Reading Response Discussions

Exposure: malaria prevalence in the region where you live (continuous measure, that could be dichotomized). Region must be defined. P_a is the prevalence exposed to at age a. For every individual we have a vector of n_i exposure measurements P_i=(P_a1, P_a2, …, P_ni).

 Outcome: being a symptomatic with malaria parasite at age 40. Binary outcome Y.

 Time dimension: age

 1)   Saturated model

• Causal contrast: Compare any life-course trajectories Y(P)-Y(P’)
• Regression with a coefficient for every age measurement and their interactions

 2)   Critical model: childhood exposure

• Causal contrast btw childhood exposures: Y(P_age=child)-Y(P’_age=child)
• Regression with a unique coefficient for childhood exposure

 3)   Cumulative: over your entire life

• Causal contrast btw cumulative exposures: Y(sum(Pa))-Y(sum(P'a))
• Regression with a unique coefficient for cumulative exposure

 4)   Social mobility: changes between childhood malaria-prevalence region and adulthood malaria-prevalence region

• Causal contrast: Compare trajectories with upward mobility to no mobility and trajectories with downward mobility to no mobility   Y(P| mobility)-Y(P’|no mobility)
• Regression with two coefficients, one for downward mobility and one for upward mobility

Malaria is an infectious disease that provide some sort of short-term immunity and repeated infections can strengthen your organism response to further infections. Therefore, I think life-course exposure to malaria affects your probability of both being infected and if infected of being symptomatic today. Following Mishra et al. recommendations, I would ideally study different complementary models but I think the cumulative model and the critical model are the most appropriated, given the sensibility of children to malaria infection and the importance of childhood years as a developmental stage. Individual longitudinal cohort data exist to test this research question but may lack variability if constrained to a small region (say a village where everyone has had the same exposure history other than the cohort effect) or be susceptible to confounding. In a context where malaria is seasonal with outbreaks of different sizes from year to year and depending on environmental conditions, confounding is serious concern.