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Predictive and Causal Analytics
Attributive Causal Modeling: Quantifying Human Health Risks Caused by Toxoplasmosis from Open System Production of Swine
Introduction This is the first of two chapters that apply predictive analytics to two very different risk prediction problems. As in the previous two chapters, the challenge in this one is to estimate human health risks from a pathogen in swine using a combination of plausible conservative estimates of relevant risk factors and probabilistic simulation. However, our focus now shifts to predicting how risks would change if some fraction of swine were shifted from totally confined production systems to more humane open systems. Predicting how interventions change risk requires a causal model, as discussed in Chapter 1. As in Chapters 5 and 6, a simple product-of-factors framework is again suitable (see equation 7.5). Instead of the terms describing propagation of changes along successive links in a causal chain, with the change in the quantity at each step being equal to a sensitivity or slope factor times the change in its predecessor, many of the factors in this chapter are estimated attribution fractions. These describe the fraction of relevant deaths or illnesses per year in the population due to (i.e., attributed to) and caused by infection with a foodborne pathogen; the fraction of them that are attributed specifically to pork consumption, and so forth. Unlike the attributable risk estimates or attributable fractions criticized in Chapter 2, which were derived purely from statistical associations, in this application the causal agent of disease, T. Gondii, is known and can be measured. Predictions for effects of interventions are therefore grounded in causal attribution calculations that can be compared to available data on prevalence and infectivity of the relevant causal agent. Chapter 8 will then turn to a pure prediction problem: how well the entries in one column in a table (indicating in vivo carcinogenicity of chemicals, or lack of it, in rodents) can be predicted from entries in other columns, representing results of relatively inexpensive high-throughput screening (HTS) assays. No causal model is required for this task: predictive analytics algorithms alone suffice.
For readers who wish to skip ahead, the main points of this chapter are as follows. Open livestock production systems, including free-range and organic livestock systems, seek to improve the welfare of animals by letting them roam in unconfined spaces. This increases their exposure to potentially harmful micro-organisms, including T. gondii. When transmitted through the food chain, T. gondii threatens human health, especially in unborn children of women infected during pregnancy, as well as the lives of patients with compromised immune systems. By contrast, conventional total confinement production systems can now keep this human health risk at or near zero. The probabilistic risk simulation model developed in the rest of this chapter quantifies the trade-off between greater use of open swine production systems and increased cases of toxoplasmosis in humans. It predicts that every 1,804 pigs shifted from conventional total confinement to open production (95% confidence interval 747-9,520) would cause the loss of one additional human quality-adjusted life year (QALY), and that increasing the fraction of U.S. swine raised in open/free range operations by 0.1% (approx. 65,000 pigs) would cause a loss of approximately 36 human QALYs per year, including between 1 and 2 extra adult deaths per year. Methods of causal analytics are valuable largely because they can quantify such tradeoffs and answer what-if questions about how human health risks would change for different interventions, such as if different fraction of pigs were shifted to open production systems. This tells risk managers and policy-makers what they need to know to make decisions that are well-informed about trade-offs and about the probable consequences of different choices.