Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.
Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.
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DOI：
10.1136/bmjgh-2020-002340
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发表时间：
2020-10
影响因子：
8.1
通讯作者：
Sgaier SK
中科院分区：
文献类型：
--
作者：
Huang VS;Morris K;Jain M;Ramesh BM;Kemp H;Blanchard J;Isac S;Sarkar B;Gothalwal V;Namasivayam V;Kumar P;Sgaier SK
用有限的资源实现雄心勃勃的全球卫生目标需要一种精确的公共卫生(PxPH)方法。在这里，我们描述如何将数据收集优化、传统分析和因果人工智能/机器学习(ML)集成在印度北方邦增加新生儿住院分娩的使用案例中。使用系统的行为框架，我们设计了一项关于妇女分娩前后行为的感知、人际和结构性驱动因素的大规模调查(n=5613)。多因素Logistic回归确定了与机构分娩相关的因素(ID)。因果ML决定了这些因素的因果顺序。利用方差分解来分析配送地点的变异来源，并使用有监督的学习算法来区分种群子群。在与ID相关的因素中，因果模型显示有分娩计划(OR=6.1，95%CI 6.0~6.3)、认为医院比家更安全(OR=5.4，95%CI 5.1~5.6)和知道经济奖励是ID的直接原因(OR=3.4，95%CI 3.3~3.5)。距离医院的距离、借送货款和主要决策者都不是因果关系。个体水平的因素对交付地点的差异贡献了69%。细分分析显示，四个不同的亚组根据ID风险感知、产次和计划进行了区分。这些发现全面了解了北方邦失禁的驱动因素和障碍，并为不同的女性提出了不同的干预点。这表明，经过优化以确定关键行为驱动因素的数据，再加上传统和ML分析，可以帮助设计一种PxPH方法，使有限资源的影响最大化。
Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India. Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women’s behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups. Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning. These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.
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DOI：
10.1016/s0140-6736(17)31758-0
发表时间：
2017-11-11
期刊：
Lancet (London, England)
影响因子：
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作者：
Golding N;Burstein R;Longbottom J;Browne AJ;Fullman N;Osgood-Zimmerman A;Earl L;Bhatt S;Cameron E;Casey DC;Dwyer-Lindgren L;Farag TH;Flaxman AD;Fraser MS;Gething PW;Gibson HS;Graetz N;Krause LK;Kulikoff XR;Lim SS;Mappin B;Morozoff C;Reiner RC Jr;Sligar A;Smith DL;Wang H;Weiss DJ;Murray CJL;Moyes CL;Hay SI
通讯作者：
Hay SI
影响因子：
5.5
作者：
Khoury MJ;Iademarco MF;Riley WT
通讯作者：
Riley WT
影响因子：
3.1
作者：
Kesterton, Amy J.;Cleland, John;Ronsmans, Carine
通讯作者：
Ronsmans, Carine
影响因子：
2.2
作者：
Kumar, Santosh;Dansereau, Emily A.;Murray, Christopher J. L.
通讯作者：
Murray, Christopher J. L.
影响因子：
--
作者：
Engl, Elisabeth;Sgaier, Sema K
通讯作者：
Sgaier, Sema K