Prospective policy analysis-a critical interpretive synthesis review

Most policy analysis methods and approaches are applied retrospectively. As a result, there have been calls for more documentation of the political-economy factors central to health care reforms in real-time. We sought to highlight the methods and previous applications of prospective policy analysis (PPA) in the literature to document purposeful use of PPA and reflect on opportunities and drawbacks. We used a critical interpretive synthesis (CIS) approach as our initial scoping revealed that PPA is inconsistently defined in the literature. While we found several examples of PPA, all were researcher-led, most were published recently and few described mechanisms for engagement in the policy process. In addition, methods used were often summarily described and reported on relatively short prospective time horizons. Most of the studies stemmed from high-income countries and, across our sample, did not always clearly outline the rationale for a PPA and how this analysis was conceptualized. That only about one-fifth of the articles explicitly defined PPA underscores the fact that researchers and practitioners conducting PPA should better document their intent and reflect on key elements essential for PPA. Despite a wide recognition that policy processes are dynamic and ideally require multifaceted and longitudinal examination, the PPA approach is not currently frequently documented in the literature. However, the few articles reported in this paper might overestimate gaps in PPA applications. More likely, researchers are embedded in policy processes prospectively but do not necessarily write their articles from that perspective, and analyses led by non-academics might not make their way into the published literature. Future research should feature examples of testing and refining the proposed framework, as well as designing and reporting on PPA. Even when policy-maker engagement might not be feasible, real-time policy monitoring might have value in and of itself.

Advancing Transportation Equity and Safety Through Autonomous Vehicles

Motor vehicle crashes are a leading cause of death in the United States, and disproportionately impact communities of color. Replacing human control with automated vehicles (AVs) holds the potential to reduce crashes and save lives. The benefits of AVs, including automated shuttles, buses, or cars could extend beyond safety to include improvements in congestion, reductions in emissions, and increased access to mobility, particularly for vulnerable populations. However, AVs have not attained the level of public trust that has been expected, given their potential to save lives and increase access to mobility. Public opinion surveys have highlighted safety and security concerns as reasons for this lack of confidence. In this study, we present the findings of an experiment we conducted to actively shift mindsets on AVs toward advancing health equity. We demonstrate through a nationally representative sample of 2265 U.S. adults that the public support for AVs can be improved by expanding their scope of application to include advancing social benefit. The survey began with questions on respondent's support for AVs based on a priori knowledge and beliefs. Consistent with prior surveys, baseline support (strong support and some degree of support) was low at 26.4% (95% confidence interval 24.0-29.0). After introducing information about how AVs could be used to provide mobility for older adults, those with limited income, or the vision-impaired, respondents were asked to reassess their support for AVs. Support significantly increased to include the majority of respondents. By prioritizing the deployment of AVs to serve individuals and communities in greatest need of mobility, AVs would not only demonstrate compelling social value by reducing disparities but would also gain widespread public support among the U.S. public.

Speeding behavior among teenage drivers during the learner and early independent driving stage: A case study approach

INTRODUCTION

Speed is a primary contributing factor in teenage driver crashes. Yet, there are significant methodological challenges in measuring real-world speeding behavior.

METHOD

This case study approach analyzed naturalistic driving data for six teenage drivers in a longitudinal study that spanned the learner and early independent driving stages of licensure in Maryland, United States. Trip duration, travel speed and length were recorded using global position system (GPS) data. These were merged with maps of the Maryland road system, which included posted speed limit (PSL) to determine speeding events in each recorded trip. Speeding was defined as driving at the speed of 10 mph higher than the posted speed limit and lasting longer than 6 s. Using these data, two different speeding measures were developed: (1) Trips with Speeding Episodes, and (2) Verified Speeding Time. Conclusions & Practical Applications: Across both measures, speeding behavior during independent licensure was greater than during the learner period. These measures improved on previous methodologies by using PSL information and eliminating the need for mapping software. This approach can be scaled for use in larger samples and has the potential to advance understanding about the trajectory of speeding behaviors among novice teenage drivers.

Design of an innovative digital application to facilitate access to healthy foods in low-income urban settings

Background

Under-resourced urban minority communities in the United States are characterized by food environments with low access to healthy foods, high food insecurity, and high rates of diet-related chronic disease. In Baltimore, Maryland, low access to healthy food largely results from a distribution gap between small food sources (retailers) and their suppliers. Digital interventions have the potential to address this gap, while keeping costs low.

