Patterns of low birth weight in greater Mexico City: A Bayesian spatio-temporal analysis

Lome-Hurtado, Alejandro, Li, Guangquan, Touza-Montero, Julia and White, Piran C.L. (2021) Patterns of low birth weight in greater Mexico City: A Bayesian spatio-temporal analysis. Applied Geography, 134. p. 102521. ISSN 0143-6228

Lome-Hurtado et al manuscript_ammended_25_June_2021.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1MB) | Preview
Official URL:


There is strong evidence that low birth weight (LBW) has a negative impact on infants' health. Children with LBW are more vulnerable to having disabilities. There are many studies on LBW, but only a small proportion has examined local geographical patterns in LBW and its determinants. LBW is a particular health concern in Mexico. The study aims to: (i) model the change in the LBW risk at the municipality level in Greater Mexico City, identifying municipalities with highest and lowest LBW risk; and (ii) explore the role of some socioeconomic and demographic risk factors in explaining LBW variations. We propose a Bayesian spatio-temporal analysis to control for space-time patterning of the data and for maternal age and prenatal care, both found to be important LBW determinants. Most of the high-risk municipalities are in the south-west and west of Greater Mexico City; and although for many of these municipalities the trend is stable, some present an increasing LBW risk over time. The results also identify those with medium-risk and with an increasing trend. These findings can support decision-makers in geographical targeting efforts to address spatial health inequalities, they may also facilitate a more proactive and cost-efficient approach to reduce LBW risk.

Item Type: Article
Uncontrolled Keywords: child health, term low birth weight, Bayesian spatio-temporal modelling, space-time variation, spatial random effects
Subjects: B900 Others in Subjects allied to Medicine
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 30 Jul 2021 09:18
Last Modified: 24 Jan 2023 08:00

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics