Rural and urban disparities in anemia among Peruvian children aged 6-59 months: a multivariate decomposition and spatial analysis

• Autores: Al-kassab-Córdova, Ali, Mendez-Guerra, Carolina, Quevedo-Ramirez, Andrés, Espinoza, Ricardo, Enriquez-Vera, Daniel, Robles-Valcarcel, Pamela
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/660260
• Enlace del recurso: http://hdl.handle.net/10757/660260
• Nivel de acceso: acceso abierto
• Materia: Anemia; Child health; Geographic information systems; Peru; Spatial analysis

Introduction: Anemia is a global public health issue that affects mainly children aged less than 5 years. In Peru, despite the reduction in the prevalence of anemia between 2010 and 2018, anemia remains a major concern, especially in high-risk zones such as rural areas. Several sociodemographic factors have been associated with anemia in children; however, components contributing to the urban–rural gap have not been previously assessed. The purpose of this study was to evaluate the determinants of the difference in anemia prevalence between urban and rural areas, and its spatial distribution in Peruvian children aged 6–59 months. Methods: A secondary data analysis was conducted using the 2019 Peruvian Demographic Health Survey. The study population included 18 846 children aged 6–59 months. A multivariate decomposition analysis for non-linear response model was performed to identify the factors contributing to the gap in the prevalence of anemia across urban and rural areas. Global Moran's I autocorrelation, Ordinary Kriging interpolation and Bernoullibased purely spatial scan statistics were employed to assess the spatial pattern of anemia. Results: Nationwide, the prevalence of anemia in Peru was 29.47% (95%CI 28.63–30.33). In rural areas, it was 38.25%, and in urban areas 26.39%. The decomposition analysis revealed that 88.61% of the difference in the prevalence of anemia between urban and rural areas was attributed to the difference in the respondents’ characteristics. Wealth index, mother's education, mother's employment status, number of living children and mother's age were key determinants contributing to the rural–urban gap. Spatial heterogeneity of anemia prevalence in childhood was observed at both inter and intradepartmental level. The SaTScan spatial analysis identified six significant cluster areas with high prevalence of anemia in childhood. Conclusion: A considerable gap of anemia prevalence between urban and rural areas was found. Targeted interventions are necessary to reduce geographic disparities.