School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
BMJ Open. 2023 Mar 17;13(3):e063354. doi: 10.1136/bmjopen-2022-063354.
Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery.
Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India.
Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) RESULTS: We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme 'Kilkari' showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.
Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
NCT03576157.
直接面向受益人的（D2B）移动健康传播方案已在全球多个国家用于向妇女及其家庭提供生殖、孕产妇、新生儿和儿童健康信息。迄今为止，这些方案向大量受益人群提供了相同的内容，以相同的频率，使用相同的渠道。本文介绍了一种概念验证方法，该方法使用机器学习将有手机接入的妇女及其丈夫群体划分为不同的群体，以支持差异化的数字方案设计和交付。
本研究中使用的数据来自印度中央邦四个区的横断面调查。
研究参与者包括有手机接入的孕妇（n=5095）及其丈夫（n=3842）。
我们使用了一种涉及 K-Means 聚类和 Lasso 回归的迭代过程，将夫妇分为三个不同的群体。第 1 组（n=1408）的男性和女性往往较为贫困，受教育程度较低，数字接入和技能水平较低。第 2 组（n=666）男性数字接入和技能水平中等，但女性则不然。第 3 组（n=1410）男性数字接入和技能水平较高，女性中等。接触 D2B 方案“Kilkari”在第 2 组中表现出最大的差异，包括在使用可逆现代避孕药具方面有 8%的差异，10 周龄儿童免疫接种率有 7%的差异，9 个月龄儿童免疫接种率有 3%的差异，10 周龄和 9 个月龄免疫接种的及时性有 4%的差异。
研究结果表明，将人群分为不同的群体，进行差异化的方案设计和交付，可能有助于提高覆盖面和影响力。
NCT03576157。
