From Antenatal Care to Skilled Delivery: Examining the Drop-off Along the Maternal Healthcare Cascade in Kenya

From Antenatal Care to Skilled Delivery: Examining the Drop-off Along the Maternal Healthcare Cascade in Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Antenatal Care to Skilled Delivery: Examining the Drop-off Along the Maternal Healthcare Cascade in Kenya charles wanjiku This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9122116/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Maternal mortality remains unacceptably high in sub-Saharan Africa, and Kenya has not achieved the reductions anticipated under the Sustainable Development Goals. A central challenge is that improving individual service indicators—antenatal care attendance, facility delivery rates—does not guarantee that women actually move through the full continuum of care. This study applies a cascade-of-care framework, adapted from HIV treatment research, to measure sequential loss along the maternal health continuum in Kenya. Methods We analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), restricting the sample to women aged 15–49 who had a live birth in the five years preceding the survey (N = 10,391). We constructed a four-level cascade variable representing sequential progression from adequate antenatal care (4 + visits) through facility delivery to timely postnatal care within 24 hours. Sociodemographic predictors were examined using chi-square tests and survey-weighted logistic regression, with all analyses accounting for the complex sampling design. Results Only 40.3% of women completed the full cascade. The largest single loss occurred at the first stage: 37.6% of women did not attend four or more antenatal visits. Among those who did, a further 10.9% delivered outside a health facility. Post-delivery attrition—women who delivered in a facility but did not receive timely postnatal care—accounted for an additional 11.2 percentage points of loss. In multivariate analysis, higher education (OR 2.97, 95% CI 2.20–4.00) and belonging to the wealthiest quintile (OR 2.94, 95% CI 2.22–3.90) were the strongest predictors of full cascade completion. Rural residence lost significance once education and wealth were controlled. Conclusions The cascade framework reveals that Kenya's maternal health system loses women at every transition point, and that these losses are concentrated among the poor and uneducated—not primarily in rural areas per se. Interventions targeting the ANC entry barrier and the facility-to-PNC transition are most urgently needed. The cascade approach should be adopted more widely in national monitoring of maternal health systems. maternal health cascade antenatal care facility delivery postnatal care Kenya continuum of care DHS 1. Introduction Maternal mortality is one of the most manageable public health concerns. Globally, approximately 287,000 women died from pregnancy-related complications in 2020, with the majority of these deaths occurring in low- and middle-income countries (WHO, 2023). Sub-Saharan Africa accounts for around 70% of all maternal deaths globally, a disproportionate burden that reflects inequalities in access, quality, and utilisation of maternal health services (Hug et al., 2019). Kenya sits within this regional pattern. Its maternal mortality ratio was estimated at 530 deaths per 100,000 live births in 2022, which, while lower than the regional average, remains far above the Sustainable Development Goal 3.1 target of fewer than 70 deaths per 100,000 live births by 2030 (Kenya National Bureau of Statistics [KNBS], 2023; United Nations, 2015). The dominant policy that has been promoted over the past decades in response to maternal mortalities has been to drive up coverage of individual maternal health interventions. That is, encouraging women to attend antenatal care (ANC), deliver in a health facility, and receive a postnatal check. These strategies have produced measurable gains. The 2022 KDHS reports that 62.4% of Kenyan women attended at least four ANC visits, while facility delivery coverage reached approximately 76%. Yet maternal mortality has not fallen commensurately. This gap between service coverage and mortality outcomes has prompted a fundamental rethinking of how maternal health systems are evaluated, shifting attention from the uptake of isolated services toward the concept of the continuum of care. The continuum of care framework, articulated by Kerber et al. (2007) in The Lancet, holds that women and newborns require an unbroken sequence of care from the preconception period through pregnancy, delivery, and the postnatal period. Achieving high coverage of any single element is insufficient if women are dropping out before reaching subsequent elements. A woman who attends four ANC visits but delivers at home misses the single most critical juncture for obstetric emergency management. A woman who delivers in a facility but receives no postnatal care remains exposed to preventable neonatal and maternal deaths in the days following birth, a period during which roughly 40% of all maternal deaths occur (Say et al., 2014). Despite this theoretical consensus, empirical measurement of the full continuum has been uneven. Studies typically report coverage of individual indicators in isolation, and the relatively few studies that examine continuity have not applied a formal cascade methodology. This approach of tracking sequential transitions from one service level to the next, with explicit quantification of loss at each stage. Cascade analysis originated in HIV treatment research, where it proved valuable for identifying exactly where patients were leaving the treatment pathway (Gardner et al., 2011). Its application to maternal health has remained rare, particularly in sub-Saharan Africa, where DHS data have the potential to operationalise the approach at population scale. This study fills that gap. Using data from the 2022 KDHS, we construct a four-level maternal health cascade adequate ANC, facility delivery, and timely postnatal care and systematically quantify attrition at each transition. We then identify the sociodemographic subgroups most likely to experience dropout at each stage. The analysis is designed to move beyond descriptions of what proportion of women use each service in isolation, toward a more precise account of where Kenya's maternal health system actually fails women and why. 2. Background and Literature Review 2.1 The Continuum of Maternal Care The continuum of care concept recognises that maternal and neonatal health outcomes are determined not by any single contact with the health system, but by the cumulative quality and completeness of care across the entire peripartum period. Kerber et al. ( 2007 ) described the continuum as operating across two dimensions. First, the time dimension spans preconception through the neonatal period, and a place dimension spanning the household, the community, and the health facility. Losses along either dimension reduce the protective effect of care even when individual service contacts occur. This framing has been endorsed by the World Health Organization (WHO) and forms the basis of global strategies including every Woman Every Child initiative (UN Secretary-General's Global Strategy, 2015 ). Empirically, a growing number of studies have shown that high ANC coverage does not reliably predict high facility delivery coverage, and high facility delivery rates do not automatically produce high PNC utilisation. A systematic review by Sines et al. ( 2007 ) found substantial attrition between ANC and delivery care across multiple low-income settings. More recent analyses using DHS data from sub-Saharan Africa have confirmed that the percentage of women completing all three contact types is consistently lower than any individual coverage figure, often by wide margins (Kerber et al., 2007 ; Tran et al., 2018 ). Kenya's own trajectory illustrates this pattern. Facility delivery coverage increased substantially following the introduction of the free maternity services policy (Linda Mama) in 2013 and its expansion in subsequent years (Gitobu et al., 2018 ). However, gains in facility delivery were not matched by equivalent increases in ANC quality or PNC coverage. The 2022 KDHS data confirm that while facility delivery is now the majority experience, meaningful proportions of women still do not complete adequate ANC, and the postnatal period remains a weak link in the chain. 2.2 Antenatal Care in Kenya ANC is widely recognised as the entry point to the maternal health continuum. The 2016 WHO recommendations established a standard of at least eight ANC contacts, but the four-visit model (known as focused antenatal care) has historically been the benchmark used in DHS surveys and national policy in sub-Saharan Africa (WHO, 2016). Even by the lower four-visit threshold, Kenya has struggled to achieve universal coverage. The 2014 KDHS reported 58% of women attending four or more visits; by 2022, this had risen to 62.4%, indicating gradual but slow improvement (KNBS, 2023). The determinants of ANC non-attendance are well documented and include long distances to facilities, costs associated with transportation and informal fees, poor prior experiences with health services, and cultural norms that discourage early presentation to formal care (Bohren et al., 2014 ; Kyei et al., 2012 ). Education and wealth are consistently the strongest socioeconomic predictors of ANC completion across sub-Saharan Africa (Mekonnen et al., 2019 ). Younger women and women of higher parity have been shown to be at elevated risk of inadequate ANC attendance in the Kenyan context (KNBS, 2023). 2.3 Facility Delivery and Postnatal Care Skilled attendance at birth, which is typically operationalised as delivery in a health facility attended by a trained health worker, is the single most effective intervention for preventing intrapartum maternal and neonatal deaths (Bhutta et al., 2014 ). Kenya has made significant progress on this indicator, driven primarily by the removal of formal delivery fees at public facilities. Nevertheless, considerable heterogeneity remains across counties, wealth quintiles, and educational levels. Women in the poorest households are still significantly less likely to deliver in a facility than their wealthier counterparts, even after controlling for geographic access (KNBS, 2023; Okonofua et al., 2021 ). Postnatal care is arguably the most neglected element of the continuum. Munos et al. ( 2010 ) estimated that effective postnatal packages could prevent 10–27% of neonatal deaths. Yet PNC coverage remains the lowest of the three major maternal health interventions in Kenya and across sub-Saharan Africa more broadly. The 2022 KDHS data reveal that timely PNC, which is defined as a check for mother or baby within 24 hours of delivery, is received by far fewer women than those who attend ANC or deliver in a facility. Structural factors contributing to low PNC coverage include early discharge from facilities, lack of awareness, and absence of community-based PNC models (Sipsma et al., 2013 ). 2.4 Applying the Cascade Framework to Maternal Health The cascade-of-care approach was developed in the HIV/AIDS field to describe sequential engagement across the treatment pathway, from HIV testing and diagnosis, through linkage to care, antiretroviral therapy initiation, retention, and viral suppression (Gardner et al., 2011 ; UNAIDS, 2014 ). The value of the approach lies in its precision. Instead of measuring aggregate coverage, it forces researchers and policymakers to confront exactly what proportion of people are lost between each successive step, and this enables targeted intervention at the points of greatest attrition. The transfer of this framework to maternal health has been explored theoretically by several authors but remains empirically underdeveloped (Tran et al., 2018 ; Mgawadere et al., 2017 ). Most existing studies that attempt a cascade-style analysis rely on data from individual health facilities or districts, limiting generalisability. Nationally representative DHS data offer an opportunity to operationalise the cascade across the full population, mapping attrition against sociodemographic variables in ways that can directly inform national planning. 