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一种用于定量监测儿童早期里程碑达成情况的发育监测评分:算法开发与验证。

A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation.

机构信息

KI Research Institute, Kfar Malal, Israel.

Neuro-Developmental Research Center, Mental Health Institute, Be'er-Sheva, Israel.

出版信息

JMIR Public Health Surveill. 2023 Aug 18;9:e47315. doi: 10.2196/47315.

Abstract

BACKGROUND

Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective.

OBJECTIVE

Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children.

METHODS

We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score's ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child's longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories.

RESULTS

The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time.

CONCLUSIONS

The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance.

摘要

背景

发育监测在全球范围内常规进行,是及时发现发育迟缓儿童的基本手段。它通常通过评估与年龄相适应的里程碑达成情况,并在健康监督访视期间运用临床判断来进行。与发育筛查和评估工具不同,监测通常缺乏标准化的定量措施,因此其解释通常是定性和主观的。

目的

本文提出了一种将发育监测评估综合为一个单一评分的新方法,该评分能够一致地描述和监测儿童的发育情况。我们描述了计算评分的过程,并展示了其有效捕捉已知人群水平关联的能力。此外,我们还表明,该评分可用于描述可能有助于跟踪和分类儿童发育轨迹的纵向发育模式。

方法

我们描述了发育监测评分(DSS),这是一种简单易用的工具,它根据最近推出的以色列发育监测计划,基于年龄依赖性未能达到发育里程碑的严重程度水平,对其进行量化。我们使用 2014 年 7 月 1 日至 2021 年 9 月 1 日期间评估的超过 100 万以色列从出生到 36 个月大的儿童的全国性队列评估了 DSS。我们测量了评分捕捉已知发育迟缓与母亲和儿童特征之间关联的能力。此外,我们还计算了连续访视中的 DSS 系列,以描述儿童的纵向发育,并应用聚类分析来识别这些发育轨迹的不同模式。

结果

分析的队列包括 1130005 名儿童。对队列亚群进行 DSS 评估,根据已知发育迟缓风险因素进行分层,结果显示发育迟缓与儿童和母亲的特征之间存在预期的关系,包括人口统计学和与产科相关的变量。平均而言,与足月儿童相比,早产儿的评分较差,与女性儿童相比,男性儿童的评分较差,而与母亲教育程度较低的评分相应较差。294000 名儿童的 6 次连续访视的评分轨迹可用。对这些轨迹的聚类揭示了 3 种主要的发育模式,这与临床经验一致:成功达到里程碑的儿童、最初倾向于失败但随着时间推移而改善的儿童以及失败倾向随时间推移而增加的儿童。

结论

所提出的评分在其基本形式下易于计算,并且可以在更精细的形式下轻松作为基于网络的工具实施。它突出了已知和新的发育迟缓与母亲和儿童特征之间的关系,展示了其在监测和研究方面的潜在用途。此外,它可以监测儿童的发育轨迹并对其进行描述。未来的工作需要对评分进行校准,使其与其他筛查工具相比,在全球范围内进行验证,并将其纳入发育监测的临床工作流程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd21/10474508/83f701f5bd1b/publichealth_v9i1e47315_fig1.jpg

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