Methods

In this paper, we describe the technical (I) front-end design and (II) back-end development process of the Baltimore Urban food Distribution (BUD) application (app). We identify and detail four main phases of the process: (I) information architecture; (II) low and high-fidelity wireframes; (III) prototype; and (IV) back-end components, while considering formative research and a pre-pilot test of a preliminary version of the BUD app.

Results

Our lessons learned provide valuable insight into developing a stable app with a user-friendly experience and interface, and accessible cloud computing services for advanced technical features.

Conclusions

Next steps will involve a pilot trial of the app in Baltimore, and eventually, other urban and rural settings nationwide. Once iterative feedback is incorporated into the app, all code will be made publicly available via an open source repository to encourage adaptation for desired communities.

Trial Registration

ClinicalTrials.gov NCT05010018.

The Baltimore Urban Food Distribution (BUD) App: Study Protocol to Assess the Feasibility of a Food Systems Intervention

Low-income urban communities in the United States commonly lack ready access to healthy foods. This is due in part to a food distribution system that favors the provision of high-fat, high-sugar, high-sodium processed foods to small retail food stores, and impedes their healthier alternatives, such as fresh produce. The Baltimore Urban food Distribution (BUD) study is a multilevel, multicomponent systems intervention that aims to improve healthy food access in low-income neighborhoods of Baltimore, Maryland. The primary intervention is the BUD application (app), which uses the power of collective purchasing and delivery to affordably move foods from local producers and wholesalers to the city's many corner stores. We will implement the BUD app in a sample of 38 corner stores, randomized to intervention and comparison. Extensive evaluation will be conducted at each level of the intervention to assess overall feasibility and effectiveness via mixed methods, including app usage data, and process and impact measures on suppliers, corner stores, and consumers. BUD represents one of the first attempts to implement an intervention that engages multiple levels of a local food system. We anticipate that the app will provide a financially viable alternative for Baltimore corner stores to increase their stocking and sales of healthier foods, subsequently increasing healthy food access and improving diet-related health outcomes for under-resourced consumers. The design of the intervention and the evaluation plan of the BUD project are documented here, including future steps for scale-up. Trial registration #: NCT05010018.

Development of a System Dynamics Model to Guide Retail Food Store Policies in Baltimore City

Policy interventions to improve food access and address the obesity epidemic among disadvantaged populations are becoming more common throughout the United States. In Baltimore MD, corner stores are a frequently used source of food for low-income populations, but these stores often do not provide a range of affordable healthy foods. This research study aimed to assist city policy makers as they considered implementing a Staple Food Ordinance (SFO) that would require small stores to provide a range and depth of stock of healthy foods. A System Dynamics (SD) model was built to simulate the complex Baltimore food environment and produce optimal values for key decision variables in SFO planning. A web-based application was created for users to access this model to optimize future SFOs, and to test out different options. Four versions of potential SFOs were simulated using this application and the advantages and drawbacks of each SFO are discussed based on the simulation results. These simulations show that a well-designed SFO has the potential to reduce staple food costs, increase corner store profits, reduce food waste, and expand the market for heathy staple foods.

Evaluating a smartphone application to increase the quantity and improve the quality of supervised practice driving

BACKGROUND

The learner stage of graduated driver licensing (GDL), when teenagers are supervised by an adult driver, represents an opportunity to develop skills that could confer a safety benefit during their years of independent driving. This paper describes the design of a teenage driving study, which aims to evaluate the impact of a smartphone application, the 'DrivingApp,' to increase the quantity and improve the quality of supervised practice driving.

METHODS

This longitudinal intervention study of teenage drivers and a parent/guardian spans the final 6 months of the learner licence and the first year of independent driving. Participants will be assigned to experimental or control groups using block allocation. Parent-teenage dyads assigned to the intervention arm will receive information about their practice driving via a smartphone application, including miles driven and total drive time. Baseline and monthly surveys will be administered to both experimental and control participants to measure the outcome measures during the learner stage: (1) practice driving amount, (2) consistency and (3) variety. Outcomes during independent driving are (1) self-reported number of attempts at the driving test and (2) number of crashes during the first year of independent driving.

DISCUSSION

Improving the quality of teenagers' supervised practice driving is an unmet research need. This study will contribute to the evidence about what can be done during the learner period of GDL to maximise teenage drivers' safety during the first years of independent driving, when crash risk is highest.

