The utility of rapid participatory appraisal for community assessment, diagnosis, and policy development

The selection of a model to guide the understanding and resolution of community problems is an important issue relating to the foundation of public health practice: assessment, policy development, and assurance. Many assessment models produce a diagnosis of community weaknesses, but fail to promote planning and interventions. Rapid Participatory Appraisal (RPA) is a participatory action research model which regards assessment as the first step in the problem solving process, and claims to achieve assessment and policy development within limited resources of time and money. Literature documenting the fulfillment of these claims, and thereby supporting the utility of the model, is relatively sparse and difficult to obtain. Very few articles discuss the changes resulting from RPA assessments in urban areas, and those that do describe studies conducted outside the U.S.A. ^ This study examines the utility of the RPA model and its underlying theories: systems theory, grounded theory, and principles of participatory change, as illustrated by the case study of a community assessment conducted for the Texas Diabetes Institute (TDI), San Antonio, Texas, and subsequent outcomes. Diabetes has a high prevalence and is a major issue in San Antonio. Faculty and students conducted the assessment by informal collaboration between two nursing and public health assessment courses, providing practical student experiences. The study area was large, and the flexibility of the model tested by its use in contiguous sub-regions, reanalyzing aggregated results for the study area. Official TDI reports, and a mail survey of agency employees, described policy development resulting from community diagnoses revealed by the assessment. ^ The RPA model met the criteria for utility from the perspectives of merit, worth, efficiency, and effectiveness. The RPA model best met the agencies' criteria (merit), met the data needs of TDI in this particular situation (worth), provided valid results within budget, time, and personnel constraints (efficiency), and stimulated policy development by TDI (effectiveness). ^ The RPA model appears to have utility for community assessment, diagnosis, and policy development in circumstances similar to the TDI diabetes study. ^

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING Publication No. _______ Carolyn A. Garby, B.S. Supervisory Professor: Banu Arun, M.D. Hereditary Breast and Ovarian Cancer (HBOC) syndrome is due to mutations in BRCA1 and BRCA2 genes. Women with HBOC have high risks to develop breast and ovarian cancers. Males with HBOC are commonly overlooked because male breast cancer is rare and other male cancer risks such as prostate and pancreatic cancers are relatively low. BRCA genetic testing is indicated for men as it is currently estimated that 4-40% of male breast cancers result from a BRCA1 or BRCA2 mutation (Ottini, 2010) and management recommendations can be made based on genetic test results. Risk assessment models are available to provide the individualized likelihood to have a BRCA mutation. Only one study has been conducted to date to evaluate the accuracy of BRCAPro in males and was based on a cohort of Italian males and utilized an older version of BRCAPro. The objective of this study is to determine if BRCAPro5.1 is a valid risk assessment model for males who present to MD Anderson Cancer Center for BRCA genetic testing. BRCAPro has been previously validated for determining the probability of carrying a BRCA mutation, however has not been further examined particularly in males. The total cohort consisted of 152 males who had undergone BRCA genetic testing. The cohort was stratified by indication for genetic counseling. Indications included having a known familial BRCA mutation, having a personal diagnosis of a BRCA-related cancer, or having a family history suggestive of HBOC. Overall there were 22 (14.47%) BRCA1+ males and 25 (16.45%) BRCA2+ males. Receiver operating characteristic curves were constructed for the cohort overall, for each particular indication, as well as for each cancer subtype. Our findings revealed that the BRCAPro5.1 model had perfect discriminating ability at a threshold of 56.2 for males with breast cancer, however only 2 (4.35%) of 46 were found to have BRCA2 mutations. These results are significantly lower than the high approximation (40%) reported in previous literature. BRCAPro does perform well in certain situations for men. Future investigation of male breast cancer and men at risk for BRCA mutations is necessary to provide a more accurate risk assessment.

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

Validity assessment of the Breast Cancer Risk Reduction Health Belief scale.

BACKGROUND: : Women at increased risk of breast cancer (BC) are not widely accepting of chemopreventive interventions, and ethnic minorities are underrepresented in related trials. Furthermore, there is no validated instrument to assess the health-seeking behavior of these women with respect to these interventions. METHODS: : By using constructs from the Health Belief Model, the authors developed and refined, based on pilot data, the Breast Cancer Risk Reduction Health Belief (BCRRHB) scale using a population of 265 women at increased risk of BC who were largely medically underserved, of low socioeconomic status (SES), and ethnic minorities. Construct validity was assessed using principal components analysis with oblique rotation to extract factors, and generate and interpret summary scales. Internal consistency was determined using Cronbach alpha coefficients. RESULTS: : Test-retest reliability for the pilot and final data was calculated to be r = 0.85. Principal components analysis yielded 16 components that explained 64% of the total variance, with communalities ranging from 0.50-0.75. Cronbach alpha coefficients for the extracted factors ranged from 0.45-0.77. CONCLUSIONS: : Evidence suggests that the BCRRHB yields reliable and valid data that allows for the identification of barriers and enhancing factors associated with use of breast cancer chemoprevention in the study population. These findings allow for tailoring treatment plans and intervention strategies to the individual. Future research is needed to validate the scale for use in other female populations. Cancer 2009. (c) 2009 American Cancer Society.

