TY - JOUR
T1 - Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India
AU - Bashingwa, Jean Juste Harrisson
AU - Mohan, Diwakar
AU - Chamberlain, Sara
AU - Scott, Kerry
AU - Ummer, Osama
AU - Godfrey, Anna
AU - Mulder, Nicola
AU - Moodley, Deshendran
AU - Lefevre, Amnesty Elizabeth
N1 - Funding Information:
Bill and Melinda Gates Foundation grant number OPP1179252.
Funding Information:
All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare that the research reported was funded by the Bill and Melinda Gates Foundation. AG and SC are employed by BBC Media Action; one of the entities supporting program implementation. The authors do not have other relationships and are not engaged in activities that could appear to have influenced the submitted work.
Publisher Copyright:
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
PY - 2023/3/17
Y1 - 2023/3/17
N2 - Objectives 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. Setting Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. Participants 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. Conclusions Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
AB - Objectives 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. Setting Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. Participants 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. Conclusions Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
KW - community child health
KW - information technology
KW - public health
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U2 - 10.1136/bmjopen-2022-063354
DO - 10.1136/bmjopen-2022-063354
M3 - Article
C2 - 36931682
AN - SCOPUS:85150666084
SN - 2044-6055
VL - 13
JO - BMJ open
JF - BMJ open
IS - 3
M1 - e063354
ER -