An Economic Analysis of Evolving Health Hazards

Health hazards (e.g., West Nile virus and antibiotic resistance) by their nature are detrimental to the health of mankind and are a vexing problem for society. Health authorities awareness of the rising health care costs associated with these health hazards highlights the need to undertake research in these areas. This dissertation presents a series of papers on these health hazards. Chapter 2 develops a spatial filtering panel data count model to examine the factors that contributed to the high prevalence of human West Nile virus (WNV) in California and Colorado using county-level data from 2003 to 2007. An econometric analysis was performed using a random effects negative binomial model to analyze the economic (income and home foreclosures) and biological (mosquitoes) factors associated with human WNV. Tests reveal the presence of spatial autocorrelation in the dependent variable (human WNV). The presence of this phenomenon implies that WNV in neighboring counties do impact the presence of WNV in adjacent counties. Consequently, the random effects negative binomial model is augmented with a spatially-lagged dependent variable and a spatial filtering term to correct for this problem and obtain unbiased estimates of the variance. Specification tests also show that income and home foreclosures are endogenous, i.e., home foreclosures, income and human WNV counts are determined jointly. Hence an instrumental variable (IV) technique is applied to the spatial filtering and spatial lag random effects negative binomial models to obtain consistent estimates. The former model is preferred because it is parsimonious in terms of a model selection criterion. Tests of over-identification (validity tests) reveal that the excluded instruments are indeed exogenous and for that matter valid. A number of hypotheses are tested regarding the economic and biological variables. The findings indicate that West Nile virus is higher in counties characterized by a low median income, high home foreclosures and high number of mosquito breeding sites. It is recommended that counties that exhibit these economic and biological characteristics should be allocated a higher percentage of resources for surveillance and monitoring of the disease. Chapter 3 is devoted to disease mapping and presentation of the variography of the various human WNV risk measures. It employs Geographic Information Systems (GIS) mapping tools to create thematic risk or hazard maps that visually depict the predicted probabilities of human WNV and the standardized morbidity ratios. The predicted probabilities were generated from the IV spatial filtering random effects negative binomial model. The hazard maps may ultimately assist policy makers in identifying areas of high and low West Nile virus risk, allocating scarce resources, and disease etiology. Variograms are estimated using geo-statistical methods to examine the spatial structure of the various risk measures. In this regard, both isotropic and anisotropic (directional) variograms are generated using exponential and Gaussian methods. They show the presence of strong spatial patterns in observed West Nile counts and the standardized morbidity ratios, but no spatial patterns in the model residuals. This study demonstrates how econometric methods can be used concurrently with GIS tools to inform public policy on the transmission of human West Nile virus. Chapter 4 builds a dynamic bio-economic model to study the impact of animal antibiotic use on the evolution of antibiotic resistance in humans. It reveals striking similarities between the theory of exhaustible resources in economics and antibiotic resistance. Antibiotic resistance is modeled as an exhaustible resource (common pool resource) extracted (used) over time. Each time an antibiotic is used it lowers the level of the resource (antibiotic effectiveness) by a small amount and thus raises the cost of using subsequent doses of an antibiotic. This process will continue and the next dose will lower the level of the resource even further making it more costly for future use of the drug. In other words, as more and more antibiotics are used the effectiveness of the drug dwindles over time. The planner\u27s problem is therefore to find the optimal use of antibiotics in animals and humans over time and this necessitates the use of capital-theoretic methods. Consequently, an optimal control model is developed to examine the trade-offs between current antibiotic use in humans and animals and future antibiotic effectiveness. The results reveal that antibiotics should be used in the animal industry to the point where the immediate net marginal benefit is just counterbalanced by the long-term cost in terms of dwindling drug effectiveness. The results of the simulation exercise show that antibiotic effectiveness decreases over time because of an accumulation of resistance to the drug by bacteria. Also the shadow value of antibiotic effectiveness decreases over time because of the decreasing levels of effectiveness. Sensitivity analyses show that increased use of antibiotics in the animal industry drastically reduces the level of antibiotic effectiveness and its shadow value in a given period. The results of this dissertation could assist health policy makers in the allocation of scarce resources. The findings underscore the importance of factors such as income, home foreclosures and the number of mosquito pools in the transmission of human WNV. Thematic maps of the standardized morbidity ratios and predicted probabilities provide information on areas of high and low WNV risks. The optimal control model provides an insightful perspective on how to allocate antibiotic resources between animal use and human medicine

Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US: A Spatial Econometric Approach

This study investigates whether the environmental Kuznets curve (EKC) relationship is supported for a measure of biodiversity risk and economic development across the United States (US). Using state-level data for all 48 contiguous states, biodiversity risk is measured using a Modified Index (MODEX). This index is an adaptation of a comprehensive National Biodiversity Risk Assessment Index. The MODEX differs from other measures in that it is takes into account the impact of human activities and conservation measures. The econometric approach includes corrections for spatial autocorrelation effects, which are present in the data. Modeling estimation results do not support the EKC hypothesis for biodiversity risk in the US. This finding is robust over ordinary least squares, spatial error, and spatial lag models, where the latter is shown to be the preferred model. Results from the spatial lag regression show that a 1% increase in human population density is associated with about a 0.19% increase in biodiversity risk. Spatial dependence in this case study explains 30% of the variation, as risk in one state spills over into adjoining states. From a policy perspective, this latter result supports the need for coordinated efforts at state and federal levels to address the problem of biodiversity loss

Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US: A Spatial Econometric Approach

This study investigates whether the environmental Kuznets curve (EKC) relationship is supported for a measure of biodiversity risk and economic development across the United States (US). Using state-level data for all 48 contiguous states, biodiversity risk is measured using a Modified Index (MODEX). This index is an adaptation of a comprehensive National Biodiversity Risk Assessment Index. The MODEX differs from other measures in that it is takes into account the impact of human activities and conservation measures. The econometric approach includes corrections for spatial autocorrelation effects, which are present in the data. Modeling estimation results do not support the EKC hypothesis for biodiversity risk in the US. This finding is robust over ordinary least squares, spatial error, and spatial lag models, where the latter is shown to be the preferred model. Results from the spatial lag regression show that a 1% increase in human population density is associated with about a 0.19% increase in biodiversity risk. Spatial dependence in this case study explains 30% of the variation, as risk in one state spills over into adjoining states. From a policy perspective, this latter result supports the need for coordinated efforts at state and federal levels to address the problem of biodiversity loss.biodiversity risk; environmental Kuznets curve; United States; spatial econometrics