<p>How to allocate resources in the health care sector is academically dealt with within the subject of health economics. Economic evaluations are within this area used to compare the costs and effects of medical interventions with the purpose to help decision makers decide how to allocate resources.</p><p>Oxygen deficiency in the foetus during birth can lead to severe life long injuries in the child. In high-risk deliveries, it is therefore considered necessary to use foetal surveillance with a scalp electrode and the choice is between surveillance with internal cardiotocography (CTG) and surveillance with ST analysis. The standard procedure is in most hospitals currently CTG, which records the foetal heart rate and the uterine contractions. The second strategy, in this thesis referred to as ST analysis, complements CTG with foetal electrocardiography(ECG) and ST analysis.</p><p>The objective of this report is to from a societal perspective determine the cost-effectiveness of using ST analysis in complicated deliveries, compared to the use of CTG alone. A cost-utility analysis was performed based on a probabilistic decision model incorporating the relevant strategies and outcomes. The costs and effects of the two different treatment strategies were compared in a decision tree. Discounted costs and quality-adjusted life-years (QALYs) were measured and simulated over a life-time perspective.</p><p>The analysis resulted in an incremental effect of 0.005 QALYs for the ST analysis strategy, when compared to the CTG strategy. ST analysis was also associated with a €30 lower cost. Thus, CTG is dominated by the ST analysis strategy. The probability that ST analysis is the cost-effective alternative is high for all values of willingness-to-pay for a QALY, which means that a decision to implement the ST analysis strategy based on the results of this thesis would be surrounded by a low degree of uncertainty.</p>

Thesis (M.B.A.)--Naval Postgraduate School, December 2003. / "MBA professional report"--Cover. Thesis advisor(s): David R. Henderson, Kevin R. Gue. Includes bibliographical references (p. 55-57). Also available online.

Compared to the relative wealth of information surrounding design in the context of system-of-systems there has been little research surrounding policy making in system-of-systems. While the adoption of a formal approach and lexicon for system-of-systems problems has been proposed by researchers, the specific inclusion of regulatory policies in system-of-systems is still largely absent or underdeveloped. Typically, there is no distinction between internal policies of an organization and exogenous policies coming through regulatory channels. Further, researchers have yet to formally employ a standardized framework to regulatory policy problems in the context of a system-of-systems. As international regulatory bodies are calling on world States to identify and select “baskets of measures” to address CO2 emissions from civil aviation, there is a growing recognition that doing so will require a framework for policy identification and selection. Despite this recognition, such a framework has yet to be established. This research develops a formal lexicon for public policy as a part of system-of-systems, and employs a formalized process to explore multiple established, planned, and potential policies in the context of the global civil aviation system. Existing architectures and lexicons will be expanded to include regulatory policies that have often been treated as exogenous forcing functions in system-of-systems problems. Ultimately, a process for informed quantitative decision making to support concurrent CO2 regulatory policy analysis and design in the civil aviation system-of-systems is established. The developed methodology will allow policy makers to systematically identify effective policy space, while maintaining the objectivity of the analyst.

Preventative interventions often demand that resources be consumed in the present in exchange for future benefits. Understanding these trade-offs, in a context of resource constraints, is essential for policy makers. Cost-effectiveness analysis is one tool to inform decision-making. Targeted testing and treatment (TTT) for latent tuberculosis infection (LTBI) consists in identifying people at high risk for LTBI for preventive treatment to decrease the risk that they will develop active tuberculosis disease (ATBD). The state of Tennessee began conducting TTT statewide in 2001. This study uses a decision tree to evaluate the cost and outcomes of TTT for LTBI in Tennessee, compared to passive ATBD case finding (PACF). Key event probabilities were obtained from the Tennessee TTT program and from the literature. Outcomes are measured in terms of Quality Adjusted Life Years (QALY). The cost-effectiveness threshold was set at $100,000/QALY saved. One-way sensitivity analyses around factors related to study design (exclusion of patient costs, secondary transmission, discount rate and analytical horizon), the program’s environment (prevalence of LTBI and drug resistance, ATBD treatment costs) and program performance (program maturity, treatment initiation and completion rate, testing in low-risk group, test characteristics, screening costs) were conducted, as was probabilistic sensitivity analysis (PSA) which takes into account the uncertainty in multiple parameters simultaneously. The base case, with a 25-year time horizon and 3% discount rate, shows that TTT prevents 47 ATBD cases, and saves 31 QALYs per 100,000 patients screened for LTBI at a societal cost of $12,579 (2011 US$) per QALY saved. Sensitivity analyses identified value thresholds that would trigger a change in preferred policy. PSA shows that the likelihood that TTT would be cost-effective is low. Decision makers interested in implementing TTT should carefully assess the characteristics of the local TB epidemic and expected program performance to determine whether TTT is preferable over PACF from a cost-effectiveness viewpoint.

