Association of social vulnerability and influenza vaccination rates for Annual Medicare Enrollees at the county-level in the United States.

INTRODUCTION: Influenza is a preventable acute respiratory illness with a high potential to cause serious complications and is associated with high mortality and morbidity in the US. We aimed to determine the specific community-level vulnerabilities for different race/ethnic communities that are most predictive of influenza vaccination rates. METHODS: We conducted a machine learning analysis (XGBoost) to identify community-level social vulnerability features that are predictive of influenza vaccination rates among Medicare enrollees across counties in the US and by race/ethnicity. RESULTS: Population density per square mile in a county is the most important feature in predicting influenza vaccination in a county, followed by unemployment rates and the percentage of mobile homes. The gain relative importance of these features are 11.6%, 9.2%, and 9%, respectively. Among whites, population density (17% gain relative importance) was followed by the percentage of mobile homes (9%) and per capita income (8.7%). For Black/African Americans, the most important features were population density (12.8%), percentage of minorities in the county (8.0%), per capita income (6.9%), and percent of over-occupied housing units (6.8%). Finally, for Hispanics, the top features were per capita income (8.4%), percentage of mobile homes (8.0%), percentage of non-institutionalized persons with a disability (7.9%), and population density (7.6%). CONCLUSIONS: Our study may have implications for the success of large vaccination programs in counties with high social vulnerabilities. Further, our findings suggest that policies and interventions seeking to increase rates of vaccination in race/ethnic minority communities may need to be tailored to address their specific socioeconomic vulnerabilities.

A decision analytic approach for social distancing policies during early stages of COVID-19 pandemic.

The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.

Perceptions of Obesity Prevention Policies: Socioeconomic Assessment in the Turkish Capital.

PURPOSE: The purpose of this study was to inform public policy opportunities to reduce childhood obesity by identifying parents' perceptions of factors contributing to childhood obesity, attribution of responsibility, and the extent of their support for public prevention policies with attention to socio-economic status. DESIGN AND METHODS: In 2015, 2066 parent-child dyads across socio-economic strata from 43 randomly selected schools in Ankara completed surveys and measurements to examine perceptions, attribution, and prevention policies related to childhood obesity. RESULTS: Parents across the socio-demographic spectrum recognized obesity as a serious problem. Unhealthy food availability was identified as the leading cause of while industry and media were credited with having the greatest responsibility for childhood obesity. There was strong public support for policy strategies targeting schools, marketing, and the built environment, though support tempered as socio-economic status and parental education decreased. CONCLUSIONS: This survey provided insight into parents' knowledge and beliefs surrounding childhood obesity as well as their endorsement of related prevention strategies. Educational messages that address variations in SES to describe the causes of childhood obesity and connect those causes to actionable community prevention strategies may improve community support for enhanced policy actions within and beyond school settings.

A decision support tool to determine cost-to-benefit of a family-centered in-home program for at-risk adolescents.

Family-centered program research has demonstrated its effectiveness in improving adolescent outcomes. However, given current fiscal constraints faced by governmental agencies, a recent report from the Institute of Medicine and National Research Council highlighted the need for cost-benefit analyses to inform decision making by policymakers. Furthermore, performance management tools such as balanced scorecards and dashboards do not generally include cost-benefit analyses. In this paper, we describe the development of an Excel-based decision support tool that can be used to evaluate a selected family-based program for at-risk children and adolescents relative to a comparison program or the status quo. This tool incorporates the use of an efficient, user-friendly interface with results provided in concise tabular and graphical formats that may be interpreted without need for substantial training in economic evaluation. To illustrate, we present an application of this tool to evaluate use of Boys Town's In-Home Family Services (IHFS) relative to detention and out-of-home placement in New York City. Use of the decision support tool can help mitigate the need for programs to contract experts in economic evaluation, especially when there are financial or time constraints.

The Association of Gasoline Prices With Hospital Utilization and Costs for Motorcycle and Nonmotorcycle Motor Vehicle Injuries in the United States.

OBJECTIVES: This study examined the association between gasoline prices and hospitalizations for motorcycle and nonmotorcycle motor vehicle crash (MVC) injuries. METHODS: Data on inpatient hospitalizations were obtained from the 2001 to 2010 Nationwide Inpatient Sample. Panel feasible generalized least squares models were used to estimate the effects of monthly inflation-adjusted gasoline prices on hospitalization rates for MVC injuries and to predict the impact of increasing gasoline taxes. RESULTS: On the basis of the available data, a $1.00 increase in the gasoline tax was associated with an estimated 8348 fewer annual hospitalizations for nonmotorcycle MVC injuries, and reduced hospital costs by $143 million. However, the increase in the gasoline tax was also associated with an estimated 3574 more annual hospitalizations for motorcycle crash injuries, and extended hospital costs by $73 million. CONCLUSIONS: This analysis of some existing data suggest that the increased utilization and costs of hospitalization from motorcycle crash injuries associated with an increase in the price of gasoline are likely to substantially offset reductions in nonmotorcycle MVC injuries. A policy decision to increase the gasoline tax could improve traffic safety if the increased tax is paired with public health interventions to improve motorcycle safety.

Community-Engaged Public Health Research to Inform Hospital Campus Planning in a Low Socioeconomic Status Urban Neighborhood.

