Exploring the implementation of an SMS-based digital health tool on maternal and infant health in informal settlements.
Antenatal care
Digital health
Informal settlements
Kenya
Maternal health
Postnatal care
Journal
BMC pregnancy and childbirth
ISSN: 1471-2393
Titre abrégé: BMC Pregnancy Childbirth
Pays: England
ID NLM: 100967799
Informations de publication
Date de publication:
27 Mar 2024
27 Mar 2024
Historique:
received:
29
09
2023
accepted:
26
02
2024
medline:
28
3
2024
pubmed:
28
3
2024
entrez:
28
3
2024
Statut:
epublish
Résumé
The rapid urbanization of Kenya has led to an increase in the growth of informal settlements. There are challenges with access to maternal, newborn, and child health (MNCH) services and higher maternal mortality rates in settlements. The Kuboresha Afya Mitaani (KAM) study aimed to improve access to MNCH services. We evaluate one component of the KAM study, PROMPTS (Promoting Mothers through Pregnancy and Postpartum), an innovative digital health intervention aimed at improving MNCH outcomes. PROMPTS is a two-way AI-enabled SMS-based platform that sends messages to pregnant and postnatal mothers based on pregnancy stage, and connects mothers with a clinical help desk to respond and refer urgent cases in minutes. PROMPTS was rolled out in informal settlements in Mathare and Kawangware in Nairobi County. The study adopted a pre-post intervention design, comparing baseline and endline population outcomes (1,416 participants, Baseline = 678, Endline = 738). To further explore PROMPTS's effect, outcomes were compared between endline participants enrolled and not enrolled in PROMPTS (738 participants). Outcomes related to antenatal (ANC) and postnatal (PNC) service uptake and knowledge were assessed using univariate and multivariate linear and logistic regression. Between baseline and enldine, mothers were 1.85 times more likely to report their babies and 1.88 times more likely to report themselves being checked by a provider post-delivery. There were improvements in moms and babies receiving care on time. 45% of the 738 endline participants were enrolled in the PROMPTS program, with 87% of these participants sending at least one message to the system. Enrolled mothers were 2.28 times more likely to report completing four or more ANC visits relative to unenrolled mothers. Similarly, enrolled mothers were 4.20 times more likely to report their babies and 1.52 times more likely to report themselves being checked by a provider post-delivery compared to unenrolled mothers. This research demonstrates that a digital health tool can be used to improve care-seeking and knowledge levels among pregnant and postnatal women in informal settlements. Additional research is needed to refine and target solutions amongst those that were less likely to enroll in PROMPTS and to further drive improved MNCH outcomes amongst this population.
Sections du résumé
BACKGROUND
BACKGROUND
The rapid urbanization of Kenya has led to an increase in the growth of informal settlements. There are challenges with access to maternal, newborn, and child health (MNCH) services and higher maternal mortality rates in settlements. The Kuboresha Afya Mitaani (KAM) study aimed to improve access to MNCH services. We evaluate one component of the KAM study, PROMPTS (Promoting Mothers through Pregnancy and Postpartum), an innovative digital health intervention aimed at improving MNCH outcomes. PROMPTS is a two-way AI-enabled SMS-based platform that sends messages to pregnant and postnatal mothers based on pregnancy stage, and connects mothers with a clinical help desk to respond and refer urgent cases in minutes.
METHODS
METHODS
PROMPTS was rolled out in informal settlements in Mathare and Kawangware in Nairobi County. The study adopted a pre-post intervention design, comparing baseline and endline population outcomes (1,416 participants, Baseline = 678, Endline = 738). To further explore PROMPTS's effect, outcomes were compared between endline participants enrolled and not enrolled in PROMPTS (738 participants). Outcomes related to antenatal (ANC) and postnatal (PNC) service uptake and knowledge were assessed using univariate and multivariate linear and logistic regression.
