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.

Predicting mobility using limited data during early stages of a pandemic.

The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize a novel optimization scheme to induce linear models containing strictly significant covariates and minimal multicollinearity. We find that there are a variety of unique factors that drive mobility in different geographic locations, as well as several factors that are common to all locations.

Impact of Florida's prescription drug monitoring program on drug- related fatal vehicle crashes: a difference-in-differences approach.

BACKGROUND: Prescription drug use has soared in the USA within the last two decades. Prescription drugs can impair motor skills essential for the safe operation of a motor vehicle, and therefore can affect traffic safety. As one of the epicentres of the opioid epidemic, Florida has been struck by high opioid misuse and overdose rates, and has concurrently suffered major threats to traffic disruptions safety caused by driving under the influence of drugs. To prevent prescription opioid misuse in Florida, Prescription Drug Monitoring Programs (PDMPs) were implemented in September 2011. OBJECTIVE: To examine the impact of Florida's implementation of a mandatory PDMP on drug-related MVCs occurring on public roads. METHODS: We employed a difference-in-differences approach to estimate the difference in prescription drug-related fatal crashes in Florida associated with its 2011 PDMP implementation relative to those in Georgia, which did not use PDMPs during the same period (2009-2013). The analyses were conducted in 2020. RESULTS: In Florida, there was a significant decline in drug-related vehicle crashes during the 22 months post-PDMP. PDMP implementation was associated with approximately two (-2.21; 95% CI -4.04 to -0.37; p<0.05) fewer prescribed opioid-related fatal crashes every month, indicating 25% reduction in the number of monthly crashes. We conducted sensitivity analyses to investigate the impact of PDMP implementation on central nervous system depressants and stimulants as well as cocaine and marijuana-related fatal crashes but found no robust significant reductions. CONCLUSIONS: The implementation of PDMPs in Florida provided important benefits for traffic safety, reducing the rates of prescription opioid-related vehicle crashes.

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.

Stockpiling Ventilators for Influenza Pandemics.

In preparing for influenza pandemics, public health agencies stockpile critical medical resources. Determining appropriate quantities and locations for such resources can be challenging, given the considerable uncertainty in the timing and severity of future pandemics. We introduce a method for optimizing stockpiles of mechanical ventilators, which are critical for treating hospitalized influenza patients in respiratory failure. As a case study, we consider the US state of Texas during mild, moderate, and severe pandemics. Optimal allocations prioritize local over central storage, even though the latter can be deployed adaptively, on the basis of real-time needs. This prioritization stems from high geographic correlations and the slightly lower treatment success assumed for centrally stockpiled ventilators. We developed our model and analysis in collaboration with academic researchers and a state public health agency and incorporated it into a Web-based decision-support tool for pandemic preparedness and response.

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.

Share of mass transit miles traveled and reduced motor vehicle fatalities in major cities of the United States.

The USA leads the developed world in motor vehicle fatalities, presenting a critical public health threat. We examined whether an increasing share of mass transit use, relative to vehicle miles traveled on public roads, was associated with reduced motor vehicle fatalities. We used annual city-level data for the USA from 1982-2010 provided by the Fatality Accident Reporting System, the Texas A&M Transportation Institute, the Census Bureau, and the National Oceanic and Atmospheric Administration to estimate a structural equation model of the factors associated with mass transit miles and motor vehicle fatalities. The final analytic data included 2,900 observations from 100 cities over 29 years. After accounting for climate, year, and the economic costs of driving, an increasing share of mass transit miles traveled per capita was associated with reduced motor vehicle fatalities. The costs of congestion to the average commuter and gas prices were positively associated with increasing the share of mass transit miles traveled. The economic costs of driving increased over time, while both the fatality rate and the share of mass transit miles traveled decreased over time. Increasing the share of mass transit miles traveled may be associated with fewer motor vehicle miles traveled. Increasing mass transit uptake may be an effective public health intervention to reduce motor vehicle fatalities in cities.

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.

Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach.

BACKGROUND: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends. OBJECTIVES: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model. METHODS: We predicted COVID-19 new cases using Google query data over three periods. Our first period covered March 1, 2020, to July 31, 2020, including the first peak of infection. We also estimated a model from October 1, 2020, to January 7, 2021, including the second wave of COVID-19 and avoiding possible biases from public interest in searching about the new pandemic. In addition, we extended our forecasting period from May 20, 2020, to January 31, 2021, to cover an extended period of time. RESULTS: Our findings show that Google relative search volume (RSV) can be used to accurately predict new COVID-19 cases.  We find that among our Google relative search volume terms, "Fever," "COVID Testing," "Signs of COVID," "COVID Treatment," and "Shortness of Breath" increase model predictive accuracy. CONCLUSIONS: Our findings highlight the value of using data sources providing near real-time data, e.g., Google Trends, to detect trends in COVID-19 cases, in order to supplement and extend existing epidemiological models.

