Factors predicting recommendations for civil commitment of sexually violent predators under Florida's Jimmy Ryce Act

This study investigated the factors considered by forensic examiners when evaluating sexually violent predators (SVP) for civil commitment under Florida's “Jimmy Ryce Act.” The project was funded by a pre-doctoral research grant awarded by the Association for the Treatment of Sexual Abusers (ATSA). ^ This study proposed two specific research questions. First, what is the direct relationship between actuarial risk assessment scores and recommendations for sex offender civil commitment? Second, which other variables are likely to influence SVP commitment decisions, and to what degree? The purpose of the study was to determine if risk assessment practices are evidence-based, and whether offenders selected for commitment meet statutory criteria. ^ The purposive sample of 450 SVPs was drawn from the population of sex offenders evaluated for civil commitment in Florida between July 1, 2000 and June 30, 2001. Data were extracted from SVP evaluations provided by the Florida Department of Children and Families. Using multivariate logistic regression, this correlational research design examined the relationship between the dependent variable, commitment decision, and several sets of independent variables. The independent variables were derived from a review of the literature, and were grouped conceptually according to their degree of correlation with sex offense recidivism. Independent variables included diagnoses, actuarial risk assessment scores, empirically validated static and dynamic risk factors, consensus based risk factors, evaluator characteristics, and demographics. This study investigated the degree to which the identified variables predicted civil commitment decisions. ^ Logistic regression results revealed that the statistically significant predictors of recommendations for sex offender civil commitment were actuarial risk assessment scores, diagnoses of Pedophilia and Paraphilia NOS, psychopathy, younger age of victim, and non-minority race. Discriminant function analysis confirmed that these variables correctly predicted commitment decisions in 90% of cases. ^ It appears that civil commitment evaluators in Florida used empirically-based assessment procedures, and did not make decisions that were heavily influenced by extraneous factors. SVPs recommended for commitment consistently met the criteria set forth by the U.S. Supreme Court in Hendricks v. Kansas (1997): they suffered from a mental abnormality predisposing them to sexual violence, and risk assessment determined that they were likely to reoffend. ^

Risk factors of coronary heart disease and correlates of type 2 diabetes among Cuban Americans

Cuban Americans, a minority Hispanic subgroup, have a high prevalence of type 2 diabetes. Persons with diabetes experience a higher rate of coronary heart disease (CHD) compared to those without diabetes. The objectives of the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) are to investigate the risk factors of CHD and the etiology of diabetes among diabetics of minority ethnic populations. No information is available on the etiology of CHD risks for Cuban Americans. ^ This cross-sectional study compared Cuban Americans with (N = 79) and without (N = 80) type 2 diabetes residing in South Florida. Data on risk factors of CHD and type 2 diabetes were collected using sociodemographics, smoking habit, Rose Angina, Modifiable Activity, and Willet's food frequency questionnaires. Anthropometrics and blood pressure (BP) were recorded. Glucose, glycated hemoglobin, lipid profile, homocysteine, and C-reactive protein were assessed in fasting blood. ^ Diabetics reported a significantly higher rate of angina symptoms than non-diabetics (P = 0.008). After adjusting for age and gender, diabetics had significantly (P < 0.001) larger waist circumference and higher systolic BP than non-diabetics. There was no significant difference in major nutrient intakes between the groups. One quarter of subjects, both diabetics and non-diabetics, exceeded the intake of percent calories from total fat and almost 60% had cholesterol intake >200 mg/d and more than 60% had fiber intake <20 gm/d. The pattern of physical activity did not differ between groups though, it was much below the recommended level. After adjusting for age and gender, diabetics had significantly (P < 0.001) higher levels of blood glucose, glycated hemoglobin, triglycerides, and homocysteine than non-diabetics. In contrast, diabetics had significantly (P < 0.01) lower levels of high-density lipoprotein cholesterol (HDL-C). ^ Multivariate logistic regression analyses showed that increasing age, male gender, large waist circumference, lack of acculturation, and high levels of triglycerides were independent risk factors of type 2 diabetes. In contrast, moderate alcohol consumption conferred protection against diabetes. ^ The study identified several risk factors of CHD and diabetes among Cuban Americans. Health care providers are encouraged to practice ethno-specific preventive measures to lower the burden of CHD and diabetes in Cuban Americans. ^

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.