LiST modelling with monitoring data to estimate impact on child mortality of an ORS and zinc programme with public sector providers in Bihar, India.

BACKGROUND: Many interventions have attempted to increase vulnerable and remote populations' access to ORS and zinc to reduce child mortality from diarrhoea. However, the impact of these interventions is difficult to measure. From 2010 to 15, Micronutrient Initiative (MI), worked with the public sector in Bihar, India to enable community health workers to treat and report uncomplicated child diarrhoea with ORS and zinc. We describe how we estimated programme's impact on child mortality with Lives Saved Tool (LiST) modelling and data from MI's management information system (MIS). This study demonstrates that using LiST modelling and MIS data are viable options for evaluating programmes to reduce child mortality. METHODS: We used MI's programme monitoring data to estimate coverage rates and LiST modelling software to estimate programme impact on child mortality. Four scenarios estimated the effects of different rates of programme scale-up and programme coverage on estimated child mortality by measuring children's lives saved. RESULTS: The programme saved an estimated 806-975 children under-5 who had diarrhoea during five-year project phase. Increasing ORS and zinc coverage rates to 19.8% & 18.3% respectively under public sector coverage with effective treatment would have increased the programme's impact on child mortality and could have achieved the project goal of saving 4200 children's lives during the five-year programme. CONCLUSIONS: Programme monitoring data can be used with LiST modelling software to estimate coverage rates and programme impact on child mortality. This modelling approach may cost less and yield estimates sooner than directly measuring programme impact with population-based surveys. However, users must be cautious about relying on modelled estimates of impact and ensure that the programme monitoring data used is complete and precise about the programme aspects that are modelled. Otherwise, LiST may mis-estimate impact on child mortality. Further, LiST software may require modifications to its built-in assumptions to capture programmatic inputs. LiST assumes that mortality rates and cause of death structure change only in response to changes in programme coverage. In Bihar, overall child mortality has decreased and diarrhoea seems to be less lethal than previously, but at present LiST does not adjust its estimates for these sorts of changes.