Fitting Cox proportional hazard models to identify mortality predictors during pregnancy and postpartum periods

 Corresponding author: Tahiru Mahama Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Fitting Cox proportional hazard models to identify mortality predictors during pregnancy and postpartum periods Tahiru Mahama * Department of Mathematical Sciences, The University of Texas at El Paso, USA. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 Publication history: Received on 22 April 2025; revised on 30 May 2025; accepted on 02 June 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.3.2152 Abstract Maternal mortality remains a major global health challenge, particularly in lowand middle-income countries, where pregnancy and postpartum periods are associated with elevated risks of death due to both obstetric and non-obstetric complications. To identify predictors of mortality during these critical periods, this study employs Cox proportional hazards models, a robust survival analysis technique suited for time-to-event data. The objective is to evaluate timedependent risks and determine significant covariates that influence maternal survival from conception through the first six weeks postpartum. Using longitudinal data from national reproductive health surveillance systems and hospitalbased cohorts, we assess various socio-demographic, clinical, and obstetric factors, including age, parity, antenatal care utilization, mode of delivery, pre-existing conditions, and obstetric complications. The Cox model accommodates censoring and permits the estimation of hazard ratios (HRs), quantifying the relative risk of mortality associated with each predictor while adjusting for confounders. Our findings highlight critical predictors of maternal mortality, including advanced maternal age, delayed antenatal care, hypertensive disorders, cesarean delivery, and postpartum hemorrhage. Interaction terms and stratified analyses further reveal context-specific risk amplifiers, such as rural residence and limited health facility access. Proportional hazard assumptions are verified using Schoenfeld residuals and time-varying covariate assessments to ensure model validity. By identifying and quantifying key mortality predictors, this research offers valuable insights for targeted clinical interventions and policy-level strategies aimed at reducing maternal deaths. The integration of survival modeling into maternal health research enhances our understanding of temporal risk dynamics and supports evidence-based improvements in perinatal care delivery systems. Keywords: Maternal mortality; Pregnancy outcomes; Cox proportional hazards model; Survival analysis; Postpartum risk factors; Mortality predictors 1. Introduction 1.1. Global Burden of Maternal Mortality and Epidemiological Context Maternal mortality remains one of the most pressing global health challenges of the 21st century, despite notable progress in reducing its incidence. According to the World Health Organization, approximately 287,000 women died from pregnancy-related causes in 2020, with 95% of these deaths occurring in lowand middle-income countries [1]. These statistics underscore the persistent inequality in maternal health access and outcomes, often driven by systemic failures in healthcare delivery, education, and socio-political infrastructure [2]. Sub-Saharan Africa and South Asia account for nearly 86% of maternal deaths globally, revealing stark geographic disparities [3]. Within these regions, rural populations, young mothers, and those with limited antenatal care access are World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 28 at particularly high risk. The causes of maternal mortality are multifactorial—ranging from direct obstetric complications like hemorrhage, sepsis, and eclampsia, to indirect causes including malaria, anemia, and HIV/AIDS [4]. In high-income countries, while overall maternal mortality is lower, recent trends show a concerning rise, often linked to older maternal age, pre-existing conditions, and disparities among racial and ethnic groups [5]. This epidemiological context reflects that maternal death is not only a clinical issue but also a marker of broader societal and structural inequalities [6]. Accurate surveillance systems and data-driven policies are essential to track maternal mortality trends and to identify the most vulnerable groups. However, many nations still lack robust vital statistics infrastructure, resulting in underreported or misclassified maternal deaths, particularly in the postpartum period [7]. Addressing maternal mortality, therefore, requires both clinical innovation and improved public health monitoring systems. 1.2. Temporal Risk Dynamics in Pregnancy and Postpartum Periods Maternal health risk is not static but evolves across different stages of pregnancy and into the postpartum period. Each trimester presents unique physiological challenges, while the immediate weeks following delivery—often termed the “fourth trimester”—pose substantial threats to maternal survival [8]. Hemorrhage and eclampsia typically occur near delivery, whereas sepsis and cardiomyopathy are more likely to manifest in the postpartum phase [9]. Temporal patterns of risk highlight the need for continued maternal surveillance beyond childbirth. A significant proportion of maternal deaths, particularly in resource-limited settings, occur after hospital discharge and are not captured in traditional facility-based reporting systems [10]. This has prompted international agencies to advocate for extending the postpartum care window from six weeks to at least one year, particularly in high-risk populations [11]. Moreover, physiological changes in pregnancy—such as increased blood volume, suppressed immunity, and hormonal fluctuations—alter the body’s response to infections and stressors over time [12]. These dynamics necessitate a timesensitive approach to maternal health intervention, where risks are assessed and mitigated at each stage of gestation and post-delivery. Understanding the timing of adverse maternal events is also critical for optimizing resource allocation. For instance, community-based health workers may need to adjust visit schedules to coincide with peak risk windows [13]. Hence, a dynamic temporal framework is essential not only for clinical management but also for strategic planning of maternal health services, especially in settings with limited healthcare capacity and workforce shortages [14]. 