3. Methods 3.1 Data Source This study uses data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional survey conducted by the Kenya National Bureau of Statistics with technical support from ICF International under the DHS Programme. The 2022 KDHS employed a stratified two-stage cluster sampling design, with the first stage selecting enumeration areas and the second stage selecting households within each area. The survey collected individual-level data on reproductive health, maternal care, child health, and household socioeconomic characteristics from women aged 15–49. Full methodological details are provided in the KDHS final report (KNBS, 2023). 3.2 Study Population The analytic sample was restricted to women aged 15–49 who had a live birth in the five years preceding the survey (N = 10,391). Women who had not had a recent birth were excluded because the ANC, delivery, and PNC variables used to construct the cascade variable pertain to the most recent birth. The five-year reference window aligns with standard DHS practice and balances sample size against recall bias. 3.3 Outcome Variable: The Cascade of Care The primary outcome was a four-level ordinal cascade variable constructed to represent sequential progression through the maternal health continuum. Level 0 (no adequate ANC) was assigned to women who attended fewer than four ANC visits, the established threshold for minimally adequate ANC under the focused antenatal care model. Level 1 (ANC only) was assigned to women who attended four or more visits but delivered outside a health facility, reflecting completion of ANC without institutional delivery. Level 2 (ANC plus facility delivery without timely PNC) was assigned to women who met both the ANC and facility delivery criteria but did not receive a postnatal check for themselves or their baby within 24 hours of delivery. Level 3 (full cascade) was assigned to women who completed all three components. Timely postnatal care was defined in accordance with WHO recommendations as a maternal or newborn check within 24 hours of delivery, using the relevant KDHS variables on the timing of postnatal visits. The 24-hour threshold was chosen because the first hours after delivery represent the period of highest risk for both maternal and neonatal death, and a check within this window is the minimum standard recommended by WHO (WHO, 2013). 3.4 Predictor Variables Based on prior literature on maternal health service utilisation in Kenya and sub-Saharan Africa, four sociodemographic predictor variables were selected: type of place of residence (urban or rural), highest educational level achieved (no education, primary, secondary, or higher), household wealth index quintile (poorest through richest), and woman's age at the time of the survey in five-year groups (15–19 through 45–49). The wealth index is computed by the DHS Programme from household asset data using principal component analysis and reflects relative household economic status rather than absolute income (Rutstein & Johnson, 2004 ). 3.5 Statistical Analysis All analyses accounted for the complex survey design of the KDHS using Stata's svy prefix commands with sampling weights (v005 divided by 1,000,000), primary sampling units (v021), and strata (v022). Descriptive statistics were computed as weighted frequencies and percentages for each sociodemographic characteristic. Bivariate associations between the cascade outcome and each predictor were examined using chi-square tests with Rao-Scott corrections for the complex design. The primary inferential analysis used survey-weighted binary logistic regression, with the outcome dichotomised as full cascade completion (Level 3) versus any incomplete cascade (Levels 0, 1, or 2). All four predictor variables were entered simultaneously. Results are reported as odds ratios with 95% confidence intervals. Statistical significance was set at p < 0.05 throughout. Analyses were conducted in Stata/MP 17 (StataCorp LLC, College Station, Texas). 4. Results 4.1 Characteristics of the Study Population A total of 10,391 women aged 15–49 with a live birth in the five years preceding the 2022 KDHS were included. Table 1 presents the sociodemographic profile of the sample. The majority of women lived in rural areas (65.4%), reflecting Kenya's predominantly rural population. Educational attainment was spread across the spectrum, with 20.0% reporting no formal education, 33.8% primary-level education, 31.6% secondary, and 14.6% higher education. Household wealth distribution showed that nearly half (48.2%) of women came from the two poorest quintiles. The largest age group was 25–29 years (27.4%), and adolescents aged 15–19 accounted for 7.6% of the analytic sample. Table 1 Sociodemographic Characteristics of the Study Population (N = 10,391) Characteristic Unweighted n Weighted % Residence Urban 3,596 34.6 Rural 6,795 65.4 Education No education 2,082 20.0 Primary 3,507 33.8 Secondary 3,283 31.6 Higher 1,519 14.6 Wealth index Poorest 3,240 31.2 Poorer 1,762 17.0 Middle 1,826 17.6 Richer 2,045 19.7 Richest 1,518 14.6 Age group 15–19 791 7.6 20–24 2,664 25.6 25–29 2,843 27.4 30–34 2,042 19.7 35–39 1,460 14.1 40–44 494 4.8 45–49 97 0.9 Source: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted. 4.2 Distribution of Women Across the Cascade Table 2 presents the distribution of women across the four cascade levels. Just over a third of women (37.6%) did not meet the threshold for adequate ANC, meaning they attended fewer than four visits for their most recent birth. This group represents the largest single point of failure in the cascade. Among the 62.4% of women who completed adequate ANC, a further 10.9 percentage points were lost at the transition to facility delivery: these women attended four or more ANC visits but ultimately delivered at home or in another non-facility setting. A subsequent 11.2 percentage points were lost at the transition to timely PNC: these women completed both ANC and facility delivery but did not receive a postnatal check within 24 hours. Only 40.3% of all women in the sample completed the full cascade, adequate ANC, facility delivery, and timely PNC. Table 2 Distribution of Women Across the Maternal Health Cascade Cascade Level Unweighted n Weighted % Level 0: No adequate ANC (< 4 visits) 3,905 37.6 Level 1: Adequate ANC only (4 + visits, home delivery) 1,133 10.9 Level 2: ANC + facility delivery (no timely PNC) 1,162 11.2 Level 3: Full cascade (ANC + facility + timely PNC) 4,191 40.3 Total 10,391 100.0 Source: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted. These figures reveal a cascade structure in which losses accumulate at every transition rather than being concentrated at a single point. While the absolute volume of loss is greatest at the ANC entry point, nearly four in ten women fail to complete four visits the proportional losses at subsequent transitions are also substantial. Among women who completed adequate ANC, the 10.9 percentage point loss to home delivery represents 17.5% of those who made it past the first stage. Among women who both completed ANC and delivered in a facility, the failure to receive timely PNC represents an additional 21.7% loss at the final transition. This pattern of compounding attrition means that even a woman who attends all her ANC visits and delivers in a facility has a meaningful probability of not receiving the postnatal check that would complete her care. 4.3 Bivariate Associations with Cascade Completion Table 3 presents the distribution across cascade levels by each sociodemographic variable. All four predictors showed statistically significant associations with cascade completion (all p < 0.001). Residence: Urban women were considerably more likely than rural women to complete the full cascade (49.3% versus 35.6%). The gap was concentrated at Levels 0 and 1: rural women were more likely than urban women to fail to complete adequate ANC (41.6% versus 30.0%) and, conditional on completing ANC, more likely to deliver outside a facility (13.1% versus 6.8%). Urban women were slightly more likely to experience a gap at Level 2, reaching facility delivery but not timely PNC, possibly reflecting the faster throughput characteristic of higher-volume urban facilities. Education: The association between education and cascade completion was monotonically positive and substantively large. Women with no formal education completed the full cascade at a rate of only 17.1%, compared with 60.3% among women with higher education. The largest absolute gap between the no-education and higher-education groups was at Level 0: 54.0% of uneducated women did not attend four ANC visits, versus 18.6% of those with higher education a difference of 35.4 percentage points. This suggests that the cascade entry point is disproportionately where uneducated women are lost. Wealth: A strong wealth gradient was also observed. Full cascade completion rose from 24.2% in the poorest quintile to 60.8% in the richest, with an approximately monotonic relationship across quintiles. As with education, the poorest women were most concentrated at Level 0 (50.0%), indicating that inadequate ANC is strongly class-patterned. The gap in timely PNC was less marked by wealth, ranging from 7.3% at the poorest to 12.5% at the richest a pattern suggesting that the PNC transition is more uniformly challenging across economic groups, at least among those who reach facility delivery. Age: Adolescent women aged 15–19 had notably low rates of full cascade completion (31.4%) and were the group most concentrated at Level 0 (47.8%), indicating that young age at first birth is a substantial barrier to ANC initiation. Women aged 20–34 showed higher completion rates (40.5–43.8%), while older women in the 35–49 range showed declining completion, reflecting possible cohort differences in norms and access patterns. The age of 45–49 had the lowest completion rate at 25.8%, though this group also had the smallest sample size (n = 97) and should be interpreted with caution. Table 3 Distribution Across Cascade Levels by Sociodemographic Characteristics Characteristic Level 0 (%) Level 1 (%) Level 2 (%) Level 3 (%) Residence p < 0.001 Urban 30.0 6.8 13.9 49.3 Rural 41.6 13.1 9.7 35.6 Education p < 0.001 No education 54.0 21.6 7.3 17.1 Primary 40.5 9.3 11.9 38.4 Secondary 32.8 6.8 12.5 47.9 Higher 18.6 8.9 12.2 60.3 Wealth index p < 0.001 Poorest 50.0 18.5 7.3 24.2 Poorer 39.8 9.3 13.1 37.9 Middle 34.6 7.2 13.6 44.6 Richer 31.7 6.6 12.7 49.0 Richest 20.0 6.7 12.5 60.8 Age group p < 0.001 15–19 47.8 8.1 12.8 31.4 20–24 35.5 9.1 11.5 43.8 25–29 34.8 10.1 12.1 43.1 30–34 35.9 13.5 10.1 40.5 35–39 41.4 12.7 10.3 35.6 40–44 40.7 13.2 9.7 36.4 45–49 55.7 14.4 4.1 25.8 Source: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted. p-values from chi-square tests with Rao-Scott correction. 