Diffusion and implementation of a tobacco prevention program: A replication study

Tobacco use is a major health hazard, and the onset of tobacco use occurs almost entirely in the teenage years. For this reason, schools are an ideal site for tobacco prevention programs. Although studies have shown that effective school-based tobacco prevention programs exist, all too frequently these programs are not used. In order for effective programs to achieve their potential impact, strategies for speeding the diffusion of these programs to school districts and seeing that, once adopted, programs are implemented as they are intended, must be developed and tested.^ This study (SC2) set out to replicate the findings of an earlier quasi-experimental study (The Smart Choices Diffusion Study, or SC1) in which strategies based on diffusion theory and social learning theory were found to be effective in encouraging adoption and implementation of an effective tobacco prevention program in schools. To increase awareness and encourage adoption, intervention strategies in both studies utilized opinion leaders, messages highlighting positive aspects of the program, and modeling of benefits and effective use through videotape and newsletters. To encourage accurate implementation of the curriculum, teacher training for the two studies utilized videotaped modeling and practice of activities by teachers. SC2 subjects were 38 school districts that make up one of Texas' 20 education service regions. These districts had served as the comparison group in SC1, and findings for the SC1 comparison and intervention groups were utilized as historic controls.^ SC2 achieved a 76.3% adoption rate and found that an average of 84% of the curriculum was taught with an 82% fidelity to methods utilized by the curriculum. These rates and rates for implementation of dissemination strategies were equal to or greater than corresponding rates for SC1. The proportion of teachers implementing the curriculum in SC2 was found to be equal to SC1's video-trained districts but lower than the SC1 workshop-trained group.^ SC2's findings corroborate and support the findings from the earlier study, and increase our confidence in its findings. Taken together, the findings from SC2 and SC1 point to the effectiveness of their theory-based intervention strategies in encouraging adoption and accurate implementation of the tobacco prevention curriculum. ^

Cigarette smoking and Ecuadorian adolescents: A cross-sectional survey and policy review

This study investigates the relationship between cigarette smoking and adolescents in Ecuador, South America. Using the Social Learning Theory as a basis, the cross-sectional survey focuses attention on such social influences as the smoking habits of family members and peers as well as, the role of cigarette advertisements. Actual use prevalence, access to cigarettes and knowledge and attitudes about smoking are also obtained.^ The survey was conducted in both urban and rural areas, with 50 schools in 40 different communities participating. Two thousand four hundred and fifty-seven adolescents aged 9 to 15 years completed a self-administered questionnaire. This part of the study was conducted in collaboration with the international health organization Amigos de las Americas (AMIGOS). Staff assigned to the AMIGOS Ecuador projects worked with local health and education officials to implement the cross-sectional survey in the field.^ The key informant survey and subsequent policy review were designed to illuminate the social, cultural and institutional environment for anti-smoking activities and interventions in Ecuador. Key individuals involved with this issue on both national and local levels were interviewed. A review of past legislative efforts and present anti-smoking laws was also conducted.^ The current smoking prevalence among the study population was 8.6 percent. Findings from the cross-sectional survey revealed significant positive associations between the smoking habits of siblings and peers and the smoking behavior of the adolescents surveyed. Significant age and gender differences were also found in association with several different variables.^ The policy review found an unfavorable environment for anti-smoking efforts. Several factors contribute to this including, most importantly, lack of funding and lack of public support. The present anti-smoking law is often vague and lacks important provisions, such as a prohibition on selling tobacco products to minors.^ Together, the two surveys provide comprehensive information for the purpose of designing smoking prevention interventions. Using the results from the two surveys, recommendations for intervention are proposed. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Classification of cellular malignancy associated changes in cervical cancer