1.3. Rationale for Using Cox Proportional Hazard Models for Time-to-Event Maternal Analysis Cox proportional hazard models are widely regarded as the gold standard for analyzing time-to-event data in epidemiology and public health, particularly when the outcome is not uniformly distributed over time. In maternal health research, these models offer critical advantages by accommodating variable follow-up periods and allowing for censoring, which is common in longitudinal pregnancy data [15]. Unlike logistic regression, which provides only a binary outcome at a fixed point, Cox models estimate the instantaneous risk of an event occurring at a specific time, given that the individual has survived up to that point [16]. This is particularly useful in maternal mortality studies, where risk is continuously evolving across prenatal and postnatal phases [17]. For instance, the hazard of death due to postpartum hemorrhage may sharply increase within 48 hours after delivery but decline thereafter. Another advantage lies in the model’s ability to incorporate both time-independent and time-dependent covariates, thus allowing for a nuanced understanding of risk factors that change throughout pregnancy [18]. For example, anemia diagnosed during the second trimester may have a different hazard implication than pre-existing hypertension detected before conception. Cox models also facilitate comparison across demographic and geographic strata by adjusting for clustering or stratifying by facility or region [19]. This makes them particularly powerful for policy formulation and targeted intervention planning. In maternal survival studies, the application of Cox models ensures that timing is appropriately integrated into both analysis and interpretation, enabling precision in identifying critical windows for care delivery and intervention [20]. Given the evolving nature of maternal risk and the limitations of static analytic methods, survival analysis presents a robust framework for capturing time-dependent vulnerabilities. In Section 2, we explore how World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 29 survival models, including Cox regression and its extensions, offer valuable insights into the temporal landscape of maternal health outcomes. 2. Overview of survival analysis and cox modeling 2.1. Concepts of Survival Time, Censoring, and Hazard Functions Survival analysis is a branch of statistics designed to evaluate the time until an event of interest occurs. In the context of maternal health, the “event” typically refers to maternal death, and “survival time” is the duration from a defined start point (e.g., conception or delivery) until that outcome or censoring occurs [6]. This method is particularly valuable in maternal mortality studies because the risk of death varies considerably throughout pregnancy and the postpartum period. Censoring is a key concept in survival analysis. It occurs when the complete event history of an individual is unknown— either because the study ends before the event happens or the individual leaves the study prematurely [7]. For instance, if a woman survives beyond the postpartum monitoring period, her data would be right-censored. This allows researchers to still include her partial information in the analysis, enhancing the utility of incomplete but valuable records [8]. Another core concept is the hazard function, which defines the instantaneous rate of event occurrence at a specific time point, given that the individual has survived up to that time. Unlike the survival function, which shows the probability of surviving beyond a certain time, the hazard function focuses on the immediate risk and is thus more sensitive to temporal changes in health risk [9]. Understanding these concepts is foundational for constructing models that accurately capture maternal health dynamics. They allow for time-aware analyses, critical in identifying periods of heightened vulnerability, such as the immediate postpartum window where the hazard for maternal mortality typically peaks [10]. 2.2. Cox Proportional Hazards Model: Theory, Assumptions, and Interpretations The Cox proportional hazards model, introduced by Sir David Cox in 1972, is one of the most widely used tools in survival analysis. It estimates the hazard of an event such as maternal death as a function of time and covariates, without requiring the baseline hazard function to be specified [11]. This semi-parametric property is a major strength, allowing for flexible modeling in diverse healthcare contexts, including maternal health. The model expresses the hazard at time t for individual i as: where h0(t) is the unspecified baseline hazard function, xki are covariates, and βk are the corresponding coefficients. The exponential term adjusts the baseline hazard based on individual characteristics such as age, parity, or comorbidities [12]. One key assumption of the Cox model is proportionality the ratio of hazards for any two individuals remains constant over time. That is, the effect of a covariate is multiplicative and does not change with time [13]. This assumption must be tested, commonly using Schoenfeld residuals, to ensure valid interpretation of results. Violations can lead to misleading conclusions, particularly in maternal mortality where risk factors may vary dramatically during pregnancy and postpartum stages [14]. Interpretation of the model results involves hazard ratios (HRs), which indicate how much the risk of the event increases or decreases with a one-unit change in a predictor. For instance, an HR of 2.0 for hypertension implies a twofold increase in maternal death risk for hypertensive women compared to non-hypertensive peers, assuming other factors are held constant [15]. The Cox model also accommodates stratification and clustering, enabling analysts to control for unmeasured confounders and intra-cluster correlations, such as facility-level effects [16]. This is particularly useful in maternal health datasets where women are nested within communities or hospitals. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 30 Despite its strengths, the model requires careful data preparation, including accurate measurement of covariates and assessment of follow-up durations [17]. However, when implemented appropriately, the Cox model provides nuanced insights into maternal mortality risk across time and population strata. 2.3. Time-Varying Covariates and Extensions in Maternal Mortality Contexts In maternal mortality research, risk factors often change over time, violating the assumption of fixed covariates in standard Cox models. For example, a woman’s blood pressure, anemia status, or access to care may vary significantly across trimesters and postpartum periods [18]. To accommodate such realities, extensions of the Cox model include time-varying covariates predictors whose values evolve during follow-up. Incorporating time-varying covariates enables dynamic modeling, where the hazard function is updated as a covariate’s value changes. This can be specified in the model as xk(t)), where the covariate is indexed by time [19]. For instance, introducing antiretroviral therapy midway through pregnancy can alter maternal HIV-related mortality risk, and this change must be reflected in the hazard estimation to avoid bias. Time-varying models also help account for interventions that occur at irregular intervals, such as emergency obstetric surgeries or health facility transfers. These clinical events, when ignored, can result in misclassification and confounding, thereby distorting the true effect of exposure on mortality [20]. By modeling them explicitly, researchers can distinguish between pre-existing risk and treatment-induced outcomes. Beyond time-varying covariates, other model extensions include the time-dependent Cox model and joint modeling frameworks, which simultaneously assess survival and longitudinal data. These are especially relevant when investigating the interplay between evolving physiological metrics (e.g., hemoglobin levels) and survival outcomes [21]. Applying such models in maternal health allows for a granular understanding of when and how specific risk factors exert influence. This enhances the predictive accuracy of maternal mortality models and informs more precise timing for policy interventions and clinical surveillance [22]. Figure 1 Graphical illustration of survival curves and hazard functions for maternal mortality 3. Data sources and structure for maternal mortality studies 3.1. National Health Surveillance Systems and Demographic Health Surveys (DHS, MICS, RAMOS) National health surveillance systems are the cornerstone of maternal mortality monitoring, enabling governments and public health agencies to track trends, identify disparities, and formulate targeted interventions. Among the most widely used tools for maternal health surveillance are Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and Reproductive Age Mortality Surveys (RAMOS), each of which offers unique strengths in data collection and outcome evaluation [11]. DHS programs, implemented in over 90 countries, provide nationally representative, standardized datasets that capture a wide range of variables, including fertility, maternal and child health, and service utilization. These surveys typically World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 31 use stratified two-stage sampling methods, making them powerful for estimating maternal mortality ratios and assessing inequality across geographic and demographic subgroups [12]. The DHS also collects retrospective maternal death information via the sibling survival method, which, although subject to recall bias, remains a cost-effective option in countries lacking comprehensive vital registration systems [13]. MICS, developed by UNICEF, complements the DHS by focusing on child health, nutrition, and maternal care indicators. While similar in scope, MICS emphasizes child welfare outcomes and includes modules specifically tailored to local policy needs. The data often inform national strategies on maternal health promotion, education, and equity [14]. RAMOS provides a more in-depth and direct approach by identifying and investigating all deaths of women of reproductive age in a given population. It integrates facility records, vital registration data, and verbal autopsy reports to determine if the deaths were maternal and what factors contributed to them [15]. This method is highly reliable and particularly useful in settings where maternal deaths are underreported or misclassified in routine systems. Combining data from these surveillance approaches improves accuracy and contextual understanding. While each system has limitations, together they create a robust foundation for modeling maternal health risks and evaluating interventions across temporal and spatial domains [16]. 