Publications

In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state,
congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

Inpatient care for children with severe traumatic brain injury (sTBI) is expensive, with inpatient charges averaging over $70,000 per case (Hospital Inpatient, Children Only, National Statistics. Diagnoses– clinical classification software (CCS) principal diagnosis category 85 coma, stupor, and brain damage, and 233 intracranial injury. Diagnoses by Aggregate charges [https://hcupnet.ahrq.gov/#setup]). This ranks sTBI in the top quartile of pediatric conditions with the greatest inpatient costs (Hospital Inpatient, Children Only, National Statistics. Diagnoses– clinical classification software (CCS) principal diagnosis category 85 coma, stupor, and brain damage, and 233 intracranial injury. Diagnoses by Aggregate charges [https://hcupnet.ahrq.gov/#setup]). The Brain Trauma Foundation developed sTBI intensive care guidelines in 2003, with revisions in 2012 (Kochanek, Carney, et. al. PCCM 3:S1-S2, 2012). These guidelines have been widely disseminated, and are associated with improved health outcomes (Pineda, Leonard. et. al. LN 12:45-52, 2013), yet research on the cost of associated hospital care is limited. The objective of this study was to assess the costs of providing hospital care to sTBI patients through a guideline-based Pediatric Neurocritical Care Program (PNCP) implemented at St. Louis Children’s Hospital, a pediatric academic medical center in the Midwest United States.

 

Objective: To evaluate the effectiveness of a physician-led rapid response team (RRT) program on morbidity and mortality following unplanned admission to the pediatric intensive care unit (PICU).

Design: Before-after study.

Setting: Single center quaternary referral PICU.

Patients: All unplanned PICU admissions from the ward from 2005-2011.

Interventions: The dataset was divided into pre- and post-RRT groups for comparison.

Measurements and Main Results: A Cox proportional hazards model was used to identify the patient characteristics associated with mortality following unplanned PICU admission. Following RRT implementation, PRISM-III illness severity was reduced 28.1%, PICU length of stay (LOS) was less 19.8%, and mortality declined 22%. Relative risk of death following unplanned admission to the PICU after RRT implementation was 0.685.

Conclusions: For children requiring unplanned admission to the PICU, RRT implementation is associated with reduced mortality, admission severity of illness and length of stay. RRT implementation led to more proximal capture and aggressive intervention in the trajectory of a decompensating pediatric ward patient.

BACKGROUND AND OBJECTIVES: Phlebotomy excess contributes to anemia in PICU patients and increases the likelihood of red blood cell transfusion, which is associated with risk of adverse outcomes. Excessive phlebotomy reduction (EPR) strategies may reduce the need for transfusion, but have not been evaluated in a PICU population. We hypothesized that EPR strategies, facilitated by implementation science methods, would decrease excess blood drawn and reduce transfusion frequency.

METHODS: Quantitative and qualitative methods were used. Patient and blood draw data were collected with survey and focus group data to evaluate knowledge and attitudes before and after EPR intervention. The Consolidated Framework for Implementation Research was used to interpret qualitative data. Multivariate regression was employed to adjust for potential confounders for blood overdraw volume and transfusion incidence.

RESULTS: Populations were similar pre- and postintervention. EPR strategies decreased blood overdraw volumes 62% from 5.5 mL (interquartile range 1–23) preintervention to 2.1 mL (interquartile range 0–7.9 mL) postintervention (P < .001). Fewer patients received red blood cell transfusions postintervention (32.1% preintervention versus 20.7% postintervention, P = .04). Regression analyses showed that EPR strategies reduced blood overdraw volume (P < .001) and lowered transfusion frequency (P = .05). Postintervention surveys reflected a high degree of satisfaction (93%) with EPR strategies, and 97% agreed EPR was a priority postintervention.

CONCLUSIONS: Implementation science methods aided in the selection of EPR strategies and enhanced acceptance which, in this cohort, reduced excessive overdraw volumes and transfusion frequency. Larger trials are needed to determine if this approach can be applied in broader PICU populations.