Cervical cancer is the leading cause of death and disease from malignant neoplasms among women in developing countries. Even though the Pap smear has significantly decreased the number of deaths from cervical cancer in the past years, it has its limitations. Researchers have developed an automated screening machine which can potentially detect abnormal cases that are overlooked by conventional screening. The goal of quantitative cytology is to classify the patient's tissue sample based on quantitative measurements of the individual cells. It is also much cheaper and potentially can take less time. One of the major challenges of collecting cells with a cytobrush is the possibility of not sampling any existing dysplastic cells on the cervix. Being able to correctly classify patients who have disease without the presence of dysplastic cells could improve the accuracy of quantitative cytology algorithms. Subtle morphologic changes in normal-appearing tissues adjacent to or distant from malignant tumors have been shown to exist, but a comparison of various statistical methods, including many recent advances in the statistical learning field, has not previously been done. The objective of this thesis is to use different classification methods applied to quantitative cytology data for the detection of malignancy associated changes (MACs). In this thesis, Elastic Net is the best algorithm. When we applied the Elastic Net algorithm to the test set, we combined the training set and validation set as "training" set and used 5-fold cross validation to choose the parameter for Elastic Net. It has a sensitivity of 47% at 80% specificity, an AUC 0.52, and a partial AUC 0.10 (95% CI 0.09-0.11).^

Development and evaluation of a video-based modeling strategy for the dissemination phase of a school health tobacco -use prevention program

Even the best school health education programs will be unsuccessful if they are not disseminated effectively in a manner that encourages classroom adoption and implementation. This study involved two components: (1) the development of a videotape intervention to be used in the dissemination phase of a 4-year, NCI-funded diffusion study and (2) the evaluation of that videotape intervention strategy in comparison with a print (information transfer) strategy. Conceptualization has been guided by Social Learning Theory, Diffusion Theory, and communication theory. Additionally, the PRECEDE Framework has been used. Seventh and 8th grade classroom teachers from Spring Branch Independent School District in west Houston participated in the evaluation of the videotape and print interventions using a 57-item preadoption survey instrument developed by the UT Center for Health Promotion Research and Development. Two-way ANOVA was used to study individual score differences for five outcome variables: Total Scale Score (comprised of 57 predisposing, enabling, and reinforcing items), Adoption Characteristics Subscale, Attitude Toward Innovation Subscale, Receptivity Toward Innovation, and Reinforcement Subscale. The aim of the study is to compare the effect upon score differences of video and print interventions alone and in combination. Seventy-three 7th and 8th grade classroom teachers completed the study providing baseline and post-intervention measures on factors related to the adoption and implementation of tobacco-use prevention programs. Two-way ANOVA, in relation to the study questions, found significant scoring differences for those exposed to the videotape intervention alone for both the Attitude Toward Innovation Subscale and the Receptivity to Adopt Subscale. No significant results were found to suggest that print alone influences favorable scoring differences between baseline and post-intervention testing. One interaction effect was found suggesting video and print combined are more effective for influencing favorable scoring differences for the Reinforcement for the Adoption Subscale.^ This research is unique in that it represents a newly emerging field in health promotion communications research with implications for Social Learning Theory, Diffusion Theory, and communication science that are applicable to the development of improved school health interventions. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Neural analogs of associative learning in {\it Aplysia\/}: Experimental and theoretical approaches

A change in synaptic strength arising from the activation of two neuronal pathways at approximately the same time is a form of associative plasticity and may underlie classical conditioning. Previously, a cellular analog of a classical conditioning protocol has been demonstrated to produce short-term associative plasticity at the connections between sensory and motor neurons in Aplysia. A similar training protocol produced long-term (24 hour) enhancement of excitatory postsynaptic potentials (EPSPs). EPSPs produced by sensory neurons in which activity was paired with a reinforcing stimulus were significantly larger than unpaired controls 24 hours after training. To examined whether the associative plasticity observed at these synapses may be involved in higher-order forms of classical conditioning, a neural analog of contingency was developed. In addition, computer simulations were used to analyze whether the associative plasticity observed in Aplysia could, in theory, account for second-order conditioning and blocking. ^

Social cognitive theory and factors associated with Mexican-American parents' beliefs, attitudes and practices regarding communication with their adolescent children about sex

