PANEL 4.3 COMPILING DISTRICT-LEVEL NUTRITION DATA IN INDIA PURNIMA MENON AND SHRUTHI CYRIAC
I
n India, POSHAN, a partnership designed to increase access to nutrition knowledge and evidence, has developed nutrition profiles for 11 districts in the states of Jharkhand, Mad- hya Pradesh, Odisha, and Uttar Pradesh. These district nutrition profiles draw on diverse sources of data to compile indicators on the state of nutrition and its drivers. The profiles are intended to be conversation starters at the district level and to enable discussions about why undernutrition levels are high and what factors, at multiple levels, might need to be addressed to improve nutrition.
In seeking recent and reliable district-level data on the drivers of undernutrition, we faced several challenges: The diversity of sectors from which data must be sourced: The data had to reflect the different sectors that influence nutrition such as food security, water and san- itation, economic status, and women’s issues.
This required using various datasets and iden- tifying nutrition-relevant indicators in them. Temporal issues: Most of the data are from different reports, and this often meant that the years when data were collected var- ied. The temporal diversity in the data made it difficult to compare nutrition data at the district and state levels or even different types of indicators for each district. Indicator definitions: While all indica- tors were initially defined as they appear in global guidelines, some of these definitions had to be altered to conform to the data avail- able. One official report on vitamin A supple- mentation, for instance, had data for children 9–59 months old, whereas another report had data for children 12–23 months old. Sampling differences: Some of the data sources provided only rural data and used smaller samples. This made it difficult to
compare data from these sources with data from national-level surveys. Data skills: Some data, such as on food security and diet diversity, require the use of unit-level data from large, complex data sources such as the National Sample Survey Organization (NSSO) and require special ana- lytical skills on the part of users. Others are less challenging, such as indicators on water, sanitation, and hygiene and access to services, which can be almost directly obtained from census data.
Despite the data challenges, the initial experiences with using the profiles to catalyze nutrition-focused conversations are encourag- ing: they highlight the problem and the data gaps, help build understanding of the roles of different sectors, and bring attention to needed short- and longer-term actions.
FIGURE 4.1 PREVALENCE OF UNDER-FIVE STUNTING AND OVERWEIGHT FOR HIGHEST AND LOWEST WEALTH QUINTILES IN SELECTED COUNTRIES (%) Q1
Wealth quintiles: OVERWEIGHT (BAZ > 2)
Q5
10 20 30 40 50
0 STUNTING (HAZ < –2)
10 20 30 40 50 60 70 80
0
Source: Figure 5 in Black et al. (2013). Reproduced with the permission of The Lancet.
Note: Red circles are the lowest wealth quintiles; blue circles are the highest wealth quintiles. BAZ = body mass index-for-age Z-score. HAZ = height-for-age Z-score. DHS = Demographic and Health Survey. MICS = Multiple Indicator Cluster Survey.
ACTIONS & ACCOUNTABILITY TO ACCELERATE THE WORLD’S PROGRESS ON NUTRITION 27
United Rep. of Tanzania (DHS 2010) Guinea Bissau (MICS 2006) Ethiopia (DHS 2011) CAR (MICS 2006) Cambodia (DHS 2010) Benin (DHS 2006) Honduras (DHS 2005) Mozambique (MICS 2008) Cameroon (MICS 2006) Somalia (MICS 2006) Nigeria (DHS 2008) Chad (DHS 2004) Rwanda (DHS 2010) Bangladesh (DHS 2007) Peru (DHS 2004) Malawi (DHS 2010) Nepal (DHS 2011) Niger (DHS 2006) Lao PDR (MICS 2006) Madagascar (DHS 2003) India (DHS 2005) Timor-Leste (DHS 2009) Guatemala (DHS 1998)
Côte d’Ivoire (MICS 2006) Burkina Faso (MICS 2006) Zambia (DHS 2007)
Sao Tome and Principe (DHS 2008) Belize (MICS 2006) Namibia (DHS 2006) Tajikistan (MICS 2005) Haiti (DHS 2005) Gabon (DHS 2000) Nicaragua (DHS 2001) Uganda (DHS 2006) Liberia (DHS 2007) Kenya (DHS 2008) Mali (DHS 2006) Guinea (DHS 2005) Lesotho (DHS 2009) Bolivia (DHS 2008) DRC (DHS 2007)
Syrian Arab Rep. (MICS 2006) Gambia (MICS 2005) Swaziland (DHS 2006) Congo (DHS 2005) Togo (MICS 2006)
Palestinians in Lebanon (MICS 2006) Vanuatu (MICS 2007) Egypt (DHS 2008) Turkey (DHS 2003) Guyana (DHS 2009) Mongolia (MICS 2005) Azerbaijan (DHS 2006) Ghana (DHS 2008) Mauritania (MICS 2007) Sierra Leone (DHS 2008) Senegal (DHS 2010) Zimbabwe (DHS 2010) Morocco (DHS 2003)
Bosnia and Herzegovina (MICS 2006) Colombia (DHS 2010) Kyrgyzstan (MICS 2005) Thailand (MICS 2005) Kazakhstan (MICS 2006) Uzbekistan (MICS 2006) Maldives (DHS 2009) Georgia (MICS 2005) Armenia (DHS 2010) Albania (DHS 2008)
The FYR Macedonia (MICS 2005) Dominican Republic (DHS 2007) Suriname (MICS 2006) Jordan (DHS 2007)
Brazil (DHS 2006) Belarus (MICS 2005) Serbia (MICS 2005) Montenegro (MICS 2005) Moldova (DHS 2005)
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