Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^

Risk factors for metabolic syndrome among overweight and obese Hispanic children attending a pediatric clinic in New Braunfels, Texas

Childhood obesity is a persistent problem in the U.S., especially among Hispanics. Health complications like hypertension, type II diabetes, and metabolic syndrome (Met-S) are being seen at younger ages, and current screening procedures may be inadequate. This study sought to describe the risk factors for Met-S present in a sample of 106 overweight and obese Hispanic children, aged 5-14 years, participating in Nutrition and Exercise Start Today (NEST), a randomized weight management intervention trial at a rural health clinic in New Braunfels, Texas; and to determine associations between these factors and other clinical and socio-demographic characteristics linked to obesity. Baseline data was analyzed for the prevalence of large waist circumference (WC), elevated blood pressure (BP), high fasting serum glucose and serum triglycerides (TG), and low serum HDL cholesterol, in relationship with selected sample characteristics. Main findings included high baseline prevalence rates of large WC (77%), reduced HDL (57%), and elevated BP (30%). WC was significantly associated with BMI percentile and the serum liver function test alanine aminotransferase (ALT) by Fisher's exact test (p<0.001 and p=0.032, respectively), while there were significant relationships between HDL and both female gender and ALT. BMI percentile and ALT were associated with all sets of Met-S diagnostic criteria examined. BMI percentile also had a strong association (p=0.005) with total number of Met-S risk factors, while ALT had a weaker association (p=0.093). WC is a low-cost, simple measure whose use may improve clinic surveillance for childhood obesity and complications like Met-S. WC, BP, HDL and ALT may be used as part of targeted screening for obesity complications like Met-S, particularly in situations where resources are limited.^

Identification of genetic risk factors for cerebellar mutism in pediatric brain tumor patients

Following posterior fossa surgery for resection of childhood medulloblastoma and primitive neuroectodermal tumor (M/PNET), cerebellar mutism (CM) may develop. This is a condition of absent or diminished speech in a conscious patient with no evidence of oral apraxia, which can be accompanied by other symptoms of the posterior fossa syndrome complex, which includes ataxia and hypotonia. Little is known about the etiology. Therefore, we conducted a SNP, gene, and pathway-level analysis to assess the role of host genetic variation on the risk of CM in M/PNET subjects following treatment. Cases (n= 20) and controls (n= 53) were recruited from the Childhood Cancer Epidemiology and Prevention Center, in Houston, TX. DNA samples were genotyped using the Illumina Human 1M Quad SNP chip. Ten pathways were identified from logistic regression used to identify the marginal effect of each SNP on CM risk. The minP test was conducted to identify associations between SNPs categorized to genes and CM risk. Pathways were assessed to determine if there was a significant enrichment of genes in the pathway compared to all other pathways. There were 78 genes that reached the threshold of min P ≤0.05 in 948 genes. The Neurotoxicity pathway was the most significant pathway after adjusting for multiple comparisons (q=0.040 and q=0.005, using Fisher's exact test and a test of proportions, respectively). Most genes within the Neurotoxicity pathway that reached a threshold of minP ≤0.05 were known to have an apoptosis function, possibly inducing neuronal apoptosis in the dentatothalamocortical pathway, and may be important in CM etiology in this population. This is the first study to assess the potential role of genetic risk factors on CM. As an exploratory study, these results should be replicated in a larger sample. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Observations from the Balcony: Directions for Pediatric Health Disparities Research and Policy

In 2002, the Institute of Medicine released Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare, a landmark monograph documenting health disparities in the U.S. health care system. Since the publication of Unequal Treatment, the field of pediatric health disparities research has advanced significantly with a proliferation of studies examining a wide array of topics concerning inequities in child health. Advances in health care policy and legislation have also added to a heightened discourse on pediatric health disparities. While there has been substantial activity in efforts to address pediatric health disparities, questions remain regarding whether these efforts have changed the trajectory of health equity among children. The aim of this paper is to examine the practical challenges of addressing pediatric health disparities in the dynamic context of global changes in health care research, policy, and legislation relevant to children. Using the Adaptive Leadership framework, this paper outlines a conceptual model for assessing the scope of progress made in addressing pediatric health disparities, diagnoses the continued adaptive challenges of pediatric health disparities, and provides an agenda for further work and future investment.

