CYTOGENETIC AND DNA CONTENT ANALYSIS IN MULTIPLE MYELOMA

In this dissertation, the cytogenetic characteristics of bone marrow cells from 41 multiple myeloma patients were investigated. These cytogenetic data were correlated with the total DNA content as measured by flow cytometry. Both the cytogenetic information and DNA content were then correlated with clinical data to determine if diagnosis and prognosis of multiple myeloma could be improved.^ One hundred percent of the patients demonstrated abnormal chromosome numbers per metaphase. The average chromosome number per metaphase ranged from 42 to 49.9, with a mean of 44.99. The percent hypodiploidy ranged from 0-100% and the percent hyperdiploidy from 0-53%. Detailed cytogenetic analyses were very difficult to perform because of the paucity of mitotic figures and the poor chromosome morphology. Thus, detailed chromosome banding analysis on these patients was impossible.^ Thirty seven percent of the patients had normal total DNA content, whereas 63% had abnormal amounts of DNA (one patient with less than normal amounts and 25 patients with greater than normal amounts of DNA).^ Several clinical parameters were used in the statistical analyses: tumor burden, patient status at biopsy, patient response status, past therapy, type of treatment and percent plasma cells. Only among these clinical parameters were any statistically significant correlations found: pretreatment tumor burden versus patient response, patient biopsy status versus patient response and past therapy versus patient response.^ No correlations were found between percent hypodiploid, diploid, hyperdiploid or DNA content, and the patient response status, nor were any found between those patients with: (a) normal plasma cells, low pretreatment tumor mass burden and more than 50% of the analyzed metaphases with 46 chromosomes; (b) normal amounts of DNA, low pretreatment tumor mass burden and more than 50% of the metaphases with 46 chromosomes; (c) normal amounts of DNA and normal quantities of plasma cells; (d) abnormal amounts of DNA, abnormal amounts of plasma cells, high pretreatment tumor mass burden and less than 50% of the metaphases with 46 chromosomes.^ Technical drawbacks of both cytogenetic and DNA content analysis in these multiple myeloma patients are discussed along with the lack of correlations between DNA content and chromosome number. Refined chromosome banding analysis awaits technical improvements before we can understand which chromosome material (if any) makes up the "extra" amounts of DNA in these patients. None of the correlations tested can be used as diagnostic or prognostic aids for multiple myeloma. ^

Household health assessment and associated water sample analysis in the Miches watershed of the Dominican Republic

Background. Diarrhea and malnutrition are the leading causes of mortality for children age one to four in the Dominican Republic. Communities within the Miches watershed lack sanitation infrastructure and water purification systems, which increases the risk of exposure to water-borne pathogens. The purpose of this cross-sectional study was to analyze health information gathered through household interviews and to test water samples for the presence of diarrheagenic pathogens and antibiotic-resistant bacteria within the Miches watershed. Methods. Frequency counts and thematic analysis were used to investigate Human Health Survey responses and Fisher's exact test was used to determine correlation between water source and reported illness. Bacteria cultured from water samples were analyzed by Gram stain, real-time PCR, API® 20E biochemical identification, and for antibiotic resistance. Results. Community members reported concerns about water sources with respect to water quality, availability, and environmental contamination. Pathogenic strains of E. coli were present in the water samples. Drinking aquifer water was positively-correlated with reported stomach aches (p=0.04) while drinking from rivers or creeks was associated with the reported absence of “gripe” (cold or flu) (p=0.01). The lack of association between reported illnesses and water source for the majority of variables suggested that there were multiple vehicles of disease transmission. Antibiotic resistant bacteria were isolated from the water samples tested. Conclusions. The presence of pathogenic E. coli in water samples suggested that water is at least one route of transmission for diarrheagenic pathogens in the Miches watershed. The presence of antibiotic-resistant bacteria in the water samples may indicate the proliferation of resistance plasmids in the environment as a result of antibiotic overuse in human and animal populations and a lack of sanitation infrastructure. An intervention that targets areas of hygiene, sanitation, and water purification is recommended to limit human exposure to diarrheagenic pathogens and antibiotic-resistant organisms. ^

The Relationship of Provincial Economic Level and Child Injury Mortality in Thailand: A Cross-Sectional Analysis in a Middle Income Country