Latinos have the highest teen birth rate nationally. Cameron County, Texas is primarily Latino (Mexican-American). This mixed-method study (n=43) examines Mexican-American parents of adolescents' beliefs, attitudes and practices regarding communication with their adolescent children about sex. Social Cognitive Theory (SCT) constructs self-efficacy, behavioral determinism, environment, outcome expectations and reciprocal determinism can be influences on frequency and quality of parent-adolescent sex communication.^ This study describes Mexican-American parents' of adolescents recollections of their own experiences associated with learning about sexuality. It also examines the attitudes and practices regarding communication about sex and the self-efficacy and behavioral capability of participants to teach their adolescent children about sex and sexually transmitted infections. ^ Negative childhood experiences (shame, lies and trauma) of the parents in this study played a key role in terms of their desire to communicate more comprehensively about sexuality with their own children than did their parents. While participants' reported low self-efficacy and behavioral capability to communicate with their adolescent children about sex, they reported relatively high frequency and quality of communication, with 75% of participants receiving a high quality score and over 44% reporting frequent communication with their adolescent children about sex. A Chi square analysis and Fisher's Exact Score revealed no association between acculturation status, gender or having a child who has mothered/fathered a baby and the frequency or quality of communication about sex with adolescent children. Study participants also gave specific recommendations for method, content and setting of sex education for their children and themselves. Promotora delivery of information and education in a comfortable, culturally appropriate neighborhood setting, as well as parent –child learning sessions were identified as possible approaches to address improve self-efficacy and behavioral capability of parents communicating with their adolescent children about sex.^ The results of this analysis provide public health practitioners and interested community entities data to identify and develop interventions that use a theoretical, evidence-based framework for culturally appropriate interventions to encourage and equip Mexican-American parents to effectively communicate with their adolescent children about sexuality, and ultimately to address the high rates of teen pregnancy in this U.S.-Mexico border community. ^

A computerized statistical framework for coalescent analysis

Coalescent theory represents the most significant progress in theoretical population genetics in the past three decades. The coalescent theory states that all genes or alleles in a given population are ultimately inherited from a single ancestor shared by all members of the population, known as the most recent common ancestor. It is now widely recognized as a cornerstone for rigorous statistical analyses of molecular data from population [1]. The scientists have developed a large number of coalescent models and methods[2,3,4,5,6], which are not only applied in coalescent analysis and process, but also in today’s population genetics and genome studies, even public health. The thesis aims at completing a statistical framework based on computers for coalescent analysis. This framework provides a large number of coalescent models and statistic methods to assist students and researchers in coalescent analysis, whose results are presented in various formats as texts, graphics and printed pages. In particular, it also supports to create new coalescent models and statistical methods. ^

Learning What Works: Demonstrating Practice Effectiveness with Children and Families Through Retrospective Investigation

Intensive family preservation services (IFPS), designed to stabilize at-risk families and avert out-of-home care, have been the focus of many randomized, experimental studies. Employing a retrospective “clinical data-mining” (CDM) methodology (Epstein, 2001), this study makes use of available information extracted from client records in one IFPS agency over the course of two years. The primary goal of this descriptive and associational study was to gain a clearer understanding of IFPS service delivery and effectiveness. Interventions provided to families are delineated and assessed for their impact on improved family functioning, their impact on the reduction of family violence, as well as placement prevention. Findings confirm the use of a wide range of services consistent with IFPS program theory. Because the study employs a quasi-experimental, retrospective use of available information, clinical outcomes described cannot be causally attributed to interventions employed as with randomized controlled trials. With regard to service outcomes, findings suggest that family education, empowerment services and advocacy are most influential in placement prevention and in ameliorating unmanageable behaviors in children as well as the incidence of family violence.

Active Learning in Nursing Education

ALINE is a pedagogical model developed to aid nursing faculty transition from passive to active learning. Based on constructionist theory, ALINE serves as a tool for organizing curriculum for online and classroom based interaction and permits positioning the student as the active player and the instructor, the facilitator to nursing competency.