Prevalence and Determinants of Maternal Mortality in Adamawa North, Nigeria: Analysis of Community-Based Evidence and the Emerging Role of AI in Health Research

Maternal mortality has been a significant public health challenge in Nigeria, particularly in rural areas of northern Nigeria, where access to quality reproductive health services is limited. This research sought to investigate the prevalence and determinants of maternal mortality in selected areas of Adamawa State, Northern Nigeria, and to examine the potential application of Artificial Intelligence (AI) to enhance maternal health surveillance and early risk detection. A descriptive cross-sectional research design was used for the study, which was carried out in Mubi North and Mubi South Local Government Areas, among others, in the Northern Senatorial Zone of Adamawa State. Structured questionnaires were distributed to 364 respondents selected through a multistage sampling design. Descriptive statistics summarized the data, and chi-square tests were used to identify relationships between variables. The study found a maternal mortality prevalence of 27.5%, consistent with previously reported high rates in northern Nigeria. The leading causes of maternal death were postpartum hemorrhage (32.4%), hypertensive disorders (21.7%), sepsis (16.5%), and delays in accessing emergency obstetric care (15.3%). Significant determinants included distance to health care facilities (χ² = 11.62, p < 0.05), shortage of skilled birth attendants (χ² = 14.31, p < 0.05), poor utilization of antenatal care (ANC) (χ² = 18.42, p < 0.05), and low socioeconomic status (SES) (χ² = 9.27, p < 0.05). Systemic challenges included inadequate referral systems, poor reporting of maternal health information, and ineffective coordination of emergency responses. Evidence from the international literature shows that AI can facilitate maternal health by enabling predictive data analysis for high-risk pregnancies, automating support for frontline health care providers, and enabling digital surveillance of maternal complications in resource-poor zones. The study recommends integrating AI-powered maternal risk prediction tools into existing reproductive health programs in Adamawa State to support efforts to prevent maternal deaths.

Maternal mortality, reproductive health, Artificial Intelligence

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