RESULTS
RESULTS
Between baseline and enldine, mothers were 1.85 times more likely to report their babies and 1.88 times more likely to report themselves being checked by a provider post-delivery. There were improvements in moms and babies receiving care on time. 45% of the 738 endline participants were enrolled in the PROMPTS program, with 87% of these participants sending at least one message to the system. Enrolled mothers were 2.28 times more likely to report completing four or more ANC visits relative to unenrolled mothers. Similarly, enrolled mothers were 4.20 times more likely to report their babies and 1.52 times more likely to report themselves being checked by a provider post-delivery compared to unenrolled mothers.
CONCLUSIONS
CONCLUSIONS
This research demonstrates that a digital health tool can be used to improve care-seeking and knowledge levels among pregnant and postnatal women in informal settlements. Additional research is needed to refine and target solutions amongst those that were less likely to enroll in PROMPTS and to further drive improved MNCH outcomes amongst this population.
Identifiants
pubmed: 38539140
doi: 10.1186/s12884-024-06373-7
pii: 10.1186/s12884-024-06373-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
222Informations de copyright
© 2024. The Author(s).
Références
Urban population growth - % Kenya. World Bank. Retrieved March 14, 2023, from https://data.worldbank.org/indicator/SP.URB.GROW?locations=KE
Essendi H, Mills S, Fotso J-C. Barriers to formal emergency obstetric care services’ utilization. J Urban Health. 2011;88:356–69. https://doi.org/10.1007/s11524-010-9481-1 .
doi: 10.1007/s11524-010-9481-1
APHRC, Population and Health Dynamics in Nairobi’s Informal Settlements: Report of the Nairobi Cross-Sectional Slums Survey (NCSS) 2012. African Population and Health Research Center. Nairobi; 2014.
Keats EC, et al. Progress and priorities for reproductive, maternal, newborn, and child health in Kenya: a Countdown to 2015 country case study. Lancet Glob Health. 2017;5(8):e782–95.
doi: 10.1016/S2214-109X(17)30246-2
pubmed: 28716350
pmcid: 5599303
Sidze EM, Wekesah FM, Kisia L, et al. Inequalities in Access and Utilization of Maternal, Newborn and Child Health Services in sub-Saharan Africa: A Special Focus on Urban Settings. Matern Child Health J. 2022;26:250–79.
doi: 10.1007/s10995-021-03250-z
pubmed: 34652595
Ziraba AK, et al. Maternal mortality in the informal settlements of Nairobi city: what do we know? Reprod Health. 2009;6:6.
doi: 10.1186/1742-4755-6-6
pubmed: 19386134
pmcid: 2675520
Leon N, Schneider H, Daviaud E. Applying a framework for assessing the health system challenges to scaling up mHealth in South Africa. BMC Med Inform Decis Mak. 2012;12(1):1–12.
doi: 10.1186/1472-6947-12-123
Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai AC. Scaling up mHealth: where is the evidence? PLoS Med. 2013;10(2): e1001382. https://doi.org/10.1371/journal.pmed.1001382 .
doi: 10.1371/journal.pmed.1001382
pubmed: 23424286
pmcid: 3570540
O’Brien N, Li E, Chaibva CN, Bravo RG, Kovacevic L, Ayisi-Boateng NK, Neves AL. Strengths Weaknesses, Opportunities, and Threats Analysis of the Use of Digital Health Technologies in Primary Health Care in the Sub-Saharan African Region: Qualitative Study. Med Internet Res. 2023;25(1):e45224.
doi: 10.2196/45224
Njoroge M, Zurovac D, Ogara EAA, et al. Assessing the feasibility of eHealth and mHealth: a systematic review and analysis of initiatives implemented in Kenya. BMC Res Notes. 2017;10:90. https://doi.org/10.1186/s13104-017-2416-0 .
doi: 10.1186/s13104-017-2416-0
pubmed: 28183341
pmcid: 5301342
Mildon A, Sellen D. Use of mobile phones for behavior change communication to improve maternal, newborn and child health: a scoping review. J Glob Health. 2019;9(2): 020425. https://doi.org/10.7189/jogh.09.020425 .
