GOAL 2: ZERO HUNGER

Percentage of under-fives falling below minus 2 standard deviations (moderate and severe) from the median height-for-age of the reference population

UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data. For WHO, see published database methodology (2). UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. UNICEF regularly reviews major repositories of potential sources (e.g. Multiple Indicator Cluster Surveys website, the Global Health Data Exchange) and also uses the UNICEF country consultation process with national authorities on selected child-related global SDG indicators, for which it is custodian or co-custodian. This allows for the emerging standards and guidelines on data flows for global reporting of SDG indicators to be met. These processes place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicits feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used and the application of global standard definitions. The World Bank Group provides estimates available through the Living Standard Measurement Surveys (LSMS) which usually requires re-analysis of datasets given that the LSMS reports often do not tabulate the stunting data.

Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere (1). Global and regional estimates are based on methodology outlined in UNICEF-WHO-The World Bank: Joint child malnutrition estimates – Levels and trends (2,3)

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Prevalence of overweight (weight for height >+2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.

UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data. For WHO, see published database methodology (de Onis et al. 2004). For UNICEF, the cadre of dedicated data and monitoring specialists working at national, regional and international levels in 190 countries routinely provide technical support for the collection and analysis of data. For the past 20 years UNICEF has undertaken an annual process to update its global databases, called Country Reporting on Indicators for Goals (CRING). This exercise is done in close collaboration with UNICEF country offices with the purpose of ensuring that UNICEF global databases contain updated and internationally comparable data. UNICEF country offices are invited to submit, through an online system, nationally representative data for over 100 key indicators on the well-being of women and children, including stunting. The country office staff work with local counterparts to ensure the most relevant data are shared. Updates sent by the country offices are then reviewed by sector specialists at UNICEF headquarters to check for consistency and overall data quality of the submitted estimates and re-analysis where possible. This review is based on a set of objective criteria to ensure that only the most reliable information is included in the databases. Once reviewed, feedback is made available on whether or not specific data points are accepted, and if not, the reasons why. UNICEF uses these data obtained through CRING to feed into the joint dataset. The World Bank Group provides estimates availabe through the Living Standard Measurement Surveys (LSMS) which ussually requires re-analysis of datasets given theat the LSMS reports often do not tabulate the stunting data.

Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere (Ref: Anthro software manual). Global and regional estimates are based on methodology outlined in UNICEF-WHO-The World Bank: Joint child malnutrition estimates – Levels and trends (UNICEF/WHO/WB 2012).

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Percentage of women aged 15-49 years with a haemoglobin level less than 120 g/L for non-pregnant women and lactating women, and less than 110 g/L for pregnant women, adjusted for altitude and smoking

A PubMed search was carried out for relevant search terms related to anaemia, haemoglobin and iron status, searching for studies published after 1 January 1990. In addition to indexed articles, many reports of national and international agencies were identified and accessed through requests to each corresponding organization. Once survey data are compiled and the Bayesian hierarchical mixture model is run to generate anaemia estimates, countries are sent a memorandum to provide a background to the estimates and explain the process. Information on the survey data used to generate the estimates for that country, estimates for the year 2015, and the resulting plots for each country are provided along with an explanation of the methodology used in generating the estimates. Countries are requested to provide feedback within six weeks.

The anaemia status of women is assessed using blood haemoglobin concentrations. In surveys, blood haemoglobin concentrations are typically measured using the direct cyanmethemoglobin method in a laboratory or with a portable, battery-operated, haemoglobin photometer in the field that uses the azide-methaemoglobin method.

Prevalence of anaemia and/or mean haemoglobin in women of reproductive age were obtained from 303 population-representative data sources from 116 countries worldwide. Data collected from 1990 to 2016 were used. Adjustment of data on blood haemoglobin concentrations for altitude and smoking was carried out whenever possible. Biologically implausible haemoglobin values (200 g/L) were excluded. A Bayesian hierarchical mixture model was used to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. Full details on statistical methods may be found here: Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data (Stevens et al, 2013). Briefly, the model calculates estimates for each country and year, informed by data from that country and year themselves, if available, and by data from other years in the same country and in other countries with data for similar time periods, especially countries in the same region. The model borrows data, to a greater extent, when data are non-existent or weakly informative, and to a lesser degree for data-rich countries and regions. The resulting estimates are also informed by covariates that help predict blood haemoglobin concentrations (e.g. maternal education, prevalence of sickle-cell disorders, mean weight-for-age z-score for children). The uncertainty ranges (credibility intervals) reflect the major sources of uncertainty, including sampling error, non-sampling error due to issues in sample design/measurement, and uncertainty from making estimates for countries and years without data.