• https://publications.iadb.org/es/situacion-de-salud-nutricion-y-saneamiento-entre-los-ninos-menores-de-12-meses-en-el-distrito-8-de

Martinez, Sebastian, et al. (2019). Community Nutrition Program in El Alto, Bolivia Data: 2014-2017. IDB Open Data. https://doi.org/10.60966/7yxk-5t52

1. Spanish

What does this dataset cover?

This dataset covers baseline conditions among households in District 8 of El Alto, Bolivia, focusing on child nutrition, maternal knowledge, feeding practices, household conditions, water and sanitation, anthropometry, anemia, and diarrhea.

Is this a baseline dataset or a final impact dataset?

This dataset is best understood as a baseline-focused dataset. It describes conditions at the start of the program and the evaluation design, rather than final causal results from the full intervention period. That means these data should not be treated as proof of program impact on their own.

What population does this dataset represent?

This dataset represents households in District 8 of El Alto that had either a child under 12 months old or a pregnant woman in the household. It is targeted at a vulnerable peri-urban population, not at all households in El Alto or in all of Bolivia.

How large is the sample?

This dataset includes information from 2,001 households. It also includes child-level health and nutrition measurements, including anthropometry for children under 36 months and hemoglobin and head circumference measures for children under 12 months.

What are the main units of analysis in this dataset?

This dataset supports analysis at more than one level:

• Household level for housing, assets, water, sanitation, and demographics
• Mother or caregiver level for nutrition and hygiene knowledge
• Child level for feeding practices, anthropometry, anemia, and diarrhea

This structure makes it useful for analyzing how household conditions relate to child outcomes.

What policy questions can this dataset help answer?

This dataset can help answer questions such as:

• What were baseline nutrition and sanitation conditions in a vulnerable peri-urban area of El Alto?
• How common were poor feeding practices, diarrhea, anemia risk, and chronic malnutrition?
• What were household water and sanitation conditions before the later outcome measurement?
• Was the target population already facing both undernutrition and overweight risk?

This dataset is more useful for baseline policy analysis than for making final claims about program impact.

How was this dataset designed for evaluation?

This dataset was developed within an experimental evaluation design for the nutrition intervention, with eligible households randomly assigned to treatment and control groups. It also captures variation in whether households were located in areas with or without access to the sewer network, allowing future analysis of nutrition and sanitation together.

Can this dataset support the analysis of nutrition and sanitation together?

Yes. One of the strengths of this dataset is that it includes both nutrition-related indicators and sanitation-related household conditions. That makes it useful for exploring how child nutrition and environmental health risks may overlap in this setting.

What outcomes are included in this dataset?

This dataset includes or supports indicators related to:

• Child weight and height
• Head circumference
• Hemoglobin
• Chronic, acute, and global malnutrition
• Overweight and obesity risk
• Diarrhea prevalence
• Maternal knowledge of infant feeding, health, and hygiene
• Household feeding and hygiene practices

This makes the dataset useful for descriptive analysis, baseline assessment, and subsequent evaluation.

What are the most important baseline findings in this dataset?

Several baseline findings stand out:

• Chronic malnutrition was reported at 21% among children under 36 months.
• Exclusive breastfeeding, under a strict definition, was reported as almost zero in this population.
• 18% of children under 36 months reportedly had diarrhea in the prior two weeks.
• 81% of households had network water access, but many used the water as obtained without additional treatment.
• 85% of households had a toilet or latrine, yet most kitchen wastewater was reportedly discharged directly to the street.

Together, these findings show overlapping risks in nutrition, behavior, and sanitation.

What does this dataset show about breastfeeding and complementary feeding?

This dataset shows a mixed picture. Early breastfeeding initiation appears relatively common, but strict exclusive breastfeeding is extremely low. Complementary feeding begins for many children by 6 to 8 months, and dietary diversity appears stronger than the breastfeeding result alone would suggest. For policymakers, that points to a need for stronger support around infant feeding practices in the first months of life.

Does this dataset include anthropometric and anemia data?

Yes. This dataset includes anthropometric measurements such as weight and height for children under 36 months, as well as hemoglobin and head circumference measurements for younger children. That makes it useful for analysts who need objective nutrition and health indicators rather than self-reported measures alone.

What sanitation variables are available in this dataset?

This dataset includes information on:

• Access to water in the home
• Water treatment practices
• Toilet or latrine access
• Type of sanitation disposal
• Kitchen wastewater disposal
• Bathroom conditions and observed housing environment

These variables are useful for policy analysis because they help explain environmental factors that may affect child health and nutrition.

Can this dataset be used to estimate national conditions in Bolivia?

No. This dataset is specific to District 8 of El Alto and to eligible households in the program area. It is valuable for understanding local conditions in this peri-urban setting, but it should not be treated as nationally representative.

Can this dataset be used to measure program impact by itself?

Not by itself. This dataset provides the baseline needed for later comparison, but impact analysis requires follow-up data and a full evaluation of treatment and control outcomes over time.

Why is this dataset useful for policy makers?

This dataset is useful because it combines household conditions, caregiver knowledge, child feeding practices, sanitation variables, and direct child health measurements within a single vulnerable urban setting. That helps policymakers see that child nutrition problems are not only about food access. They are also connected to caregiver behavior, health knowledge, sanitation, and exposure to infectious disease.