doi: 10.7189/jogh.09.020425
pubmed: 31893032
pmcid: 6925966
Hall CS, Fottrell E, Wilkinson S, Byass P. Assessing the impact of mHealth interventions in low-and middle-income countries–what has been shown to work? Glob Health Action. 2014;7(1):25606.
doi: 10.3402/gha.v7.25606
pubmed: 25361730
Shiferaw S, Spigt M, Tekie M, Abdullah M, Fantahun M, Dinant GJ. The effects of a locally developed mHealth intervention on delivery and postnatal care utilization; a prospective controlled evaluation among health centres in Ethiopia. PLoS ONE. 2016;11(7): e0158600.
doi: 10.1371/journal.pone.0158600
pubmed: 27383186
pmcid: 4934867
Kabongo EM, Mukumbang FC, Delobelle P, et al. Explaining the impact of mHealth on maternal and child health care in low- and middle-income countries: a realist synthesis. BMC Pregnancy Childbirth. 2021;21:196. https://doi.org/10.1186/s12884-021-03684-x .
doi: 10.1186/s12884-021-03684-x
pubmed: 33750340
pmcid: 7941738
Beguy Donatien, et al. Health & demographic surveillance system profile: the Nairobi urban health and demographic surveillance system (NUHDSS). International journal of epidemiology. 2015;44(2):462–71.
doi: 10.1093/ije/dyu251
pubmed: 25596586
Jones, Rachel. “A Short Message Service (SMS) increases postpartum care-seeking behavior and uptake of family planning of mothers in peri-urban public facilities in Kenya.” PLOS One, vol. 15, no. 9, 2020, n/a. PLOS one
WHO, Monitoring immunization services using the Lot Quality Technique 1996.
Pezzoli L, Kim SH. Monitoring health interventions-who’s afraid of LQAS? Glob Health Action. 2013;6:21921. https://doi.org/10.3402/gha.v6i0.21921 .
doi: 10.3402/gha.v6i0.21921
pubmed: 24206650
Valadez J, Weiss W, Leburg C, Davis R. A trainer’s guide for baseline surveys and regular monitoring. Using LQAS for assessing field programs in community health in developing countries. Washington, DC: NGO Networks for Health; 2002.
Kierie, Helen. “The COVID-19 pandemic and disruptions to essential health services in Kenya: a retrospective time-series analysis.” The Lancet Global Health, vol. 10, no. 9, 2022, pp. E1257-E1267. The Lancet, https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(22)00285-6/fulltext .
Mburu S, Oboko R. A model for predicting utilization of mHealth interventions in low-resource settings: case of maternal and newborn care in Kenya. BMC Med Inform Decis Mak. 2018;18:67. https://doi.org/10.1186/s12911-018-0649-z .
doi: 10.1186/s12911-018-0649-z
pubmed: 30016943
pmcid: 6050709
Venkataramanan R, Subramanian SV, Alajlani M, Arvanitis TN. Effect of mobile health interventions in increasing utilization of Maternal and Child Health care services in developing countries: A scoping review. DIGITAL HEALTH. 2022;8. doi: https://doi.org/10.1177/20552076221143236
Kazi, Abdul Momin, et al. “Assessing Mobile Phone Access and Perceptions for Texting-Based MHealth Interventions among Expectant Mothers and Child Caregivers in Remote Regions of Northern Kenya: A Survey-Based Descriptive Study.” JMIR Public Health and Surveillance, U.S. National Library of Medicine, 30 Jan. 2017, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306611/ .
Mathur, Mitali Roy. “How to identify the best length and time for a phone survey.” IDinsight, 3 April 2020, https://www.idinsight.org/article/how-to-identify-the-best-length-and-time-for-a-phone-survey/ . Accessed 23 February 2023.