Cited by 7Global Maternal and Child HealthMobile Health and mHealth ApplicationsMaternal and Neonatal Healthcare

Description of Maternal Morbidities Amongst 1000 Women During Pregnancy in Ambanja, Madagascar – Opportunities and Challenges of Using an mHealth System

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Garcia Vilaplana, Elisabeth, Petignat, Patrick, Benski, Anne-Caroline, Soaroby, Adelia, Sormani, Jessica, Vassilakos, Pierre, Schmidt, Nicole C·2020·International Journal of Women's Health

Abstract

BACKGROUND: The estimated burden of maternal morbidities in lower-income countries, such as Madagascar, is high. However, there is still a lack of data on maternal morbidities, in part due to an absence of standardized assessment tools. This cross-sectional study aims to report maternal morbidities among 1015 women in the district of Ambanja, Madagascar, and to describe the advantages and limitations of a mHealth system. METHODS: Data were collected using the PANDA (pregnancy and newborn diagnosis assessment) system, an mHealth device that incorporates the WHO recommendations for antenatal care (ANC). Data, including personal and medical information, but also clinical data such as hypertension, anemia or HIV were collected from more than 1000 women attending ANC. RESULTS: A total of 1015 pregnant women were recruited from January 2015 to August 2018. The average age was 24.6 years old, and most women were married (82.3%). The majority lived in urban areas and were unemployed. Prevalence of hypertension and gestational diabetes was relatively low (4% vs 2.2%). Malaria infection was diagnosed in 2.2% and HIV was diagnosed in 1.2% of pregnant women. The most common morbidity was anemia (68.4%) and the only significant factor associated was being single compared to being married (OR 1.68, 95% CI 1.05-2.70, p-value 0.032). DISCUSSION: The prevalence of anemia in our study population was much higher than previously reported in Sub-Saharan Africa. This finding highlights the need for regular iron supplementation during pregnancy, especially in vulnerable (eg, single) women. The PANDA mHealth system provides unique opportunities due to its standardization of ANC and creation of a digital database accessible from a distance. However, one of the main challenges was that even a mHealth system such as the PANDA remains dependent on the local procurement chain. Therefore, future studies need to access opportunities of mHealth systems to support health service delivery. TRIAL REGISTRATION: Registered on ISRCTN on 14th October 2015, number ISRCTN18270380. Retrospectively registered.