Modeling hospital energy and economic costs for COVID-19 infection control interventions

The study objective assessed the energy demand and economic cost of two hospital-based COVID-19 infection control interventions: negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models quantified increases in energy demand and reductions in infections. The NP intervention was applied to 11, 22, and 44 rooms for small, medium, and large hospitals, while the XP-UV equipment was used eight, nine, and ten hours a day. For small, medium, and large hospitals, the annum kWh for NP rooms were 116,700 kWh, 332,530 kWh, 795,675 kWh, which correspond to annum energy costs of $11,845 ($1,077/room), $33,752 ($1,534/room), and $80,761 ($1,836/room). For XP-UV, the annum-kilowatt-hours (and costs) were 438 ($45), 493 ($50), and 548 ($56) for small, medium, and large hospitals. While energy efficiencies may be expected for the large hospital, the hospital contained more energy-intensive use rooms (ICUs) which resulted in higher operational and energy costs. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals

OBJECTIVE

Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors.

MATERIALS AND METHODS

We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success.

RESULTS

We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations.

CONCLUSIONS

Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02449-3.

Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals

OBJECTIVES

Our goal was to optimize infection control of paired environmental control interventions within hospitals to reduce methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), and vancomycin-resistant Enterococci (VRE).

BACKGROUND

The most widely used infection control interventions are deployment of handwashing (HW) stations, control of relative humidity (RH), and negative pressure (NP) treatment rooms. Direct costs of multidrug-resistant organism (MDRO) infections are typically not included in the design of such interventions.

METHODS

We examined the effectiveness of pairing HW with RH and HW with NP. We used the following three data sets: A meta-analysis of progression rates from uncolonized to colonized to infected, 6 years of MDRO treatment costs from 400 hospitals, and 8 years of MDRO incidence rates at nine army hospitals. We used these data as inputs into an Infection De-Escalation Model with varying budgets to obtain optimal intervention designs. We then computed the infection and prevention rates and cost savings resulting from these designs.

RESULTS

The average direct cost of an MDRO infection was $3,289, $1,535, and $1,067 for MRSA, CRE, and VRE. The mean annual incidence rates per facility were 0.39%, 0.034%, and 0.011% for MRSA, CRE, and VRE. After applying the cost-minimizing intervention pair to each scenario, the percentage reductions in infections (and annual direct cost savings) in large, community, and small acute care hospitals were 69% ($1.5 million), 73% ($631K), 60% ($118K) for MRSA, 52% ($460.5K), 58% ($203K), 50% ($37K) for CRE, and 0%, 0%, and 50% ($12.8K) for VRE.

CONCLUSION

The application of this Infection De-Escalation Model can guide cost-effective decision making in hospital built environment design to improve control of MDRO infections.

Applying an Innovative Model of Disaster Resilience at the Neighborhood Level : The COPEWELL New York City Experience

Community resilience is a community's ability to maintain functioning (ie, delivery of services) during and after a disaster event. The Composite of Post-Event Well-Being (COPEWELL) is a system dynamics model of community resilience that predicts a community's disaster-specific functioning over time. We explored COPEWELL's usefulness as a practice-based tool for understanding community resilience and to engage partners in identifying resilience-strengthening strategies. In 2014, along with academic partners, the New York City Department of Health and Mental Hygiene organized an interdisciplinary work group that used COPEWELL to advance cross-sector engagement, design approaches to understand and strengthen community resilience, and identify local data to explore COPEWELL implementation at neighborhood levels. The authors conducted participant interviews and collected shared experiences to capture information on lessons learned. The COPEWELL model led to an improved understanding of community resilience among agency members and community partners. Integration and enhanced alignment of efforts among preparedness, disaster resilience, and community development emerged. The work group identified strategies to strengthen resilience. Searches of neighborhood-level data sets and mapping helped prioritize communities that are vulnerable to disasters (eg, medically vulnerable, socially isolated, low income). These actions increased understanding of available data, identified data gaps, and generated ideas for future data collection. The COPEWELL model can be used to drive an understanding of resilience, identify key geographic areas at risk during and after a disaster, spur efforts to build on local metrics, and result in innovative interventions that integrate and align efforts among emergency preparedness, community development, and broader public health initiatives.

