Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Perspective transformation: Women in long-term recovery from alcohol abuse

Objective. The purpose of this study was to determine the meaning of personal transformation for twenty women in long term, stable recovery from alcohol abuse; to identify themes or patterns of this recovery, and; to determine the extent to which they experienced the phenomenon of perspective transformation. ^ Method. Volunteers were recruited by advertisement, word of mouth, and through a closed circuit web based broadcast. A descriptive, exploratory study, which analyzed perspective transformation from the standpoint of five action phases, was conducted. Data was collected using in-depth personal interviews and questionnaires. Subjects' responses were analyzed by qualitative methods. Triangulation was performed on the grouped data comparing the interviews to the data produced by the questionnaires. Quantitative analysis of questionnaire items explored behavioral changes experienced before and after alcoholism recovery. ^ Results. Five phases of recovery were identified. Phase I which involved recognition that alcohol was a problem and change might be possible took several years during which 3 major transitions occurred: (1) from often being alienated to having relationships with family and friends; (2) from daily upheavals to eventually a more peaceful existence, and; (3) from denial that alcohol was a problem to acceptance and willingness to change. Recovery was often seen in a spiritual context, which also required ongoing support. During Phase II there was an assessment of self, others, and the environment which revealed a pattern of intense unhappiness and negative feelings toward self and others with a disregard for cultural norms. Phase III revealed a period of desperation as life became unmanageable, but gradual willingness to accept support and guidance and a desire to improve self and help others. This led to improvement of existing role performance and the willingness to try out new roles. In Phase IV there was a pattern of personal growth which included: the establishment of boundaries, setting priorities, a willingness to place others' needs above their own, acceptance of responsibility, and learning to cope without alcohol, often with the use of tools learned in AA. During Phase V, many experienced knowledge of frailties but growing respect for self and others, with an improved ability to function in giving relationships. Implications for Prevention and Recovery: Early education concerning addiction and recovery may play a crucial role in prevention and early recovery, as it did for children of women in this study. Recovery requires persistent effort and organized support. ^

Multilevel analysis for a longitudinal clinical trial

This study applies the multilevel analysis technique to longitudinal data of a large clinical trial. The technique accounts for the correlation at different levels when modeling repeated blood pressure measurements taken throughout the trial. This modeling allows for closer inspection of the remaining correlation and non-homogeneity of variance in the data. Three methods of modeling the correlation were compared. ^

Analysis of recurrent failure times: A time-dependent Yule process approach

Analysis of recurrent events has been widely discussed in medical, health services, insurance, and engineering areas in recent years. This research proposes to use a nonhomogeneous Yule process with the proportional intensity assumption to model the hazard function on recurrent events data and the associated risk factors. This method assumes that repeated events occur for each individual, with given covariates, according to a nonhomogeneous Yule process with intensity function λx(t) = λ 0(t) · exp( x′β). One of the advantages of using a non-homogeneous Yule process for recurrent events is that it assumes that the recurrent rate is proportional to the number of events that occur up to time t. Maximum likelihood estimation is used to provide estimates of the parameters in the model, and a generalized scoring iterative procedure is applied in numerical computation. ^ Model comparisons between the proposed method and other existing recurrent models are addressed by simulation. One example concerning recurrent myocardial infarction events compared between two distinct populations, Mexican-American and Non-Hispanic Whites in the Corpus Christi Heart Project is examined. ^

Forecasting cost based on a stochastic utilization plan and a fixed cost function

The application of Markov processes is very useful to health-care problems. The objective of this study is to provide a structured methodology of forecasting cost based upon combining a stochastic model of utilization (Markov Chain) and deterministic cost function. The perspective of the cost in this study is the reimbursement for the services rendered. The data to be used is the OneCare database of claim records of their enrollees over a two-year period of January 1, 1996–December 31, 1997. The model combines a Markov Chain that describes the utilization pattern and its variability where the use of resources by risk groups (age, gender, and diagnosis) will be considered in the process and a cost function determined from a fixed schedule based on real costs or charges for those in the OneCare claims database. The cost function is a secondary application to the model. Goodness-of-fit will be used checked for the model against the traditional method of cost forecasting. ^