4.4 Multivariate Predictors of Full Cascade Completion Table 4 presents results from survey-weighted logistic regression examining predictors of full cascade completion (Level 3 versus Levels 0–2). After adjusting for all predictors simultaneously, education and wealth emerged as the dominant independent predictors. Rural residence, which appeared substantively important in bivariate analysis, was no longer statistically significant in the multivariate model (OR 1.00, 95% CI 0.83–1.20, p = 0.963). This suggests that the urban-rural gap observed in bivariate analysis is largely explained by confounding with education and wealth rural women are poorer and less educated, and these characteristics, not rural residence itself, drive reduced cascade completion. Among education categories, each successive level was independently associated with higher odds of completing the full cascade compared to no education: primary education (OR 1.92, 95% CI 1.52–2.41), secondary (OR 2.40, 95% CI 1.89–3.05), and higher education (OR 2.97, 95% CI 2.20–4.00). The dose-response relationship across educational levels is consistent with a causal interpretation, though the cross-sectional design precludes causal inference. Household wealth also showed a graded association. Women in the middle, richer, and richest quintiles had significantly higher odds of full cascade completion than the poorest women (OR 1.47, 1.75, and 2.94 respectively), while the difference between the poorest and poorer quintiles did not reach statistical significance (OR 1.13, 95% CI 0.96–1.33, p = 0.129). The attenuation of the poorer-versus-poorest difference in the multivariate model may reflect educational confounding within the lower wealth strata. Age effects were attenuated but partially retained in the multivariate model. Women aged 20–24, 25–29, and 30–34 had significantly higher odds of completing the full cascade compared with adolescents aged 15–19 (OR 1.52, 1.38, and 1.44 respectively). Women aged 35 and above showed no significant advantage over adolescents after controlling for education and wealth, suggesting that the apparent peak in cascade completion in the 20–34 age range reflects the combined effects of developmental stage, parity, and socioeconomic characteristics rather than age alone. Table 4 Survey-Weighted Logistic Regression: Predictors of Full Cascade Completion (N = 10,391) Characteristic Odds Ratio 95% CI p-value Residence (ref: Urban) Rural 1.00 0.83–1.20 0.963 Education (ref: No education) Primary 1.92 1.52–2.41 < 0.001 Secondary 2.40 1.89–3.05 < 0.001 Higher 2.97 2.20–4.00 < 0.001 Wealth index (ref: Poorest) Poorer 1.13 0.96–1.33 0.129 Middle 1.47 1.22–1.76 < 0.001 Richer 1.75 1.40–2.19 < 0.001 Richest 2.94 2.22–3.90 < 0.001 Age group (ref: 15–19) 20–24 1.52 1.22–1.88 < 0.001 25–29 1.38 1.11–1.73 0.004 30–34 1.44 1.13–1.84 0.003 35–39 1.20 0.94–1.53 0.146 40–44 1.07 0.78–1.47 0.667 45–49 0.96 0.49–1.88 0.897 Source: 2022 Kenya Demographic and Health Survey. OR = odds ratio; CI = confidence interval. All analyses survey-weighted with complex design correction. 5. Discussion 5.1 Interpreting the Cascade Structure The central finding of this study is that only four in ten Kenyan women who gave birth between 2017 and 2022 completed the full maternal health cascade of adequate ANC, facility delivery, and timely postnatal care. This figure is arresting in a health system context where facility delivery alone has been widely promoted as the primary solution to maternal mortality. It reveals that aggregate service coverage statistics which present each indicator independently systematically overstate the extent to which women are actually benefiting from the full continuum of care. The cascade structure observed here shows losses at every transition, but they are not equal in magnitude. The largest absolute loss is at entry, 37.6% of women do not complete four ANC visits. This is a longstanding problem in Kenya, and the 2022 data suggest it has not been resolved. Addressing this entry barrier is the single change that would produce the largest immediate gain in the number of women on the cascade. Strategies that reduce ANC attendance including financial barriers, distance, poor provider behaviour, and lack of community mobilization must remain central to the policy agenda. However, the cascade framework draws attention to a second problem that aggregate coverage figures obscure. The conditional losses at later transitions. Among women who completed adequate ANC, 17.5% did not reach facility delivery. Among those who completed both ANC and facility delivery, 21.7% did not receive timely PNC. These figures indicate that a substantial fraction of the women most engaged with the health system, those who proved themselves motivated enough to attend four or more ANC visits, are still slipping out of care before the continuum is complete. Interventions specifically designed to retain engaged women across transitions are therefore needed, not just strategies for bringing disengaged women into ANC. 5.2 The Significance of the Postnatal Gap Perhaps the most policy-relevant finding from a proportional perspective is the loss at the ANC-to-facility-delivery and facility-delivery-to-PNC transitions. The post-delivery PNC gap is particularly concerning because women who have already entered the formal system and delivered in a facility represent an accessible population. Their failure to receive a timely postnatal check is not explained by geographic distance from services in the way that ANC non-attendance might be. Rather, it reflects either system-side failures early discharge before a PNC check has been completed, inadequate staffing to conduct checks or individual-level constraints such as rapid return home for family or economic reasons. The literature supports the severity of this gap. Munos et al. ( 2010 ) demonstrated that postnatal packages targeting the neonatal period could prevent 10–27% of neonatal deaths, and maternal deaths in the 24–72 hours post-partum are concentrated precisely in the window within which timely PNC would be administered. In the Kenyan context, the Linda Mama free maternity services policy extended to facility delivery but did not specifically address the retention of women for PNC, a gap that this study's findings suggest has persisted. The bivariate data reveal a counterintuitive pattern at Level 2: urban women (13.9%) were slightly more likely than rural women (9.7%) to complete facility delivery but fail to receive timely PNC. This warrants further investigation. One plausible explanation is that higher-volume urban facilities discharge women more rapidly, reducing the window within which a PNC check can occur within the 24-hour threshold. Another is that urban women face stronger competing demands—returning to wage employment or other family obligations that prompt earlier departure from the facility. This would represent a quality-of-care problem specific to the high-throughput urban setting. 5.3 Education, Wealth, and the Attenuation of Rural Effects The finding that rural residence loses statistical significance in the multivariate model is important for how policymakers conceptualise the urban-rural divide in maternal health. It suggests that geographic residence is largely a proxy for the true drivers of cascade incompletion—namely, educational disadvantage and material poverty. Women living in rural areas are substantially more likely to be poor and less educated than urban women, and it is these characteristics, not location per se, that determine cascade completion. This reframing has significant policy implications. Spatially targeted interventions—mobile health clinics, rural facility upgrading—address geographic access constraints and are necessary where distance is a genuine barrier. But they are unlikely to be sufficient if the underlying educational and economic constraints remain unaddressed. The very strong independent effects of education and wealth point toward the need for upstream social investments—girls' education, poverty reduction—that operate well beyond the health sector. These results align with a growing body of evidence that maternal health outcomes are deeply shaped by intersecting social determinants that health systems alone cannot modify (Bhutta et al., 2014 ; Okonofua et al., 2021 ). The strength of the education effect deserves particular attention. Women with higher education were nearly three times as likely as uneducated women to complete the full cascade (OR 2.97), an effect size that dwarfs most individual health intervention effects reported in the literature. Educated women are not simply better at navigating the health system; they are also more likely to have economic resources, greater autonomy in health decision-making, better knowledge of what care is recommended, and stronger capacity to demand quality from providers. Education appears to operate through multiple pathways simultaneously, producing cumulative advantages across the entire cascade. 5.4 Adolescent Women and the Risk of Early Cascade Exit Adolescents aged 15–19 had the highest rate of Level 0 non-completion (47.8%) and the lowest rate of full cascade completion (31.4%), patterns that persisted as an age effect even after adjusting for education and wealth. This is consistent with evidence that adolescent women face particular barriers to ANC attendance, including stigma associated with pregnancy outside marriage, parental or partner control over health decisions, and fear of disclosure at health facilities (Mekonnen et al., 2019 ). In Kenya, adolescent pregnancy also intersects with socioeconomic disadvantage: young mothers are more likely to be from poor households and to have left school early, reinforcing the combined education-wealth-age gradient observed in this study. The multivariate findings showing sustained advantages for women in the 20–34 age range over adolescents, even after controlling for education and wealth, suggest that some element of developmental stage or parity experience is independently facilitating cascade completion. Primiparity—which is strongly concentrated in younger age groups—has been associated with lower utilisation of maternal health services in previous DHS-based studies from Kenya and other African settings, possibly reflecting unfamiliarity with the care pathway (Kyei et al., 2012 ). 5.5 Methodological Contribution and Limitations This study makes a methodological contribution by demonstrating the feasibility and analytic utility of cascade analysis applied to nationally representative DHS data. The approach forces a more precise accounting of system failure than is possible when ANC, delivery, and PNC are reported as separate indicators. It also naturally generates a sequentially ordered outcome variable that aligns with the temporal structure of care, which is conceptually more appropriate than treating each indicator independently. We encourage DHS-based researchers in other national contexts to adopt this framework as a tool for comparing maternal health system performance across countries. Several limitations should be noted. First, the data are cross-sectional and rely on retrospective recall of health behaviours up to five years prior to the survey, introducing the possibility of recall bias. Recall error regarding ANC visit counts and timing of postnatal checks may produce misclassification at cascade boundaries. Second, the study captures only whether specific care contacts occurred, not the quality of care delivered. A woman classified as having completed the full cascade may have received care of very poor quality at each contact. This study therefore measures coverage adequacy, not care adequacy. Third, the cascade definition is conservative in one respect and may understate PNC inadequacy, it requires timely PNC within 24 hours, but the full recommended schedule includes additional contacts in the days and weeks following delivery that are not captured in this analysis. Fourth, the study design does not permit causal inference; the sociodemographic associations observed are descriptive of population patterns rather than estimates of causal effects. 