The effect of acculturation on diabetes prevalence in Hispanics: A systematic review

Context. The high prevalence of diabetes among Hispanics in the U.S. has stimulated increased interest in the role culture plays in Hispanics' risk of diabetes. It is critical to identify gaps in the existing research and to determine the relationship between acculturation and diabetes prevalence in the Hispanic population. ^ Objective. To review the current literature to evaluate the effects of acculturation on diabetes prevalence among Hispanic Americans. ^ Methods. A literature search of diabetes-related studies was conducted. Studies were selected for review if they reported at least one acculturation measure, used Hispanics adults (ages 18 and older) and included information regarding the diabetes prevalence of Hispanics and/or Latinos. Only those that examined acculturation by diabetes prevalence for Hispanics were included in the review. ^ Results. Sixteen studies were reviewed that met the search criteria and these studies used distinct measures of acculturation that captured four primary dimensions: time (duration of exposure to U.S. culture), language, culture and residence. Data represented studies conducted in a variety of settings, such as healthcare facilities in a state or region of the U.S. and nationally representative surveys. The data indicate positive, negative and no significant relationship with diabetes. Depending on the measure of acculturation used and gender the association between acculturation and diabetes varied. ^ Conclusions. There is no clear association between acculturation and diabetes prevalence; it can not be determined based on the available literature. Many of the studies examining this relationship found non-significant results and the directionality of the relationship varied greatly depending on the type of measure used, the number of measures used, and the study population. Ideal studies of acculturation should concentrate on investigating the links between time measures of acculturation, location of residence and changing beliefs, values and norms. A comprehensive acculturation scale is needed to better understand the complex relationship between diabetes prevalence amongst Hispanics and acculturation. ^

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.^

A retrospective cohort study of the duration of hepatitis B vaccine immunity and associated factors in pediatric dialysis patients

Dialysis patients are at high risk for hepatitis B infection, which is a serious but preventable disease. Prevention strategies include the administration of the hepatitis B vaccine. Dialysis patients have been noted to have a poor immune response to the vaccine and lose immunity more rapidly. The long term immunogenicity of the hepatitis B vaccine has not been well defined in pediatric dialysis patients especially if administered during infancy as a routine childhood immunization.^ Purpose. The aim of this study was to determine the median duration of hepatitis B immunity and to study the effect of vaccination timing and other cofactors on the duration of hepatitis B immunity in pediatric dialysis patients.^ Methods. Duration of hepatitis B immunity was determined by Kaplan-Meier survival analysis. Comparison of stratified survival analysis was performed using log-rank analysis. Multivariate analysis by Cox regression was used to estimate hazard ratios for the effect of timing of vaccine administration and other covariates on the duration of hepatitis B immunity.^ Results. 193 patients (163 incident patients) had complete data available for analysis. Mean age was 11.2±5.8 years and mean ESRD duration was 59.3±97.8 months. Kaplan-Meier analysis showed that the total median overall duration of immunity (since the time of the primary vaccine series) was 112.7 months (95% CI: 96.6, 124.4), whereas the median overall duration of immunity for incident patients was 106.3 months (95% CI: 93.93, 124.44). Incident patients had a median dialysis duration of hepatitis B immunity equal to 37.1 months (95% CI: 24.16, 72.26). Multivariate adjusted analysis showed that there was a significant difference between patients based on the timing of hepatitis B vaccination administration (p<0.001). Patients immunized after the start of dialysis had a hazard ratio of 6.13 (2.87, 13.08) for loss of hepatitis B immunity compared to patients immunized as infants (p<0.001).^ Conclusion. This study confirms that patients immunized after dialysis onset have an overall shorter duration of hepatitis B immunity as measured by hepatitis B antibody titers and after the start of dialysis, protective antibody titer levels in pediatric dialysis patients wane rapidly compared to healthy children.^

Impact of real time polymerase chain reaction technology on the therapeutic approach to treatment of staphylococcal infections

Can the early identification of the species of staphylococcus responsible for infection by the use of Real Time PCR technology influence the approach to the treatment of these infections? ^ This study was a retrospective cohort study in which two groups of patients were compared. The first group, ‘Physician Aware’ consisted of patients in whom physicians were informed of specific staphylococcal species and antibiotic sensitivity (using RT-PCR) at the time of notification of the gram stain. The second group, ‘Physician Unaware’ consisted of patients in whom treating physicians received the same information 24–72 hours later as a result of blood culture and antibiotic sensitivity determination. ^ The approach to treatment was compared between ‘Physician Aware’ and ‘Physician Unaware’ groups for three different microbiological diagnoses—namely MRSA, MSSA and no-SA (or coagulase negative Staphylococcus). ^ For a diagnosis of MRSA, the mean time interval to the initiation of Vancomycin therapy was 1.08 hours in the ‘Physician Aware’ group as compared to 5.84 hours in the ‘Physician Unaware’ group (p=0.34). ^ For a diagnosis of MSSA, the mean time interval to the initiation of specific anti-MSSA therapy with Nafcillin was 5.18 hours in the ‘Physician Aware’ group as compared to 49.8 hours in the ‘Physician Unaware’ group (p=0.007). Also, for the same diagnosis, the mean duration of empiric therapy in the ‘Physician Aware’ group was 19.68 hours as compared to 80.75 hours in the ‘Physician Unaware’ group (p=0.003) ^ For a diagnosis of no-SA or coagulase negative staphylococcus, the mean duration of empiric therapy was 35.65 hours in the ‘Physician Aware’ group as compared to 44.38 hours in the ‘Physician Unaware’ group (p=0.07). However, when treatment was considered a categorical variable and after exclusion of all cases where anti-MRS therapy was used for unrelated conditions, only 20 of 72 cases in the ‘Physician Aware’ group received treatment as compared to 48 of 106 cases in the ‘Physician Unaware’ group. ^ Conclusions. Earlier diagnosis of MRSA may not alter final treatment outcomes. However, earlier identification may lead to the earlier institution of measures to limit the spread of infection. The early diagnosis of MSSA infection, does lead to treatment with specific antibiotic therapy at an earlier stage of treatment. Also, the duration of empiric therapy is greatly reduced by early diagnosis. The early diagnosis of coagulase negative staphylococcal infection leads to a lower rate of unnecessary treatment for these infections as they are commonly considered contaminants. ^

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.

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^