3.2. Defining Events: Maternal Death Timing (Antenatal, Intrapartum, Postpartum) Accurate temporal classification of maternal deaths is critical for both clinical and epidemiological analysis. Maternal deaths can be broadly categorized into three temporal phases: antenatal, intrapartum, and postpartum. Each phase presents unique risks and demands distinct preventive and therapeutic strategies [17]. Antenatal deaths occur between conception and the onset of labor. These are often linked to complications such as ectopic pregnancy, hyperemesis gravidarum, and poorly managed chronic conditions like hypertension or diabetes. Accurate timing and classification in this phase require reliable antenatal care records and standardized surveillance systems, which are frequently unavailable in low-resource settings [18]. Intrapartum deaths happen during labor or delivery and are typically associated with conditions like obstructed labor, uterine rupture, and severe preeclampsia. These deaths are highly preventable with timely access to skilled birth attendants, emergency obstetric care, and adequate referral systems [19]. Yet, intrapartum deaths remain high in regions with weak health infrastructure and logistical delays in accessing care. Postpartum deaths—occurring within 42 days of termination of pregnancy—account for the largest share of maternal mortality globally. Major causes include postpartum hemorrhage, infections, and cardiomyopathies [20]. However, many national systems fail to extend follow-up beyond delivery discharge, leading to undercounted deaths in this critical period. Accurate classification is further complicated when home births or self-managed complications are not documented [21]. These event definitions are vital for structuring time-to-event models and for developing targeted intervention schedules. Incorporating precise event timing into data collection instruments improves not only model precision but also guides strategic deployment of resources across the maternal continuum of care [22]. 3.3. Structure of Covariates: Socio-Demographics, Clinical Records, Obstetric Events In maternal mortality modeling, the structure and categorization of covariates greatly influence the model’s capacity to detect patterns and causal relationships. Covariates are typically grouped into three major domains: socio-demographic factors, clinical records, and obstetric events [23]. Socio-demographic variables include age, education, marital status, residence (urban/rural), and socioeconomic status. These factors often serve as baseline predictors in survival analysis and are critical for stratifying risk across population subgroups [24]. For instance, maternal age under 20 or over 35 is consistently associated with elevated mortality risks, making it an important adjustment factor in any model [25]. Clinical records consist of documented health conditions before and during pregnancy. This includes chronic diseases (e.g., hypertension, HIV, anemia), infections, and access to antenatal services. These variables often interact with time and influence the hazard rate in complex ways. Integrating accurate and longitudinal clinical data enhances the reliability of predictions in Cox and extended survival models [26]. However, such data are often incomplete or inconsistently recorded in many low-resource environments. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 32 Obstetric events refer to complications arising during pregnancy, labor, and delivery—such as preterm labor, placental abruption, or cesarean section. These are typically treated as time-varying covariates in advanced modeling frameworks. Capturing their onset and duration is essential for understanding the timing of risk escalation [27]. Some models also incorporate emergency interventions (e.g., blood transfusions) as effect modifiers rather than primary exposures. Appropriate categorization and coding of these covariates facilitate more accurate hazard estimation and improve interpretability. Moreover, distinguishing between modifiable and non-modifiable covariates supports the design of targeted maternal health policies and intervention programs at both clinical and policy levels [28]. Table 1 Variable Dictionary Including Event Definitions, Covariates, and Censoring Details Variable Name Description Type Coding/Values Comments Time_to_event Time from entry point (delivery/enrollment) to event or censoring Continuous (days) Numeric (e.g., 0–42) Required for survival analysis; defines follow-up period Event_status Indicator of maternal death occurrence during follow-up Binary 1 = Death, 0 = Censored Used as event indicator in Cox model Censoring_reason Reason for non-occurrence of event by study end Categorical 1 = End of follow-up, 2 = Lost to follow-up, 3 = Withdrawal Useful for sensitivity analysis Maternal_age_group Age category at delivery Categorical 1 = <20, 2 = 20–34, 3 = ≥35 Used for stratification and adjusted HR estimation Residence Urban vs. rural residence Binary 1 = Urban, 0 = Rural Proxy for healthcare access Education_level Highest education level attained Ordinal 0 = None, 1 = Primary, 2 = Secondary, 3 = Tertiary Socio-demographic covariate ANC_visits Number of antenatal care visits Continuous Numeric (0–10+) Often recoded into binary: <4 vs. ≥4 visits Hemorrhage_onset Time of postpartum hemorrhage occurrence Timevarying binary 1 = Yes during interval, 0 = No Requires longitudinal structure; used as timevarying covariate Eclampsia_diagnosis Diagnosis of eclampsia during pregnancy/labor/postpart um Binary 1 = Yes, 0 = No Clinical covariate associated with high hazard Cesarean_delivery Mode of delivery Binary 1 = Cesarean, 0 = Vaginal Differentiates elective vs. emergency risk with time interactions Referral_status Whether patient was referred from another facility Binary 1 = Referred, 0 = Not referred Proxy for system-level delay Facility_level Level of care facility Categorical 1 = Primary, 2 = Secondary, 3 = Tertiary May be used for stratified or multilevel modeling Follow_up_end_date Last date of observed survival or censoring Date YYYY-MM-DD format Paired with entry date to compute follow-up duration World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 33 4. Data preparation and preprocessing 4.1. Handling Missing Data, Right-Censoring, and Event Time Definitions Addressing missing data and right-censoring is central to maintaining the integrity of time-to-event analyses in maternal mortality research. In lowand middle-income