TABLE 1—
Estimated Prevalence of Undernutrition in Children Younger Than 5 Years Covered by the Universal Health Coverage Programs: Argentina, 2005–2013
| Population | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 |
| % Stunting per year (HAZ < –2) | |||||||||
| National | |||||||||
| Total | 20.6 | 19.0 | 17.5 | 16.2 | 15.0 | 13.9 | 12.9 | 12.0 | 11.3 |
| Urban | 20.4 | 18.8 | 17.4 | 16.0 | 14.8 | 13.7 | 12.7 | 11.9 | 11.1 |
| Rural | 23.0 | 21.4 | 20.0 | 18.6 | 17.4 | 16.3 | 15.3 | 14.4 | 13.7 |
| Boys | 22.0 | 20.4 | 18.9 | 17.6 | 16.4 | 15.3 | 14.3 | 13.4 | 12.7 |
| Girls | 19.2 | 17.6 | 16.2 | 14.8 | 13.6 | 12.5 | 11.5 | 10.7 | 9.9 |
| Region | |||||||||
| NOA | 17.8 | 16.6 | 15.5 | 14.5 | 13.5 | 12.6 | 11.7 | 10.9 | 10.2 |
| NEA | 21.0 | 20.0 | 18.9 | 17.9 | 16.9 | 15.9 | 14.9 | 14.0 | 13.1 |
| Centro | 14.2 | 14.3 | 14.1 | 13.9 | 13.4 | 12.8 | 12.1 | ||
| Cuyo | 16.9 | 15.6 | 14.4 | 13.3 | 12.3 | 11.3 | 10.5 | ||
| Patagonia | 13.3 | 12.1 | 11.0 | 10.1 | 9.4 | 8.9 | 8.7 | ||
| % Underweight per year (WAZ < −2) | |||||||||
| National | |||||||||
| Total | 4.0 | 3.9 | 3.9 | 3.8 | 3.6 | 3.4 | 3.1 | 2.8 | 2.5 |
| Urban | 4.0 | 4.0 | 3.9 | 3.8 | 3.6 | 3.4 | 3.2 | 2.8 | 2.5 |
| Rural | 3.9 | 3.9 | 3.8 | 3.7 | 3.5 | 3.3 | 3.1 | 2.7 | 2.4 |
| Boys | 4.3 | 4.3 | 4.3 | 4.2 | 4.0 | 3.8 | 3.5 | 3.2 | 2.8 |
| Girls | 3.6 | 3.6 | 3.5 | 3.4 | 3.2 | 3.0 | 2.8 | 2.5 | 2.1 |
| Region | |||||||||
| NOA | 3.3 | 3.1 | 3.0 | 2.8 | 2.7 | 2.5 | 2.3 | 2.1 | 1.9 |
| NEA | 5.0 | 4.8 | 4.5 | 4.3 | 4.0 | 3.7 | 3.3 | 3.0 | 2.6 |
| Centro | 9.7 | 8.0 | 6.6 | 5.3 | 4.2 | 3.2 | 2.4 | ||
| Cuyo | 6.2 | 5.2 | 4.3 | 3.6 | 3.0 | 2.5 | 2.2 | ||
| Patagonia | 7.7 | 6.0 | 4.5 | 3.5 | 2.8 | 2.5 | 2.5 | ||
Note. HAZ = height-for-age z-score; LME = linear mixed effects; NEA = northeast Argentina; NOA = northwest Argentina; WAZ = weight-for-age z-score. We estimated the prevalence of stunting and underweight fitting LME model A to the whole data set and then conditioning to the mean (observed) values of gender, urban vs rural residence, and age (national), gender and age (for urban and rural), urban vs rural residence, and age (for boys and girls). For regions, we fitted the model splitting the database by region and then conditioning on mean values of gender, urban vs rural residence, and age.