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.

The importance of widespread testing for COVID-19 pandemic: systems thinking for drive-through testing sites.

On 11 March 2020, the World Health Organisation (WHO) declared COVID-19 a pandemic. Early epidemiological estimates show that COVID-19 is highly transmissible, infecting populations across the globe in a short amount of time. WHO has recommended widespread clinical testing in order to contain COVID-19. However, mass testing in emergency department (ED) settings may result in crowded EDs and increase transmission risk for healthcare staff and other ED patients. Drive-through COVID-19 testing sites are an effective solution to quickly collect samples from suspected cases with minimal risk to healthcare personnel and other patients. Nevertheless, there are many logistical and operational challenges, such as shortages of testing kits, limited numbers of healthcare staff and long delays for collecting samples. Solving these problems requires an understanding of disease dynamics and epidemiology, as well as the logistics of mass distribution. In this position paper, we provide a conceptual framework for addressing these challenges, as well as some insights from prior literature and experience on developing decision support tools for public health departments.

Dynamic Empirically Based Model for Understanding Future Trends in US Obesity Prevalence in the Context of Social Influences.

OBJECTIVE: This study aimed to (1) identify mechanistic model structures that produced quality fit to historic obesity prevalence trends and (2) evaluate the sensitivity of future obesity prevalence to social transmission and nonsocial parameters. METHODS: An age- and gender-structured compartmental model was used to describe transitions between weight status groups. Four model structures with different combinations of social transmission and nonsocial mechanisms were calibrated to match historic time series and assessed for quality of fit. Projections of overall obesity prevalence to 2052 were simulated, and sensitivity analyses were conducted. RESULTS: The model structure that included only nonsocial mechanisms indicated that the overall obesity prevalence in the United States has already stabilized and will increase little more; however, it underestimated observed obesity prevalence since 2013. If social transmission mechanisms influence obesity, the model estimated continued increases in obesity prevalence, reaching 48.0% to 55.1% by 2050. Obesity prevalence was most sensitive to changes in the adult social transmission parameters, especially among women. CONCLUSIONS: The model projected that US obesity prevalence in the overall population will likely continue to increase for decades. The findings that obesity prevalence was most sensitive to adult parameters can be used to inform conversations about priorities for public health and health care programs and policies.

Prevalence of childhood obesity and related parental factors across socioeconomic strata in Ankara, Turkey.

BACKGROUND: Among low- and middle-income nations, the highest prevalence of child overweight and associated metabolic disorders has been found in Middle Eastern and Eastern European countries. Obesity has been on the rise in Turkey and past research has shown regional variations among adults. However, the prevalence of childhood obesity in different socioeconomic groups in the largest metropolitan areas in the country has not been reported. AIMS: This study aimed to investigate the prevalence of child obesity with a population-representative, SES-stratified random sample with objective measures of body mass index (BMI) in the capital city of Turkey. METHODS: Weight status was measured by the WHO growth curve and analyzed by socioeconomic status (SES), sex, and parental factors in a population-representative sample of 2066 parent-child dyads. Chi-square and logistic regression were conducted. RESULTS: Rates of overweight and obesity were 21.2% and 14.6% (35.8% combined) but significantly higher in high (24.5% and 18.9%) vs. low SES (20.1% and 13.8%) (P = 0.02). Boys were at higher risk for obesity than girls, especially in high vs. low SES (Odds Ratio [OR] = 3.0 [95% CI: 1.4-6.5] vs. 1.7 [95% CI: 1.2-2.5]). Having both parents being overweight or obese increased the risk for obesity, particularly in medium and high SES (OR = 5.8 [95% CI: 2.3-14.1]) and 6.3 (95% CI: 1.5-26.2). CONCLUSIONS: Higher maternal education was a risk factor in low-to-medium but not high SES. In Ankara, child overweight and obesity appears to be 1.5 times more prevalent than national estimates. Higher SES may signify greater exposure to an obesogenic environment and greater obesity risk.

Assessing the validity and reliability of family factors on physical activity: A case study in Turkey.