The objective of this cross-sectional study was to examine the relationship of provincial economic development indices with incidences of child injury mortality in Thailand from 1999 - 2001. All injury deaths among children age 1-14 years were included. The independent variables included gross provincial product per capita (GPP/c), poverty and inequality indices, material and social deprivation indices, population in rural/ urban areas, and migration. Due to multicollinearity of such variables, the 76 provinces were categorized by GPP/c quartile, and means of overall injury, drowning, and transport-related mortality rates were compared among quartile groups. Spearman’s rho correlation between GPP/c and injury mortality rates was also performed. Finally, factor analysis was employed to create a set of factors to be treated as uncorrelated variables and stepwise multiple regression was carried out for the effects of the factors on injury mortality rates. A significant direct relationship was observed between GPP/c and overall injury mortality among children age 1-4 years, and 10-14 year-olds of both genders. Drowning was the main cause of this relationship among children age 1-4 years, and transport-related injury was the principle cause among children age 10-14 years. Conversely, provinces with lower GPP/c experienced higher injury mortality rates among school-age children 5-9 years old for both genders, mostly due to drowning. Factor analysis, and multiple regression results confirmed the relationships between economic development and injury mortality rates. These findings revealed that economic development had an adverse impact on injury-related mortality among children 1 to 4 and 10 to14 in Thailand.

Environmental, health and safety cost benefit analysis in the United States chemical industry

The purpose of this research project is to determine whether there is a cost/benefit to allocating financial and other company-related resources to improve environmental, health and safety performance beyond that which is required by law. The issue of whether a company benefits from spending dollars to achieve environmental, health and safety performance beyond legal compliance is an important issue to the chemical manufacturing industry in the United States because of the voluminous and complex legal requirements impacting environmental, health and safety expenditures. The cost/benefit issue has practical significance because many U.S. chemical manufacturing companies base their environmental, health and safety management strategies on just achieving and maintaining compliance with legal requirements when in reality this strategy may actually be a higher cost way of managing environmental, health and safety practices. This difference in environmental, health and safety management strategy is being investigated to determine if managing environmental, health and safety to achieve performance beyond that which is required by law results in a greater benefit to companies in the U.S. chemical manufacturing sector.

Traditional linkage analysis in admixed families

Currently there is no general method to study the impact of population admixture within families on the assumptions of random mating and consequently, Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE) and on the inference obtained from traditional linkage analysis. ^ First, through simulation, the effect of admixture of two populations on the log of the odds (LOD) score was assessed, using Prostate Cancer as the typical disease model. Comparisons between simulated mixed and homogeneous families were performed. LOD scores under both models of admixture (within families and within a data set of homogeneous families) were closest to the homogeneous family scores of the population having the highest mixing proportion. Random sampling of families or ascertainment of families with disease affection status did not affect this observation, nor did the mode of inheritance (dominant/recessive) or sample size. ^ Second, after establishing the effect of admixture on the LOD score and inference for linkage, the presence of induced disequilibria by population admixture within families was studied and an adjustment procedure was developed. The adjustment did not force all disequilibria to disappear but because the families were adjusted for the population admixture, those replicates where the disequilibria exist are no longer affected by the disequilibria in terms of maximization for linkage. Furthermore, the adjustment was able to exclude uninformative families or families that had such a high departure from HWE and/or LE that their LOD scores were not reliable. ^ Together these observations imply that the presence of families of mixed population ancestry impacts linkage analysis in terms of the LOD score and the estimate of the recombination fraction. ^

Prevalence of depressive symptoms among Hispanic women attending a public health clinic

Background. Estimates of perinatal depression have ranged from 5% to more than 25% of women (Gavin et al. 2005). Although Hispanics have one of the highest birthrates, few studies have looked at the prevalence of depression among this population. This study aims to describe the prevalence of depressive symptoms among a sample of Hispanic women. Methods. A convenience sample of 439 Hispanic women were screened for depression using the Center for Epidemiologic Studies Depression Scale. Sociodemographic data relating to pregnancy were also collected. Results. Although bivariate analysis found several variables to be significant, multivariate analysis found only marital and pregnancy status to be significant in predicting depression. Conclusions. While marital and pregnancy status proved to the strongest predictors for depression, future research would benefit from collecting information on timing of pregnancy and postpartum to further explore the role of pregnancy status and depressive symptoms. ^

A new combined endpoint using U-statistic in analysis in clinical trials

Common endpoints can be divided into two categories. One is dichotomous endpoints which take only fixed values (most of the time two values). The other is continuous endpoints which can be any real number between two specified values. Choices of primary endpoints are critical in clinical trials. If we only use dichotomous endpoints, the power could be underestimated. If only continuous endpoints are chosen, we may not obtain expected sample size due to occurrence of some significant clinical events. Combined endpoints are used in clinical trials to give additional power. However, current combined endpoints or composite endpoints in cardiovascular disease clinical trials or most clinical trials are endpoints that combine either dichotomous endpoints (total mortality + total hospitalization), or continuous endpoints (risk score). Our present work applied U-statistic to combine one dichotomous endpoint and one continuous endpoint, which has three different assessments and to calculate the sample size and test the hypothesis to see if there is any treatment effect. It is especially useful when some patients cannot provide the most precise measurement due to medical contraindication or some personal reasons. Results show that this method has greater power then the analysis using continuous endpoints alone. ^