Risk Index Score (RISc)

The main objective of this study was to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that can be translated into a simple scoring system in order to ascertain stroke cases using hospital admission medical records data. This algorithm, the Risk Index Score (RISc), was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christ (BASIC) project. The validity of the RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment accomplished by physician review of hospital admission records. The goal of this study was to develop a rapid, simple, efficient, and accurate method to ascertain the incidence of stroke from routine hospital admission hospital admission records for epidemiologic investigations. ^ The main objectives of this study were to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that could be translated into a simple scoring system to ascertain stroke cases using hospital admission medical records data. (Abstract shortened by UMI.)^

Adherence to a lifestyle physical activity program: Trends over time and associations with processes of change and physical activity

Research on lifestyle physical activity interventions suggests that they help individuals meet the new recommendations for physical activity made by the Centers for Disease Control and Prevention (CDC) and the American College of Sports Medicine (ACSM). The purpose of this research was to describe the rates of adherence to two lifestyle physical activity intervention arms and to examine the association between adherence and outcome variables, using data from Project PRIME, a lifestyle physical activity intervention based on the transtheoretical model and conducted by the Cooper Institute of Aerobics Research, Dallas, Texas. Participants were 250 sedentary healthy adults, aged 35 to 70 years, primarily non-Hispanic White, and in the contemplation and preparation stages of readiness to change. They were randomized to a group (PRIME G) or a mail- and telephone-delivered condition (PRIME C). Adherence measures included attending class (PRIME G), completing a monthly telephone call with a health educator (PRIME C), and completing homework assignments and self-monitoring minutes of moderate- to vigorous physical activity (both groups). In the first results paper, adherence over time and between conditions was examined: Attendance in group, completing the monthly telephone call, and homework completion decreased over time, and participants in PRIME G were more likely to complete homework than those in PRIME C. Paper 2 aimed to determine whether the adherence measures predicted achievement of the CDC/ACSM physical activity guideline. In separate models for the two conditions, a latent variable measuring adherence was found to predict achievement of the guideline. Paper 3 examined the association between adherence measures and the transtheoretical model's processes of change within each condition. For both, participants who completed at least two thirds of the homework assignments improved their use of the processes of change more than those who completed less than that amount. These results suggest that encouraging adherence to a lifestyle physical activity intervention, at least among already motivated volunteers, may increase the likelihood of beneficial changes in the outcomes. ^

Outcomes and cost effectiveness of treating type 2 diabetes with a nurse case manager following treatment algorithms versus primary care physicians

Background. Diabetes places a significant burden on the health care system. Reduction in blood glucose levels (HbA1c) reduces the risk of complications; however, little is known about the impact of disease management programs on medical costs for patients with diabetes. In 2001, economic costs associated with diabetes totaled $100 billion, and indirect costs totaled $54 billion. ^ Objective. To compare outcomes of nurse case management by treatment algorithms with conventional primary care for glycemic control and cardiovascular risk factors in type 2 diabetic patients in a low-income Mexican American community-based setting, and to compare the cost effectiveness of the two programs. Patient compliance was also assessed. ^ Research design and methods. An observational group-comparison to evaluate a treatment intervention for type 2 diabetes management was implemented at three out-patient health facilities in San Antonio, Texas. All eligible type 2 diabetic patients attending the clinics during 1994–1996 became part of the study. Data were obtained from the study database, medical records, hospital accounting, and pharmacy cost lists, and entered into a computerized database. Three groups were compared: a Community Clinic Nurse Case Manager (CC-TA) following treatment algorithms, a University Clinic Nurse Case Manager (UC-TA) following treatment algorithms, and Primary Care Physicians (PCP) following conventional care practices at a Family Practice Clinic. The algorithms provided a disease management model specifically for hyperglycemia, dyslipidemia, hypertension, and microalbuminuria that progressively moved the patient toward ideal goals through adjustments in medication, self-monitoring of blood glucose, meal planning, and reinforcement of diet and exercise. Cost effectiveness of hemoglobin AI, final endpoints was compared. ^ Results. There were 358 patients analyzed: 106 patients in CC-TA, 170 patients in UC-TA, and 82 patients in PCP groups. Change in hemoglobin A1c (HbA1c) was the primary outcome measured. HbA1c results were presented at baseline, 6 and 12 months for CC-TA (10.4%, 7.1%, 7.3%), UC-TA (10.5%, 7.1%, 7.2%), and PCP (10.0%, 8.5%, 8.7%). Mean patient compliance was 81%. Levels of cost effectiveness were significantly different between clinics. ^ Conclusion. Nurse case management with treatment algorithms significantly improved glycemic control in patients with type 2 diabetes, and was more cost effective. ^