Acute and chronic methylphenidate dose-response assessment on three adolescent male rat strains.

Methylphenidate (MPD), commonly known as Ritalin, is the most frequently prescribed drug to treat children and adults with attention deficit hyperactivity disorder (ADHD). Adolescence is a period of development involving numerous neuroplasticities throughout the central nervous system (CNS). Exposure to a psychostimulant such as MPD during this crucial period of neurodevelopment may cause transient or permanent changes in the CNS. Genetic variability may also influence these differences. Thus, the objective of the present study was to determine whether acute and chronic administration of MPD (0.6, 2.5, or 10.0mg/kg, i.p.) elicit effects among adolescent WKY, SHR, and SD rats and to compare whether there were strain differences. An automated, computerized, open-field activity monitoring system was used to study the dose-response characteristics of acute and repeated MPD administration throughout the 11-day experimental protocol. Results showed that all three adolescent rat groups exhibited dose-response characteristics following acute and chronic MPD administration, as well as strain differences. These strain differences depended on the MPD dose and locomotor index. Chronic treatment of MPD in these animals did not elicit behavioral sensitization, a phenomenon described in adult rats that is characterized by the progressive augmentation of the locomotor response to repeated administration of the drug. These results suggest that the animal's age at time of drug treatment and strain/genetic variability play a crucial role in the acute and chronic effect of MPD and in the development of behavioral sensitization.

Outcomes assessment of a pediatric practice guideline

Objective. This study examines the structure, processes, and data necessary to assess the outcome variables, length of stay and total cost, for a pediatric practice guideline. The guideline was developed by a group of physicians and ancillary staff members representing the services that most commonly provide treatment for asthma patients at Texas Children's Hospital, as a means of standardizing care. Outcomes have needed to be assessed to determine the practice guideline's effectiveness.^ Data sources and study design. Data for the study were collected retrospectively from multiple hospital data bases and from inpatient chart reviews. All patients in this quasi-experimental study had a diagnosis of Asthma (ICD-9-CM Code 493.91) at the time of admission.^ The study examined data for 100 patients admitted between September 15, 1995 and November 15, 1995, whose physician had elected to apply the asthma practice guideline at the time of the patient's admission. The study examined data for 66 inpatients admitted between September 15, 1995 and November 15, 1995, whose physician elected not to apply the asthma practice guideline. The principal outcome variables were identified as "Length of Stay" and "Cost".^ Principal findings. The mean length of stay for the group in which the practice guideline was applied was 2.3 days, and 3.1 days for the comparison group, who did not receive care directed by the practice guideline. The difference was statistically significant (p value = 0.008). There was not a demonstrable difference in risk factors, health status, or quality of care between the groups. Although not showing statistical significance in the univariate analysis, private insurance showed a significant difference in the logistic regression model presenting an elevated odds ratio (odds ratio = 2.2 for a hospital stay $\le$2 days to an odds ratio = 4.7 for a hospital stay $\le$3 days) showing that patients with private insurance experienced greater risk of a shorter hospital stay than the patients with public insurance in each of the logistic regression models. Public insurance included; Medicaid, Medicare, and charity cases. Private insurance included; private insurance policies whether group, individual, or managed care. The cost of an admission was significantly less for the group in which the practice guideline was applied, with a mean difference between the two groups of $1307 per patient.^ Conclusion. The implementation and utilization of a pediatric practice guideline for asthma inpatients at Texas Children's Hospital has a significant impact in terms of reducing the total cost of the hospital stay and length of the hospital stay for asthma patients admitted to Texas Children's Hospital. ^

