Assessment and validation of the Community Maternal Danger Score algorithm.

Journal article


Bola, R., Ujoh, F., Ukah, U.V. and Lett, R. (2022). Assessment and validation of the Community Maternal Danger Score algorithm. Global health research and policy. 7 (1), p. 6. https://doi.org/10.1186/s41256-022-00240-8
AuthorsBola, R., Ujoh, F., Ukah, U.V. and Lett, R.
Abstract<h4>Background</h4>High rates of maternal mortality in low-and-middle-income countries (LMICs) are associated with the lack of skilled birth attendants (SBAs) at delivery. Risk analysis tools may be useful to identify pregnant women who are at risk of mortality in LMICs. We sought to develop and validate a low-cost maternal risk tool, the Community Maternal Danger Score (CMDS), which is designed to identify pregnant women who need an SBA at delivery.<h4>Methods</h4>To design the CMDS algorithm, an initial scoping review was conducted to identify predictors of the need for an SBA. Medical records of women who delivered at the Federal Medical Centre in Makurdi, Nigeria (2019-2020) were examined for predictors identified from the literature review. Outcomes associated with the need for an SBA were recorded: caesarean section, postpartum hemorrhage, eclampsia, and sepsis. A maternal mortality ratio (MMR) was determined. Multivariate logistic regression analysis and area under the curve (AUC) were used to assess the predictive ability of the CMDS algorithm.<h4>Results</h4>Seven factors from the literature predicted the need for an SBA: age (under 20 years of age or 35 and older), parity (nulliparity or grand-multiparity), BMI (underweight or overweight), fundal height (less than 35 cm or 40 cm and over), adverse obstetrical history, signs of pre-eclampsia, and co-existing medical conditions. These factors were recorded in 589 women of whom 67% required an SBA (n = 396) and 1% died (n = 7). The MMR was 1189 per 100,000 (95% CI 478-2449). Signs of pre-eclampsia, obstetrical history, and co-existing conditions were associated with the need for an SBA. Age was found to interact with parity, suggesting that the CMDS requires adjustment to indicate higher risk among younger multigravida and older primigravida women. The CMDS algorithm had an AUC of 0.73 (95% CI 0.69-0.77) for predicting whether women required an SBA, and an AUC of 0.85 (95% CI 0.67-1.00) for in-hospital mortality.<h4>Conclusions</h4>The CMDS is a low-cost evidence-based tool that uses 7 risk factors assessed on 589 women from Makurdi. Non-specialist health workers can use the CMDS to standardize assessment and encourage pregnant women to seek an SBA in preparation for delivery, thus improving care in countries with high rates of maternal mortality.
KeywordsRisk analysis; Maternal mortality; Nigeria; Antenatal Care; Skilled Birth Attendants; Lmic
Year2022
JournalGlobal health research and policy
Journal citation7 (1), p. 6
PublisherBMC
ISSN2397-0642
Digital Object Identifier (DOI)https://doi.org/10.1186/s41256-022-00240-8
Publication dates
Online11 Feb 2022
Publication process dates
Accepted03 Nov 2022
Deposited18 Mar 2022
Publisher's version
License
File Access Level
Open
LicenseCC BY
Permalink -

https://openresearch.lsbu.ac.uk/item/8z876

Download files


Publisher's version
s41256-022-00240-8.pdf
License: CC BY 4.0
File access level: Open

  • 31
    total views
  • 3
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

Identification and mitigation of high-risk pregnancy with the Community Maternal Danger Score Mobile Application in Gboko, Nigeria.
Bola, R., Ujoh, F. and Lett, R. (2022). Identification and mitigation of high-risk pregnancy with the Community Maternal Danger Score Mobile Application in Gboko, Nigeria. PLoS ONE. 17 (9), p. e0275442. https://doi.org/10.1371/journal.pone.0275442
Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño−Southern Oscillation (ENSO)
Henchiri, M., Igbawua, T., Javed, T., Bai, Y., Zhang, S., Essifi, B., Ujoh, F. and Zhang, J. (2021). Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño−Southern Oscillation (ENSO). Remote Sensing. 13 (23), p. e4730. https://doi.org/10.3390/rs13234730
Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data
Shao, Z., Sumari, N., Portnov, A., Ujoh, F., Musakwa, W. and Mandela, Paulo J. (2020). Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data. Geo-spatial Information Science. 24 (1), pp. 241-255. https://doi.org/10.1080/10095020.2020.1787800
Suitability mapping for rice cultivation in Benue State, Nigeria using satellite data
Ujoh, F., Igbawua, T. and Ogidi Paul, M. (2019). Suitability mapping for rice cultivation in Benue State, Nigeria using satellite data. Geo-spatial Information Science. 22 (4), pp. 332-344. https://doi.org/10.1080/10095020.2019.1637075