To understand how the nature of scientific collaboration between individuals and sites in team-based research initiatives affect

collaboration and research output, we examined four waves of prospective survey data to measure collaboration across investigators, disciplines, and sites to measure structural determinants of research success. 116 investigators in the five sites of the NIH-funded U54 Transdisciplinary Research on Energetics and Cancer (TREC) initiative were surveyed about their research ties with a 2011 baseline measure and followed by three additional iterations and augmented by bibliometric data.

Social network analysis describes the changing structure of contact and cooperation. We found that the network structure of a team science project affects the nature and rate of publications, implying that funded projects vary in research output based on how investigators interact with each other and that the design of scientific research projects affects research output by determining levels of contact between actual and potential collaborators.

Keywords: cancer; research; transdisciplinarity; team science; network models. 

Objective: Our goal was to identify risk factors for acute kidney injury (AKI) in children surviving cardiac arrest (CA).

Design: Retrospective analysis of a public-access dataset.

Setting: Fifteen children’s hospitals associated with the Pediatric Emergency Care Applied Research Network.

Patients: Two hundred ninety-six subjects between 1 day and 18 years of age who experienced in-hospital or out-of-hospital CA between July 1, 2003, and December 31, 2004.

Interventions: None.

Measurements and Main Results: Our primary outcome was development of AKI as defined by the Acute Kidney Injury Network (AKIN) staged criteria. An ordinal logistic model was developed using 8 candidate variables. We found 6 critical explanatory variables, including total number of epinephrine doses, post-CA blood pressure, arrest location, presence of a chronic lung condition, pH nadir, and presence of an abnormal baseline creatinine.

Conclusions: This study is the first to identify risk factors for AKI in children after CA. Our findings regarding the impact of epinephrine dosing are of particular interest and suggest potential for epinephrine toxicity with regard to AKI. The ability to identify and potentially modify risk factors for AKI after CA may lead to improved morbidity and mortality in this challenging population.

Key Words: cardiac arrest; children; pediatric; outcome; acute kidney injury; epinephrine.

Purpose

To quantify the coinciding improvement in the clinical diagnosis of sepsis, its documentation in the electronic health records, and subsequent medical coding of sepsis for billing purposes in recent years.

Methods

We examined 98,267 hospitalizations in 66,208 patients who met systemic inflammatory response syndrome criteria at a tertiary care center from 2008 to 2012. We used g-computation to estimate the causal effect of the year of hospitalization on receiving an International Classification of Diseases, Ninth Revision, Clinical Modification discharge diagnosis code for sepsis by estimating changes in the probability of getting diagnosed and coded for sepsis during the study period.

Results

When adjusted for demographics, Charlson-Deyo comorbidity index, blood culture frequency per hospitalization, and intensive care unit admission, the causal risk difference for receiving a discharge code for sepsis per 100 hospitalizations with systemic inflammatory response syndrome, had the hospitalization occurred in 2012, was estimated to be 3.9% (95% confidence interval [CI], 3.8%–4.0%), 3.4% (95% CI, 3.3%–3.5%), 2.2% (95% CI, 2.1%–2.3%), and 0.9% (95% CI, 0.8%–1.1%) from 2008 to 2011, respectively.

Conclusions

Patients with similar characteristics and risk factors had a higher of probability of getting diagnosed, documented, and coded for sepsis in 2012 than in previous years, which contributed to an apparent increase in sepsis incidence.

Keywords:

Causality, ICD-9-CM, Sepsis, Systemic inflammatory response syndrome, Risk difference

An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person’s activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis more challenging. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation is performed at the minute level and follows the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using 2003−2004 National Health and Nutrition Examination Survey data.

Keywords Accelerometer, physical activity, missing count data, multiple imputation, zero-inflated model, Poisson log-normal

 

Background.  The long-term and cumulative effect of multiple episodes of bacteremia and sepsis across multiple hospitalizations on the development of cardiovascular (CV) events is uncertain. 