Debriefing after resuscitations in a pediatric emergency department

Multiple guidelines recommend debriefing of actual resuscitations to improve clinical performance. We implemented a novel standardized debriefing program using a Debriefing In Situ Conversation after Emergent Resuscitations Now (DISCERN) tool. Following the development of the evidence-based DISCERN tool, we conducted an observational study of all resuscitations (intubation, CPR, and/or defibrillation) at a pediatric emergency department (ED) over one year. Resuscitation interventions, patient survival, and physician team leader characteristics were analyzed as predictors for debriefing. Each debriefing's participants, time duration, and content were recorded. Thematic content of debriefings was categorized by framework approach into Team Emergency Assessment Measure (TEAM) elements. There were 241 resuscitations and 63 (26%) debriefings. A higher proportion of debriefings occurred after CPR (p<0.001) or ED death (p<0.001). Debriefing participants always included an attending and nurse; the median number of staff roles present was six. Median interval (from resuscitation end to start of debriefing) & debriefing durations were 33 (IQR 15,67) and 10 minutes (IQR 5,12), respectively. Common TEAM themes included co-operation/coordination (30%), communication (22%), and situational awareness (15%). Stated reasons for not debriefing included: unnecessary (78%), time constraints (19%), or other reasons (3%). Debriefings with the DISCERN tool usually involved higher acuity resuscitations, involved most of the indicated personnel, and lasted less than 10 minutes. This qualitative tool could be adapted to other settings. Future studies are needed to evaluate for potential impacts on education, quality improvement programming, and staff emotional well-being.^

The prevention of catheter related bloodstream infections in a pediatric population

Catheter related bloodstream infections are a significant barrier to success in many inpatient healthcare facilities. The goal of this study was to analyze and determine if an evidence based methodology to reduce the number of catheter related bloodstream infections in a pediatric inpatient healthcare facility had significant impact on the infection rate. Catheter related bloodstream infection rates were compared before and after program implementation. The patient population was selected based upon a recommendation in the 2010 National Healthcare Safety Network report on device related infections. This report indicated a need for more data on pediatric populations requiring admission to a long term care facility. The study design is a retrospective cohort study. Catheter related bloodstream infection data was gathered between 2008 and 2011. In October of 2008 a program implementation began to reduce the number of catheter related bloodstream infections. The key components of this initiative were to implement a standardized catheter maintenance checklist, introduce the usage of a chlorhexadine gluconate based product for catheter maintenance and skin antisepsis, and a multidisciplinary education plan that focused on hand hygiene and aseptic technique. The catheter related bloodstream infection rate in 2008 was 21.21 infections per 1000 patient-line days. After program implementation the 2009 catheter related bloodstream infection rate dropped to 1.11 per 1000 patient-line days. The infection rates in 2010 and 2011 were 2.19 and 1.47 respectively. Additionally, this study demonstrated that there was a potential cost savings of $620,000 to $1,240,000 between 2008 and 2009. In conclusion, an evidence based program based upon CDC guidelines can have a significant impact on catheter related bloodstream infection rates. ^

Weight-for-age percentile and its association to community acquired methicillin resistant Staphylococcus aureus among pediatric patients in Houston, TX

BACKGROUND: Weight has been implicated as a risk factor for symptomatic community-acquired methicillin resistant Staphylococcus Aureus (CA-MRSA). Information from Texas Children's Hospital (TCH) in Houston, TX was used to implement a case-control study to assess weight-for-age percentile (WFA), race and seasonal exposure as risk factors. ^ METHODS: A retrospective chart review to collect data from TCH was conducted covering the time period January 1st, 2008 to May 31st, 2011. Cases were confirmed and identified by the infectious disease department and were matched on a 1:1 ratio to controls that were seen by the emergency department for non-infected fractures from June 1st, 2008 to May 31st, 2011. Data abstraction was performed using TCH's electronic medical records (EMR) system (EPIC ®). ^ RESULTS: Of 702 CA-MRSA identified cases, ages 9 to 16.99, 564 (80.3%) had the variable `weight' present in their EMR, were not duplicates and not determined to be outliers. Cases were randomly matched to a pool of available controls (n=1864) according to age and gender, yielding 539 1:1 matched pairs (95.5% case matching success) with a total study sample size, N=1078. Case median age was 13.38 years with the majority being White (66.05%) and male (59.4%). Adjusted conditional logistic regression analysis of the matched pairs identified the following risk factors to presenting with CA-MRSA infection among pediatric patients, ages 9 to 16.99 years: a) Individual weight in the highest (75th-99.9th) WFA quartile (OR=1.36; 95% confidence interval [CI]=1.06-1.74; P= 0.016), b) Infection during summer months (OR: 1.69; 95% CI=1.2-2.38; P= 0.003), c) patients of African American race/ethnicity (OR= 1.48; 95% CI=1.13-1.95; P= 0.004). ^ CONCLUSIONS: Pediatric patients, 9 to 16.99 years of age, in the highest WFA quartile (75th-99.9th), or of African-American race had an associated increased risk of presenting with CA-MRSA infection. Furthermore, children in this population were at a higher risk of contracting CA-MRSA infection during the summer season.^