Exposure: poverty level (e.g. % below federal poverty level)
Outcome: HIV infection
Hypothesis: people exposed to higher levels of poverty are more likely to develop an HIV infection, and at a younger age.
Lifecourse model: I think the “social mobility hypothesis,” or as described by Gita et al as the “life course model of a critical period with later effect modification (where the irreversible change of the critical period can be either enhanced or diminished by a later effect)” is most appropriate, because I believe poverty during childhood/teenage years has a different effect on the likelihood of HIV infection than poverty during adulthood.
Regression model: The most appropriate regression model would be a Cox model, since I’m interested in the time to event (event being testing positive for HIV infection). This could be written as:
Log(h(t|x)) = log(h0(t)) + β2S2 + β3S3 + θ23S2S3,
Where β2 gives the slope of childhood poverty level, β3 gives the slope of the adult poverty level, and θ23 is the coefficient for the interaction term.
Dataset: I could use the Community Health Applied Research Network (CHARN) data. CHARN is a research network of community health centers and universities that was established to conduct research on patient-centered outcomes among underserved populations. Advantages of this dataset include that it comprises different geographical areas of the US and is very large (over 500,000 patients). Potential problems with using this dataset are that because it focuses on underserved populations, there may not be much variability in the exposure; because it is comprised of electronic health record data, family income is unlikely to be recorded and participants will have to be asked directly (which leads to another problem – may not accurately recall early life family income levels, especially at the level of detail that would be needed for research purposes).