Agent-based modelling for rethinking the socioeconomic determinants of child health in sub-Saharan Africa

Socioeconomic factors play distal roles in shaping populations’ health. In sub-Saharan Africa, these structural health determinants are strongly associated with intermediate determinants of under-5 mortality such as lifestyle factors, health seeking behaviour, or exposure to a health threat. The aim of the study was to use simulation tools for rethinking the dynamics between socioeconomic factors, preventive health measures, and child health. An agent-based model was developed, consisting of rules and equations based on data from four Demographic and Health Surveys conducted in sub-Saharan countries. The model, visualizing the impact of different factors and complex effects, enhanced the understanding and debate on causal pathways of socioeconomic inequalities in under-5 mortality

The Net Benefit of a treatment should take the correlation between benefits and harms into account

OBJECTIVE: The assessment of benefits and harms from experimental treatments often ignores the association between outcomes. Generalized pairwise comparisons (GPC) can be used to assess the Net Benefit of treatment in a randomized trial accounting for that association.STUDY DESIGN AND SETTINGS: We use GPC to analyze a fictitious trial of treatment versus control, with a binary efficacy outcome (response) and a binary toxicity outcome, as well as data from two actual randomized trials in oncology. In all cases, we compute the Net Benefit for scenarios with different orders of priority between response and toxicity, and a range of odds ratios (ORs) for the association between outcomes.RESULTS: The GPC Net Benefit was quite different from the benefit/harm computed using marginal treatment effects on response and toxicity. In the fictitious trial using response as first priority, treatment had an unfavorable Net Benefit if OR1. With OR=1, the Net Benefit was 0. Results changed drastically using toxicity as first priority.CONCLUSION: Even in a simple situation, marginal treatment effects can be misleading. In contrast, GPC assesses the Net Benefit as a function of the treatment effects on each outcome, the association between outcomes, and individual patient priorities

Agent-based modelling to inform health intervention strategies : the case of severe acute malnutrition in children in high-burden low-income countries

Health interventions improve the management of severe acute malnutrition (SAM) in children under 5 in high-burden low-income countries. However, evaluation of their implementation faces a paucity of information and could benefit from a system perspective derived from the knowledge of implementers and experts. These challenges could be addressed using simulation modelling. We compared Markov and agent-based models of interventions for improving the management of SAM and assessed benefits and limitations in informing complex health intervention strategy designs. Based on a conceptual framework developed with existing evidence and expert advice, the agent-based model generated simulated data representing the complex evolution of the system. Multiple scenarios were investigated by varying parameters and mimicking rules of interventions. This study pointed out possible synergies between interventions enhancing early start of treatment and increasing recovery from SAM. When these interventions were adequately combined, outcomes of coverage, recovery and overall survival improved. Benefits of agent-based modelling were use of history, if-then rules to uncover mechanisms behind probabilities, and modifiable transition rates. Limitations related to model validation, choices of assumptions, and simplification. Agent-based modelling could be used to adapt intervention strategies to local contexts and support scale-up. As such, modelling could complement the methodological toolkit of health intervention strategy designs for improved policy decision

Agent-Based Modelling to Inform Health Intervention Strategies: The case of severe acute malnutrition in children in high-burden low-income countries

Health interventions improve the management of severe acute malnutrition (SAM) in children under 5 in high-burden low-income countries. However, evaluation of their implementation faces a paucity of information and could benefit from a system perspective derived from the knowledge of implementers and experts. These challenges could be addressed using simulation modelling. We compared Markov and agent-based models of interventions for improving the management of SAM and assessed benefits and limitations in informing complex health intervention strategy designs. Based on a conceptual framework developed with existing evidence and expert advice, the agent-based model generated simulated data representing the complex evolution of the system. Multiple scenarios were investigated by varying parameters and mimicking rules of interventions. This study pointed out possible synergies between interventions enhancing early start of treatment and increasing recovery from SAM. When these interventions were adequately combined, outcomes of coverage, recovery and overall survival improved. Benefits of agent-based modelling were use of history, if-then rules to uncover mechanisms behind probabilities, and modifiable transition rates. Limitations related to model validation, choices of assumptions, and simplification. Agent-based modelling could be used to adapt intervention strategies to local contexts and support scale-up. As such, modelling could complement the methodological toolkit of health intervention strategy designs for improved policy decision