6. Conclusion This study demonstrates that fewer than half of Kenyan women who gave birth between 2017 and 2022 completed the recommended sequence of maternal health care, adequate ANC, facility delivery, and timely PNC. The cascade framework reveals a system that loses women at every transition, with the largest absolute loss at the ANC entry point and significant proportional losses at each subsequent stage. Education and household wealth are the dominant independent predictors of cascade completion, while the urban-rural gap is largely explained by these underlying socioeconomic factors. Adolescent women are disproportionately represented among those who never enter the cascade. These findings have three principal implications for policy. First, ANC coverage remains the highest-priority target, given that more than a third of women fail to reach even the minimum standard of four visits. Demand-side incentives, community health worker outreach, and the removal of financial and logistical barriers should be sustained and strengthened. Second, the facility-to-PNC transition deserves far greater policy attention than it has received. Retaining women who have already delivered in a facility long enough to receive a postnatal check is a tractable problem that existing facility-based systems should be able to address through structured discharge protocols. Third, investments in girls' education and poverty reduction are not peripheral to maternal health strategy—they are central to it. The strength of the education and wealth effects observed here indicates that health systems will continue to struggle to serve the women most at risk until broader social inequalities are addressed. The cascade approach to monitoring maternal health continuity should be incorporated into national health information systems and DHS reporting frameworks. Tracking individual service indicators in isolation has historically masked the true scale of care incompletion. Measuring the full cascade and the losses between each of its stages provides a more honest and actionable picture of where maternal health systems are actually failing women. The ethics declaration This research was performed in accordance with the principles of the Declaration of Helsinki. The study used secondary data from the 2022 Kenya Demographic and Health Survey (KDHS), which is publicly available through the DHS Program website ( https://dhsprogram.com ). Ethical approval for the original KDHS data collection was obtained from the ICF Institutional Review Board (Project Number: 132989) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (Protocol Number: KEMRI/RES/7/3/1). All survey respondents provided written informed consent before participation, including consent for anonymized data to be used in future research. Since this analysis involved de-identified, publicly available data, it did not require further ethical clearance. Declarations The ethics declaration This research was performed in accordance with the principles of the Declaration of Helsinki. The study used secondary data from the 2022 Kenya Demographic and Health Survey (KDHS), which is publicly available through the DHS Program website (https://dhsprogram.com). Ethical approval for the original KDHS data collection was obtained from the ICF Institutional Review Board (Project Number: 132989) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (Protocol Number: KEMRI/RES/7/3/1). All survey respondents provided written informed consent before participation, including consent for anonymized data to be used in future research. Since this analysis involved de-identified, publicly available data, it did not require further ethical clearance . Funding The authors received no financial support for the research, authorship, and/or publication of this article. This study was conducted using publicly available data from the Demographic and Health Surveys (DHS) Program, and all work was performed as part of the authors' academic affiliations without external funding. Human Ethics and Consent to Participate All participants in the original surveys provided written informed consent before participation, including consent for anonymized data to be used in future research. As this study involved secondary analysis of fully anonymized, publicly available data, it was exempt from additional ethical review. Human Ethics and Consent to Participate declarations: not applicable for this secondary analysis Consent to Publish Consent to Publish declaration: not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos) that would require consent for publication. All data presented are aggregated, anonymized, and publicly available from the Demographic and Health Surveys (DHS) Program Data Availability The datasets generated and/or analyzed during the current study are available in the Demographic and Health Surveys (DHS) Program repository and the Kenya National Bureau of Statistics https://statistics.knbs.or.ke/nada/index.php/catalog/128/related-materials and https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. Access to the data requires free registration and approval of a research proposal by The DHS Program, in accordance with the data use agreements with the Government of Kenya. The data are publicly available for legitimate research purposes. The authors confirm that they did not have any special access privileges to these data Competing interests The authors declare that they have no competing interests. No financial or non-financial interests that could be construed as influencing the research or interpretation of the findings exist. Author Contributions Charles, John: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing – original draft. Mary, Charles: Conceptualization, Methodology, Investigation, Validation, Writing – review & editing, Project administration. Charles, erick: Resources, Validation, Writing – review & editing, Supervision. All authors have read and approved the final manuscript References Bhutta, Z. A., Das, J. K., Bahl, R., Lawn, J. E., Salam, R. A., Paul, V. K., Sankar, M. J., Blencowe, H., Rizvi, A., Chou, V. B., & Walker, N. (2014). Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? The Lancet, 384(9940), 347–370. https://doi.org/10.1016/S0140-6736(14)60792-3 Bohren, M. A., Hunter, E. C., Munthe-Kaas, H. M., Souza, J. P., Vogel, J. P., & Gülmezoglu, A. M. (2014). Facilitators and barriers to facility-based delivery in low- and middle-income countries: A qualitative evidence synthesis. Reproductive Health, 11(1), 71. https://doi.org/10.1186/1742-4755-11-71 Gardner, E. M., McLees, M. P., Steiner, J. F., Del Rio, C., & Burman, W. J. (2011). The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clinical Infectious Diseases, 52(6), 793–800. https://doi.org/10.1093/cid/ciq243 Gitobu, C. M., Gichangi, P. B., & Mwanda, W. O. (2018). The effect of Kenya's free maternal healthcare policy on the utilization of health facility delivery services and maternal and neonatal mortality in public health facilities. Journal of Pregnancy, 2018, Article 9648059. https://doi.org/10.1155/2018/9648059 Hug, L., Alexander, M., You, D., & Alkema, L. (2019). National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: A systematic analysis. The Lancet Global Health, 7(6), e710–e720. https://doi.org/10.1016/S2214-109X(19)30163-9 Kerber, K. J., de Graft-Johnson, J. E., Bhutta, Z. A., Okong, P., Starrs, A., & Lawn, J. E. (2007). Continuum of care for maternal, newborn, and child health: From slogan to service delivery. The Lancet, 370(9595), 1358–1369. https://doi.org/10.1016/S0140-6736(07)61578-5 Kenya National Bureau of Statistics (KNBS). (2023). Kenya Demographic and Health Survey 2022: Final report. Nairobi: KNBS & ICF. https://dhsprogram.com/publications/publication-FR370-DHS-Final-Reports.cfm Kyei, N. N. A., Campbell, O. M. R., & Gabrysch, S. (2012). The influence of distance and level of service provision on antenatal care use in rural Zambia. PLOS ONE, 7(10), e46475. https://doi.org/10.1371/journal.pone.0046475 Mekonnen, T., Dune, T., & Perz, J. (2019). Maternal health service utilisation of adolescent women in sub-Saharan Africa: A systematic review. BMC Pregnancy and Childbirth, 19(1), 426. https://doi.org/10.1186/s12884-019-2386-8 Mgawadere, F., Unkels, R., Kazembe, A., & van den Broek, N. (2017). Factors associated with maternal mortality in Malawi: Application of the three delays model. BMC Pregnancy and Childbirth, 17(1), 219. https://doi.org/10.1186/s12884-017-1406-8 Munos, M. K., Walker, C. L. F., & Black, R. E. (2010). The effect of postnatal care quality on neonatal mortality: An analysis of 18 countries. International Journal of Epidemiology, 39(Suppl 1), i108–i117. https://doi.org/10.1093/ije/dyq174 Okonofua, F. E., Ntoimo, L. F. C., Ogu, R., Galadanci, H., Gana, M., Adetokunbo, S., Imaralu, J. O., & Iliyasu, Z. (2021). Prevalence and determinants of emergency obstetric complications in Nigerian public hospitals. Reproductive Health, 18(1), 1. https://doi.org/10.1186/s12978-020-01055-3 Rutstein, S. O., & Johnson, K. (2004). The DHS wealth index. DHS Comparative Reports No. 6. ORC Macro. https://dhsprogram.com/publications/publication-cr6-comparative-reports.cfm Say, L., Chou, D., Gemmill, A., Tunçalp, Ö., Moller, A. B., Daniels, J., Gülmezoglu, A. M., Temmerman, M., & Alkema, L. (2014). Global causes of maternal death: A WHO systematic analysis. The Lancet Global Health, 2(6), e323–e333. https://doi.org/10.1016/S2214-109X(14)70227-X Sines, E., Tinker, A., & Ruben, J. (2007). The continuum of care for maternal, newborn, and child health: From slogan to service delivery. Save the Children and Population Reference Bureau. https://www.prb.org/resources/the-continuum-of-care/ Sipsma, H. L., Curry, L. A., & Bradley, E. H. (2013). Perspectives on delays in antenatal and delivery care in Ghana: Findings from a qualitative study. African Journal of Reproductive Health, 17(1), 117–128. Tran, T. K., Gottvall, K., Nguyen, H. D., Ascher, H., & Petzold, M. (2018). Factors associated with antenatal care adequacy in rural and urban contexts: Results from two health and demographic surveillance sites in Vietnam. BMC Health Services Research, 18(1), 600. https://doi.org/10.1186/s12913-018-3411-3 UNAIDS. (2014). 90-90-90: An ambitious treatment target to help end the AIDS epidemic. UNAIDS. https://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf UN Secretary-General's Global Strategy. (2015). Every woman every child: Saving lives, protecting futures. Progress report on the Every Woman Every Child movement. United Nations. https://www.everywomaneverychild.org/global-strategy/ United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015 (A/RES/70/1). https://sdgs.un.org/2030agenda World Health Organization. (2013). WHO recommendations on postnatal care of the mother and newborn. WHO. https://www.who.int/publications/i/item/9789241506649 World Health Organization. (2023). Trends in maternal mortality: 2000 to 2020. Estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. WHO. https://www.who.int/publications/i/item/9789240068759 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 24 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 20 Mar, 2026 First submitted to journal 14 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Introduction","content":"\u003cp\u003eMaternal mortality is one of the most manageable public health concerns. Globally, approximately 287,000 women died from pregnancy-related complications in 2020, with the majority of these deaths occurring in low- and middle-income countries (WHO, 2023). Sub-Saharan Africa accounts for around 70% of all maternal deaths globally, a disproportionate burden that reflects inequalities in access, quality, and utilisation of maternal health services (Hug et al., 2019). Kenya sits within this regional pattern. Its maternal mortality ratio was estimated at 530 deaths per 100,000 live births in 2022, which, while lower than the regional average, remains far above the Sustainable Development Goal 3.1 target of fewer than 70 deaths per 100,000 live births by 2030 (Kenya National Bureau of Statistics [KNBS], 2023; United Nations, 2015).\u003c/p\u003e\n\u003cp\u003eThe dominant policy that has been promoted over the past decades in response to maternal mortalities has been to drive up coverage of individual maternal health interventions. That is, encouraging women to attend antenatal care (ANC), deliver in a health facility, and receive a postnatal check. These strategies have produced measurable gains. The 2022 KDHS reports that 62.4% of Kenyan women attended at least four ANC visits, while facility delivery coverage reached approximately 76%. Yet maternal mortality has not fallen commensurately. This gap between service coverage and mortality outcomes has prompted a fundamental rethinking of how maternal health systems are evaluated, shifting attention from the uptake of isolated services toward the concept of the continuum of care.\u003c/p\u003e\n\u003cp\u003eThe continuum of care framework, articulated by Kerber et al. (2007) in The Lancet, holds that women and newborns require an unbroken sequence of care from the preconception period through pregnancy, delivery, and the postnatal period. Achieving high coverage of any single element is insufficient if women are dropping out before reaching subsequent elements. A woman who attends four ANC visits but delivers at home misses the single most critical juncture for obstetric emergency management. A woman who delivers in a facility but receives no postnatal care remains exposed to preventable neonatal and maternal deaths in the days following birth, a period during which roughly 40% of all maternal deaths occur (Say et al., 2014).