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This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative. [ABSTRACT FROM AUTHOR]

Introduction: The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models.Methods: To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work.Results: The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals.Conclusions: This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models. [ABSTRACT FROM AUTHOR]

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction. [ABSTRACT FROM AUTHOR]

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs. [ABSTRACT FROM AUTHOR]

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

In the last 50 years Thailand has achieved advanced demographic and health transitions. Many infectious diseases are controlled and infant and maternal mortality rates are among the lowest in the region. Within such a low mortality setting, however, substantial variations in health persist, with education being a major driver. This paper assesses the ongoing mortality transition in Thailand, examining relationships between risk factors and mortality outcomes among a large nationwide cohort of distance-learning Open University students, as well as examining the differential mortality benefit enjoyed by this educated group when compared with the general Thai population. The cohort comprised 87,151 participants, who in 2005 completed a questionnaire collecting detailed information on socio-demographics, health behaviours and health outcomes. Participants were aged 15-87 at baseline and lived in all regions of the country. Cohort members' citizen identification numbers were matched with official death registration records to identify mortality among participants. A total of 1401 deaths were identified up to November 2016. Results show cohort study participants experiencing mortality at approximately one-third of the rate of the general population in the same age and sex groups. The gap between the mortality rates in the two groups widened with increased age. Differential risk factor prevalence among the two populations, particularly lower overweight and obesity prevalence and lower cigarette smoking rates in the cohort, can explain some of this variation. The largely unmeasurable effect of aspiration for life improvement through distance education while embedded in their communities is shown by this study to have a powerful effect on mortality risk. With overall education levels, including higher education, rising in Thailand this growing group of educated aspirational Thais may represent future trends in Thai mortality. Identifying the drivers and characteristics of this mortality variation can help inform policies to provide health services and to help reduce mortality in the whole population. [ABSTRACT FROM AUTHOR]

Background: Norway has achieved a noteworthy reduction in smoking prevalence over the past forty years. In 2015, 13% of Norwegians aged 13-74 smoked daily and a further 9% smoked occasionally. One of the objectives of the Norwegian 2013-16 national strategy for tobacco control is to achieve a reduction in the daily smoking prevalence to<10% by 2016. This paper aims to estimate how long it will take for Norway to achieve the 10% smoking prevalence. Methods: A dynamic forecasting model using smoking prevalence data from national survey data on the prevalence of current, former and never smokers in the Norwegian population from 1985 to 2012 was used to estimate future smoking prevalence in the Norwegian population based on a continuation of current patterns in smoking cessation and initiation rates. Results: The result suggests that Norway's smoking prevalence among men and women will continue to decline if current trends of smoking initiation and cessation continue. Our model predicts, based on figures for 1985-2012, that the prevalence of current daily and occasional smoking among men and women will not fall below the 10% mark until 2029 for men and 2026 for women, if current trends continue. Conclusion: Smoking is likely to remain an important public health issue in Norway for many years. New strategies are needed to accelerate the decline in smoking in Norway. [ABSTRACT FROM AUTHOR]

This editorial is a commentary on the review paper by Ramirez Varela et al. entitled "Mapping the historical development of physical activity and health research: a structured literature review and citation network analysis." This editorial highlights the significance and implications of this review, with a particular focus on future research and policy directions. [ABSTRACT FROM AUTHOR]

The incidence and prevalence of diabetes (primarily type 2 diabetes) has risen sharply since 1990. It is projected to increase another 64% between 2010 and 2025, affecting 53.1 million people and resulting in medical and societal costs of a half trillion dollars a year. We know how to prevent many cases of diabetes and how to treat it effectively. Early appropriate treatment makes a significant difference in preventing major complications and reducing premature death, but it does not cure the disease. Early detection of prediabetes, in conjunction with lifestyle changes, can reduce the number of people with diabetes. A dramatic reduction in diabetes prevalence over time will require significant lifestyle changes on the part of society as a whole. The purpose of this study is to increase public awareness of the severity of regional diabetes trends by providing detailed forecasts for all states and several metropolitan areas for 2010, 2015, and 2025. A model was created to utilize the latest national diabetes and population data and projections, and to transform these into state and metropolitan area forecasts for the whole population and major subgroups. These forecasts were then summarized in easy-to-understand briefing papers for each state and selected metro areas, which are provided online for easy public access. This research is important because little data exist that project the future prevalence and potential costs of diabetes at the state and metro area level. With this data, key stakeholders can make informed decisions concerning diabetes, its impact on their communities, and resource allocation. ( Population Health Management 2012;15:194-200) [ABSTRACT FROM AUTHOR]

Patients' experience of stereotype threat in clinical settings and encounters may be one contributor to health care disparities. Stereotype threat occurs when cues in the environment make negative stereotypes associated with an individual's group status salient, triggering physiological and psychological processes that have detrimental consequences for behavior. By recognizing and understanding the factors that can trigger stereotype threat and understanding its consequences in medical settings, providers can prevent it from occurring or ameliorate its consequences for patient behavior and outcomes. In this paper, we discuss the implications of stereotype threat for medical education and trainee performance and offer practical suggestions for how future providers might reduce stereotype threat in their exam rooms and clinics. [ABSTRACT FROM AUTHOR]

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of "imminent drinking" (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant's current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness. [ABSTRACT FROM AUTHOR]