The Mind Behind the Metrics: How Statistical Modelling Is Strengthening Nigeria’s Maternal-Health Intelligence

8 December 2020 In Nigeria’s public health system, where emergencies and long-term planning struggles for space, researchers who bring clarity through data are becoming increasingly important. Among the voices contributing to this shift is Akinpeloye Olajide, a Nigerian biostatistician and epidemiology researcher, whose work applies rigorous statistical modelling to one of the country’s most challenging indicators: maternal mortality.

His approach reflects a broader movement toward evidence-driven decision-making, where analytical tools and quantitative insight support more informed conversations across Nigeria’s evolving health landscape.

Akinpeloye is a co-author of the study “Probability Distribution Fitting to Maternal Mortality in Nigeria,” published in the International Journal of Mathematical Modelling and Computations. Using a decade-long dataset sourced from University College Hospital, Ibadan, the research team evaluated how maternal-mortality counts change over time and which probability distribution best describes those changes.
Three classical models; Gamma, Weibull, and Exponential were tested and assessed using established criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), standard tools for comparing how well statistical models fit real life data.

The analysis showed that the Exponential distribution provided the closest fit to the observed data, recording the lowest AIC and BIC values among all tested models.

By establishing this fit, the study introduces a mathematically grounded framework for examining long-term mortality patterns.

The research also highlights important variations in maternal-mortality rates across the ten-year period reviewed.
As documented in the publication, the highest rates of approximately 8 maternal deaths per 1,000 live births occurred in 2011 and 2012, followed by a gradual decline and a renewed increase in 2016.

These fluctuations illustrate not only the volatility of maternal outcomes in Nigeria but also the need for methods that can interpret such patterns more precisely. Rather than viewing each year in isolation, a modelling approach helps analysts understand whether mortality is rising, stabilizing, or deviating from its expected course, thereby enriching discussions around program performance and resource planning.
Beyond the statistical findings themselves, Akinpeloye’s work demonstrates how probability modelling can enhance public-health intelligence. In settings where routine data systems are often fragmented or incomplete, having a mathematical distribution that captures the underlying structure of mortality introduces a new level of clarity. Such tools can support the monitoring of long-term trends, the identification of unusual shifts in outcomes, and the evaluation of whether maternal-health interventions are producing measurable improvements.

By giving planners access to a more structured way of interpreting data, the research contributes to a stronger evidence base for maternal-health decision-making.

From an economic and social standpoint, maternal mortality carries wide-reaching consequences from emergency care costs and medical complications to the long-term impact on families and communities. While the study does not quantify these burdens directly, its analytical framework can support forecasting exercises and deeper evaluations of where maternal-health investments may have the greatest impact. In a system where health indicators must compete for limited resources, the ability to describe mortality patterns with statistical precision adds meaningful value to planning and evaluation efforts.

Looking ahead, Nigeria’s growing emphasis on data-informed governance creates an important opportunity for integrating statistical methods more fully into public-health practice. Although policymaking involves many procedural steps, the modelling framework introduced in Akinpeloye’s research represents the type of analytic tool that can help institutions study maternal mortality more systematically.

As national and state-level agencies work to strengthen surveillance systems, improve data quality, and evaluate progress toward maternal-health goals, methodological contributions like his will remain essential.

They offer clarity where routine reporting may fall short and provide the foundation for more consistent, scientifically grounded interpretation of health trends.

In a landscape where reliable data has often been challenging to obtain or utilize effectively, Akinpeloye Olajide’s work stands out for its rigor and relevance. His research underscores the importance of statistical thinking in public health and reflects a broader shift toward analytical approaches that can inform planning, monitoring, and long-term strategy.

As Nigeria continues to advance its commitment to evidence-based decision-making, contributions like these will play an increasingly important role in shaping how the country understands and responds to maternal-health challenges.