Food insecurity remains one of the greatest challenges in Africa, hindering the economic development. Related indicators have been almost invariably monitored in the achievement of the Millennium Development Goals. Women’s body mass index (BMI) measures women's nutritional status, a key indicator of the socio-economic development of a country. Despite recent intervention programmes, geographic and socio-economic disparities remain in the BMI distribution. Therefore, it would be important to rely on accurate estimates of women’s mean BMI levels across domains. We consider a small area model with area-specific random effects that capture the large regional differences in BMI levels. We propose a Bayesian model to investigate the role on BMI of a number of socio-economic characteristics such as age, wealth, parity, education, while accounting for regional variation. Since it is reasonable to assume that some of these variables are measured with error, we develop a suitable methodology and investigate the effect of neglecting measurement error in covariates on the assessment of the regression effects and on the prediction of area-specific BMI mean levels. We apply the proposed model to 2011 DHS data to explore the geographic variability of the BMI in two regions, Ethiopia and Nigeria, and compare the determinants of women’s nutritional status.

Determinants and geographical disparities of BMI in African Countries: a measurement error small area approach

Serena Arima;
2018

Abstract

Food insecurity remains one of the greatest challenges in Africa, hindering the economic development. Related indicators have been almost invariably monitored in the achievement of the Millennium Development Goals. Women’s body mass index (BMI) measures women's nutritional status, a key indicator of the socio-economic development of a country. Despite recent intervention programmes, geographic and socio-economic disparities remain in the BMI distribution. Therefore, it would be important to rely on accurate estimates of women’s mean BMI levels across domains. We consider a small area model with area-specific random effects that capture the large regional differences in BMI levels. We propose a Bayesian model to investigate the role on BMI of a number of socio-economic characteristics such as age, wealth, parity, education, while accounting for regional variation. Since it is reasonable to assume that some of these variables are measured with error, we develop a suitable methodology and investigate the effect of neglecting measurement error in covariates on the assessment of the regression effects and on the prediction of area-specific BMI mean levels. We apply the proposed model to 2011 DHS data to explore the geographic variability of the BMI in two regions, Ethiopia and Nigeria, and compare the determinants of women’s nutritional status.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/472108
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact