Policy implications of public health workforce development for disaster preparedness in the United States

Public health efforts were initiated in the United States with legislative actions for enhancing food safety and ensuring pure drinking water. Some additional policy initiatives during the early 20th century helped organize and coordinate relief efforts for victims of natural disasters. By 1950's the federal government expanded its role for providing better health and safety to the communities, and its disaster relief activities became more structured. A rise in terrorism related incidents during the late 1990's prompted new proactive policy directions. The traditional policy and program efforts for rescue, recovery, and relief measures changed focus to include disaster preparedness and countermeasures against terrorism.^ The study took a holistic approach by analyzing all major disaster related policies and programs, in regard to their structure, process, and outcome. Study determined that United States has a strong disaster preparedness agenda and appropriate programs are in place with adequate policy support, and the country is prepared to meet all possible security challenges that may arise in the future. The man-made disaster of September 11th gave a major thrust to improve security and enhance preparedness of the country. These new efforts required large additional funding from the federal government. Most existing preparedness programs at the local and national levels are run with federal funds which is insufficient in some cases. This discrepancy arises from the fact that federal funding for disaster preparedness programs at present are not allocated by the level of risks to individual states or according to the risks that can be assigned to critical infrastructures across the country. However, the increased role of the federal government in public health affairs of the states is unusual, and opposed to the spirit of our constitution where sovereignty is equally divided between the federal government and the states. There is also shortage of manpower in public health to engage in disaster preparedness activities, despite some remarkable progress following the September 11th disaster.^ Study found that there was a significant improvement in knowledge and limited number of studies showed improvement of skills, increase in confidence and improvement in message-mapping. Among healthcare and allied healthcare professionals, short-term training on disaster preparedness increased knowledge and improved personal protective equipment use with some limited improvement in confidence and skills. However, due to the heterogeneity of these studies, the results and interpretation of this systematic review may be interpreted with caution.^

DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.

Adaptive clinical trial design for targeted therapy development

The development of targeted therapy involve many challenges. Our study will address some of the key issues involved in biomarker identification and clinical trial design. In our study, we propose two biomarker selection methods, and then apply them in two different clinical trial designs for targeted therapy development. In particular, we propose a Bayesian two-step lasso procedure for biomarker selection in the proportional hazards model in Chapter 2. In the first step of this strategy, we use the Bayesian group lasso to identify the important marker groups, wherein each group contains the main effect of a single marker and its interactions with treatments. In the second step, we zoom in to select each individual marker and the interactions between markers and treatments in order to identify prognostic or predictive markers using the Bayesian adaptive lasso. In Chapter 3, we propose a Bayesian two-stage adaptive design for targeted therapy development while implementing the variable selection method given in Chapter 2. In Chapter 4, we proposed an alternate frequentist adaptive randomization strategy for situations where a large number of biomarkers need to be incorporated in the study design. We also propose a new adaptive randomization rule, which takes into account the variations associated with the point estimates of survival times. In all of our designs, we seek to identify the key markers that are either prognostic or predictive with respect to treatment. We are going to use extensive simulation to evaluate the operating characteristics of our methods.^