OBJECTIVE: To compare sociodemographic and motivational factors for healthcare use and identify desirable health-promoting resources among groups in a low socioeconomic status (SES) community in Chicago, IL. BACKGROUND: Disparities in health services and outcomes are well established in low SES urban neighborhoods in the United States and many factors beyond service availability and quality impact community health. Yet there is no clear process for engaging communities in building resources to improve population-level health in such locales. METHODS: A hospital building project led to a partnership of public health researchers, architects, and planners who conducted community-engaged research. We collected resident data and compared factors for healthcare use and choice and likelihood of engaging new health-promoting services. RESULTS: Neighborhood areas were strongly associated with ethnic groupings, and there were differences between groups in healthcare choice and service needs, such as, proximity to home was more important to Latinos than African Americans in choice of healthcare facility ( p adj = .001). Latinos expressed higher likelihood to use a fitness facility ( p adj = .001). Despite differences in vehicle ownership, >75% of all respondents indicated that nearby public transportation was important in choosing healthcare. CONCLUSION: Knowledge of community needs and heterogeneity is essential to decision makers of facility and community development plans. Partnerships between public health, urban planning, architecture, and local constituents should be cultivated toward focus on reducing health disparities. Further work to integrate community perspectives through the planning and design process and to evaluate the long-term impact of such efforts is needed.

Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska.

INTRODUCTION: Emergency department (ED) visits increase during the influenza seasons. It is essential to identify statistically significant correlates in order to develop an accurate forecasting model for ED visits. Forecasting influenza-like-illness (ILI)-related ED visits can significantly help in developing robust resource management strategies at the EDs. METHODS: We first performed correlation analyses to understand temporal correlations between several predictors of ILI-related ED visits. We used the data available for Douglas County, the biggest county in Nebraska, for Omaha, the biggest city in the state, and for a major hospital in Omaha. The data set included total and positive influenza test results from the hospital (ie, Antigen rapid (Ag) and Respiratory Syncytial Virus Infection (RSV) tests); an Internet-based influenza surveillance system data, that is, Google Flu Trends, for both Nebraska and Omaha; total ED visits in Douglas County attributable to ILI; and ILI surveillance network data for Douglas County and Nebraska as the predictors and data for the hospital's ILI-related ED visits as the dependent variable. We used Seasonal Autoregressive Integrated Moving Average and Holt Winters methods with3 linear regression models to forecast ILI-related ED visits at the hospital and evaluated model performances by comparing the root means square errors (RMSEs). RESULTS: Because of strong positive correlations with ILI-related ED visits between 2008 and 2012, we validated the use of Google Flu Trends data as a predictor in an ED influenza surveillance tool. Of the 5 forecasting models we have tested, linear regression models performed significantly better when Google Flu Trends data were included as a predictor. Regression models including Google Flu Trends data as a predictor variable have lower RMSE, and the lowest is achieved when all other variables are also included in the model in our forecasting experiments for the first 5 weeks of 2013 (with RMSE = 57.61). CONCLUSIONS: Google Flu Trends data statistically improve the performance of predicting ILI-related ED visits in Douglas County, and this result can be generalized to other communities. Timely and accurate estimates of ED volume during the influenza season, as well as during pandemic outbreaks, can help hospitals plan their ED resources accordingly and lower their costs by optimizing supplies and staffing and can improve service quality by decreasing ED wait times and overcrowding.

Simulating school closure policies for cost effective pandemic decision making.

BACKGROUND: Around the globe, school closures were used sporadically to mitigate the 2009 H1N1 influenza pandemic. However, such closures can detrimentally impact economic and social life. METHODS: Here, we couple a decision analytic approach with a mathematical model of influenza transmission to estimate the impact of school closures in terms of epidemiological and cost effectiveness. Our method assumes that the transmissibility and the severity of the disease are uncertain, and evaluates several closure and reopening strategies that cover a range of thresholds in school-aged prevalence (SAP) and closure durations. RESULTS: Assuming a willingness to pay per quality adjusted life-year (QALY) threshold equal to the US per capita GDP ($46,000), we found that the cost effectiveness of these strategies is highly dependent on the severity and on a willingness to pay per QALY. For severe pandemics, the preferred strategy couples the earliest closure trigger (0.5% SAP) with the longest duration closure (24 weeks) considered. For milder pandemics, the preferred strategies also involve the earliest closure trigger, but are shorter duration (12 weeks for low transmission rates and variable length for high transmission rates). CONCLUSIONS: These findings highlight the importance of obtaining early estimates of pandemic severity and provide guidance to public health decision-makers for effectively tailoring school closures strategies in response to a newly emergent influenza pandemic.

A new method of exercising pandemic preparedness through an interactive simulation and visualization.

As seen in the spring 2009 A/H1N1 influenza outbreak, influenza pandemics can have profound social, legal and economic effects. This experience has also made the importance of public health preparedness exercises more evident. Universities face unique challenges with respect to pandemic preparedness due to their dense student populations, location within the larger community and frequent student/faculty international travel. Depending on the social structure of the community, different mitigation strategies should be applied for decreasing the severity and transmissibility of the disease. To this end, Arizona State University has developed a simulation model and tabletop exercise that facilitates decision-maker interactions around emergency-response scenarios. This simulation gives policy makers the ability to see the real-time impact of their decisions. Therefore, tabletop exercises with computer simulations are developed to practice these decisions, which can possibly give opportunities for practicing better policy implementations. This paper introduces a new method of designing and performing table-top exercises for pandemic influenza via state-of-the-art technologies including system visualization and group decision making with a supporting simulation model. The presented exercise methodology can increase readiness for a pandemic through the support of computer and information technologies and the survey results that we include in this paper certainly support this result. The video scenarios and the computer simulation model make the exercise appear very compelling and real, which makes this presented method of exercising different than the other table-top exercises in the literature. Finally, designing a pandemic preparedness exercise with supporting technologies can help identifying the communication gaps between responsible authorities and advance the table-top exercising methodology.