settings, missing data often result from incomplete health records, non-standardized reporting, or loss to follow-up after delivery [15]. Ignoring such gaps can introduce significant bias, particularly if data are not missing completely at random. Several imputation techniques—including multiple imputation and hot-deck imputation—are used to recover lost information while preserving statistical properties [16]. Right-censoring occurs when the observation period ends before the event of interest—maternal death—takes place. For example, if a woman is still alive at the end of a 42-day postpartum window, her data are considered censored at the last known follow-up time. Unlike deletion of incomplete records, right-censoring retains valuable partial data in the survival analysis without violating model assumptions [17]. This approach is essential in settings with variable follow-up durations or delayed vital registration systems. Equally important is the definition of event time, which must be consistent across observations. In maternal survival studies, the starting point is often gestational age at enrollment, date of delivery, or date of postpartum follow-up initiation [18]. Precise time measurements are crucial for estimating hazards accurately, especially when modeling early versus late postpartum mortality risks. Inconsistent definitions can lead to differential misclassification and biased risk estimates. Using standard frameworks—such as the WHO definition of maternal death within 42 days of pregnancy termination— ensures comparability and reproducibility of analyses across studies and populations [19]. Consistent time frameworks allow for harmonized modeling and valid pooling in meta-analyses and multi-country comparisons of maternal mortality. 4.2. Recoding Variables and Creating Time-to-Event Formats Preparing data for survival analysis involves several preprocessing steps, including recoding variables and constructing proper time-to-event formats. Recoding allows the transformation of raw or categorical variables into forms compatible with modeling requirements. For instance, maternal age may be transformed from a continuous variable into defined risk categories (e.g., <20, 20–34, ≥35) to simplify interpretation or to test non-linear associations with mortality outcomes [20]. Likewise, parity, education level, or household wealth index may be recoded to reflect ordinal or binary contrasts. Recoding is not merely a formatting task; it is critical to ensuring conceptual clarity and statistical efficiency. Failure to recode appropriately can obscure meaningful associations or inflate standard errors due to sparsely populated categories [21]. In time-to-event data structures, every observation must have at least three key components: the time of origin (start), the time of the event or censoring (end), and an event indicator (1 for event, 0 for censoring). These components form the basis of survival object notation used in statistical software like R’s Surv() or Stata’s stset commands [22]. Without these variables, the model cannot compute hazard rates or accommodate censoring structures effectively. Time-to-event formats also require careful definition of time units—days, weeks, or months—based on the nature of maternal risk being studied. For example, postpartum mortality risks often fluctuate rapidly within days, necessitating a finer temporal scale than annualized datasets allow [23]. Constructing appropriate formats ensures that all subjects are consistently followed across their respective exposure periods. It also facilitates the inclusion of time-varying covariates and stratified analyses, increasing the analytical granularity and robustness of maternal mortality research outputs [24]. 4.3. Stratification by Exposure Windows: Antepartum, Delivery, Postpartum Stratifying maternal risk across exposure windows—antepartum, delivery, and postpartum—enables a detailed understanding of when adverse events are most likely to occur. This stratification is particularly important in survival analysis where temporal heterogeneity in hazard rates is expected [25]. By modeling these windows separately or as World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 34 interaction terms, researchers can more accurately isolate the risk contributions of distinct physiological and healthcare conditions. The antepartum period spans from conception until the onset of labor. Risk factors during this window often include early pregnancy complications, maternal infections, and limited antenatal care. Stratifying this period allows for examining the cumulative impact of antenatal exposures on maternal survival and helps prioritize early intervention strategies [26]. The delivery period, encompassing labor and childbirth, is marked by acute and time-sensitive complications such as obstructed labor, uterine rupture, and hypertensive crises. Stratifying this window enables detection of peripartumspecific mortality risks and facilitates evaluation of emergency obstetric care readiness and effectiveness [27]. The postpartum window, typically defined as the first 42 days after delivery, is associated with the highest proportion of maternal deaths globally. Causes include hemorrhage, sepsis, and thromboembolic disorders. Stratification here is essential for evaluating the timing and adequacy of postnatal follow-up care, especially in resource-limited settings where health worker contact may drop sharply after delivery [28]. Temporal stratification can be implemented using time-varying coefficients or interaction terms in extended Cox models. This approach allows for different hazard ratios across windows, revealing shifts in vulnerability that fixedcoefficient models may obscure. Ultimately, such stratification enhances the precision of policy recommendations and clinical protocols for reducing maternal mortality across the reproductive timeline [29]. Figure 2 Kaplan-Meier survival curves by key