Methods.  We conducted a longitudinal study of 156 380 hospitalizations in 47 009 patients (≥18 years old) who had at least 2 inpatient admissions at an academic tertiary care center in St Louis, Missouri, from 1 January 2008 through 31 December 2012. We used marginal structural models, estimated by inverse probability weighting (IPW) of bacteremia or sepsis and IPW of censoring, to estimate the marginal causal effects of bacteremia and sepsis on developing the first observed incident CV event, including stroke, transient ischemic attack, and myocardial infarction (MI), during the study period. 

Results.  Bacteremia and sepsis occurred during 4923 (3.1%) and 5544 (3.5%) hospitalizations among 3932 (8.4%) and 4474 (9.5%) patients, respectively. CV events occurred in 414 (10.5%) and 538 (12.0%) patients with prior episodes of bacteremia or sepsis, respectively, vs 3087 (7.2%) and 2963 (7.0%) patients without prior episodes of bacteremia or sepsis. The causal odds of experiencing a CV event was 1.52-fold (95% confidence interval [CI], 1.21- to 1.90-fold) and 2.39-fold (95% CI, 1.88- to 3.03-fold) higher in patients with prior instances of bacteremia or sepsis, respectively, compared to those without. Prior instances of septic shock resulted in a 6.91-fold (95% CI, 5.34- to 8.93-fold) increase in the odds of MI. 

Conclusions.  Prior instances of bacteremia and sepsis substantially increase the 5-year risk of CV events. 

KEYWORDS: bloodstream infection, causal inference, inverse probability weighting, marginal structural model, time-varying confounding

Purpose

Past studies of sepsis epidemiology did not address misclassification bias due to imperfect verification of sepsis detection methods to estimate the true prevalence.

Methods

We examined 273,126 hospitalizations from 2008 to 2012 at a tertiary-care center to develop surveillance-aimed sepsis detection criteria, based on the presence of the sepsis-explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes (995.92 or 785.52), blood culture orders, and antibiotics administration. We used Bayesian multinomial latent class models to estimate the true prevalence of sepsis, while adjusting for the imperfect sensitivity and specificity and the conditional dependence among the individual criteria.

Results

The apparent annual prevalence of sepsis hospitalizations based on explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes were 1.5%, 1.4%, 1.6%, 2.2%, and 2.5% for the years 2008 to 2012. Bayesian posterior estimates for the true prevalence of sepsis suggested that it remained stable from 2008, 19.2% (95% credible interval [CI]: 17.9%, 22.9%), to 2012, 17.8% (95% CI: 16.8%, 20.2%). The sensitivity of sepsis-explicit codes, however, increased from 7.6% (95% CI: 6.4%, 8.4%) in 2008 to 13.8% (95% CI: 12.2%, 14.9%) in 2012.

Conclusions

The true prevalence of sepsis remained high, but stable despite an increase in the sensitivity of sepsis-explicit codes in administrative data.

We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved
heterogeneity among immigrant voters. Our approach is based on \emph{generalized linear mixed Dirichlet models} (GLMDM) where
random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for
unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent
effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult
problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2)
smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive
understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among
immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant
literature suggests.  When looking at vote choice we also found that an immigrant's degree of structural integration does not
affect the vote in favor of the CDU/CSU, a party which is traditionally associated with restrictive immigration policy.

