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.

有强有力的证据表明，低出生体重 (LBW) 对婴儿的健康有负面影响。患有 LBW 的儿童更容易患上残疾。关于 LBW 的研究很多，但只有一小部分研究了 LBW 的局部地理模式及其决定因素。LBW 在墨西哥是一个特别的健康问题。该研究的目的是：(i) 模拟大墨西哥城城市层面 LBW 风险的变化，确定 LBW 风险最高和最低的城市；(ii) 探索一些社会经济和人口风险因素在解释 LBW 变化中的作用。我们提出了贝叶斯时空分析来控制数据的时空模式以及孕产妇年龄和产前护理，两者都是重要的 LBW 决定因素。大多数高风险城市位于大墨西哥城的西南部和西部；尽管这些城市中的许多城市的趋势是稳定的，但随着时间的推移，有些城市的 LBW 风险会增加。结果还确定了那些具有中等风险和增加趋势的人。这些发现可以支持决策者进行地理定位，以解决空间健康不平等问题，还可以促进采取更主动和更具成本效益的方法来降低 LBW 风险。