\u003c/p\u003e\n\u003cp\u003eDespite this theoretical consensus, empirical measurement of the full continuum has been uneven. Studies typically report coverage of individual indicators in isolation, and the relatively few studies that examine continuity have not applied a formal cascade methodology. This approach of tracking sequential transitions from one service level to the next, with explicit quantification of loss at each stage. Cascade analysis originated in HIV treatment research, where it proved valuable for identifying exactly where patients were leaving the treatment pathway (Gardner et al., 2011). Its application to maternal health has remained rare, particularly in sub-Saharan Africa, where DHS data have the potential to operationalise the approach at population scale.\u003c/p\u003e\n\u003cp\u003eThis study fills that gap. Using data from the 2022 KDHS, we construct a four-level maternal health cascade adequate ANC, facility delivery, and timely postnatal care and systematically quantify attrition at each transition. We then identify the sociodemographic subgroups most likely to experience dropout at each stage. The analysis is designed to move beyond descriptions of what proportion of women use each service in isolation, toward a more precise account of where Kenya's maternal health system actually fails women and why.\u003c/p\u003e"},{"header":"2. Background and Literature Review","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Continuum of Maternal Care\u003c/h2\u003e \u003cp\u003eThe continuum of care concept recognises that maternal and neonatal health outcomes are determined not by any single contact with the health system, but by the cumulative quality and completeness of care across the entire peripartum period. Kerber et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) described the continuum as operating across two dimensions. First, the time dimension spans preconception through the neonatal period, and a place dimension spanning the household, the community, and the health facility. Losses along either dimension reduce the protective effect of care even when individual service contacts occur. This framing has been endorsed by the World Health Organization (WHO) and forms the basis of global strategies including every Woman Every Child initiative (UN Secretary-General's Global Strategy, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirically, a growing number of studies have shown that high ANC coverage does not reliably predict high facility delivery coverage, and high facility delivery rates do not automatically produce high PNC utilisation. A systematic review by Sines et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found substantial attrition between ANC and delivery care across multiple low-income settings. More recent analyses using DHS data from sub-Saharan Africa have confirmed that the percentage of women completing all three contact types is consistently lower than any individual coverage figure, often by wide margins (Kerber et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKenya's own trajectory illustrates this pattern. Facility delivery coverage increased substantially following the introduction of the free maternity services policy (Linda Mama) in 2013 and its expansion in subsequent years (Gitobu et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, gains in facility delivery were not matched by equivalent increases in ANC quality or PNC coverage. The 2022 KDHS data confirm that while facility delivery is now the majority experience, meaningful proportions of women still do not complete adequate ANC, and the postnatal period remains a weak link in the chain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Antenatal Care in Kenya\u003c/h2\u003e \u003cp\u003eANC is widely recognised as the entry point to the maternal health continuum. The 2016 WHO recommendations established a standard of at least eight ANC contacts, but the four-visit model (known as focused antenatal care) has historically been the benchmark used in DHS surveys and national policy in sub-Saharan Africa (WHO, 2016). Even by the lower four-visit threshold, Kenya has struggled to achieve universal coverage. The 2014 KDHS reported 58% of women attending four or more visits; by 2022, this had risen to 62.4%, indicating gradual but slow improvement (KNBS, 2023).\u003c/p\u003e \u003cp\u003eThe determinants of ANC non-attendance are well documented and include long distances to facilities, costs associated with transportation and informal fees, poor prior experiences with health services, and cultural norms that discourage early presentation to formal care (Bohren et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kyei et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Education and wealth are consistently the strongest socioeconomic predictors of ANC completion across sub-Saharan Africa (Mekonnen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Younger women and women of higher parity have been shown to be at elevated risk of inadequate ANC attendance in the Kenyan context (KNBS, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Facility Delivery and Postnatal Care\u003c/h2\u003e \u003cp\u003eSkilled attendance at birth, which is typically operationalised as delivery in a health facility attended by a trained health worker, is the single most effective intervention for preventing intrapartum maternal and neonatal deaths (Bhutta et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Kenya has made significant progress on this indicator, driven primarily by the removal of formal delivery fees at public facilities. Nevertheless, considerable heterogeneity remains across counties, wealth quintiles, and educational levels. Women in the poorest households are still significantly less likely to deliver in a facility than their wealthier counterparts, even after controlling for geographic access (KNBS, 2023; Okonofua et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePostnatal care is arguably the most neglected element of the continuum. Munos et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) estimated that effective postnatal packages could prevent 10\u0026ndash;27% of neonatal deaths. Yet PNC coverage remains the lowest of the three major maternal health interventions in Kenya and across sub-Saharan Africa more broadly. The 2022 KDHS data reveal that timely PNC, which is defined as a check for mother or baby within 24 hours of delivery, is received by far fewer women than those who attend ANC or deliver in a facility. Structural factors contributing to low PNC coverage include early discharge from facilities, lack of awareness, and absence of community-based PNC models (Sipsma et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Applying the Cascade Framework to Maternal Health\u003c/h2\u003e \u003cp\u003eThe cascade-of-care approach was developed in the HIV/AIDS field to describe sequential engagement across the treatment pathway, from HIV testing and diagnosis, through linkage to care, antiretroviral therapy initiation, retention, and viral suppression (Gardner et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; UNAIDS, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The value of the approach lies in its precision. Instead of measuring aggregate coverage, it forces researchers and policymakers to confront exactly what proportion of people are lost between each successive step, and this enables targeted intervention at the points of greatest attrition.\u003c/p\u003e \u003cp\u003eThe transfer of this framework to maternal health has been explored theoretically by several authors but remains empirically underdeveloped (Tran et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mgawadere et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Most existing studies that attempt a cascade-style analysis rely on data from individual health facilities or districts, limiting generalisability. Nationally representative DHS data offer an opportunity to operationalise the cascade across the full population, mapping attrition against sociodemographic variables in ways that can directly inform national planning.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Source\u003c/h2\u003e \u003cp\u003eThis study uses data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional survey conducted by the Kenya National Bureau of Statistics with technical support from ICF International under the DHS Programme. The 2022 KDHS employed a stratified two-stage cluster sampling design, with the first stage selecting enumeration areas and the second stage selecting households within each area. The survey collected individual-level data on reproductive health, maternal care, child health, and household socioeconomic characteristics from women aged 15\u0026ndash;49. Full methodological details are provided in the KDHS final report (KNBS, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study Population\u003c/h2\u003e \u003cp\u003eThe analytic sample was restricted to women aged 15\u0026ndash;49 who had a live birth in the five years preceding the survey (N\u0026thinsp;=\u0026thinsp;10,391). Women who had not had a recent birth were excluded because the ANC, delivery, and PNC variables used to construct the cascade variable pertain to the most recent birth. The five-year reference window aligns with standard DHS practice and balances sample size against recall bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Outcome Variable: The Cascade of Care\u003c/h2\u003e \u003cp\u003eThe primary outcome was a four-level ordinal cascade variable constructed to represent sequential progression through the maternal health continuum. Level 0 (no adequate ANC) was assigned to women who attended fewer than four ANC visits, the established threshold for minimally adequate ANC under the focused antenatal care model. Level 1 (ANC only) was assigned to women who attended four or more visits but delivered outside a health facility, reflecting completion of ANC without institutional delivery. Level 2 (ANC plus facility delivery without timely PNC) was assigned to women who met both the ANC and facility delivery criteria but did not receive a postnatal check for themselves or their baby within 24 hours of delivery. Level 3 (full cascade) was assigned to women who completed all three components.\u003c/p\u003e \u003cp\u003eTimely postnatal care was defined in accordance with WHO recommendations as a maternal or newborn check within 24 hours of delivery, using the relevant KDHS variables on the timing of postnatal visits. The 24-hour threshold was chosen because the first hours after delivery represent the period of highest risk for both maternal and neonatal death, and a check within this window is the minimum standard recommended by WHO (WHO, 2013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictor Variables\u003c/h2\u003e \u003cp\u003eBased on prior literature on maternal health service utilisation in Kenya and sub-Saharan Africa, four sociodemographic predictor variables were selected: type of place of residence (urban or rural), highest educational level achieved (no education, primary, secondary, or higher), household wealth index quintile (poorest through richest), and woman's age at the time of the survey in five-year groups (15\u0026ndash;19 through 45\u0026ndash;49). The wealth index is computed by the DHS Programme from household asset data using principal component analysis and reflects relative household economic status rather than absolute income (Rutstein \u0026amp; Johnson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses accounted for the complex survey design of the KDHS using Stata's svy prefix commands with sampling weights (v005 divided by 1,000,000), primary sampling units (v021), and strata (v022). Descriptive statistics were computed as weighted frequencies and percentages for each sociodemographic characteristic. Bivariate associations between the cascade outcome and each predictor were examined using chi-square tests with Rao-Scott corrections for the complex design. The primary inferential analysis used survey-weighted binary logistic regression, with the outcome dichotomised as full cascade completion (Level 3) versus any incomplete cascade (Levels 0, 1, or 2). All four predictor variables were entered simultaneously. Results are reported as odds ratios with 95% confidence intervals. