Nonresponse is a challenge in many fields, including demography, economics, public health, sociology, and business. This chapter explores nonpolitical manifestations of nonignorable nonresponse by focusing on population health. For many conditions, the decision to get tested or the willingness to allow a test is deeply wrapped up in the likelihood of having the condition. During Covid, for example, people who thought they might have been exposed to the virus were almost certainly more likely to get tested meaning that nonignorable nonresponse complicated our ability to understand the Covid outbreak. Section 13.1 discusses the challenge of estimating public health variables in terms of a nonignorable missing data problem. Section 13.2 explores how first-stage instruments can improve the efficiency and accuracy of efforts to assess prevalence. Section 13.3 presents a framework for comparing Covid positivity rates across regions even when testing rates differ.

This chapter highlights the critical importance of having the right kind of data for selection models that address nonignorable nonresponse. In general, we need a variable that is included in our response model and excluded from our outcome model. The best approach is creating a randomized response instrument that affects whether someone responds, but does not affect the content of their response. In many polling contexts, it is easy to create randomized response instruments. The pollster simply needs to figure out some protocol that affects response rate and then randomize it. Section 10.1 makes it clear that knowing the correct functional form is not enough to save a selection model. Section 10.2 highlights the difficulty of using observational response instruments. Section 10.3 discusses how and why to create randomized response instruments. Section 10.4 shows how to use randomized response instruments in a simple test for diagnosing nonignorable nonresponse. Section 10.5 shows how randomized response instruments enable us to use the full suite of selection models even when we do not observe data for nonrespondents.