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Rationale: Asthma is a variable condition with an apparent tendency for a natural decline in asthma symptoms and health care use occurring as children age. As a result, asthma interventions using a pre-post design may overestimate the intervention effect when no proper control group is available. Objectives: Investigate patterns of natural decline over time with increasing age in asthma symptoms and health care use of children. Develop a statistical procedure that enables adjustment that accounts for expected declines in these outcomes and is useable when intervention evaluations must rely solely on pre-post data. Methods: Mixed-effects models with mixture distributions were used to describe the pattern of symptoms and health care use in 3,021 children aged 2 to 15 years in a combined sample from three controlled trials. An adaptive least squares estimation was used to account for overestimation of intervention effects and make adjustments for pre-post only data. Termed "Adjustment for Natural Declines in Asthma Outcomes (ANDAO)," the adjustment method uses bootstrap sampling to create control cohorts comparable to subjects in the intervention study from existing control subjects. ANDAO accounts for expected declines in outcomes and is beneficial when intervention evaluations must rely solely on pre-post data. Measurements and Main Results. Children under 10 years of age experienced 18% (95% confidence interval, 15-21%) fewer symptom days and 28% (95% confidence interval, 24-32%) fewer symptom nights with each additional year of age. The decline was less than 10% after age 10 years, depending on baseline asthma severity. Emergency department visits declined regardless of baseline symptom frequency (P = 0.02). The adjustment method corrected estimates to within 2.4% of true effects through simulations using control cohorts. Conclusions. Because of the declines in symptoms and health care use expected with increasing age of children with asthma, pre-post comparisons will greatly overestimate intervention effects. The ANDAO provides means to adequately estimate treatment effects when a control group design is not possible.

Summary: Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper. [ABSTRACT FROM AUTHOR]

Background: The U.S. uninsured rate has dropped significantly since the passage of the Affordable Care Act (ACA), yet insurance coverage remains lower in historically marginalized communities than in the overall population. New consumer engagement approaches that involve these populations are needed. The purpose of this study was to evaluate the effectiveness of the Insuring Good Health Intervention, a website and video series designed to improve engagement with health insurance and ACA reforms. The study was designed and implemented using a community-based participatory research approach., Method: We conducted a lagged-control cluster randomized controlled trial to evaluate Insuring Good Health with racially and ethnically diverse adults seeking services in medically underserved areas within the Detroit, Michigan metropolitan area. Outcomes were assessed at baseline and at 6 and 9 months postintervention, and including self-efficacy, knowledge, beliefs, and intention to seek help with insurance navigation and care., Results: Among 243 participants, mean age was 43.4 ( SD = 13) years, and all participants met federal guidelines for poverty. The study had an 86% response rate at 9-month follow-up ( n = 209). Compared with the lagged-control group, intervention participants had more positive beliefs concerning preventive care (estimate 0.51, standard error 0.16; p < .01), and intention to seek help with insurance navigation and care (estimate 0.43, standard error 0.17; p < .001) at 9-month follow-up. Hispanic participants benefitted the most from the intervention, including improved knowledge of health insurance eligibility over 9 months compared with other racial/ethnic groups (estimate -0.97, standard error 0.40, p < .01)., Discussion: Insuring Good Health was associated with increased intention to seek help with insurance navigation and care, and improved beliefs around preventive care., Conclusions: Insuring Good Health has the potential to be an important consumer engagement tool to reduce disparities in health insurance coverage and care seeking.

Injury resulting from motor vehicle crashes is the leading cause of death among teenagers in the US. Few programs or policies have been found to be effective in reducing the risk of fatal car crashes for young novice drivers. One effective policy that has been widely implemented is Graduated Driver Licensing (GDL). Published articles have mostly reported on the temporal effectiveness of GDL in the US. This article reports on the development of spatial statistical modeling approaches to evaluate and compare the effectiveness of GDL policy across eighty-three counties in the state of Michigan. Data were gathered from several publicly available databases, including the US Fatality Analysis Reporting System (FARS), US Census Bureau, US Bureau of Labor Statistics, and US Department of Agriculture. To account for spatial dependence among crash counts from adjacent counties we invoke spatial random effects, which we provide with a Conditionally AutoRegressive (CAR) prior. Our analysis confirms previous findings that GDL in Michigan is an effective policy that significantly reduces the risk of fatal car crashes among novice teenage drivers. In addition, it indicates that rurality is an important contextual variable associated with spatial differences in GDL effectiveness across the state of Michigan. Finally, our findings provide information that can be used to strengthen GDL policy and its implementation to further enhance teenage-driver safety., (Copyright © 2013 Elsevier Ltd. All rights reserved.)