Overview of Data Management Planning Tools

These Data Management Plans are more comprehensive and complex than in the past. Libraries around the nation are trying to put together tools to help researchers write plans that conform to the new requirements. This session will look at some of these tools.

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. ^

A model for use by local public health departments to evaluate pandemic influenza plans

The events of the 1990's and early 2000's demonstrated the need for effective planning and response to natural and man-made disasters. One of those potential natural disasters is pandemic flu. Once defined, the CDC stated that program, or plan, effectiveness is improved through the process of program evaluation. (Centers for Disease Control and Prevention, 1999) Program evaluation should be accomplished not only periodically, but in the course of routine administration of the program. (Centers for Disease Control and Prevention, 1999) Accomplishing this task for a "rare, but significant event" is challenging. (Herbold, John R., PhD., 2008) To address this challenge, the RAND Corporation (under contract to the CDC) developed the "Facilitated Look-Backs" approach that was tested and validated at the state level. (Aledort et al., 2006).^ Nevertheless, no comprehensive and generally applicable pandemic influenza program evaluation tool or model is readily found for use at the local public health department level. This project developed such a model based on the "Facilitated Look-Backs" approach developed by RAND Corporation. (Aledort et al., 2006) Modifications to the RAND model included stakeholder additions, inclusion of all six CDC program evaluation steps, and suggestions for incorporating pandemic flu response plans in seasonal flu management implementation. Feedback on the model was then obtained from three LPHD's—one rural, one suburban, and one urban. These recommendations were incorporated into the final model. Feedback from the sites also supported the assumption that this model promotes the effective and efficient evaluation of both pandemic flu and seasonal flu response by reducing redundant evaluations of pandemic flu plans, seasonal flu plans, and funding requirement accountability. Site feedback also demonstrated that the model is comprehensive and flexible, so it can be adapted and applied to different LPHD needs and settings. It also stimulates evaluation of the major issues associated with pandemic flu planning. ^ The next phase in evaluating this model should be to apply it in a program evaluation of one or more LPHD's seasonal flu response that incorporates pandemic flu response plans.^

Disease risk factors and humoral antibody response to periodontal-associated pathogens: A model to evaluate periodontal disease in older adults

Periodontal diseases (PD) are infectious, inflammatory, and tissue destructive events which affect the periodontal ligament that surround and support the teeth. Periodontal diseases are the major cause of tooth loss after age 35, with gingivitis and periodontitis affecting 75% of the adult population. A select group of bacterial organisms are associated with periodontal pathogenesis. There is a direct association between oral hygiene and prevention of PD. The importance of genetic differences and host immune response capabilities in determining host, susceptibility or resistance to PD has not been established. This study examined the risk factors and serum (humoral) immune response to periodontal diseased-associated pathogens in a 55 to 80+ year old South Texas study sample with PD. This study sample was described by: age, sex, ethnicity, the socioeconomic factors marital status, income and occupation, IgG, IgA, IgM immunoglobulin status, and the autoimmune response markers rheumatoid factor (RF) and antinuclear antibody (ANA). These variables were used to determine the risk factors associated with development of PD. Serum IgG, IgA, IgM antibodies to bacterial antigens provided evidence for disease exposure.^ A causal model for PD was constructed from associations for risk factors (ethnicity, marital status, income, and occupation) with dental exam and periodontitis. The multiple correlation between PD and ethnicity, income and dental exam was significant. Hispanics of low income were least likely to have had a dental exam in the last year and most likely to have PD. The etiologic agents for PD, as evidenced by elevated humoral antibody responses, were the Gram negative microorganisms Bacteroides gingivalis, serotypes FDC381 and SUNYaBA7A1-28, and Wolinella recta. Recommendation for a PD prevention and control program are provided. ^

Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates

Campus behavior management is important for ensuring classroom order and promoting positive academic outcomes. Previous studies have shown the importance of individual student and campus personnel characteristics and campus context for explaining campus discipline rates (e.g., rates of suspension and expulsion). Assessing campus discipline rates, while controlling for these individual and campus characteristics, is important for the monitoring, evaluation, and intervention role of policymakers as well as state and federal level education agencies. Systems or metrics exist that measure other student outcomes (i.e., academic performance) with controls for individual and campus characteristics, but none exist that monitor these differences for discipline rates across campuses. In this paper, we use a multivariate model to analyze a longitudinal, statewide dataset for all secondary students in Texas from 2000 to 2008 in order to examine how campus discipline rates differ across schools with statistically similar students, teachers, and campus characteristics. The findings are important for understanding that some schools with similar characteristics have significantly different exclusionary discipline rates, and they are important for informing policy and agency level decision-making. The methodology described can easily be used by monitoring agencies as well as local school districts.