Maternal mortality refers to deaths of mothers as a result of complications during pregnancy, delivery, and up to 42 days after childbirth or loss of products of conception; and is a major global health concern associated with the death of one in every four women of reproductive age [1–3]. The maternal mortality ratio (MMR) at a given time provides estimates of the number of women dying per 100,000 live births, and is a proxy indicator of the quality of healthcare and wellbeing [3]. The Sustainable Development Goal (SDG 3.1) target is to reduce global MMR to less than 70 deaths per 100,000 live births by 2030 [1, 4, 5]. The global MMR estimate in 2021 was 158.8 deaths per 100,000 live births, and is projected to be 140.9 deaths per 100,000 live births in 2030, missing the SDG 3.1 target [6, 7]. A disproportionate number of these deaths occur in developing countries [3, 8], and robust interventions in these settings are required to enable attainment of this SDG target. In Kenya, MMR is more than double the global estimate at 378 deaths per 100,000 live births, in 2021, and there are likely to be sub-national variation in MMR, calling for tailored approaches for different geographic regions [3, 6, 9]. The current national policies, in Kenya, use a blanket approach to reduce MMR, and include; waiving fees for maternal and child health services in public hospitals, increased use of mobile clinics, recruitment of more primary healthcare workers, establishment of new healthcare facilities to increase access to vulnerable women [9, 10]. However, MMR still remains high implying that there are unmet gaps. A data driven approach that takes into account disparities due to socio-economic status, cultural practices, disease prevalence, climatic and environmental differences and other factors may explain sub-national MMR variations, and in turn allow optimization of interventions that improve healthcare outcomes. We hypothesized that targeted interventions and better guided resource allocation at sub-national level, in Kenya, will significantly improve maternal health by reducing MMR, and fast-track SDG-3 attainment, compared to national blanket approaches [11].
We used four broad classes of WHO defined indicators (direct, indirect, cultural and socio-demographic) of maternal mortality to highlight targeted interventions required for different sub-national regions, in Kenya [12]. Direct indicators have an immediate link to clinical complications resulting in death and include: hemorrhage, eclampsia/pre-eclampsia, infectious diseases, cardiovascular diseases, obstructed labor, and raptured uterus [10–13]. Examples of infectious diseases that are endemic to developing countries and significantly increase MMR are: malaria, which causes up to 10,000 maternal deaths annually, and has more severe outcomes during pregnancy [14]. HIV accounts for up to 20% of the global maternal deaths [15–18]. Indirect indicates increase the likelihood of death as a result of direct indicators, and include: inaccessible ante- and postnatal clinics, skilled birth attendants, emergency services, and health facilities [19]. Socio-demographic indicators are a proxy of barriers to accessing high quality healthcare services and include; wealth status, and educational attainment by mothers and their caregivers [20]. They influence the ability to seek medical during pregnancy complications; facilitate delivery at a health facility; and seek skilled assistance during delivery [11, 21]. Education status of male partners in communities where the mothers do not have complete autonomy on health expenditure also limits healthcare access [22]. Cultural indicators help to target interventions towards ethnic groups with detrimental cultural practices that impede maternal health [22]. These practices include: the utilization of unskilled traditional birth attendants for delivery as dictated by birth rituals [7, 16]; female genital mutilation (FGM) that leaves the mothers vulnerable to obstructed labor, hemorrhage, and raises the risk of maternal mortality [23]. Gender-based violence especially during pregnancy; early child marriages, which place a large number of underage mothers at risk of sexually transmitted diseases, illicit abortions that may lead to infertility; and compromised autonomy of these mothers on their healthcare decisions, which dictates that they seek the consent of their male partners who may be oblivious to women-specific healthcare needs [24].
Here, we highlight how these four categories of indicators can be used to prioritise interventions aimed at reducing MMR at sub-national level, in Kenya. Our study leverages uniformly sampled, retrospective data from the most recent Kenyan demographic and health survey (DHS) data, which was collated in 2014 [18]. Contemporary national DHS data (from 2015) due every five years is still being collated, further emphasizing the urgent need for data driven approaches to resolve high MMR in most developing countries. Our findings can also be used as an entry point for generating robust predictive statistical models that can circumvent this deficiency of publicly accessible DHS datasets. We highlight and quantify factors contributing to differentiated MMR across different geographic populations during the survey period that can inform more targeted interventions to significantly reduce MMR, and fast-track SDG-3.1 attainment.