Incorporating co-morbidity: understanding inequality to improve the value of targeted public health strategies Authors: David Jeffries & Warren Stevens Population Services International COMPLEX SYSTEMS AND AGENT-BASED MODELS Public health interventions are usually targeted by single latent proxies which attempt to identify vulnerable groups. This oversimplifies the multi-variable dynamics of the co-morbidities that should influence targeting. The probabilistic model presented here compares age, wealth and z-score as markers for targeting strategies of ARI. In reality there are many more non-linear interactions, which cannot be adequately modeled by conventional stochastic models and are more appropriately represented by simulation models, such as an agent-based model. What is appealing about the construct of agent-based models with regard to understanding the complexity of health policy resource allocation in context is that consequences on the collective level are often neither obvious nor predictable, even when the assumptions used to predict individual agents are relatively simple. BACKGROUND Attempts to achieve Millennium Development Goals 4 and 5 have focused primarily on achieving high levels of population-wide coverage of interventions such as immunizations, antenatal care and ITN use, but how much has the focus on nationwide coverage marginalized targeted approaches? Disease burden is not evenly distributed across populations and using ‘wealth’ to measure that inequality underestimates its severity. Also, single-disease, single-intervention models may not capture the full compound effect of co-morbidities in the most vulnerable sub-populations. METHODS The probability of an episode of ARI was estimated using logistic regression (adjusted for household clustering), giving age-stratified estimates for wealth and z-score percentiles. The age-stratified mortality rates were then estimated with probability of death = 1 – (1 – prob of death from ARI)number of episodes. Figure 1 compares the death rates from ARI for z and wealth scores in all U5s. Source data: Demographic Health Survey from India 2006 (n= 30,076) and two Mortality Surveys (n=1,298; n= 1,677). Figure 3 – Cumulative mortality CONCLUSIONS Wealth or socio-economics status is not a good reference variable for measuring health inequality in young children. Single-disease, single-death models underestimate the role of co-morbidity in relative risk of death. Variance in risk of death may be much higher than is currently assumed, and this may directly affect the potential impact of population-wide interventions For a copy of this poster go to www.psi.org