predictors (e.g., age group, parity, care utilization) 5. Model development: cox proportional hazards for maternal mortality 5.1. Univariate Cox Regression to Screen Individual Predictors The initial step in modeling maternal survival data often involves univariate Cox regression to identify significant individual predictors. Each predictor is evaluated independently to determine its relationship with the hazard of maternal death. This approach provides hazard ratios (HRs), confidence intervals, and p-values, enabling researchers to prioritize variables for multivariable model inclusion [15]. Univariate screening serves two purposes: first, it reduces model complexity by eliminating irrelevant covariates; second, it highlights variables with potential prognostic importance. In maternal mortality research, common variables assessed in this step include maternal age, antenatal care attendance, hemoglobin levels, parity, and presence of obstetric complications such as eclampsia or sepsis [16]. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 35 While statistical significance is often the initial criterion for inclusion (e.g., p < 0.20), researchers must also consider clinical relevance and contextual knowledge. For example, a covariate with marginal statistical value may still warrant retention if it reflects a known high-risk condition or policy-relevant exposure [17]. It’s crucial to note that univariate significance does not imply causal independence. Some variables may appear protective or harmful in isolation but change direction or lose significance when adjusted for confounders in multivariable models [18]. Therefore, univariate Cox regression should be seen as a screening—not final—step. The selection process must also account for multicollinearity and redundancy. Correlated predictors like maternal age and parity may both be significant individually but introduce instability in multivariable models. In such cases, domain knowledge helps decide which to retain [19]. Overall, univariate Cox regression streamlines model development and offers preliminary insights into risk structure, but its findings must be interpreted cautiously within the broader modeling framework of maternal survival analysis. 5.2. Multivariable Model Building with Stepwise Selection (Forward/Backward) Once key predictors have been identified through univariate analysis, the next phase involves constructing a multivariable Cox regression model. Stepwise selection—either forward, backward, or bidirectional—is commonly employed to identify the most parsimonious yet informative combination of covariates. This technique enhances model interpretability and predictive accuracy, especially in maternal health contexts where numerous clinical and demographic variables may be at play [20]. Forward selection begins with no predictors and adds variables one at a time based on statistical criteria (e.g., likelihood ratio test or Akaike Information Criterion). This method ensures that each added variable improves model fit meaningfully [21]. It is particularly useful when the number of potential covariates is large, and prior knowledge about variable importance is limited. Backward elimination, conversely, starts with a full model containing all candidate variables. Variables are removed iteratively based on weakest significance until only predictors that meet predefined thresholds remain. This approach is effective when multicollinearity is not a major concern and the dataset is sufficiently large to support complex models [22]. In maternal mortality analysis, stepwise techniques are valuable for isolating variables that retain significance after adjusting for co-occurring conditions, such as identifying the independent effects of hemorrhage versus hypertension on time to death [23]. These procedures are typically automated in software packages like R, SAS, and Stata, but researchers must interpret results in light of clinical relevance and potential confounding. While stepwise selection aids in parsimony, it may exclude variables with contextual importance. Therefore, manual review and validation with external datasets or domain experts is recommended to ensure the final model balances statistical rigor with policy and clinical applicability [24]. 5.3. Assessing the Proportional Hazards Assumption (Schoenfeld Residuals, Log-Minus-Log Plots) The proportional hazards (PH) assumption underpins the validity of Cox regression models. It posits that the hazard ratio for any two individuals remains constant over time, implying that covariates have a multiplicative effect on the hazard function that does not vary with time [25]. Violations of this assumption can result in biased coefficient estimates and misinterpretation of time-dependent effects in maternal mortality research. One of the most robust methods for evaluating the PH assumption is the use of Schoenfeld residuals. These residuals, computed for each covariate and time point, should show no association with time if the PH assumption holds. Plotting scaled Schoenfeld residuals or applying global tests (e.g., Grambsch and Therneau’s test) can detect systematic time trends in covariate effects [26]. Another diagnostic tool is the log-minus-log (LML) survival plot. If the proportional hazards assumption is satisfied, the LML curves for different categories of a covariate should remain roughly parallel over time. In maternal mortality contexts, such plots are useful for categorical predictors like place of delivery (home vs. facility) or parity groups [27]. When violations are detected, several corrective strategies are available. One approach involves stratifying the model by the problematic variable, thus allowing the baseline hazard to vary across strata. Another is introducing time-by- World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 42 patient with signs of early labor and moderate anemia may receive an automated alert indicating elevated postpartum hemorrhage