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

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The objective of this dissertation is to develop and evaluate quantitative models that have the potential to improve cardiovascular disease (CVD) screening and projection efforts in the U.S. Paper 1 assesses the exchangeability of a non-laboratory-based CVD risk score (predictors do not include cholesterol) with more commonly-used laboratory-based scores, such as the Framingham risk equations. Under conventional thresholds for identifying high-risk individuals, 92-96% of adults in the National Health and Nutrition Examination Survey (NHANES III) were equivalently characterized as high- or low-risk using either type of score. The 10-year CVD death results also suggest that simple CVD risk assessment could be a useful proxy for more expensive laboratory-based screening strategies in the U.S. or other resource-limited settings. Paper 2 uses micro-simulation modeling techniques to evaluate the cost effectiveness of primary cardiovascular disease (CVD) screening using staged laboratory-based and/or non-laboratory-based total CVD risk assessment. The results imply that efficient screening guidelines should include non-laboratory-based risk assessment, either as a single stage or as part of multistage screening approach. Compared to current CVD screening guidelines, fewer cholesterol tests would be administered and more adults would receive low-cost statins under cost-effective screening policies. Paper 3 examines the trends of CVD risk factors, treatment, and total risk in the U.S. from 1973-2010, and offers projections of these variables for 2015-2030. Nine waves of cross-sectional NHANES data show that the divergent, observed trends in common CVD risk factors (such as smoking, BMI, total cholesterol, and blood pressure) are expected to continue in future years. Age-adjusted CVD risk has decreased over time (during the observed and projected periods), but total risk has increased when considering the impact of aging on CVD risk. Scenario analyses suggest that strategies targeting cholesterol and blood pressure treatment have the greatest potential to reduce future CVD burden in the U.S.

This dissertation is motivated by two of the health-related Millennium Development Goals (MDGs): MDG 4, focused on reducing child mortality, and MDG 5, which aims to improve maternal health. My three papers evaluate the health and economic impact, and cost-effectiveness, of interventions to improve maternal and child health in three areas of the developing world using methods from decision sciences and statistics. In paper 1, I use a decision-analytic model that simulates the natural history of pregnancy and pregnancy-related complications to assess the expected health outcomes, costs, and cost-effectiveness of strategies to reduce maternal mortality in Afghanistan. Increasing family planning was found to be the most effective single intervention to reduce maternal mortality. Further findings suggest that a stepwise approach that couples increased family planning with incremental improvements in access to appropriate intrapartum care could prevent 3 out of 4 maternal deaths and would be cost-effective. Paper 2 explores the value of community-based disease management programs for reducing mortality from childhood pneumonia and malaria in 24 countries of sub-Saharan Africa. I use a model-based framework that combines symptom patterns, care-seeking behavior, and treatment coverage from an empirical assessment of household survey data with information on diagnostic algorithms and disease progression from the literature. Results indicate that a community health worker program modeled on currently-existing programs could avert over 100,000 under-five deaths combined across the 24 countries and would be regarded as cost-effective compared to the status quo under typical benchmarks for international cost-effectiveness analysis. My third paper evaluates the effect of Janani Suraksha Yojana (JSY), a conditional cash transfer program intended to promote the use of reproductive health services in India, on childhood immunizations and other reproductive and child health indicators. Using observational data from the most recent district-level household survey, I conduct a matching analysis with logistic regression to assess the associations of interest. Results show that receipt of financial assistance from JSY led to a significant increase in childhood immunizations rates, post-partum check-ups, and some healthy breastfeeding practices, but no impact was found on exclusive breastfeeding and care-seeking behaviors.

This dissertation examines methods used to evaluate the cost-effectiveness of childhood obesity interventions in order to help decision-makers prioritize among competing health programs using standardized outcomes. Chapter 1 generates inputs for use in cost-effectiveness analyses (CEAs) of childhood obesity interventions. In Chapter 1.1, I use data from the Medical Expenditure Panel Survey to predict expenditures associated with obesity in childhood and adolescence. I found that obese children and adolescents have significantly different expenditures than their normal weight counterparts. I conclude that exclusion of obesity-related medical expenditures can potentially undervalue the cost-effectiveness of interventions. In Chapter 1.2, I use data from the Study of Early Child Care and Youth Development to examine the longitudinal trajectory of child weight. I derived probabilities of transitioning between weight classes that can be used in a decision-analytic model to extrapolate the effectiveness of childhood obesity interventions beyond childhood. I found that deviating from CDC BMI reference categories can more accurately capture the risk of future obesity. In Chapter 2, I evaluate the cost-effectiveness of a primary care-based obesity prevention program, High Five for Kids. Over two years, High Five for Kids was low-cost, but only marginally effective in reducing BMI. I used a decision analytic simulation model to extrapolate trial outcomes over a 10-year horizon, and found that in the long-term, primary care based obesity prevention was likely to be cost-effective relative to usual care. I also found that key methodological considerations can meaningfully influence the cost-effectiveness of childhood obesity interventions. In Chapter 3, I develop an agent-based model to explore the dynamics of the potential spread of obesity within families. I found that the “contagion” of obesity could result in significant collateral weight loss in family members not targeted in an intervention. As a result, CEAs may underestimate the benefits of obesity interventions. Moreover, I found that unless interventions are targeted toward all obese children in a family, the contagion of obesity can hinder weight loss in intervention targets. This model can be leveraged as a tool to optimize family-based obesity intervention strategies and inform randomized controlled obesity prevention trials.