THE DESIGN AND DEVELOPMENT OF A SURVEY INSTRUMENT FOR THE ASSESSMENT OF OCCUPATIONAL STRESS

Using stress and coping as a unifying theoretical concept, a series of five models was developed in order to synthesize the survey questions and to classify information. These models identified the question, listed the research study, described measurements, listed workplace data, and listed industry and national reference data.^ A set of 38 instrument questions was developed within the five coping correlate categories. In addition, a set of 22 stress symptoms was also developed. The study was conducted within two groups, police and professors, on a large university campus. The groups were selected because their occupations were diverse, but they were a part of the same macroenvironment. The premise was that police officers would be more highly stressed than professors.^ Of a total study group of 80, there were 37 respondents. The difference in the mean stress responses was observable between the two groups. Not only were the responses similar within each group, but the stress level of response was also similar within each group. While the response to the survey instrument was good, only 3 respondents answered the stress symptom survey properly. It was determined that none of the 37 respondents believed that they were ill. This perception of being well was also evidenced by the grand mean of the stress scores of 2.76 (3.0 = moderate stress). This also caused fewer independent variables to be entered in the multiple regression model.^ The survey instrument was carefully designed to be universal. Universality is the ability to transcend occupational or regional definitions as applied to stress. It is the ability to measure responses within broad categories such as physiological, emotional, behavioral, social, and cognitive functions without losing the ability to measure the detail within the individual questions, or the relationships between questions and categories.^ Replication is much easier to achieve with standardized categories, questions, and measurement procedures such as those developed for the universal survey instrument. Because the survey instrument is universal it can be used as an analytical device, an assessment device, a basic tool for planning and a follow-up instrument to measure individual response to planned reductions in occupational stress. (Abstract shortened with permission of author.) ^

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Assessment of occupational exposures to organic chemicals in a plastics manufacturing facility

The objective of this dissertation was to design and implement strategies for assessment of exposures to organic chemicals used in the production of a styrene-butadiene polymer at the Texas Plastics Company (TPC). Linear statistical retrospective exposure models, univariate and multivariate, were developed based on the validation of historical industrial hygiene monitoring data collected by industrial hygienists at TPC, and additional current industrial hygiene monitoring data collected for the purposes of this study. The current monitoring data served several purposes. First, it provided information on current exposure data, in the form of unbiased estimates of mean exposure to organic chemicals for each job title included. Second, it provided information on homogeneity of exposure within each job title, through the use of a carefully designed sampling scheme which addressed variability of exposure both between and within job titles. Third, it permitted the investigation of how well current exposure data can serve as an evaluation tool for retrospective exposure estimation. Finally, this dissertation investigated the simultaneous evaluation of exposure to several chemicals, as well as the use of values below detection limits in a multivariate linear statistical model of exposures. ^

A proposed application of the attributable risk technique in the assessment of unmet need among newly organized populations

In the midst of health care reform, and as health care organizations reorganize to provide more cost-effective healthcare, the population is being shifted into new healthcare delivery systems such as health insurance purchasing alliances, and health maintenance organizations. These new models of delivery are usually organized within resource restricted and data limited environments. Health care planners are faced with the challenge of identifying priorities for preventive and primary care services within these newly organized populations (Medicare HMO, Medicaid HMO, etc.). The author proposes a technique usually employed in epidemiology--attributable risk estimation--as a planning methodology to establish preventive health priorities within newly organized populations. Illustrations of the methodology are provided utilizing the Texas 1992 population. ^

COSTS AND EFFECTS OF ARKANSAS' LONG-TERM CARE DEMONSTRATION PROJECT (MEDICAID, CHANELLING, COMMUNITY ASSESSMENT)

The purpose of this study was to assess the impact of the Arkansas Long-Term Care Demonstration Project upon Arkansas' Medicaid expenditures and upon the clients it serves. A Retrospective Medicaid expenditure study component used analyses of variance techniques to test for the Project's effects upon aggregated expenditures for 28 demonstration and control counties representing 25 percent of the State's population over four years, 1979-1982.^ A second approach to the study question utilized a 1982 prospective sample of 458 demonstration and control clients from the same 28 counties. The disability level or need for care of each patient was established a priori. The extent to which an individual's variation in Medicaid utilization and costs was explained by patient need, presence or absence of the channeling project's placement decision or some other patient characteristic was examined by multiple regression analysis. Long-term and acute care Medicaid, Medicare, third party, self-pay and the grand total of all Medicaid claims were analyzed for project effects and explanatory relationships.^ The main project effect was to increase personal care costs without reducing nursing home or acute care costs (Prospective Study). Expansion of clients appeared to occur in personal care (Prospective Study) and minimum care nursing home (Retrospective Study) for the project areas. Cost-shifting between Medicaid and Medicare in the project areas and two different patterns of utilization in the North and South projects tended to offset each other such that no differences in total costs between the project areas and demonstration areas occurred. The project was significant ((beta) = .22, p < .001) only for personal care costs. The explanatory power of this personal care regression model (R('2) = .36) was comparable to other reported health services utilization models. Other variables (Medicare buy-in, level of disability, Social Security Supplemental Income (SSI), net monthly income, North/South areas and age) explained more variation in the other twelve cost regression models. ^