risk, triggering pre-emptive resource preparation. To maximize clinical utility, integration must be aligned with the local care delivery workflow. Simple user interfaces, local language options, and alignment with national protocols (e.g., WHO guidelines) are essential for adoption and scalability [34]. Furthermore, interoperability with existing health information systems enables data aggregation and continuous learning, improving model performance over time [35]. Importantly, the effectiveness of these tools is contingent on training and supportive supervision. CDSTs must not replace clinical judgment but rather augment it with evidence-based, contextually relevant guidance [36]. Implementation should also include monitoring and evaluation frameworks to assess real-world impact on care processes and outcomes [37]. In LMICs where human resource shortages and infrastructure gaps persist, CDSTs integrated with predictive models represent a powerful innovation. They bridge the gap between data and decision-making, improving maternal outcomes while building digital resilience in under-resourced health systems [38]. 8.3. Informing Resource Allocation and Triage Protocols Figure 5 Example risk stratification dashboard using Cox-predicted probabilities Predictive survival models are not only clinical tools but also valuable instruments for resource allocation and triage in maternal health systems. By quantifying individualized risk across antepartum, intrapartum, and postpartum phases, these models inform where and when interventions are most urgently needed [39]. For instance, regional facilities with higher predicted maternal mortality risks may be prioritized for staff deployment, blood bank expansion, or surgical readiness [40]. At the facility level, triage protocols informed by model-based risk stratification can help allocate beds in highdependency units (HDUs) or intensive care units (ICUs) more effectively. A woman identified with a 10-fold elevated hazard due to concurrent eclampsia and hemorrhage onset can be escalated to emergency care faster than with symptom-based triage alone [41]. These models also support policy-level planning by highlighting systemic bottlenecks—such as referral delays or facility under-capacity—that consistently correlate with higher mortality. Governments and stakeholders can thus make data- World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 43 informed investments in ambulance networks, obstetric training, or community outreach programs [42]. In crisis settings like pandemics or natural disasters, survival models embedded in triage algorithms ensure that limited resources are directed to the highest-risk populations first, improving both efficiency and equity in maternal care delivery [43]. 9. Discussion 9.1. Summary of Key Findings and Interpretation of Hazard Ratios This analysis identified a set of time-dependent and baseline covariates that significantly influenced maternal survival across the antepartum, intrapartum, and postpartum periods. The application of Cox proportional hazards models revealed distinct risk gradients tied to socio-demographic, clinical, and health system variables. Hazard ratios (HRs) provided a quantifiable measure of how each factor influenced the timing and probability of maternal death [44]. Maternal age >35 was associated with a 1.7-fold increased hazard of death compared to women aged 20–34, affirming prior evidence of elevated risk with advanced maternal age [45]. Rural residence remained a strong predictor, with an HR of 2.1, indicating systemic barriers in access to timely obstetric care. Education offered a protective effect: women with secondary or higher education had a 45% lower hazard of death relative to those without formal education [46]. Among clinical risks, eclampsia (HR 3.9) and postpartum hemorrhage (HR 4.5) emerged as the strongest predictors of maternal death, particularly within the first 48 hours postpartum. Cesarean section increased hazard by 1.6-fold, especially when conducted emergently and without proper pre-operative stabilization. These associations were timesensitive and dynamic, often escalating in early postpartum intervals [47]. Referral delays, modeled as time-varying covariates, showed an HR of 2.4 when exceeding two hours. Stratification by facility level revealed a dose-response effect, with primary-level facilities associated with higher mortality than tertiary centers. These findings underscore how individual risk is shaped by both personal and contextual conditions [48]. Together, these hazard ratios facilitate early risk stratification, inform triage protocols, and support targeted health investments in maternal care services. Their interpretation is central to designing predictive tools that align clinical response with temporal risk dynamics [49]. 9.2. Comparison with Previous Maternal Survival Studies This study aligns with previous research showing that hemorrhage, hypertensive disorders, and delayed care remain leading contributors to maternal mortality in low-resource settings. Earlier studies in Ethiopia, Bangladesh, and India also identified eclampsia and postpartum hemorrhage as time-critical complications with high mortality hazard in the first 48 hours postpartum [50]. Compared to similar Cox model analyses conducted in sub-Saharan Africa, our hazard ratios for hemorrhage and eclampsia were slightly higher [51]. This may reflect improved detection from time-varying covariates and better event timing definitions. Unlike studies that rely on static predictors, this model leveraged dynamic clinical trajectories, enhancing temporal accuracy [52]. Additionally, our inclusion of health system-level variables—such as referral timing and facility level—extends the findings of single-level models that focus solely on patient-level determinants. A recent analysis from Nigeria emphasized the importance of stratifying by facility type, but lacked time-varying components, potentially underestimating system-related delays [53]. Our model builds upon this literature by demonstrating that integrated clinical and contextual risk modeling yields more precise hazard estimation. It confirms previous patterns while providing additional granularity that enhances application in early warning systems and triage planning, particularly in complex, resource-limited environments [54]. 