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  1. [5]World Health Organization. WHO Recommendations on Antenatal care for positive pregnancy experience; 2016 Available from: http://apps.who.int/iris/bitstream/10665/250796/1/9789241549912-eng.pdf. Accessed 9 3, 2020. 28079998
  2. [6]Barreix M, Barbour K, McCaw-Binns A, et al. Standardizing the measurement of maternal morbidity: pilot study results. Int J Gynecol Obstet. 2018; 141: 10– 19. doi: 10.1002/ijgo.12464 PMC6001807 29851115 · DOI
  3. [9]Say L, Souza JP, Pattinson RC. Maternal near miss - towards a standard tool for monitoring quality of maternal health care. Best Pract Res Clin Obstet Gynaecol. 2009; 23(3): 287– 296. doi: 10.1016/j.bpobgyn.2009.01.007 19303368 · DOI · Paper page
  4. [10]Say L, Barreix M, Chou D, et al. Maternal morbidity measurement tool pilot: study protocol. Reprod Health. 2016; 13(1): 1– 6. doi: 10.1186/s12978-016-0164-6 27277959 PMC4899915 · DOI · Paper page
  5. [11]Benski AC, Stancanelli G, Scaringella S, et al. Usability and feasibility of a mobile health system to provide comprehensive antenatal care in low-income countries: PANDA mHealth pilot study in Madagascar. J Telemed Telecare. 2016; 23(5): 536– 543. doi: 10.1177/1357633X16653540 27365320 · DOI
  6. [12]Shaffer J, Doumbia S, Ndiaye D, et al. Development of a data collection and management system in West Africa: challenges and sustainability. Infect Dis Poverty. 2018; 7(1). doi: 10.1186/s40249-018-0494-4. PMC6292095 30541626 · DOI
  7. [13]Mbondji P, Kebede D, Soumbey-Alley E, Zielinski C, Kouvividila W, Lusamba-Dikassa P. Resources, indicators, data management, dissemination and use in health information systems in sub-Saharan Africa: results of a questionnaire-based survey. J R Soc Med. 2016; 107(1_suppl). PMC4109353 24739381
  8. [14]Magee LA, Pels A, Helewa M, et al. Diagnosis, evaluation, and management of the hypertensive disorders of pregnancy: executive summary. J Obstet Gynaecol Can. 2014; 36(5): 416– 438. doi: 10.1016/S1701-2163(15)30588-0 24927294 · DOI
  9. [15]World Health Organization. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy. 2016 Available from: https://extranet.who.int/rhl/topics/preconception-pregnancy-childbirth-and-postpartum-care/antenatal-care/who-recommendation-diagnosis-gestational-diabetes-pregnancy-0. Accessed 9 3, 2020. 24199271
  10. [17]Filippi V, Chou D, Ronsmans C, et al. Levels and causes of maternal mortality and morbidity Black RE, Laxminarayan R, Temmerman M, et al. editors. Reproductive, Maternal, Newborn, and Child Health: Disease Control Priorities. Third Vol. 2 Washington (DC): The International Bank for Reconstruction and Development/The World Bank; 2016 27227205
  11. [18]Geller SE, Koch AR, Garland CE, MacDonald EJ, Storey F, Lawton B. A global view of severe maternal morbidity: moving beyond maternal mortality. Reprod Health. 2018; 15(Suppl 1). doi: 10.1186/s12978-018-0527-2 PMC6019990 29945657 · DOI
  12. [19]Stephen G, Mgongo M, Hashim TH, Katanga J, Stray-pedersen B, Msuya SE. Anaemia in pregnancy: prevalence, risk factors, and adverse perinatal outcomes in Northern Tanzania. Anemia. 2018; 2018: 1– 9. doi: 10.1155/2018/1846280 PMC5954959 29854446 · DOI
  13. [20]Fokom-Domgue J, Noubiap JJN. Diagnosis of hypertensive disorders of pregnancy in sub-Saharan Africa: a poorly assessed but increasingly important issue. J Clin Hypertens. 2015; 17(1): 70– 73. doi: 10.1111/jch.12429 PMC8031762 25348088 · DOI
  14. [24]Mbhenyane X, Cherane M. Compliance with the consumption of iron and folate supplements by pregnant women in Mafikeng local municipality, North West Province, South Africa. Afr Health Sci. 2017; 17(3): 657– 670. doi: 10.4314/ahs.v17i3.8 29085393 PMC5656199 · DOI · Paper page
  15. [25]Smith-Greenaway E. Premarital childbearing in sub-Saharan Africa: can investing in women’s education offset disadvantages for children? SSM Popul Health. 2016; 2: 164– 174. doi: 10.1016/j.ssmph.2016.02.001 29349137 PMC5757959 · DOI
  16. [27]Ihantamalala FA, Rakotoarimanana FMJ, Ramiadantsoa T, et al. Spatial and temporal dynamics of malaria in Madagascar. Malar J. 2018; 17(1): 1– 13. doi: 10.1186/s12936-018-2206-8 29391023 PMC5796477 · DOI
  17. [28]Ikoh MU, Udo AU, Charles AO, Charles JO. The influence of ‘’stock out” on health-seeking behaviour of low income women in Uyo urban, Akwa Ibom State, Nigeria. Int Q Community Health Educ. 2008–2009; 29(3): 257– 273. doi: 10.2190/IQ.29.3.e 19720589 · DOI
  18. [29]Wagenaar B, Gimbel S, Hoek R, et al. Stock-outs of essential health products in Mozambique - longitudinal analyses from 2011 to 2013. Trop Med Int Health. 2014; 19(7): 791– 801. 24724617 PMC4479203
  19. [30]Mikkelsen-Lopez I, Shango W, Barrington J, Ziegler R, Smith T, Desavigny D. The challenge to avoid anti-malarial medicine stock-outs in an era of funding partners: the case of Tanzania. Malar J. 2014; 13(1): 1– 9. doi: 10.1186/1475-2875-13-181 24885420 PMC4030285 · DOI
  20. [31]Githinji S, Kigen S, Memusi D, et al. Reducing stock-outs of life saving malaria commodities using mobile phone text-messaging: SMS for life study in Kenya. PLoS One. 2013; 8(1): 1– 8. doi: 10.1371/journal.pone.0054066 PMC3547931 23349786 · DOI

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Authors
Garcia Vilaplana, E., Petignat, P., Benski, A.-C., Soaroby, A., Sormani, J., Vassilakos, P., & Schmidt, N. C.
Affiliations
  • University Hospital of Geneva (CH)
  • Harvard University (US)
  • Centre Hospitalier de la Dracénie (FR)
Corresponding author
Schmidt, Nicole C
Venue
International Journal of Women's Health
Published
2020-10
Volume
Volume 12
Pages
823-833
ISSN
1179-1411 (ISSN-L)
OpenAlex
W3092559073
Cited by
7
References
3
Citations by year
2026: 1 · 2025: 1 · 2023: 3 · 2022: 1 · 2021: 1
FWCI
1.20
Type
journal article
Language
en
Open access
Locations
  • International Journal of Women s Health (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Global Maternal and Child Health
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › General Health Professions › Maternal and Neonatal Healthcare
Keywords
maternal morbidity, pregnancy, prenatal care, mobile health, anemia in pregnancy, hypertension in pregnancy, gestational diabetes mellitus, malaria in pregnancy, HIV in pregnancy, iron supplementation, marital status

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