Public health principles to inform testing and build trust in automated vehicles

Highly publicised crashes involving self-driving or autonomous vehicles (AVs) have raised questions about safety and eroded public trust in the technology. In this State of the Art Review, we draw on previous successes in injury prevention and public health to focus attention on three strategies to reduce risk and build public confidence as AVs are being tested on public roads. Data pooling, a graduated approach to risk exposure, and harm reduction principles each offer practical lessons for AV testing. The review points out how the eventual deployment of AV technology could have a substantial impact on public health. In this regard, inclusive testing, public education and smart policy could extend the social value of AVs by improving access to mobility and by directing deployments towards scenarios with the greatest population health impact. The application of these strategies does not imply slowing down progress; rather, their implementation could accelerate adoption and result in realising the benefits of AVs more quickly and comprehensively while minimising risks.

Cost-Effectiveness of Multifaceted Built Environment Interventions for Reducing Transmission of Pathogenic Bacteria in Healthcare Facilities

OBJECTIVES

The objective of this study is to determine the optimal allocation of budgets for pairs of alterations that reduce pathogenic bacterial transmission. Three alterations of the built environment are examined: handwashing stations (HW), relative humidity control (RH), and negatively pressured treatment rooms (NP). These interventions were evaluated to minimize total cost of healthcare-associated infections (HAIs), including medical and litigation costs.

BACKGROUND

HAIs are largely preventable but are difficult to control because of their multiple mechanisms of transmission. Moreover, the costs of HAIs and resulting mortality are increasing with the latest estimates at US$9.8 billion annually.

METHOD

Using 6 years of longitudinal multidrug-resistant infection data, we simulated the transmission of pathogenic bacteria and the infection control efforts of the three alterations using Chamchod and Ruan's model. We determined the optimal budget allocations among the alterations by representing them under Karush-Kuhn-Tucker conditions for this nonlinear optimization problem.

RESULTS

We examined 24 scenarios using three virulence levels across three facility sizes with varying budget levels. We found that in general, most of the budget is allocated to the NP or RH alterations in each intervention. At lower budgets, however, it was necessary to use the lower cost alterations, HW or RH.

CONCLUSIONS

Mathematical optimization offers healthcare enterprise executives and engineers a tool to assist with the design of safer healthcare facilities within a fiscally constrained environment. Herein, models were developed for the optimal allocation of funds between HW, RH, and negatively pressured treatment rooms (NP) to best reduce HAIs. Specific strategies vary by facility size and virulence.

Applications of systems modelling in obesity research

Obesity is a complex system problem involving a broad spectrum of policy, social, economic, cultural, environmental, behavioural, and biological factors and the complex interrelated, cross-sector, non-linear, dynamic relationships among them. Systems modelling is an innovative approach with the potential for advancing obesity research. This study examined the applications of systems modelling in obesity research published between 2000 and 2017, examined how the systems models were developed and used in obesity studies and discussed related gaps in current research. We focused on the applications of two main systems modelling approaches: system dynamics modelling and agent-based modelling. The past two decades have seen a growing body of systems modelling in obesity research. The research topics ranged from micro-level to macro-level energy-balance-related behaviours and policies (19 studies), population dynamics (five studies), policy effect simulations (eight studies), environmental (10 studies) and social influences (15 studies) and their effects on obesity rates. Overall, systems analysis in public health research is still in its early stages, with limitations linked to model validity, mixed findings and its actual use in guiding interventions. Challenges in theory and modelling practices need to be addressed to realize the full potential of systems modelling in future obesity research and interventions.

Improving health systems performance in low- and middle-income countries: a system dynamics model of the pay-for-performance initiative in Afghanistan

System dynamics methods were used to explore effective implementation pathways for improving health systems performance through pay-for-performance (P4P) schemes. A causal loop diagram was developed to delineate primary causal relationships for service delivery within primary health facilities. A quantitative stock-and-flow model was developed next. The stock-and-flow model was then used to simulate the impact of various P4P implementation scenarios on quality and volume of services. Data from the Afghanistan national facility survey in 2012 was used to calibrate the model. The models show that P4P bonuses could increase health workers' motivation leading to higher levels of quality and volume of services. Gaming could reduce or even reverse this desired effect, leading to levels of quality and volume of services that are below baseline levels. Implementation issues, such as delays in the disbursement of P4P bonuses and low levels of P4P bonuses, also reduce the desired effect of P4P on quality and volume, but they do not cause the outputs to fall below baseline levels. Optimal effect of P4P on quality and volume of services is obtained when P4P bonuses are distributed per the health workers' contributions to the services that triggered the payments. Other distribution algorithms such as equal allocation or allocations proportionate to salaries resulted in quality and volume levels that were substantially lower, sometimes below baseline. The system dynamics models served to inform, with quantitative results, the theory of change underlying P4P intervention. Specific implementation strategies, such as prompt disbursement of adequate levels of performance bonus distributed per health workers' contribution to service, increase the likelihood of P4P success. Poorly designed P4P schemes, such as those without an optimal algorithm for distributing performance bonuses and adequate safeguards for gaming, can have a negative overall impact on health service delivery systems.

