Collins Joseph H, Cambiano Valentina, Phillips Andrew N, Colbourn Tim
Institute for Global Health, University College London, London, United Kingdom.
PLoS One. 2024 Dec 2;19(12):e0296540. doi: 10.1371/journal.pone.0296540. eCollection 2024.
Mathematical modelling is a commonly utilised tool to predict the impact of policy on health outcomes globally. Given the persistently high levels of maternal and perinatal morbidity and mortality in sub-Saharan Africa, mathematical modelling is a potentially valuable tool to guide strategic planning for health and improve outcomes.
The aim of this scoping review was to explore the characteristics of mathematical models and modelling studies evaluating the impact of maternal and/or perinatal healthcare interventions or services on health-related outcomes in the region. A search across three databases was conducted on 2nd November 2023 which returned 8660 potentially relevant studies, from which 60 were included in the final review. Characteristics of these studies, the interventions which were evaluated, the models utilised, and the analyses conducted were extracted and summarised.
Findings suggest that the popularity of modelling within this field is increasing over time with most studies published after 2015 and that population-based, deterministic, linear models were most frequently utilised, with the Lives Saved Tool being applied in over half of the reviewed studies (n = 34, 57%). Much less frequently (n = 6) models utilising system-thinking approaches, such as individual-based modelling or systems dynamics modelling, were developed and applied. Models were most applied to estimate the impact of interventions or services on maternal mortality (n = 34, 57%) or neonatal mortality outcomes (n = 39, 65%) with maternal morbidity (n = 4, 7%) and neonatal morbidity (n = 6, 10%) outcomes and stillbirth reported on much less often (n = 14, 23%).
Going forward, given that healthcare delivery systems have long been identified as complex adaptive systems, modellers may consider the advantages of applying systems-thinking approaches to evaluate the impact of maternal and perinatal health policy. Such approaches allow for a more realistic and explicit representation of the systems- and individual- level factors which impact the effectiveness of interventions delivered within health systems.
数学建模是一种常用工具，用于预测全球政策对健康结果的影响。鉴于撒哈拉以南非洲地区孕产妇和围产期发病率和死亡率一直居高不下，数学建模是指导卫生战略规划和改善结果的潜在有价值工具。
本范围综述的目的是探讨评估孕产妇和/或围产期保健干预措施或服务对该地区健康相关结果影响的数学模型和建模研究的特征。2023年11月2日对三个数据库进行了检索，返回了8660项潜在相关研究，最终纳入综述的有60项。提取并总结了这些研究的特征、评估的干预措施、使用的模型以及进行的分析。
研究结果表明，随着时间的推移，该领域建模的受欢迎程度在增加，大多数研究在2015年之后发表，并且最常使用基于人群的确定性线性模型，超过一半的综述研究（n = 34，57%）应用了“挽救生命工具”。使用系统思维方法（如基于个体的建模或系统动力学建模）的模型开发和应用较少（n = 6）。模型最常用于估计干预措施或服务对孕产妇死亡率（n = 34，57%）或新生儿死亡率结果（n = 39，65%）的影响，而关于孕产妇发病率（n = 4，7%）、新生儿发病率（n = 6，10%）结果和死产的报告则少得多（n = 14，23%）。
展望未来，鉴于长期以来医疗服务提供系统被视为复杂适应系统，建模者可能会考虑应用系统思维方法来评估孕产妇和围产期卫生政策影响的优势。此类方法能够更现实、明确地呈现影响卫生系统内干预措施有效性的系统层面和个体层面因素。