Background: The National Cancer Institute's Transdisciplinary Research in Energetics and Cancer initiative is in its second round of funding. Despite increasing agreement that trans-disciplinary team-based research is valuable in addressing complex problems like energy balance and cancer, methods for constructing and maintaining transdisciplinary teams is lacking. Purpose: We articulate a method for assessing trans-disciplinary teams that relies on social network analysis and using this knowledge to improve their functioning. Methods: Using data from the Washington University TREC site in 2011 and 2013, we demonstrate the use of social network analysis to assess and provide feedback on team functioning. Results: We portray broker functioning in both years. By 2013, the director and co-director had begun to share broker functions with other members. Some brokers fostered communication with less central network members. Conclusions: The information obtained can help to train a new generation of investigators to optimally participate on transdisciplinary research teams.
A Social Network Analysis Approach to... (PDF Download Available). Available from: https://www.researchgate.net/publication/279790188_A_Social_Network_Analysis_Approach_to_Diagnosing_and_Improving_the_Functioning_of_Transdisciplinary_Teams_in_Public_Health 

Identification of modifiable risk factors is gravely needed to prevent adverse prostate health outcomes. We previously developed a murine precancer model in which exposure to maternal obesity stimulated prostate hyperplasia in offspring. Here, we used generalized linear modeling to evaluate the influence of additional environmental covariates on prostate hyperplasia. As expected from our previous work, the model revealed that aging and maternal diet-induced obesity (DIO) each correlated with prostate hyperplasia. However, prostate hyperplasia was not correlated with the length of maternal DIO. Cage density positively associated with both prostate hyperplasia and offspring body weight. Expression of the glucocorticoid receptor in prostates also positively correlated with cage density and negatively correlated with age of the animal. Together, these findings suggest that prostate tissue was adversely patterned during early life by maternal overnutrition and was susceptible to alteration by environmental factors such as cage density. Additionally, prostate hyperplasia may be acutely influenced by exposure to DIO, rather than occurring as a response to worsening obesity and comorbidities experienced by the mother. Finally, cage density correlated with both corticosteroid receptor abundance and prostate hyperplasia, suggesting that overcrowding influenced offspring prostate hyperplasia. These results emphasize the need for multivariate regression models to evaluate the influence of coordinated variables in complicated animal systems.

Keywords prostate hyperplasia, cage overcrowding, maternal obesity, developmental programming, generalized linear modeling

Purpose

Recognition of the complex, multidimensional relationship between excess adiposity and cancer control outcomes has motivated the scientific community to seek new research models and paradigms.

Methods

The National Cancer Institute developed an innovative concept to establish a center grant mechanism in nutrition, energetics, and physical activity, referred to as the Transdisciplinary Research on Energetics and Cancer (TREC) Initiative. This paper gives an overview of the 2011–2016 TREC Collaborative Network and the 15 research projects being conducted at the centers.

Results

Four academic institutions were awarded TREC center grants in 2011: Harvard University, University of California San Diego, University of Pennsylvania, and Washington University in St. Louis. The Fred Hutchinson Cancer Research Center is the Coordination Center. The TREC research portfolio includes three animal studies, three cohort studies, four randomized clinical trials, one cross-sectional study, and two modeling studies. Disciplines represented by TREC investigators include basic science, endocrinology, epidemiology, biostatistics, behavior, medicine, nutrition, physical activity, genetics, engineering, health economics, and computer science. Approximately 41,000 participants will be involved in these studies, including children, healthy adults, and breast and prostate cancer survivors. Outcomes include biomarkers of cancer risk, changes in weight and physical activity, persistent adverse treatment effects (e.g., lymphedema, urinary and sexual function), and breast and prostate cancer mortality.

Conclusion

The NIH Science of Team Science group will evaluate the value added by this collaborative science. However, the most important outcome will be whether this transdisciplinary initiative improves the health of Americans at risk of cancer as well as cancer survivors.

There is some confusion in political science, and the social sciences in general, about the meaning and interpretation of
interaction effects in models with non-interval, non-normal outcome variables.  Often these terms are casually thrown into a model
specification without observing that their presence fundamentally changes the interpretation of the resulting coefficients.  The
work here explains the conditional nature of reported coefficients and their standard errors in models with interactions, defining
the necessarily different interpretation required by generalized linear models, and providing a general analytical method for
correctly calculating coefficient standard errors in models with second-order or higher interactions.  Methodological issues are
illustrated with an application of generalized linear models with interactions applied to voter information structured by
electoral systems and resulting legislative representation in comparative politics.