Descriptive Analytics for Public Health: Socioeconomic and Air Pollution Correlates of Adult Asthma, Heart Attack, and Stroke Risks

Abstract

This is the first of four chapters emphasizing the application of descriptive analytics to characterize public and occupational health risks. Much of risk analysis addresses basic descriptive information: how big is a risk now, how is it changing over time or with age, how does it differ for people or situations with different characteristics, on what factors does it depend, with what other risks or characteristics does it cluster? Such questions arise not only for public and occupational health and safety risks, but also for risks of failures or degraded performance in engineering infrastructure or technological systems, financial systems, political systems, or other “systems of systems” (Guo and Haimes 2016). Simply knowing how large a risk is now and whether it is increasing, staying steady, or decreasing may be enough to decide whether a proposed costly intervention to reduce it is worth considering further. This chapter shows how to use basic tools of descriptive analytics, especially interaction plots (showing the conditional expected value of one variable at different levels of one or more other variables), together with more advanced methods from Chap. 2, such as regression trees, partial dependence plots, Bayesian networks (BNs), to describe risks and how they vary with other factors. A brief discussion and motivation of these methods is given for readers who have skipped Chap. 2. Chapter 4 introduces additional descriptive techniques, including plots that use non-parametric regression to pass smooth curves or surfaces through data clouds. It shows how they can be used, together with simple mathematical analysis, to resolve a puzzle that has occasioned some debate among toxicologists: that some studies have concluded that workers form disproportionately high levels of benzene metabolites at very low occupational exposure concentrations compared to higher concentrations, while other studies conclude that metabolism of benzene at low concentrations is approximately linear, and proportional to concentrations in inhaled air. Chapter 5 emphasizes the value of descriptive plots, upper-bounding analyses, and qualitative assumptions, as well as more quantitative risk assessment modeling, in bounding the size of human health risks from use of antibiotics in food animals. Chapter 6 calculates plausible bounds on the sizes of the quantitative risks to human health of infection with a drug-resistant “super-bug” from swine farming operations. Together, these chapters illustrate how descriptive analytics can be used to obtain and present useful quantitative characterizations of human health risks despite realistic scientific uncertainties about the details of relevant causal processes.