Determining acquisition and cessation antecedents to adolescent cigarette smoking for development of intervention strategies

Our national focus and emphasis on the promotion of healthy behavior choices regarding tobacco and other drugs continues to target adolescents. Multiple studies have shown that adolescence is the optimum period for the prevention of substance use initiation as life-long patterns of health behaviors are established during this critical developmental stage. Tobacco use is associated with an increase in morbid and mortal health conditions of which prevalence increases throughout the lifespan. Attention to the antecedents of preventable health conditions aims to modify the risks and identify health promotion factors. Modifying antecedent factors for tobacco initiation in youth and identifying protective factors for successful smoking cessation has major public health implications across the lifespan. Of foremost interest are those risk factors and resultant behaviors that predict a youth's probability of initiating cigarette use and their cessation of cigarette use. Specifically, this dissertation supports previous results identifying intervention variables on the initiation/cessation continuum model especially with the established predictors of smoking (decisional balance and susceptibility) and with more recently identified predictors of smoking (nicotine dependence and withdrawal symptoms) in current and former smokers in a sample of high school students in Austin and Houston, Texas. These results offer insight for the development of appropriate intervention program strategies for our youth. ^

Family stress, coping, communication, and adaptation in unintended adolescent pregnancy

Objective. This study examines post-crisis family stress, coping, communication, and adaptation using the Double ABC-X Model of Family Adaptation in families with a pregnant or postpartum adolescent living at home. ^ Methods. Ninety-eight pregnant and parenting adolescents between ages 14 and 18 years (Group 1 at 20 or more weeks gestation; Group 2 at delivery and 8 weeks postpartum) and their parent(s) completed instruments congruent with the model to measure family stress, coping, communication, and adaptation. Descriptive family data was obtained. Mother-daughter data was analyzed for differences between subjects and within subjects using paired t-tests. Correlational analysis was used to examine relationships among variables. ^ Results. More than 90% of families were Hispanic. There were no significant differences between mother and daughter mean scores for family stress or communication. Adolescent coping was not significantly correlated to family coping at any interval. Adolescent family adaptation scores were significantly lower than mothers' scores at delivery and 8 weeks postpartum. Mean individual ratings of family variables did not differ significantly between delivery and 8 weeks postpartum. Simultaneous multiple regression analysis showed that stress, coping, and communication significantly influenced adaptation for mothers and daughters at all three intervals. The relative contributions of the three independent variables exhibited different patterns for mothers and daughters. Parent-adolescent communication accounted for most of the variability in adaptation for daughters at all three intervals. Daughters' family stress ratings were significant for adaptability (p = .01) during the pregnancy and for cohesion (p = .03) at delivery. Adolescent coping (p = .03) was significant for cohesion at 8 weeks postpartum. Family stress was a significant influence at all three intervals for mothers' ratings of family adaptation. Parent-adolescent communication was significant for mother's perception of both family cohesion (p < .001) and adaptability (p < .001) at delivery and 8 weeks, but not during pregnancy. ^ Conclusions. Mothers' and daughters' ratings of family processes were similar regarding family stress and communication, but were significantly different for family adaptation. Adolescent coping may not reflect family coping. Family communication is a powerful component in family functioning and may be an important focus for interventions with adolescents and parents. ^

Physiologic and psychosocial stage-based differences for dietary fat consumption in women with type 2 diabetes

