative to baseline model estimates to achieve a corresponding 6% increase in smoking prevalence by 2060. CONCLUSIONS: Based on current knowledge of the patterns of e-cigarette use by smoking status and the heavy concentration of e-cigarette use among current smokers, the simulated effects of e-cigarettes on smoking cessation generate substantially larger changes to smoking prevalence compared with their effects on smoking initiation.

Evaluation of a Personalized, Web-Based Decision Aid for Lung Cancer Screening.

INTRODUCTION: Informed decision making has been highlighted as an important aspect of lung cancer screening programs. This study seeks to assess the efficacy of a web-based patient decision aid for lung cancer screening, www.shouldiscreen.com. METHODS: A before-and-after study (August through December 2014) was conducted where participants navigated a web-based decision aid that provided information about low-dose computed tomography lung cancer screening. Using an established prediction model, the decision aid computed baseline lung cancer risk and an individual's chances of benefiting from, and risk of being harmed by, screening. Outcome measures included knowledge of lung cancer risk factors and lung cancer screening, decisional conflict, concordance, and acceptability of the decision aid. Data were collected from 60 participants who were current or former smokers, had no history of lung cancer, and had not received a chest computed tomographic scan in the previous year. Analysis took place in 2015. RESULTS: Knowledge increased after seeing the decision aid compared with before (p<0.001), whereas the score on the Decisional Conflict Scale decreased (p<0.001). Concordance between a participant's preference to screen and the U.S. Preventive Services Task Force recommendation improved after seeing the decision aid (p<0.001). Risk perceptions among the screen-ineligible group changed (n=49), contrary to those who were eligible (n=11). Ninety-seven percent of the participants reported that the decision aid was likely useful for lung cancer screening decision making. CONCLUSIONS: The web-based decision aid should be a helpful resource for individuals considering lung cancer screening, as well as for practitioners and health systems with lung cancer screening programs.

A participatory model of the paradox of primary care.

PURPOSE: The paradox of primary care is the observation that primary care is associated with apparently low levels of evidence-based care for individual diseases, but systems based on primary care have healthier populations, use fewer resources, and have less health inequality. The purpose of this article is to explore, from a complex systems perspective, mechanisms that might account for the effects of primary care beyond disease-specific care. METHODS: In an 8-session, participatory group model-building process, patient, caregiver, and primary care clinician community stakeholders worked with academic investigators to develop and refine an agent-based computer simulation model to test hypotheses about mechanisms by which features of primary care could affect health and health equity. RESULTS: In the resulting model, patients are at risk for acute illness, acute life-changing illness, chronic illness, and mental illness. Patients have changeable health behaviors and care-seeking tendencies that relate to their living in advantaged or disadvantaged neighborhoods. There are 2 types of care available to patients: primary and specialty. Primary care in the model is less effective than specialty care in treating single diseases, but it has the ability to treat multiple diseases at once. Primary care also can provide disease prevention visits, help patients improve their health behaviors, refer to specialty care, and develop relationships with patients that cause them to lower their threshold for seeking care. In a model run with primary care features turned off, primary care patients have poorer health. In a model run with all primary care features turned on, their conjoint effect leads to better population health for patients who seek primary care, with the primary care effect being particularly pronounced for patients who are disadvantaged and patients with multiple chronic conditions. Primary care leads to more total health care visits that are due to more disease prevention visits, but there are reduced illness visits among people in disadvantaged neighborhoods. Supplemental appendices provide a working version of the model and worksheets that allow readers to run their own experiments that vary model parameters. CONCLUSION: This simulation model provides insights into possible mechanisms for the paradox of primary care and shows how participatory group model building can be used to evaluate hypotheses about the behavior of such complex systems as primary health care and population health.

Development and validation of a personalized, web-based decision aid for lung cancer screening using mixed methods: a study protocol.

BACKGROUND: The National Lung Screening Trial demonstrated that low-dose computed tomography (LDCT) screening could be an effective way to reduce lung cancer mortality. Informed decision-making in the context of lung cancer screening requires that potential screening subjects accurately recognize their own lung cancer risk, as well as the harms and benefits associated with screening, while taking into account their personal values and preferences. OBJECTIVE: Our objective is to develop a Web-based decision aid in accordance with the qualifying and certification criteria in the International Patient Decision Aid Standards instrument version 4.0 that will assist patients in making informed decisions with regard to lung cancer screening. METHODS: In "alpha" testing, a prototype of the decision aid was tested for usability with 10 potential screening participants in focus groups. Feedback was also sought from public health and health risk communication experts external to the study. Following that, improvements to the prototype were made accordingly, and "beta" testing was done in the form of a quasi-experimental design-a before-after study-with a group of 60 participants. Outcomes tested were knowledge, risk perception of lung cancer and lung cancer screening, decisional conflict, and acceptability of the decision aid as determined by means of a self-administered electronic survey. Focus groups of a subsample of survey participants will be conducted to gain further insight into usability issues. RESULTS: Alpha testing is completed. Beta testing is currently being carried out. As of 2014 December 7, 60 participants had completed the before-after study. We expect to have results by 2015 January 31. Qualitative data collection and analysis are expected to be completed by 2015 May 31. CONCLUSIONS: We hypothesize that this Web-based, interactive decision aid containing personalized, graphical, and contextual information on the benefits and harms of LDCT screening will increase knowledge, reduce decisional conflict, and improve concordance between patient preferences and the current US Preventive Services Task Force's screening guidelines.

Provider dismissal policies and clustering of vaccine-hesitant families: an agent-based modeling approach.

Many pediatric practices have adopted vaccine policies that require parents who refuse to vaccinate according to the ACIP schedule to find another health care provider. Such policies may inadvertently cluster unvaccinated patients into practices that tolerate non vaccination or alternative schedules, turning them into risky pockets of low herd immunity. The objective of this study was to assess the effect of provider zero-tolerance vaccination policies on the clustering of intentionally unvaccinated children. We developed an agent-based model of parental vaccine hesitancy, provider non-vaccination tolerance, and selection of patients into pediatric practices. We ran 84 experiments across a range of parental hesitancy and provider tolerance scenarios. When the model is initialized, all providers accommodate refusals and intentionally unvaccinated children are evenly distributed across providers. As provider tolerance decreases, hesitant children become more clustered in a smaller number of practices and eventually are not able to find a practice that will accept them. Each of these effects becomes more pronounced as the level of hesitancy in the population rises. Heterogeneity in practice tolerance to vaccine-hesitant parents has the unintended result of concentrating susceptible individuals within a small number of tolerant practices, while providing little if any compensatory protection to adherent individuals. These externalities suggest an agenda for stricter policy regulation of individual practice decisions.