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.

A predictive model of rat calorie intake as a function of diet energy density

Easy access to high-energy food has been linked to high rates of obesity in the world. Understanding the way that access to palatable (high fat or high calorie) food can lead to overconsumption is essential for both preventing and treating obesity. Although the body of studies focused on the effects of high-energy diets is growing, our understanding of how different factors contribute to food choices is not complete. In this study, we present a mathematical model that can predict rat calorie intake to a high-energy diet based on their ingestive behavior to a standard chow diet. Specifically, we propose an equation that describes the relation between the body weight ( W), energy density ( E), time elapsed from the start of diet ( T), and daily calorie intake ( C). We tested our model on two independent data sets. Our results show that the suggested model can predict the calorie intake patterns with high accuracy. Additionally, the only free parameter of our proposed equation (ρ), which is unique to each animal, has a strong correlation with their calorie intake.

Evolution of vulnerability of communities facing repeated hazards

The decisions that individuals make when recovering from and adapting to repeated hazards affect a region's vulnerability in future hazards. As such, community vulnerability is not a static property but rather a dynamic property dependent on behavioral responses to repeated hazards and damage. This paper is the first of its kind to build a framework that addresses the complex interactions between repeated hazards, regional damage, mitigation decisions, and community vulnerability. The framework enables researchers and regional planners to visualize and quantify how a community could evolve over time in response to repeated hazards under various behavioral scenarios. An illustrative example using parcel-level data from Anne Arundel County, Maryland-a county that experiences fairly frequent hurricanes-is presented to illustrate the methodology and to demonstrate how the interplay between individual choices and regional vulnerability is affected by the region's hurricane experience.

Taking dietary habits into account: A computational method for modeling food choices that goes beyond price

Computational models have gained popularity as a predictive tool for assessing proposed policy changes affecting dietary choice. Specifically, they have been used for modeling dietary changes in response to economic interventions, such as price and income changes. Herein, we present a novel addition to this type of model by incorporating habitual behaviors that drive individuals to maintain or conform to prior eating patterns. We examine our method in a simulated case study of food choice behaviors of low-income adults in the US. We use data from several national datasets, including the National Health and Nutrition Examination Survey (NHANES), the US Bureau of Labor Statistics and the USDA, to parameterize our model and develop predictive capabilities in 1) quantifying the influence of prior diet preferences when food budgets are increased and 2) simulating the income elasticities of demand for four food categories. Food budgets can increase because of greater affordability (due to food aid and other nutritional assistance programs), or because of higher income. Our model predictions indicate that low-income adults consume unhealthy diets when they have highly constrained budgets, but that even after budget constraints are relaxed, these unhealthy eating behaviors are maintained. Specifically, diets in this population, before and after changes in food budgets, are characterized by relatively low consumption of fruits and vegetables and high consumption of fat. The model results for income elasticities also show almost no change in consumption of fruit and fat in response to changes in income, which is in agreement with data from the World Bank's International Comparison Program (ICP). Hence, the proposed method can be used in assessing the influences of habitual dietary patterns on the effectiveness of food policies.

Simulated Models Suggest That Price per Calorie Is the Dominant Price Metric That Low-Income Individuals Use for Food Decision Making

BACKGROUND

The price of food has long been considered one of the major factors that affects food choices. However, the price metric (e.g., the price of food per calorie or the price of food per gram) that individuals predominantly use when making food choices is unclear. Understanding which price metric is used is especially important for studying individuals with severe budget constraints because food price then becomes even more important in food choice.

OBJECTIVE

We assessed which price metric is used by low-income individuals in deciding what to eat.

METHODS

With the use of data from NHANES and the USDA Food and Nutrient Database for Dietary Studies, we created an agent-based model that simulated an environment representing the US population, wherein individuals were modeled as agents with a specific weight, age, and income. In our model, agents made dietary food choices while meeting their budget limits with the use of 1 of 3 different metrics for decision making: energy cost (price per calorie), unit price (price per gram), and serving price (price per serving). The food consumption patterns generated by our model were compared to 3 independent data sets.