Purpose. This cross-sectional, observational study explored differences among groups staged for intent to decrease dietary fat intake in women with type 2 diabetes in relation to demographic, weight concern, physiological, and psychosocial variables. ^ Methods. A sample of 100 community-dwelling, English-speaking women, who were over age 30 and had type 2 diabetes for at least a year, was accessed through a culturally diverse endocrinology clinic. Subjects completed 7 self-report instruments: demographic sheet, with 11-point weight satisfaction scale; staging algorithm; fat intake (MEDFICTS); depression (CES-D); diabetes-specific dietary knowledge (ADKnowl), social support and self-efficacy scales (SE-Type 2). Physiological variables were abstracted from the medical record (HbA 1c, blood pressure, serum cholesterol and triglycerides). ^ Results. The women's average age was 57.69 years ( SD = 3.07); 50% were married. Subjects were well-educated ( M = 14 years; SD = 3.33), with average diabetes duration of 10.57 years (SD = 9.11), high body mass index (M = 35.72; SD = 8.36), low diabetes-specific dietary knowledge, low weight satisfaction, but in good diabetes control. Racial/ethnic composition was 44% non-Hispanic-White-American, 18% Hispanic-White-American, 15% non-Hispanic-African-American, 16% Hispanic-African-American and 5% other. Fat intake was low and differed by racial/ethnic demographics. The highest fat intake scores were for non-Hispanic-African-Americans (M = 53), followed by Hispanic-White-Americans (M = 51), non-Hispanic-White-Americans (M = 45), and Hispanic-African-Americans (M = 32), who had the lowest fat intake scores. ^ MANOVA analyses revealed no significant differences between stages of behavior change in relation to psychosocial or weight concern variables, age, education, HbA1c, or cholesterol levels. Single women were more likely to be in the three preaction stages (precontemplation, contemplation, and preparation); married women were equally distributed across stages (the preaction stages plus action and maintenance). African-American women (Hispanic and non-Hispanic) were more likely in contemplation and preparation. Triglycerides were higher in women in the action stage than contemplation or preparation. Systolic blood pressure was higher in action than preparation; diastolic blood pressure was higher in action than preaction. ^ Conclusions. Healthcare professionals should consider race, ethnicity, and marital status in client interactions. Dietary intake can vary according to both race and ethnicity; collapsing racial/ethnic groups can alter means and distributions, generating faulty conclusions. Further research is warranted to explore relationships between dietary self-care and marital status, race, ethnicity, and physiological variables. ^

Depressive symptoms and sexual risk behaviors in adolescents

The purpose of this study was to determine whether depression is a factor in explaining the difference in sex behaviors among adolescents with different ethnic backgrounds, family and school contexts. We hypothesize that adolescents with a higher number of depressive symptoms are more likely to engage in sexual risk behaviors than adolescents with fewer depressive symptoms. Further, adolescent depression and sexual behaviors are mediated or moderated by individual characteristics, family and school contexts. ^ Background. large ethnic disparities exist in adolescent engagement in risky sexual behaviors, yet, there is little in the literature that explains these disparities. Studies of sexual behavior of youths abound; yet, there is little literature on the prevalence and correlates of depression or the association between depression and sexual behaviors among different ethnic groups. Objectives. (1) To determine ethnic differences in the prevalence of depressive symptoms using data collected through the National Longitudinal Study of Adolescent Health (Add Health). (2) To determine predictors of sex risk behaviors among adolescents, including the role of depression. (3) To identify predictors of depression among these adolescents. Methods. Add Health data from wave 1 and wave 2 interviews of 7th–12th graders were analyzed using multivariate models constructed with both depression and sexual behavior as outcome variables. Logistic regression models determined whether and to what extent the independent variables, including depression, sex behaviors, demographic factors, individual and family characteristics, and school context were related to the probability of engaging in risky sexual behaviors. Results. Ethnic differences in depressive symptoms did not persist after demographic and contextual variables were included in the model. Sex behaviors all shared the hypothesized relationship with depressive symptoms. The odds of risky sex behaviors increased as number of depressive symptoms increased. Depression was predicted by marijuana use and having a serious argument with father for males at Wave 1 and by age and future orientation for females. Wave 2 depression was predicted by Wave 1 depression. ^