Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants
Objectives: A limitation in the design and monitoring of public health policies is the lack of conceptual models to explain their results. The objective of this study was to develop a predictive model of stunting in children under 5 years of age in the central Andean region of Peru, using socioecono...
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doaj-dc79a7ebc9714548a939c32f9790ea222021-04-06T04:03:58ZengElsevierPublic Health in Practice2666-53522021-11-012100112Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinantsJorge Castro-Bedriñana0Doris Chirinos-Peinado1Gina De La Cruz-Calderón2Specialized Research Center in Nutritional Food Security, Universidad Nacional del Centro del Perú, Huancayo, Peru; Corresponding author. Av. Mariscal Castilla, 3909 El Tambo, Huancayo, Peru.Specialized Research Center in Nutritional Food Security, Universidad Nacional del Centro del Perú, Huancayo, PeruProfessional School of Agroindustrial Engineering, Universidad Nacional Autónoma de Chota, Cajamarca, PeruObjectives: A limitation in the design and monitoring of public health policies is the lack of conceptual models to explain their results. The objective of this study was to develop a predictive model of stunting in children under 5 years of age in the central Andean region of Peru, using socioeconomic and agro-productive predictors. Study design: Cross-sectional data of 380 families in 15 districts of the central region of Peru. WHO criteria were used to define stunting prediction model. Methods: An explanatory and predictive study of stunting in children was carried out considering causality criteria through Chi-square tests and bivariate logistic regression. Family food production, maternal education, breastfeeding practices and others determinant related to rural zone conditions were considered as explanatory variables in stunting of children under 5 years old. Three exhaustive models for predicted the presence of stunting was developed. Results: Stunting percentage was 40.3. To Hosmer and Lemeshow test, the best fit was the model that considered the level of maternal education, timely consumption of colostrum, birth weight and guinea pig rearing, having high reliability (P < 0.05). Conclusions: A predictive model for early detection of stunting risk in rural areas of the Andean region was developed based on simple and easily applied indicators. Effective policies are required to improve the feeding practices of pregnant women, increase breastfeeding and promote guinea pig raising for self-consumption and improve the nutritional status of children.http://www.sciencedirect.com/science/article/pii/S2666535221000379Socioeconomic disparitiesPeruvian high andesStunting predictionAgri-food determinantsGuinea pig Raising |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jorge Castro-Bedriñana Doris Chirinos-Peinado Gina De La Cruz-Calderón |
spellingShingle |
Jorge Castro-Bedriñana Doris Chirinos-Peinado Gina De La Cruz-Calderón Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants Public Health in Practice Socioeconomic disparities Peruvian high andes Stunting prediction Agri-food determinants Guinea pig Raising |
author_facet |
Jorge Castro-Bedriñana Doris Chirinos-Peinado Gina De La Cruz-Calderón |
author_sort |
Jorge Castro-Bedriñana |
title |
Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants |
title_short |
Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants |
title_full |
Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants |
title_fullStr |
Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants |
title_full_unstemmed |
Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants |
title_sort |
predictive model of stunting in the central andean region of peru based on socioeconomic and agri-food determinants |
publisher |
Elsevier |
series |
Public Health in Practice |
issn |
2666-5352 |
publishDate |
2021-11-01 |
description |
Objectives: A limitation in the design and monitoring of public health policies is the lack of conceptual models to explain their results. The objective of this study was to develop a predictive model of stunting in children under 5 years of age in the central Andean region of Peru, using socioeconomic and agro-productive predictors. Study design: Cross-sectional data of 380 families in 15 districts of the central region of Peru. WHO criteria were used to define stunting prediction model. Methods: An explanatory and predictive study of stunting in children was carried out considering causality criteria through Chi-square tests and bivariate logistic regression. Family food production, maternal education, breastfeeding practices and others determinant related to rural zone conditions were considered as explanatory variables in stunting of children under 5 years old. Three exhaustive models for predicted the presence of stunting was developed. Results: Stunting percentage was 40.3. To Hosmer and Lemeshow test, the best fit was the model that considered the level of maternal education, timely consumption of colostrum, birth weight and guinea pig rearing, having high reliability (P < 0.05). Conclusions: A predictive model for early detection of stunting risk in rural areas of the Andean region was developed based on simple and easily applied indicators. Effective policies are required to improve the feeding practices of pregnant women, increase breastfeeding and promote guinea pig raising for self-consumption and improve the nutritional status of children. |
topic |
Socioeconomic disparities Peruvian high andes Stunting prediction Agri-food determinants Guinea pig Raising |
url |
http://www.sciencedirect.com/science/article/pii/S2666535221000379 |
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