BACKGROUND: Childhood obesity rates have been rising rapidly in developing countries. A better understanding of the risk factors and social context is necessary to inform public health interventions and policies. This paper describes the validation of several measurement scales for use in Turkey, which relate to child and parent perceptions of physical activity (PA) and enablers and barriers of physical activity in the home environment. METHOD: The aim of this study was to assess the validity and reliability of several measurement scales in Turkey using a population sample across three socio-economic strata in the Turkish capital, Ankara. Surveys were conducted in Grade 4 children (mean age = 9.7 years for boys; 9.9 years for girls), and their parents, across 6 randomly selected schools, stratified by SES (n = 641 students, 483 parents). Construct validity of the scales was evaluated through exploratory and confirmatory factor analysis. Internal consistency of scales and test-retest reliability were assessed by Cronbach's alpha and intra-class correlation. RESULTS: The scales as a whole were found to have acceptable-to-good model fit statistics (PA Barriers: RMSEA = 0.076, SRMR = 0.0577, AGFI = 0.901; PA Outcome Expectancies: RMSEA = 0.054, SRMR = 0.0545, AGFI = 0.916, and PA Home Environment: RMSEA = 0.038, SRMR = 0.0233, AGFI = 0.976). The PA Barriers subscales showed good internal consistency and poor to fair test-retest reliability (personal α = 0.79, ICC = 0.29, environmental α = 0.73, ICC = 0.59). The PA Outcome Expectancies subscales showed good internal consistency and test-retest reliability (negative α = 0.77, ICC = 0.56; positive α = 0.74, ICC = 0.49). Only the PA Home Environment subscale on support for PA was validated in the final confirmatory model; it showed moderate internal consistency and test-retest reliability (α = 0.61, ICC = 0.48). DISCUSSION: This study is the first to validate measures of perceptions of physical activity and the physical activity home environment in Turkey. Our results support the originally hypothesized two-factor structures for Physical Activity Barriers and Physical Activity Outcome Expectancies. However, we found the one-factor rather than two-factor structure for Physical Activity Home Environment had the best model fit. This study provides general support for the use of these scales in Turkey in terms of validity, but test-retest reliability warrants further research.

Social Networking in an Agricultural Research Center: Using Data to Enhance Outcomes.

The purpose of this article is to present a case study of one midwestern Agricultural Center (Ag Center) that used social network analysis (SNA) to (1) evaluate its collaborations with extramural stakeholders and (2) strategically plan for extending outreach for goal achievement. An evaluation team developed a data collection instrument based on SNA principles. It was administered to the Ag Center's intramural stakeholders (N = 9), who were asked to identify the key extramural stakeholders with whom they had collaborated within the previous 12 months. Additional questions about each extramural stakeholder helped to categorize them according to SNA network measures for degree of centrality, betweenness centrality, and closeness centrality. Findings showed the Ag Center had N = 305 extramural stakeholders. Most of these were other researchers and did not represent the diverse group of stakeholders that the Ag Center had targeted for engagement. Only a few of the intramural stakeholders had national or international connections. Findings were used to improve and diversify connections in order to leverage the Ag Center's expertise and ability to translate research into new best practices and policies. The SNA case study has implications for other evaluators and project directors looking for methodologies that can monitor networks in large science consortia and help leaders plan for translating research into practice and policies by networking with those who can influence such change.

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.

Modeling social transmission dynamics of unhealthy behaviors for evaluating prevention and treatment interventions on childhood obesity.

Research evidence indicates that obesity has spread through social networks, but lever points for interventions based on overlapping networks are not well studied. The objective of our research was to construct and parameterize a system dynamics model of the social transmission of behaviors through adult and youth influence in order to explore hypotheses and identify plausible lever points for future childhood obesity intervention research. Our objectives were: (1) to assess the sensitivity of childhood overweight and obesity prevalence to peer and adult social transmission rates, and (2) to test the effect of combinations of prevention and treatment interventions on the prevalence of childhood overweight and obesity. To address the first objective, we conducted two-way sensitivity analyses of adult-to-child and child-to-child social transmission in relation to childhood overweight and obesity prevalence. For the second objective, alternative combinations of prevention and treatment interventions were tested by varying model parameters of social transmission and weight loss behavior rates. Our results indicated child overweight and obesity prevalence might be slightly more sensitive to the same relative change in the adult-to-child compared to the child-to-child social transmission rate. In our simulations, alternatives with treatment alone, compared to prevention alone, reduced the prevalence of childhood overweight and obesity more after 10 years (1.2-1.8% and 0.2-1.0% greater reduction when targeted at children and adults respectively). Also, as the impact of adult interventions on children was increased, the rank of six alternatives that included adults became better (i.e., resulting in lower 10 year childhood overweight and obesity prevalence) than alternatives that only involved children. The findings imply that social transmission dynamics should be considered when designing both prevention and treatment intervention approaches. Finally, targeting adults may be more efficient, and research should strengthen and expand adult-focused interventions that have a high residual impact on children.

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.