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 throughout. Analyses were conducted in Stata/MP 17 (StataCorp LLC, College Station, Texas).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eA total of 10,391 women aged 15\u0026ndash;49 with a live birth in the five years preceding the 2022 KDHS were included. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic profile of the sample. The majority of women lived in rural areas (65.4%), reflecting Kenya's predominantly rural population. Educational attainment was spread across the spectrum, with 20.0% reporting no formal education, 33.8% primary-level education, 31.6% secondary, and 14.6% higher education. Household wealth distribution showed that nearly half (48.2%) of women came from the two poorest quintiles. The largest age group was 25\u0026ndash;29 years (27.4%), and adolescents aged 15\u0026ndash;19 accounted for 7.6% of the analytic sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic Characteristics of the Study Population (N\u0026thinsp;=\u0026thinsp;10,391)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Distribution of Women Across the Cascade\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of women across the four cascade levels. Just over a third of women (37.6%) did not meet the threshold for adequate ANC, meaning they attended fewer than four visits for their most recent birth. This group represents the largest single point of failure in the cascade. Among the 62.4% of women who completed adequate ANC, a further 10.9 percentage points were lost at the transition to facility delivery: these women attended four or more ANC visits but ultimately delivered at home or in another non-facility setting. A subsequent 11.2 percentage points were lost at the transition to timely PNC: these women completed both ANC and facility delivery but did not receive a postnatal check within 24 hours. Only 40.3% of all women in the sample completed the full cascade, adequate ANC, facility delivery, and timely PNC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Women Across the Maternal Health Cascade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCascade Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 0: No adequate ANC (\u0026lt;\u0026thinsp;4 visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 1: Adequate ANC only (4\u0026thinsp;+\u0026thinsp;visits, home delivery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 2: ANC\u0026thinsp;+\u0026thinsp;facility delivery (no timely PNC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel 3: Full cascade (ANC\u0026thinsp;+\u0026thinsp;facility\u0026thinsp;+\u0026thinsp;timely PNC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10,391\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted.\u003c/p\u003e \u003cp\u003eThese figures reveal a cascade structure in which losses accumulate at every transition rather than being concentrated at a single point. While the absolute volume of loss is greatest at the ANC entry point, nearly four in ten women fail to complete four visits the proportional losses at subsequent transitions are also substantial. Among women who completed adequate ANC, the 10.9 percentage point loss to home delivery represents 17.5% of those who made it past the first stage. Among women who both completed ANC and delivered in a facility, the failure to receive timely PNC represents an additional 21.7% loss at the final transition. This pattern of compounding attrition means that even a woman who attends all her ANC visits and delivers in a facility has a meaningful probability of not receiving the postnatal check that would complete her care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Bivariate Associations with Cascade Completion\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution across cascade levels by each sociodemographic variable. All four predictors showed statistically significant associations with cascade completion (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eResidence: Urban women were considerably more likely than rural women to complete the full cascade (49.3% versus 35.6%). The gap was concentrated at Levels 0 and 1: rural women were more likely than urban women to fail to complete adequate ANC (41.6% versus 30.0%) and, conditional on completing ANC, more likely to deliver outside a facility (13.1% versus 6.8%). Urban women were slightly more likely to experience a gap at Level 2, reaching facility delivery but not timely PNC, possibly reflecting the faster throughput characteristic of higher-volume urban facilities.\u003c/p\u003e \u003cp\u003eEducation: The association between education and cascade completion was monotonically positive and substantively large. Women with no formal education completed the full cascade at a rate of only 17.1%, compared with 60.3% among women with higher education. The largest absolute gap between the no-education and higher-education groups was at Level 0: 54.0% of uneducated women did not attend four ANC visits, versus 18.6% of those with higher education a difference of 35.4 percentage points. This suggests that the cascade entry point is disproportionately where uneducated women are lost.\u003c/p\u003e \u003cp\u003eWealth: A strong wealth gradient was also observed. Full cascade completion rose from 24.2% in the poorest quintile to 60.8% in the richest, with an approximately monotonic relationship across quintiles. As with education, the poorest women were most concentrated at Level 0 (50.0%), indicating that inadequate ANC is strongly class-patterned. The gap in timely PNC was less marked by wealth, ranging from 7.3% at the poorest to 12.5% at the richest a pattern suggesting that the PNC transition is more uniformly challenging across economic groups, at least among those who reach facility delivery.\u003c/p\u003e \u003cp\u003eAge: Adolescent women aged 15\u0026ndash;19 had notably low rates of full cascade completion (31.4%) and were the group most concentrated at Level 0 (47.8%), indicating that young age at first birth is a substantial barrier to ANC initiation. Women aged 20\u0026ndash;34 showed higher completion rates (40.5\u0026ndash;43.8%), while older women in the 35\u0026ndash;49 range showed declining completion, reflecting possible cohort differences in norms and access patterns. The age of 45\u0026ndash;49 had the lowest completion rate at 25.8%, though this group also had the smallest sample size (n\u0026thinsp;=\u0026thinsp;97) and should be interpreted with caution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution Across Cascade Levels by Sociodemographic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 0 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 1 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel 2 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel 3 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey; all percentages are survey-weighted. p-values from chi-square tests with Rao-Scott correction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Multivariate Predictors of Full Cascade Completion\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents results from survey-weighted logistic regression examining predictors of full cascade completion (Level 3 versus Levels 0\u0026ndash;2). After adjusting for all predictors simultaneously, education and wealth emerged as the dominant independent predictors. Rural residence, which appeared substantively important in bivariate analysis, was no longer statistically significant in the multivariate model (OR 1.00, 95% CI 0.83\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.963). This suggests that the urban-rural gap observed in bivariate analysis is largely explained by confounding with education and wealth rural women are poorer and less educated, and these characteristics, not rural residence itself, drive reduced cascade completion.\u003c/p\u003e \u003cp\u003eAmong education categories, each successive level was independently associated with higher odds of completing the full cascade compared to no education: primary education (OR 1.92, 95% CI 1.52\u0026ndash;2.41), secondary (OR 2.40, 95% CI 1.89\u0026ndash;3.05), and higher education (OR 2.97, 95% CI 2.20\u0026ndash;4.00). The dose-response relationship across educational levels is consistent with a causal interpretation, though the cross-sectional design precludes causal inference.\u003c/p\u003e \u003cp\u003eHousehold wealth also showed a graded association. Women in the middle, richer, and richest quintiles had significantly higher odds of full cascade completion than the poorest women (OR 1.47, 1.75, and 2.94 respectively), while the difference between the poorest and poorer quintiles did not reach statistical significance (OR 1.13, 95% CI 0.96\u0026ndash;1.33, p\u0026thinsp;=\u0026thinsp;0.129). The attenuation of the poorer-versus-poorest difference in the multivariate model may reflect educational confounding within the lower wealth strata.\u003c/p\u003e \u003cp\u003eAge effects were attenuated but partially retained in the multivariate model. Women aged 20\u0026ndash;24, 25\u0026ndash;29, and 30\u0026ndash;34 had significantly higher odds of completing the full cascade compared with adolescents aged 15\u0026ndash;19 (OR 1.52, 1.38, and 1.44 respectively). Women aged 35 and above showed no significant advantage over adolescents after controlling for education and wealth, suggesting that the apparent peak in cascade completion in the 20\u0026ndash;34 age range reflects the combined effects of developmental stage, parity, and socioeconomic characteristics rather than age alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey-Weighted Logistic Regression: Predictors of Full Cascade Completion (N\u0026thinsp;=\u0026thinsp;10,391)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (ref: Urban)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (ref: No education)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52\u0026ndash;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026ndash;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.20\u0026ndash;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index (ref: Poorest)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026ndash;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026ndash;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (ref: 15\u0026ndash;19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval. All analyses survey-weighted with complex design correction.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Interpreting the Cascade Structure\u003c/h2\u003e \u003cp\u003eThe central finding of this study is that only four in ten Kenyan women who gave birth between 2017 and 2022 completed the full maternal health cascade of adequate ANC, facility delivery, and timely postnatal care. This figure is arresting in a health system context where facility delivery alone has been widely promoted as the primary solution to maternal mortality. It reveals that aggregate service coverage statistics which present each indicator independently systematically overstate the extent to which women are actually benefiting from the full continuum of care.\u003c/p\u003e \u003cp\u003eThe cascade structure observed here shows losses at every transition, but they are not equal in magnitude. The largest absolute loss is at entry, 37.6% of women do not complete four ANC visits. This is a longstanding problem in Kenya, and the 2022 data suggest it has not been resolved. Addressing this entry barrier is the single change that would produce the largest immediate gain in the number of women on the cascade. Strategies that reduce ANC attendance including financial barriers, distance, poor provider behaviour, and lack of community mobilization must remain central to the policy agenda.