The interorganizational implementation of state health policy: A network approach

This is an implementation analysis of three consecutive state health policies whose goal was to improve access to maternal and child health services in Texas from 1983 to 1986. Of particular interest is the choice of the unit of analysis, the policy subsystem, and the network approach to analysis. The network approach analyzes and compares the structure and decision process of six policy subsystems in order to explain program performance. Both changes in state health policy as well as differences in implementation contexts explain evolution of the program administrative and service unit, the policy subsystem. And, in turn, the evolution of the policy subsystem explains changes in program performance. ^

An evaluation of a behavioral change intervention for smokeless tobacco use

During the last three decades considerable attention has been placed on the reduction of tobacco use due to cigarette smoking. During this time, studies have been funded and programs have been developed that focus on both prevention and cessation of cigarette smoking. This intense focus has led to a significant decline in cigarette smoking. But now, use of another form of tobacco--smokeless tobacco--is gaining in popularity.^ In 1989, the National Cancer Institute funded a research study at The University of Texas M. D. Anderson Cancer Center, called Working Well, to develop, implement, and evaluate worksite health promotion programs aimed at reducing cancer risks. As part of this program, a behavioral intervention for smokeless tobacco use was developed. This dissertation evaluates the impact of that behavioral change intervention for smokeless tobacco use.^ Data collected during the Working Well program were analyzed to determine the effect of the intervention. The primary outcomes analyzed were smokeless tobacco cessation, stages of change movement, and prevalence. The secondary outcomes analyzed included the prediction of smokeless tobacco use, stage movement, and cessation. Primary outcome analyses were conducted using the worksite as the unit of analysis, while the secondary analyses were conducted using the individual as the unit of analysis.^ Approximately 20% of the male population used smokeless tobacco. Results of intervention analyses indicate that the Working Well program produced no intervention effect on any of the primary outcomes. At the final observation, the experimental worksites achieved a quit rate of 27%, while the control worksites achieved a quit rate of 26% (P = 0.78). Stage movement for the experimental worksites was 49%, while the control worksites experienced stage movement of 43% (P = 0.20). The results of the analyses on smokeless tobacco prevalence followed the same pattern. Predictors of smokeless tobacco use, cessation, and stage movement were also identified.^ Based on the results found in this study, smokeless tobacco should remain a research priority. Future research should focus on smokeless tobacco use, including the identification of the determinants of smokeless tobacco use and the development of measures and effective intervention strategies. ^

Development and evaluation of a nutrition education program designed to increase fruit and vegetable consumption in girls, ages 9--12

The purpose of this study is to evaluate the theory-based Eat 5 nutrition badge. It is designed to increase fruit and vegetable (F&V) intake in 4th-6th grade junior Girl Scouts. Twenty-two troops were recruited and randomized by grade level (4th, 5th, 6th, or mixed) into either the intervention or control conditions. The leaders in the intervention condition received a brief training and the materials and conducted the program with their troops during four meetings. The Girl Scouts in the intervention condition completed 1-day Food Frequency Questionnaires and Nutrition Questionnaires both before and after completing the Eat 5 badge, and a third measurement of F&V intake three months after the posttest. Girl Scouts in the control condition were only evaluated at the three time periods.^ The primary hypotheses were that the Girl Scouts in the intervention condition would increase their daily intake of fruits and vegetables at both the posttest and three months later, compared to the Girl Scouts in the control condition. Other study questions investigated the impact of the Eat 5 program on intervening variables such as knowledge, self-efficacy, barriers, norms, F&V preference, and F&V selection and preparation skills.^ A nested ANOVA, with troop as the unit of analysis nested within condition, was used to assess the effects of the program. Pretest F&V intake and grade level were used as covariates. Pretest mean F&V intake for the total sample of 210 girls was 2.50 servings per day; 3.0 for the intervention group (n = 101). Significant increases in F&V intake (to 3.4 servings per day), knowledge, and fruit and vegetable preference were found for the intervention condition troops compared to the troops in the control condition. Three months later, the mean F&V intake had returned to pretest levels.^ This study indicates that social groups such as Girl Scouts can provide a channel for nutrition education. Long term effects were not sustained by the intervention; a possible cause was the lack of change in self-efficacy. Therefore, additional interventions are recommended such as booster lessons to maintain increased F&V intake by Girl Scouts. ^

COMPARISON OF THE ORGANIZATIONAL STRUCTURES AND EFFECTIVENESS OF THE INVESTOR-OWNED AND VOLUNTARY MULTIHOSPITAL SECTORS

Objectives. The central objective of this study was to systematically examine the internal structure of multihospital systems, determining the management principles used and the performance levels achieved in medical care and administrative areas.^ The Universe. The study universe consisted of short-term general American hospitals owned and operated by multihospital corporations. Corporations compared were the investor-owned (for-profit) and the voluntary multihospital systems. The individual hospital was the unit of analysis for the study.^ Theoretical Considerations. The contingency theory, using selected aspects of the classical and human relations schools of thought, seemed well suited to describe multihospital organization and was used in this research.^ The Study Hypotheses. The main null hypotheses generated were that there are no significant differences between the voluntary and the investor-owned multihospital sectors in their (1) hospital structures and (2) patient care and administrative performance levels.^ The Sample. A stratified random sample of 212 hospitals owned by multihospital systems was selected to equally represent the two study sectors. Of the sampled hospitals approached, 90.1% responded.^ The Analysis. Sixteen scales were constructed in conjunction with 16 structural variables developed from the major questions and sub-items of the questionnaire. This was followed by analysis of an additional 7 structural and 24 effectiveness (performance) measures, using frequency distributions. Finally, summary statistics and statistical testing for each variable and sub-items were completed and recorded in 38 tables.^ Study Findings. While it has been argued that there are great differences between the two sectors, this study found that with a few exceptions the null hypotheses of no difference in organizational and operational characteristics of non-profit and for-profit hospitals was accepted. However, there were several significant differences found in the structural variables: functional specialization, and autonomy were significantly higher in the voluntary sector. Only centralization was significantly different in the investor owned. Among the effectiveness measures, occupancy rate, cost of data processing, total manhours worked, F.T.E. ratios, and personnel per occupied bed were significantly higher in the voluntary sector. The findings indicated that both voluntary and for-profit systems were converging toward a common hierarchical corporate management approach. Factors of size and management style may be better descriptors to characterize a specific multihospital group than its profit or nonprofit status. (Abstract shortened with permission of author.) ^

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.