An in-depth review of the UNICEF NutriDash platform, lessons learnt and future perspectives: a mixed-methods study.

OBJECTIVES: Robust data on nutrition are essential to realise the right to nutrition for every child. Created in 2009, UNICEF's Nutrition Dashboard (NutriDash) collects nutrition programme information from 125 countries. An in-depth review of NutriDash was conducted to understand its strengths and identify key actions to increase its effectiveness and efficiency. METHODS: Adapting the Centres for Disease Control and Prevention updated guidelines for evaluating public health surveillance systems, a mixed-methods approach was used. A questionnaire was designed to capture information on key attributes of NutriDash and disseminated to UNICEF country offices for quantitative feedback on user experiences. Structured key informant interviews were held with internal and external stakeholders to gain qualitative perceptions on data generated from NutriDash. Analysis involved producing frequency distributions for the questionnaire data and performing thematic analyses on interview data. RESULTS: A total of 53 respondents completed the questionnaire (42% response rate), representing 48 countries and good regional geographic representation. Most respondents (96%) worked in UNICEF country offices. The percentages of participants who agreed or strongly agreed with each attribute of the NutriDash system were as follows: acceptability: 71%, stability: 68%, simplicity; 63%, data quality: 60%, flexibility: 58% and usefulness: 43%. Internal and external stakeholders commented on the value of NutriDash; its use ranging from nutrition global trend monitoring for programme planning to producing reports and dashboards. Key themes derived from this review as areas for improvement included communication, access to data and data quality. CONCLUSIONS: This review has identified key themes that will inform improvements to NutriDash and form a baseline for future periodic reviews to continuously enhance the system to improve availability of timely quality nutrition programme data. UNICEF will continue to engage with countries, key partners and governments to improve the NutriDash data value chain and ensure the right to nutrition for every child.

Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites.

INTRODUCTION: Estimates of incident cases of severe wasting among young children are not available for most settings but are needed for optimal planning of treatment programmes and burden estimation. To improve programme planning, global guidance recommends a single 'incidence correction factor' of 1.6 be applied to available prevalence estimates to account for incident cases. This study aimed to update estimates of the incidence correction factor to improve programme planning and inform the approach to burden estimation for severe wasting. METHODS: A global call was issued for secondary data from severe wasting treatment programmes including prevalence, population size, programme admission and programme coverage through a UNICEF-led effort. Site-specific incidence correction factors were calculated as the number of incident cases (annual programme admissions/programme coverage) divided by the number of prevalent cases (prevalence*population size). Estimates were aggregated by country, region and overall using inverse-variance weighted random-effects meta-analysis. RESULTS: We estimated incidence correction factors from 352 sites in 20 countries. Estimates aggregated by country ranged from 1.3 (Nigeria) to 30.1 (Burundi). Excluding implausible values, the overall incidence correction factor was 3.6 (95% CI 3.4 to 3.9). CONCLUSION: Our results suggest that incidence correction factors vary between sites and that the burden of severe wasting will often be underestimated using the currently recommended incidence correction factor of 1.6. Application of updated incidence correction factors represents a simple way to improve programme planning when incidence data are not available and could inform the approach to burden estimation.

Assessment of Anthropometric Data Following Investments to Ensure Quality: Kenya Demographic Health Surveys Case Study, 2008 to 2009 and 2014.

BACKGROUND: Evidence-based nutrition programs depend on accurate estimates of malnutrition derived from data collected in population representative surveys. The feasibility of obtaining accurate anthropometric data as part of national, multisectoral surveys has been a debated issue. OBJECTIVES: The study aimed to evaluate changes in anthropometric data quality corresponding to investments by the Kenya Ministry of Health and nutrition sector partners for the 2014 Kenya Demographic Health Survey. METHODS: Anthropometric data collected during the 2008 to 2009 and 2014 Kenya surveys were reanalyzed to assess standard parameters of quality: standard deviation, skewness, and kurtosis of z-score values for 3 anthropometric indicators (weight for height, height for age, and weight for age), percentage of children with missing measurements and outlier values, digit preference, and heaping of age. RESULTS: A total of 9936 households were selected in 2008 to 2009, and 39 679 households were selected in 2014. Standard deviation of z-scores for all 3 indicators was smaller in 2014 than in 2008 to 2009. Applying original Demographic and Health Survey exclusion criteria, weight for height z-scores were 1.16 in 2014, 10.1% narrower than 2008 to 2009. The percentage of outlying values declined significantly from 2008 to 2009 to 2014 for both height for age and weight for height ( P < .001). Digit preference scores in 2014 improved for both weight ( P = .011) and height ( P < .001) suggesting less rounding of terminal digits. CONCLUSIONS: All tests of data quality suggest an improvement in 2014 relative to 2008 to 2009, despite the complexity implied by the larger sample. This improvement corresponds with efforts to enhance training and supervision of anthropometry, suggesting a positive effect of these enhancements.