\u003c/p\u003e \u003cp\u003eHowever, the cascade framework draws attention to a second problem that aggregate coverage figures obscure. The conditional losses at later transitions. Among women who completed adequate ANC, 17.5% did not reach facility delivery. Among those who completed both ANC and facility delivery, 21.7% did not receive timely PNC. These figures indicate that a substantial fraction of the women most engaged with the health system, those who proved themselves motivated enough to attend four or more ANC visits, are still slipping out of care before the continuum is complete. Interventions specifically designed to retain engaged women across transitions are therefore needed, not just strategies for bringing disengaged women into ANC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 The Significance of the Postnatal Gap\u003c/h2\u003e \u003cp\u003ePerhaps the most policy-relevant finding from a proportional perspective is the loss at the ANC-to-facility-delivery and facility-delivery-to-PNC transitions. The post-delivery PNC gap is particularly concerning because women who have already entered the formal system and delivered in a facility represent an accessible population. Their failure to receive a timely postnatal check is not explained by geographic distance from services in the way that ANC non-attendance might be. Rather, it reflects either system-side failures early discharge before a PNC check has been completed, inadequate staffing to conduct checks or individual-level constraints such as rapid return home for family or economic reasons.\u003c/p\u003e \u003cp\u003eThe literature supports the severity of this gap. Munos et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated that postnatal packages targeting the neonatal period could prevent 10\u0026ndash;27% of neonatal deaths, and maternal deaths in the 24\u0026ndash;72 hours post-partum are concentrated precisely in the window within which timely PNC would be administered. In the Kenyan context, the Linda Mama free maternity services policy extended to facility delivery but did not specifically address the retention of women for PNC, a gap that this study's findings suggest has persisted.\u003c/p\u003e \u003cp\u003eThe bivariate data reveal a counterintuitive pattern at Level 2: urban women (13.9%) were slightly more likely than rural women (9.7%) to complete facility delivery but fail to receive timely PNC. This warrants further investigation. One plausible explanation is that higher-volume urban facilities discharge women more rapidly, reducing the window within which a PNC check can occur within the 24-hour threshold. Another is that urban women face stronger competing demands\u0026mdash;returning to wage employment or other family obligations that prompt earlier departure from the facility. This would represent a quality-of-care problem specific to the high-throughput urban setting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Education, Wealth, and the Attenuation of Rural Effects\u003c/h2\u003e \u003cp\u003eThe finding that rural residence loses statistical significance in the multivariate model is important for how policymakers conceptualise the urban-rural divide in maternal health. It suggests that geographic residence is largely a proxy for the true drivers of cascade incompletion\u0026mdash;namely, educational disadvantage and material poverty. Women living in rural areas are substantially more likely to be poor and less educated than urban women, and it is these characteristics, not location per se, that determine cascade completion.\u003c/p\u003e \u003cp\u003eThis reframing has significant policy implications. Spatially targeted interventions\u0026mdash;mobile health clinics, rural facility upgrading\u0026mdash;address geographic access constraints and are necessary where distance is a genuine barrier. But they are unlikely to be sufficient if the underlying educational and economic constraints remain unaddressed. The very strong independent effects of education and wealth point toward the need for upstream social investments\u0026mdash;girls' education, poverty reduction\u0026mdash;that operate well beyond the health sector. These results align with a growing body of evidence that maternal health outcomes are deeply shaped by intersecting social determinants that health systems alone cannot modify (Bhutta et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Okonofua et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strength of the education effect deserves particular attention. Women with higher education were nearly three times as likely as uneducated women to complete the full cascade (OR 2.97), an effect size that dwarfs most individual health intervention effects reported in the literature. Educated women are not simply better at navigating the health system; they are also more likely to have economic resources, greater autonomy in health decision-making, better knowledge of what care is recommended, and stronger capacity to demand quality from providers. Education appears to operate through multiple pathways simultaneously, producing cumulative advantages across the entire cascade.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Adolescent Women and the Risk of Early Cascade Exit\u003c/h2\u003e \u003cp\u003eAdolescents aged 15\u0026ndash;19 had the highest rate of Level 0 non-completion (47.8%) and the lowest rate of full cascade completion (31.4%), patterns that persisted as an age effect even after adjusting for education and wealth. This is consistent with evidence that adolescent women face particular barriers to ANC attendance, including stigma associated with pregnancy outside marriage, parental or partner control over health decisions, and fear of disclosure at health facilities (Mekonnen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Kenya, adolescent pregnancy also intersects with socioeconomic disadvantage: young mothers are more likely to be from poor households and to have left school early, reinforcing the combined education-wealth-age gradient observed in this study.\u003c/p\u003e \u003cp\u003eThe multivariate findings showing sustained advantages for women in the 20\u0026ndash;34 age range over adolescents, even after controlling for education and wealth, suggest that some element of developmental stage or parity experience is independently facilitating cascade completion. Primiparity\u0026mdash;which is strongly concentrated in younger age groups\u0026mdash;has been associated with lower utilisation of maternal health services in previous DHS-based studies from Kenya and other African settings, possibly reflecting unfamiliarity with the care pathway (Kyei et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Methodological Contribution and Limitations\u003c/h2\u003e \u003cp\u003eThis study makes a methodological contribution by demonstrating the feasibility and analytic utility of cascade analysis applied to nationally representative DHS data. The approach forces a more precise accounting of system failure than is possible when ANC, delivery, and PNC are reported as separate indicators. It also naturally generates a sequentially ordered outcome variable that aligns with the temporal structure of care, which is conceptually more appropriate than treating each indicator independently. We encourage DHS-based researchers in other national contexts to adopt this framework as a tool for comparing maternal health system performance across countries.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. First, the data are cross-sectional and rely on retrospective recall of health behaviours up to five years prior to the survey, introducing the possibility of recall bias. Recall error regarding ANC visit counts and timing of postnatal checks may produce misclassification at cascade boundaries. Second, the study captures only whether specific care contacts occurred, not the quality of care delivered. A woman classified as having completed the full cascade may have received care of very poor quality at each contact. This study therefore measures coverage adequacy, not care adequacy. Third, the cascade definition is conservative in one respect and may understate PNC inadequacy, it requires timely PNC within 24 hours, but the full recommended schedule includes additional contacts in the days and weeks following delivery that are not captured in this analysis. Fourth, the study design does not permit causal inference; the sociodemographic associations observed are descriptive of population patterns rather than estimates of causal effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study demonstrates that fewer than half of Kenyan women who gave birth between 2017 and 2022 completed the recommended sequence of maternal health care, adequate ANC, facility delivery, and timely PNC. The cascade framework reveals a system that loses women at every transition, with the largest absolute loss at the ANC entry point and significant proportional losses at each subsequent stage. Education and household wealth are the dominant independent predictors of cascade completion, while the urban-rural gap is largely explained by these underlying socioeconomic factors. Adolescent women are disproportionately represented among those who never enter the cascade.\u003c/p\u003e \u003cp\u003eThese findings have three principal implications for policy. First, ANC coverage remains the highest-priority target, given that more than a third of women fail to reach even the minimum standard of four visits. Demand-side incentives, community health worker outreach, and the removal of financial and logistical barriers should be sustained and strengthened. Second, the facility-to-PNC transition deserves far greater policy attention than it has received. Retaining women who have already delivered in a facility long enough to receive a postnatal check is a tractable problem that existing facility-based systems should be able to address through structured discharge protocols. Third, investments in girls' education and poverty reduction are not peripheral to maternal health strategy\u0026mdash;they are central to it. The strength of the education and wealth effects observed here indicates that health systems will continue to struggle to serve the women most at risk until broader social inequalities are addressed.\u003c/p\u003e \u003cp\u003eThe cascade approach to monitoring maternal health continuity should be incorporated into national health information systems and DHS reporting frameworks. Tracking individual service indicators in isolation has historically masked the true scale of care incompletion. Measuring the full cascade and the losses between each of its stages provides a more honest and actionable picture of where maternal health systems are actually failing women.