A review of economic analysis in the vaccination program; decision making and governance: Adivisory Committee on Immunization Practices (ACIP)

The Advisory Committee on Immunization Practices (ACIP) develops written recommendations for the routine administration of vaccines to children and adults in the U.S. civilian population. The ACIP is the only entity in the federal government that makes such recommendations. ACIP elaborates on selection of its members and rules out concerns regarding its integrity, but fails to provide information about the importance of economic analysis in vaccine selection. ACIP recommendations can have large health and economic consequences. Emphasis on economic evaluation in health is a likely response to severe pressures of the federal and state health budget. This study describes the economic aspects considered by the ACIP while sanctioning a vaccine, and reviews the economic evaluations (our economic data) provided for vaccine deliberations. A five year study period from 2004 to 2009 is adopted. Publicly available data from ACIP web database is used. Drummond et al. (2005) checklist serves as a guide to assess the quality of economic evaluations presented. Drummond et al.'s checklist is a comprehensive hence it is unrealistic to expect every ACIP deliberation to meet all of their criteria. For practical purposes we have selected seven criteria that we judge to be significant criteria provided by Drummond et al. Twenty-four data points were obtained in a five year period. Our results show that out of the total twenty-four data point‘s (economic evaluations) only five data points received a score of six; that is six items on the list of seven were met. None of the data points received a perfect score of seven. Seven of the twenty-four data points received a score of five. A minimum of a two score was received by only one of the economic analyses. The type of economic evaluation along with the model criteria and ICER/QALY criteria met at 0.875 (87.5%). These three criteria were met at the highest rate among the seven criteria studied. Our study findings demonstrate that the perspective criteria met at 0.583 (58.3%) followed by source and sensitivity analysis criteria both tied at 0.541 (54.1%). The discount factor was met at 0.250 (25.0%).^ Economic analysis is not a novel concept to the ACIP. It has been practiced and presented at these meetings on a regular basis for more than five years. ACIP‘s stated goal is to utilize good quality epidemiologic, clinical and economic analyses to help policy makers choose among alternatives presented and thus achieve a better informed decision. As seen in our study the economic analyses over the years are inconsistent. The large variability coupled with lack of a standardized format may compromise the utility of the economic information for decision-making. While making recommendations, the ACIP takes into account all available information about a vaccine. Thus it is vital that standardized high quality economic information is provided at the ACIP meetings. Our study may provide a call for the ACIP to further investigate deficiencies within the system and thereby to improve economic evaluation data presented. ^

Performance tiers: Implementing comparative effectiveness analysis in the health care setting through operations research and spatial methods

This dissertation develops and tests a comparative effectiveness methodology utilizing a novel approach to the application of Data Envelopment Analysis (DEA) in health studies. The concept of performance tiers (PerT) is introduced as terminology to express a relative risk class for individuals within a peer group and the PerT calculation is implemented with operations research (DEA) and spatial algorithms. The analysis results in the discrimination of the individual data observations into a relative risk classification by the DEA-PerT methodology. The performance of two distance measures, kNN (k-nearest neighbor) and Mahalanobis, was subsequently tested to classify new entrants into the appropriate tier. The methods were applied to subject data for the 14 year old cohort in the Project HeartBeat! study.^ The concepts presented herein represent a paradigm shift in the potential for public health applications to identify and respond to individual health status. The resultant classification scheme provides descriptive, and potentially prescriptive, guidance to assess and implement treatments and strategies to improve the delivery and performance of health systems. ^

THE USE OF MULTIVARIATE ANALYSIS OF COVARIANCE IN EVALUATING THE COMPARABILITY OF LABORATORY DETERMINATIONS IN COOPERATIVE STUDIES

The role of clinical chemistry has traditionally been to evaluate acutely ill or hospitalized patients. Traditional statistical methods have serious drawbacks in that they use univariate techniques. To demonstrate alternative methodology, a multivariate analysis of covariance model was developed and applied to the data from the Cooperative Study of Sickle Cell Disease.^ The purpose of developing the model for the laboratory data from the CSSCD was to evaluate the comparability of the results from the different clinics. Several variables were incorporated into the model in order to control for possible differences among the clinics that might confound any real laboratory differences.^ Differences for LDH, alkaline phosphatase and SGOT were identified which will necessitate adjustments by clinic whenever these data are used. In addition, aberrant clinic values for LDH, creatinine and BUN were also identified.^ The use of any statistical technique including multivariate analysis without thoughtful consideration may lead to spurious conclusions that may not be corrected for some time, if ever. However, the advantages of multivariate analysis far outweigh its potential problems. If its use increases as it should, the applicability to the analysis of laboratory data in prospective patient monitoring, quality control programs, and interpretation of data from cooperative studies could well have a major impact on the health and well being of a large number of individuals. ^

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.