Healthcare

Maternal Mortality in Nigeria: How can data shape policy?

As global and local organizations respond to the call to action to address maternal mortality, the limited resources available to the Nigerian Ministry of Health (MoH) and the lack of robust data collection pose a unique challenge. In this article, we explore how benevolent organizations have used healthcare data to inform their strategy, and how public and non-profit organizations can leverage data for targeted initiatives and programs in Nigeria.

Nov 21, 2020 • 4 min read

According to the WHO, roughly 20% of all global maternal deaths occur in Nigeria. Nigerian women also have a 1 in 22-lifetime risk of dying during pregnancy, childbirth, or postpartum; in most developed countries, the risk is 1 in 4,900. These stats reveal a significant unmet need for effective policies that minimize the barriers and improve the quality of maternal care in Nigeria.

As global and local organizations respond to the call to action to address maternal mortality, the limited resources available to the Nigerian Ministry of Health (MoH) and the lack of robust data collection pose a unique challenge. In this article, we explore how benevolent organizations have used healthcare data to inform their strategy, and how public and non-profit organizations can leverage data for targeted initiatives and programs in Nigeria.

The Bill and Melinda Gates Foundation (BMGF) has established a significant presence in the fight to minimize global maternal mortality. As part of their maternal mortality program, the BMGF has developed a Strategic Data Analysis & Synthesis Initiative that collects, analyzes and shares maternal and infant health indicators across their investment portfolio to inform their strategies and policies. Additionally, the BMGF plans to standardize data collection across a variety of their programs and use the evidence-based recommendations generated to facilitate the adoption of their innovations by public decision-makers.

However, the creation of these standardized data collection initiatives raises important questions – what kind of data are these organizations collecting and how exactly are they using it?

The BMGF’s guide to actionable measurement provides some useful insights. The organization encourages grantees to collect data that allows them to focus investments based on ‘what has worked, what has not and what may be promising’. For example, The Family Planning and Reproductive Health department funded a grant to monitor progress in raising contraceptive use in urban slums across four countries in Sub-Saharan Africa and South Asia in order to identify key success factors that can be replicated in other programs. Rapid-surveys are fielded to gather data on the incentives and barriers for the adoption of available BMGF family planning programs, such as misconceptions on the risk of pregnancy, cost deterrents and concerns of side effects. These program-specific metrics are collected with the intent to use the data to inform their strategy and to hold donors and governments accountable.

A woman in Makoko and her new borns

To analyze and synthesize the large datasets collected across their programs, the BMGF has recruited the help of third-party organizations like Third Sector Intelligence (3SI). 3SI synthesizes data into evidence-based options the BMGF can use to inform their investment priorities and help drive outcomes across multiple projects. For example, 3SI analyzed the results of a $1 billion Agricultural Development grant portfolio in Kenya, Tanzania, Uganda and Zambia to provide recommendations on how to more effectively collect direct farmer feedback using technology. Outsourcing data analysis/synthesis represents a transferable learning for governments with limited resources to more efficiently make informed policy decisions.

But does data collection and synthesis actually improve maternal mortality outcomes when used to inform strategy? Remarkably so. After the launch of the BMGF’s Health Extension Program in Ethiopia in 2003, child mortality decreased at a significant rate while maternal mortality only showed modest decreases. Through the Ethiopian MoH’s data collection efforts, the organization identified a correlation between high maternal mortality rates and home births. Program resources were diverted to campaign for an increased proportion of women giving birth at hospitals; hospital births increased from 20% to 73% between 2011 and 2016, effectively changing the landscape of childbirth in Ethiopia.

In the Nigerian context of scarce resources and poor data collection, some organizations have developed creative approaches. The Wellbeing Foundation Africa (WBFA) has expanded MamaCare, antenatal classes led by qualified midwives, to the whole state of Kwara. As a result, Kwara, one of Nigeria’s poorest states, has effectively met the 2030 Sustainable Development Goal targets for reducing neonatal and under-5 mortality rates. How did they do this? Toyin Saraki, the founder of the WBFA, recently lauded the organization’s use of evidence-based initiatives and WhatsApp group monitoring to identify mothers at risk and direct resources accordingly. The data collected is also used to tailor the MamaCare curriculum to meet the specific needs of women in different regions; the success of this program through the COVID-19 pandemic has inspired the WBFA to formally commit to launch an official MamaCare Whatsapp ‘chatbot’ in October 2020.

Despite the harrowing maternal mortality statistics in Nigeria, the emergence of more sophisticated data analysis tools presents a unique opportunity to strategically reduce the number of avoidable deaths. For public decision-makers tackling maternal mortality in Nigeria, data collection will be a key strategy. These examples make clear that a successful maternal mortality strategy should consider several factors to ensure optimal outcomes:

  • A comprehensive network of data collection systems should be established across primary healthcare facilities to monitor outcomes for maternal mortality. This enables governments to track indicators of poor maternal health outcomes (e.g. number of maternal and neonatal / infant deaths, number of elective cesarean sections, Health Care Personnel-to-patient ratio, provider delivery volume, etc.) and direct funds to the areas/institutions most in need.
    • In an increasingly digital world, electronic medical records and other health tech data can be leveraged to more accurately monitor the progress of these programs.
  • Fielding surveys, questionnaires or in-person interviews can capture valuable data that can inform policies. Additionally, with the rapidly increasing mobile internet use in Nigeria, the ability to capture this data remotely presents a feasible, low-cost opportunity, particularly in a post-COVID 19 world.
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Healthcare
The Helium Team
Nov 21, 2020 • 4 min read

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