RESULTS

The food choice behaviors observed in 2 of the data sets were found to be closest to the simulated dietary patterns generated by the price per calorie metric. The behaviors observed in the third data set were equidistant from the patterns generated by price per calorie and price per serving metrics, whereas results generated by the price per gram metric were further away.

CONCLUSIONS

Our simulations suggest that dietary food choice based on price per calorie best matches actual consumption patterns and may therefore be the most salient price metric for low-income populations.

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.

Incorporating Systems Science Principles into the Development of Obesity Prevention Interventions: Principles, Benefits, and Challenges

Systems modeling represents an innovative approach for addressing the obesity epidemic at the community level. We developed an agent-based model of the Baltimore City food environment that permits us to assess the relative impact of different programs and policies, alone and in combination, and potential unexpected consequences. Based on this experience, and a review of literature, we have identified a set of principles, potential benefits, and challenges. Some of the key principles include the importance of early and multilevel engagement with the community prior to initiating model development and continued engagement and testing with community stakeholders. Important benefits include improving community stakeholder understanding of the system, testing of interventions before implementation, and identification of unexpected consequences. Challenges in these models include deciding on the most important, yet parsimonious factors to consider, how to model food source and food selection behavior in a realistic yet transferable manner, and identifying the appropriate outcomes and limitations of the model.

Examining social norm impacts on obesity and eating behaviors among US school children based on agent-based model

Background

Although the importance of social norms in affecting health behaviors is widely recognized, the current understanding of the social norm effects on obesity is limited due to data and methodology limitations. This study aims to use nontraditional innovative systems methods to examine: a) the effects of social norms on school children’s BMI growth and fruit and vegetable (FV) consumption, and b) the effects of misperceptions of social norms on US children’s BMI growth.

Methods

We built an agent-based model (ABM) in a utility maximization framework and parameterized the model based on empirical longitudinal data collected in a US nationally representative study, the Early Childhood Longitudinal Study – Kindergarten Cohort (ECLS-K), to test potential mechanisms of social norm affecting children’s BMI growth and FV consumption.

Results

Intraclass correlation coefficients (ICC) for BMI were 0.064-0.065, suggesting that children’s BMI were similar within each school. The correlation between observed and ABM-predicted BMI was 0.87, indicating the validity of our ABM. Our simulations suggested the follow-the-average social norm acts as an endogenous stabilizer, which automatically adjusts positive and negative deviance of an individual’s BMI from the group mean of a social network. One unit of BMI below the social average may lead to 0.025 unit increase in BMI per year for each child; asymmetrically, one unit of BMI above the social average, may only cause 0.015 unit of BMI reduction. Gender difference was apparent. Social norms have less impact on weight reduction among girls, and a greater impact promoting weight increase among boys. Our simulation also showed misperception of the social norm would push up the mean BMI and cause the distribution to be more skewed to the left. Our simulation results did not provide strong support for the role of social norms on FV consumption.

Conclusions

Social norm influences US children’s BMI growth. High obesity prevalence will lead to a continuous increase in children’s BMI due to increased socially acceptable mean BMI. Interventions promoting healthy body image and desirable socially acceptable BMI should be implemented to control childhood obesity epidemic.

Influenza Forecasting with Google Flu Trends

Objective

We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model.

Introduction

Each year, influenza results in increased Emergency Department crowding which can be mitigated through early detection linked to an appropriate response. Although current surveillance systems, such as Google Flu Trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases.

Methods

Forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear, and autoregressive methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Models were developed and evaluated through statistical measures of global deviance and log-likelihood ratio tests. An additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model.

Results

A generalized autoregressive Poisson (GARMA) forecast model integrating previous influenza cases with Google Flu Trends information provided the most accurate influenza case predictions. Google Flu Trend data was the only source of external information providing significant forecast improvements (p = 0.00002). The final model, a GARMA intercept model with the addition of Google Flu Trends, predicted weekly influenza cases during 4 out-of-sample outbreaks within 7 cases for 80% of estimates (Figure 1).

Conclusions

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Influenza forecasting with Google Flu Trends

BACKGROUND

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.

METHODS

Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.

RESULTS

A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.

CONCLUSIONS

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.