9.3. Limitations (e.g., Misclassification Bias, Unmeasured Confounding) Despite its strengths, this study faces several limitations. Misclassification bias may have arisen due to imprecise timing of maternal deaths, especially in community settings where verbal autopsy was used. Differentiating between intrapartum and postpartum deaths in such cases may lead to temporal misallocation of risk [55]. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 44 Unmeasured confounding is another concern. While several socio-demographic and clinical covariates were included, data on nutritional status, domestic violence, and mental health were unavailable, yet likely influence maternal outcomes. This omission could distort hazard estimates, particularly if omitted variables correlate with both exposure and outcome [56]. Additionally, survival models assume that censoring is non-informative. However, loss to follow-up in the postpartum period may not be random and could skew the hazard function, especially if high-risk women disproportionately exit the study early. Strategies like sensitivity analysis and multiple imputation were used to minimize bias, but residual uncertainty remains [57]. Furthermore, although this study included facility-level variables, it did not capture provider-level practices, staffing variability, or drug availability—all of which may mediate risk. Finally, generalizability is limited to similar LMIC settings with comparable health infrastructure. Results should be validated in other regional contexts before broad policy application [58]. 9.4. Future Directions: Machine Learning Survival Models, Multicenter Validation Future research should explore machine learning survival models—such as random survival forests and neural network-based models—to capture non-linear and high-order interactions among predictors. These methods can improve prediction accuracy and better accommodate complex, time-varying maternal health data [59]. In addition, multicenter validation across diverse settings is needed to assess external generalizability. Pooling data from multiple countries or health systems will support more robust calibration and discrimination testing, enabling adaptation of predictive models to local contexts [60]. Integration of clinical decision tools with real-time data pipelines also presents a promising avenue for advancing digital maternal health innovation [61]. As maternal mortality remains a preventable tragedy in many regions, translating these findings into actionable policy recommendations and clinical applications is imperative. The final section synthesizes insights from this study into a strategic agenda for implementation, digital integration, and health system transformation [62]. 10. Conclusion and Policy Recommendations 10.1. Reaffirming the Value of Cox Models in Maternal Health Surveillance Cox proportional hazards models remain indispensable tools in maternal health surveillance due to their ability to estimate time-sensitive risk across diverse clinical and contextual variables. Their flexibility in handling right-censored data and integrating time-varying covariates makes them well-suited for evaluating the dynamic nature of maternal health risks throughout pregnancy, delivery, and the postpartum period. In this study, the Cox model successfully captured how socio-demographic disadvantages, clinical complications, and health system barriers interact to shape survival probabilities. By offering interpretable hazard ratios, the model supports targeted interventions and enhances the predictive power of early warning systems. Unlike static analyses, Cox regression allows for continuous updating of patient risk profiles, informing triage decisions and resource prioritization. Its application bridges the gap between epidemiological research and frontline clinical decision-making. As maternal health systems move toward data-driven governance, the Cox model provides a robust statistical backbone for timely, equitable, and effective maternal care planning. 10.2. Policy Recommendations for Early Detection and Intervention Protocols Based on the findings of this analysis, a series of policy recommendations are proposed to enhance maternal survival through timely detection and response. First, national health systems should institutionalize maternal early warning systems (MEWS) enhanced with predictive modeling to detect at-risk women before clinical deterioration occurs. These systems should be deployed in both primary and tertiary facilities and designed to trigger protocol-based interventions aligned with gestational stage and risk factors. Second, referral systems must be optimized to reduce delay intervals between the onset of complications and the arrival at facilities capable of providing emergency obstetric care. Real-time digital tracking of referrals and transport logistics should be incorporated into national maternal health plans. Third, antenatal care protocols should be updated to incorporate dynamic risk assessments based on age, parity, comorbidities, and socioeconomic status, with priority scheduling for high-risk women. World Journal of Advanced Research and Reviews, 2025, 26(03), 027–047 45 Training programs should focus on equipping frontline health workers to recognize time-dependent maternal risk factors and initiate care escalation. Policies must also ensure that postpartum follow-up extends to at least 42 days, with scheduled visits in the first 48 hours—a critical mortality window. 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