Factors Affecting Nutrition around Nepal: District and Subregional Snapshots

Nepal, like other Scaling Up Nutrition (SUN) countries, is facing competing demands for time, human resources, and funding within the realm of nutrition programming. The priorities assigned by the international agreements to which Nepal is a signatory (such as SUN, the Millennium Development Goals [MDGs], and others) include a wide array of goals and corresponding targets, many of which are integrated into Nepal's 2013-2017 Multi-Sector Nutrition Plan (MSNP). Given the wide and diverse mandate needed to achieve these targets, it is important to learn how key decision-makers choose what activities to prioritize, when, and where in the country.

This set of quantitative snapshots acts as a complement to the qualitative and financial analysis being conducted as part of SPRING's Pathways to Better Nutrition Case Study Series, with all work converging to answer the question of how countries prioritize activities with national nutrition plans to produce significant reductions in undernutrition. Details on the methods used to create the quantitative snapshots can be found in the download (above).

When thinking of "scaling up" nutrition, it is important to understand the mixture of causes of undernutrition across the country. These snapshots provide a look at the prevalence of key MSNP indicators for the 13 subregions of Nepal, which is the lowest level of representativeness of the surveys that serve as source data. Data are presented on selected indicators for each of the strategic national objectives named by the MSNP for reducing undernutrition in Nepal. One can best interpret these snapshots as a set, looking at each subregion to assess which objectives or set of constraints are most pressing in each area, and across subregions to see how indicators vary across the country. There may be some universal concerns, while other concerns may be acute only in select geographic areas. In each of the following tables, the colors denote subregional performance compared to the national average (red is worse, green is better, and yellow is within one percentage point of the average; for women's daily workload, the cutoffs are within one half-hour of the average).

Table 1 summarizes the key impact indicators across Nepal. The first tab shows how the subregion averages compare to the national average, whereas the second tab shows the subregion averages compared to the national MSNP targets meant to be achieved by 2017.

Table 1. Key MSNP Indicator Summary Table

Compared to National Average

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Compared to MSNP National Target (2017)

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From Table 1 it is evident that malnutrition is an issue that cuts across all ecological zones (mountain, hill, and Terai) and development regions (Far-Western, Mid-Western, Western, Central, and Eastern)¹, and, although the MSNP names education as an overarching measure of success, poor nutritional outcomes occur in both better- and worse-educated areas. Western mountain, for instance, has an above-average primary education completion rate coupled with the highest stunting rate in the country (60 percent). Conversely, the Western hill region has lower-than-average education levels, but is the only subregion in the country that has already surpassed the 2017 MSNP underweight targets for both women and children. Eastern Terai is the only subregion that does average or better than the national average on all key indicators, though it has not yet met any of the MSNP targets.

To achieve these targets, the MSNP aims to influence a number of intermediate outcomes, presented by subregion in Table 2. Although these results do not have specific targets assigned to them, their baseline levels are nonetheless important in understanding the potential pathways in determining nutritional status.

Table 2. Intermediate Outcome Indicator Summary Table

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When comparing Tables 1 and 2, no consistent patterns emerge between any intermediate outcome and impact indicator, suggesting that the relative contributions of each outcome to undernutrition are complex and may be highly dependent on context. Diarrhea incidence among young children, for instance, is relatively similar across subregions except for in Central and Western Terai, where it is several percentage points higher. Although these two Terai regions have higher than average child underweight rates, they are not the highest in the country; additionally, their stunting rates are close to the national average. There also seems to be no systematic relationship between child anemia rates and other nutritional status indicators; however, anemia remains a severe public health problem in its own right² in 11 out of 13 subregions, and appears to be highest in the Terai subregions. Another finding of note is that the 3 IYCF practices (feeding children breastmilk/dairy products and the recommended minimum dietary diversity and meal frequency) do not always track with early initiation of breastfeeding, suggesting that behavior change communication strategies for child feeding should be tailored to regional context.

Notably, several of the intermediate outcome indicators mentioned by the MSNP will require considerable effort to measure or have little existing data on them. For instance, although anemia is measured as part of the Demographic and Health Surveys, no nationwide survey on vitamin A and iodine deficiency has been conducted in Nepal since 1998 (Internet Global Micronutrient Project 2014). Child vitamin A supplementation and household use of iodized salt (shown in the next table), however, may be used as proxies for these two indicators. Access to severe acute malnutrition (SAM) services, also explicitly named in the MSNP, requires a population assessment of children with SAM paired with a measurement of both the frequency and quality of SAM services within the health system.

Variation in the key and outcome indicators may be at least partially explained by contextual drivers or barriers in each subregion. The most appropriate indicators for these outputs (based on relevance to MSNP activities, data quality, availability, and regional variation) are displayed in Table 3, grouped under outputs from the MSNP.³ Because this exercise focuses on the differences in subregional data, indicators were not selected for outputs that related to the plan's rollout at the national (1–2) and local (7–8) levels. For the remaining outputs, the MSNP has assigned one ministry responsible for leading activities in each output; the ministries are listed in the first column. These outputs are:

• Output 3: Maternal and child nutritional care service utilization improved
• Output 4: Adolescent girls' parental education, life skills, and nutrition status enhanced
• Output 5: Diarrheal diseases and ARI episodes reduced among young mothers, adolescent girls, and infants and young children.
• Output 6: Availability and consumption of appropriate foods (in terms of quality, quantity, frequency, and safety) enhanced and women's workload reduced.

Table 3. Barriers and Drivers of Better Nutrition, by Selected MSNP Output

The sectoral organization of MSNP activities allows for a rapid prioritization of the activities or entire sectors to which each subregion might be able to devote greater or lesser degrees of effort. As an example, the Central hill subregion (which includes Kathmandu) outperforms the national average in terms of water and sanitation indicators but its health services indicators are more mixed, suggesting the need for a renewed focus on improving the quality of antenatal care and child nutrition programs. The opposite is true in the Mid-western Terai.

Similar to the intermediate outcomes, several relevant indicators for barriers and drivers could not be found at the subnational level. Output 4, for instance, calls for improving adolescent girls' nutrition knowledge and future parenting skills. National surveys on adolescent populations exist, but do not contain that information (MOHP 2012b). Other activities, such as providing iron–folic acid and deworming drugs to adolescents, are new programs. The MSNP acknowledges this lack of data by noting that baseline and endline surveys should be conducted as means of evaluating the education sector's performance (GON and NPC 2012).

Nevertheless, adolescent female education attainment (the sole indicator for Output 4) is useful to further understanding each subregion's context when compared to overall education attainment in Table 1. One striking example is the Western mountain area, where overall education rates are higher than the national average but the lowest of any subregion among adolescent women (33 percent). Western mountain also has the highest child stunting rate in the country, and in general nutritional incomes track more closely with adolescent female education levels rather than overall education levels.