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe ethics declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003e This research was performed in accordance with the principles of the Declaration of Helsinki. The study used secondary data from the 2022 Kenya Demographic and Health Survey (KDHS), which is publicly available through the DHS Program website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Ethical approval for the original KDHS data collection was obtained from the ICF Institutional Review Board (Project Number: 132989) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (Protocol Number: KEMRI/RES/7/3/1). All survey respondents provided written informed consent before participation, including consent for anonymized data to be used in future research. Since this analysis involved de-identified, publicly available data, it did not require further ethical clearance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eThe ethics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was performed in accordance with the principles of the Declaration of Helsinki. The study used secondary data from the 2022 Kenya Demographic and Health Survey (KDHS), which is publicly available through the DHS Program website (https://dhsprogram.com). Ethical approval for the original KDHS data collection was obtained from the ICF Institutional Review Board (Project Number: 132989) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (Protocol Number: KEMRI/RES/7/3/1). All survey respondents provided written informed consent before participation, including consent for anonymized data to be used in future research. Since this analysis involved de-identified, publicly available data, it did not require further ethical clearance\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article. This study was conducted using publicly available data from the Demographic and Health Surveys (DHS) Program, and all work was performed as part of the authors' academic affiliations without external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in the original surveys provided written informed consent before participation, including consent for anonymized data to be used in future research. As this study involved secondary analysis of fully anonymized, publicly available data, it was exempt from additional ethical review. Human Ethics and Consent to Participate declarations: not applicable for this secondary analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos) that would require consent for publication. All data presented are aggregated, anonymized, and publicly available from the Demographic and Health Surveys (DHS) Program\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the Demographic and Health Surveys (DHS) Program repository and the Kenya National Bureau of Statistics https://statistics.knbs.or.ke/nada/index.php/catalog/128/related-materials and https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. Access to the data requires free registration and approval of a research proposal by The DHS Program, in accordance with the data use agreements with the Government of Kenya. The data are publicly available for legitimate research purposes. The authors confirm that they did not have any special access privileges to these data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. No financial or non-financial interests that could be construed as influencing the research or interpretation of the findings exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharles, John: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing – original draft. Mary, Charles: Conceptualization, Methodology, Investigation, Validation, Writing – review \u0026amp; editing, Project administration. Charles, erick: Resources, Validation, Writing – review \u0026amp; editing, Supervision. All authors have read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhutta, Z. A., Das, J. K., Bahl, R., Lawn, J. E., Salam, R. A., Paul, V. K., Sankar, M. J., Blencowe, H., Rizvi, A., Chou, V. B., \u0026amp; Walker, N. (2014). Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? The Lancet, 384(9940), 347\u0026ndash;370. https://doi.org/10.1016/S0140-6736(14)60792-3\u003c/li\u003e\n\u003cli\u003eBohren, M. A., Hunter, E. C., Munthe-Kaas, H. M., Souza, J. P., Vogel, J. P., \u0026amp; G\u0026uuml;lmezoglu, A. M. (2014). Facilitators and barriers to facility-based delivery in low- and middle-income countries: A qualitative evidence synthesis. Reproductive Health, 11(1), 71. https://doi.org/10.1186/1742-4755-11-71\u003c/li\u003e\n\u003cli\u003eGardner, E. M., McLees, M. P., Steiner, J. F., Del Rio, C., \u0026amp; Burman, W. J. (2011). The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clinical Infectious Diseases, 52(6), 793\u0026ndash;800. https://doi.org/10.1093/cid/ciq243\u003c/li\u003e\n\u003cli\u003eGitobu, C. M., Gichangi, P. B., \u0026amp; Mwanda, W. O. (2018). The effect of Kenya\u0026apos;s free maternal healthcare policy on the utilization of health facility delivery services and maternal and neonatal mortality in public health facilities. Journal of Pregnancy, 2018, Article 9648059. https://doi.org/10.1155/2018/9648059\u003c/li\u003e\n\u003cli\u003eHug, L., Alexander, M., You, D., \u0026amp; Alkema, L. (2019). National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: A systematic analysis. The Lancet Global Health, 7(6), e710\u0026ndash;e720. https://doi.org/10.1016/S2214-109X(19)30163-9\u003c/li\u003e\n\u003cli\u003eKerber, K. J., de Graft-Johnson, J. E., Bhutta, Z. A., Okong, P., Starrs, A., \u0026amp; Lawn, J. E. (2007). Continuum of care for maternal, newborn, and child health: From slogan to service delivery. The Lancet, 370(9595), 1358\u0026ndash;1369. https://doi.org/10.1016/S0140-6736(07)61578-5\u003c/li\u003e\n\u003cli\u003eKenya National Bureau of Statistics (KNBS). (2023). Kenya Demographic and Health Survey 2022: Final report. Nairobi: KNBS \u0026amp; ICF. https://dhsprogram.com/publications/publication-FR370-DHS-Final-Reports.cfm\u003c/li\u003e\n\u003cli\u003eKyei, N. N. A., Campbell, O. M. R., \u0026amp; Gabrysch, S. (2012). The influence of distance and level of service provision on antenatal care use in rural Zambia. PLOS ONE, 7(10), e46475. https://doi.org/10.1371/journal.pone.0046475\u003c/li\u003e\n\u003cli\u003eMekonnen, T., Dune, T., \u0026amp; Perz, J. (2019). Maternal health service utilisation of adolescent women in sub-Saharan Africa: A systematic review. BMC Pregnancy and Childbirth, 19(1), 426. https://doi.org/10.1186/s12884-019-2386-8\u003c/li\u003e\n\u003cli\u003eMgawadere, F., Unkels, R., Kazembe, A., \u0026amp; van den Broek, N. (2017). Factors associated with maternal mortality in Malawi: Application of the three delays model. BMC Pregnancy and Childbirth, 17(1), 219. https://doi.org/10.1186/s12884-017-1406-8\u003c/li\u003e\n\u003cli\u003eMunos, M. K., Walker, C. L. F., \u0026amp; Black, R. E. (2010). The effect of postnatal care quality on neonatal mortality: An analysis of 18 countries. International Journal of Epidemiology, 39(Suppl 1), i108\u0026ndash;i117. https://doi.org/10.1093/ije/dyq174\u003c/li\u003e\n\u003cli\u003eOkonofua, F. E., Ntoimo, L. F. C., Ogu, R., Galadanci, H., Gana, M., Adetokunbo, S., Imaralu, J. O., \u0026amp; Iliyasu, Z. (2021). Prevalence and determinants of emergency obstetric complications in Nigerian public hospitals. Reproductive Health, 18(1), 1. https://doi.org/10.1186/s12978-020-01055-3\u003c/li\u003e\n\u003cli\u003eRutstein, S. O., \u0026amp; Johnson, K. (2004). The DHS wealth index. DHS Comparative Reports No. 6. ORC Macro. https://dhsprogram.com/publications/publication-cr6-comparative-reports.cfm\u003c/li\u003e\n\u003cli\u003eSay, L., Chou, D., Gemmill, A., Tun\u0026ccedil;alp, \u0026Ouml;., Moller, A. B., Daniels, J., G\u0026uuml;lmezoglu, A. M., Temmerman, M., \u0026amp; Alkema, L. (2014). Global causes of maternal death: A WHO systematic analysis. The Lancet Global Health, 2(6), e323\u0026ndash;e333. https://doi.org/10.1016/S2214-109X(14)70227-X\u003c/li\u003e\n\u003cli\u003eSines, E., Tinker, A., \u0026amp; Ruben, J. (2007). The continuum of care for maternal, newborn, and child health: From slogan to service delivery. Save the Children and Population Reference Bureau. https://www.prb.org/resources/the-continuum-of-care/\u003c/li\u003e\n\u003cli\u003eSipsma, H. L., Curry, L. A., \u0026amp; Bradley, E. H. (2013). Perspectives on delays in antenatal and delivery care in Ghana: Findings from a qualitative study. African Journal of Reproductive Health, 17(1), 117\u0026ndash;128.\u003c/li\u003e\n\u003cli\u003eTran, T. K., Gottvall, K., Nguyen, H. D., Ascher, H., \u0026amp; Petzold, M. (2018). Factors associated with antenatal care adequacy in rural and urban contexts: Results from two health and demographic surveillance sites in Vietnam. BMC Health Services Research, 18(1), 600. https://doi.org/10.1186/s12913-018-3411-3\u003c/li\u003e\n\u003cli\u003eUNAIDS. (2014). 90-90-90: An ambitious treatment target to help end the AIDS epidemic. UNAIDS. https://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf\u003c/li\u003e\n\u003cli\u003eUN Secretary-General\u0026apos;s Global Strategy. (2015). Every woman every child: Saving lives, protecting futures. Progress report on the Every Woman Every Child movement. United Nations. https://www.everywomaneverychild.org/global-strategy/\u003c/li\u003e\n\u003cli\u003eUnited Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015 (A/RES/70/1). https://sdgs.un.org/2030agenda\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2013). WHO recommendations on postnatal care of the mother and newborn. WHO. https://www.who.int/publications/i/item/9789241506649\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2023). Trends in maternal mortality: 2000 to 2020. Estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. WHO. https://www.who.int/publications/i/item/9789240068759\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"maternal health cascade, antenatal care, facility delivery, postnatal care, Kenya, continuum of care, DHS","lastPublishedDoi":"10.21203/rs.3.rs-9122116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9122116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaternal mortality remains unacceptably high in sub-Saharan Africa, and Kenya has not achieved the reductions anticipated under the Sustainable Development Goals. A central challenge is that improving individual service indicators\u0026mdash;antenatal care attendance, facility delivery rates\u0026mdash;does not guarantee that women actually move through the full continuum of care. This study applies a cascade-of-care framework, adapted from HIV treatment research, to measure sequential loss along the maternal health continuum in Kenya.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), restricting the sample to women aged 15\u0026ndash;49 who had a live birth in the five years preceding the survey (N\u0026thinsp;=\u0026thinsp;10,391). We constructed a four-level cascade variable representing sequential progression from adequate antenatal care (4\u0026thinsp;+\u0026thinsp;visits) through facility delivery to timely postnatal care within 24 hours. Sociodemographic predictors were examined using chi-square tests and survey-weighted logistic regression, with all analyses accounting for the complex sampling design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOnly 40.3% of women completed the full cascade. The largest single loss occurred at the first stage: 37.6% of women did not attend four or more antenatal visits. Among those who did, a further 10.9% delivered outside a health facility. Post-delivery attrition\u0026mdash;women who delivered in a facility but did not receive timely postnatal care\u0026mdash;accounted for an additional 11.2 percentage points of loss. In multivariate analysis, higher education (OR 2.97, 95% CI 2.20\u0026ndash;4.00) and belonging to the wealthiest quintile (OR 2.94, 95% CI 2.22\u0026ndash;3.90) were the strongest predictors of full cascade completion. Rural residence lost significance once education and wealth were controlled.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe cascade framework reveals that Kenya's maternal health system loses women at every transition point, and that these losses are concentrated among the poor and uneducated\u0026mdash;not primarily in rural areas per se. Interventions targeting the ANC entry barrier and the facility-to-PNC transition are most urgently needed. The cascade approach should be adopted more widely in national monitoring of maternal health systems.\u003c/p\u003e","manuscriptTitle":"From Antenatal Care to Skilled Delivery: Examining the Drop-off Along the Maternal Healthcare Cascade in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 18:51:38","doi":"10.21203/rs.3.rs-9122116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"121427400127885781019192153630164669858","date":"2026-05-15T09:22:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:13:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41337802626141236280031762195975020957","date":"2026-04-20T04:34:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T08:19:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T18:10:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T12:55:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T12:55:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-14T11:10:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a48aae2-1eb8-4687-a5c6-9cffe3c08953","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"121427400127885781019192153630164669858","date":"2026-05-15T09:22:58+00:00","index":122,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:13:01+00:00","index":105,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T18:51:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 18:51:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9122116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9122116","identity":"rs-9122116","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}