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Modeling Issues

This paper explores individual awareness of chronic health conditions, emphasizing its significance in seeking early care. The authors investigate whether awareness varies by socioeconomic factors such as race, gender, and income, utilizing a three-step sequential model based on the Health and Retirement Survey (HRS). The model assesses the probability of participation in medical examinations and the likelihood of being aware of a chronic condition. With two conceptual frameworks, the authors encourage creative problem-solving and thorough data utilization to address selection bias and enhance understanding in health awareness research.

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Modeling Issues

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  1. Modeling Issues An example

  2. Always good to consider different ways of thinking of the same issue. • In this example, the authors are trying to assess individual awareness of having a chronic health condition. • Awareness is important for seeking care early. • Question is if awareness differs by socioeconomic characteristics like race, gender and income.

  3. They consider a three-step process towards awareness, based on data from the Health and Retirement Survey (HRS). • They assume the survey is random and not subject to selection bias with regards to participation in the survey. Is this a good assumption?

  4. Build a three-step sequential binary response model • The probability that nn individual in the HRS agrees to participate in a medical examination that is an optional part of the survey. • The likelihood that the individual has a chronic health condition. • The probability that the individual is aware of having a chronic health condition if she does indeed have one (ie, the probability of awareness). • Gives a nice trivariateprobit with selection problem.

  5. Conceptual framework used in the paper (figure 3)

  6. An alternative conceptual framework paper (figure 4)

  7. Each implies a 3-step sequential binary choice model • For figure 3 • Participation • Examination • Self-report (unawareness is 38%) • For figure 4 • Participation • Self-report • Examination (unawareness is 13%)

  8. Think about your data – what else can it tell you?

  9. And for figure 4

  10. Message • Conceptualize your problem in more ways than one. • Try to think creatively. • Make sure you are using all your data in an empirical model. Think about how information can carry forward to future steps, especially when dealing with issues like selection or censored data.

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