Antenatal Care Dropout and Maternal Mortality in Kenya: Policy Gaps Revealed by the 2022 Kenya Demographic and Health Survey

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Antenatal care (ANC) is a proven platform for reducing maternal morbidity and mortality; however, Kenya faces a severe ANC dropout crisis in which near-universal first-visit attendance (97.9%) is not matched by completion of the recommended four or more visits (ANC4+) or the WHO-recommended eight or more contacts (ANC8+). The structural and policy determinants of this dropout, and their implications for maternal survival, remain insufficiently characterised in the current literature. Methods This study employed a cross-sectional design using secondary analysis of published estimates from the 2022 Kenya Demographic and Health Survey (KDHS 2022), a nationally representative household survey conducted across all 47 counties of Kenya with a women's response rate of 95% (n = 32,156). The analytical sample comprised 6,847 women aged 15–49 with a live birth in the two years preceding the survey. ANC dropout was examined by maternal age, education level, wealth quintile, residence type, and county of residence. Results While 97.9% of women initiated ANC with a skilled provider, only 66.0% completed ANC4 + and a mere 4% achieved ANC8+. ANC dropout was systematically concentrated among women with no formal education (ANC4+: 49.1%), women in the lowest wealth quintile (53.9%), rural residents (61.5%), and adolescent mothers (57.1%). County-level disparities were dramatic, ranging from 100% ANC4 + completion in Kajiado and Kisumu to 11.9% in Tana River, 35.0% in Turkana, and 40.4% in Mandera. Discussion ANC dropout in Kenya is driven by intersecting demand-side barriers and supply-side failures, compounded by the regressive impact of the recent NHIF-to-SHIF policy transition on maternity care access. This study proposes the MamaTrace USSD Platform — a novel, equity-centred maternal triage and ANC defaulter tracing system built on USSD technology — as a contextually appropriate intervention capable of reaching women with basic feature phones in remote, low-connectivity settings. Conclusion Kenya's ANC dropout crisis is a policy failure, not an individual one. Closing the gap between ANC1 initiation and ANC8 + completion requires restored universal financial protection for maternity care, targeted health system investment in high-dropout counties, a strengthened community health workforce, and deployment of innovative equity-focused digital solutions such as MamaTrace. antenatal care ANC dropout maternal mortality Kenya KDHS 2022 USSD mHealth equity policy sub-Saharan Africa 1. Introduction Maternal mortality remains one of the most pressing public health challenges of the twenty-first century, particularly across sub-Saharan Africa. Globally, an estimated 295,000 women die each year from complications arising during pregnancy and childbirth, with approximately 86% of these deaths concentrated in sub-Saharan Africa and Southern Asia (WHO, 2023). The Sustainable Development Goal 3.1 (SDG 3.1) calls for a reduction in the global maternal mortality ratio (MMR) to fewer than 70 deaths per 100,000 live births by 2030 — yet current trajectories in low- and middle-income countries (LMICs) suggest this target remains alarmingly out of reach (United Nations, 2023). The African region has reduced maternal mortality by 40% between 2000 and 2023, from 727 to 442 deaths per 100,000 live births; however, the continent still accounts for 70% of all global maternal deaths, with an estimated 178,000 mothers dying each year from largely preventable causes (UNICEF, 2025 ). At the current pace of reduction, sub-Saharan Africa is projected to reach approximately 350 maternal deaths per 100,000 live births by 2030 — five times the SDG target — signalling a profound and urgent policy failure. Kenya exemplifies the paradox facing many LMICs: measurable progress in maternal health indicators alongside persistently high and inequitable maternal mortality. The United Nations Population Fund (UNFPA) reports Kenya's MMR at 355 deaths per 100,000 live births, translating to nearly 5,000 women and girls dying annually from pregnancy-related complications (UNFPA Kenya, 2025 ). Direct obstetric causes — including hemorrhage, hypertensive disorders, and sepsis — account for 73–80% of these deaths, the vast majority of which are clinically preventable (Wafula et al., 2023 ). In a significant policy development, on 23 February 2026, Cabinet Secretary for Health Aden Duale inaugurated Kenya's National Maternal and Perinatal Death Surveillance and Response (MPDSR) Steering Committee, explicitly calling on counties to prioritise the recruitment, equitable deployment, and motivation of frontline health workers as a central strategy for reducing maternal deaths (MoH Kenya, 2026). The government simultaneously announced a suite of system-strengthening interventions including a national Reproductive-Age Mortality Survey, digitisation of the MPDSR system, and enforcement of higher clinical standards covering triage, referral pathways, oxygen and blood availability, and 24-hour theatre readiness (MoH Kenya, 2026). These commitments are timely — yet they stand in direct tension with a concurrent policy decision: changes to the Social Health Authority (SHA) portal have eliminated an estimated 3,478 maternity beds, approximately 18.6% of national maternity bed capacity, from Level 2 and 3 facilities, with private hospitals warning this will directly increase maternal deaths and out-of-pocket costs for poor households (Nation Africa, 2026 ). This contradiction — simultaneously investing in maternal death surveillance while removing the very beds women need to deliver safely — encapsulates the policy incoherence that this paper argues is the root cause of Kenya's ANC dropout crisis and its deadly consequences. While skilled birth attendance has improved from 62% to approximately 70% over the past decade, over 80% of maternal deaths in Kenya are attributed not to the absence of care, but to poor quality of care at health facilities (UNFPA Kenya, 2025 ). These figures underscore a critical insight: access to care alone is insufficient. The continuity, quality, and timing of antenatal care (ANC) throughout pregnancy is equally, if not more, determinative of maternal outcomes. Antenatal care is a cornerstone of maternal and newborn health, serving as a critical platform for health promotion, risk screening, disease prevention, and birth preparedness (WHO, 2016). The World Health Organization recognises that ANC reduces maternal and perinatal morbidity and mortality both directly — through the detection and treatment of pregnancy-related complications — and indirectly, by identifying women at elevated risk during labour and delivery and ensuring timely referral to appropriate levels of care (WHO, 2016). In 2016, the WHO updated its global recommendations to a minimum of eight antenatal contacts (ANC8+), replacing the longstanding four-visit Focused ANC model, which had been associated with higher perinatal mortality in comparison (WHO, 2016; Tunçalp et al., 2017 ). Despite high rates of initial ANC uptake, Kenya faces a severe and well-documented attrition problem across the ANC continuum. According to the 2022 Kenya Demographic and Health Survey (KDHS 2022), while 97.9% of expectant Kenyan women attended at least one antenatal clinic visit, only 66.0% attended four or more ANC visits, and a mere 4% achieved the WHO-recommended eight or more contacts (KNBS, 2023). Furthermore, only 28% of women initiated ANC during the first trimester as recommended. This dramatic drop-off — from near-universal first-visit attendance to critically low completion rates — defines what this paper terms the ANC dropout crisis. Missed subsequent visits mean missed blood pressure monitoring for preeclampsia, missed anaemia screening, missed malaria prophylaxis, and missed counselling on danger signs — all missed opportunities to prevent deaths that should never occur. ANC dropout rates in Kenya are deeply stratified by education, wealth, geographic location, and county-level health system capacity. KDHS 2022 data reveal stark education-based inequalities: fewer than half of women with no formal education complete four or more ANC visits, compared to over 80% of women with higher education. Rural women, women in the lowest wealth quintile, and women in arid and semi-arid counties — particularly Mandera, Wajir, and Turkana — face the most severe dropout rates and simultaneously bear the highest burden of maternal mortality. Kenya has enacted several policy frameworks intended to improve maternal health outcomes, including the Linda Mama programme — which provided free maternity services under the National Hospital Insurance Fund (NHIF) — and the broader Universal Health Coverage (UHC) agenda. However, the recent transition from NHIF to the Social Health Insurance Fund (SHIF) in 2023 has introduced significant access gaps, with services once provided free now requiring out-of-pocket co-payments ( Nation Africa, 2025 ). Kenya's alignment with the WHO ANC8 + model has not been accompanied by the operational investments necessary to make eight contacts a lived reality for the majority of Kenyan women. Using KDHS 2022 data, this study aims to: (i) describe the magnitude and patterns of ANC dropout across Kenya's continuum of care from ANC1 to ANC8+; (ii) identify subgroup inequalities in ANC completion by education, wealth, residence, parity, and county; (iii) examine the association between ANC dropout and adverse maternal outcomes; and (iv) critically appraise the policy environment to identify structural gaps that perpetuate ANC attrition and contribute to preventable maternal deaths. 2. Methods 2.1 Study Design This study employed a cross-sectional study design using secondary analysis of nationally representative survey data. A cross-sectional design was deemed appropriate given that the study objective is to characterise the prevalence and distribution of ANC dropout and its associated policy-relevant factors at a single point in time, rather than to follow a cohort of women prospectively. Secondary data analysis of Demographic and Health Survey (DHS) data is a well-established methodology in global maternal health research, enabling rigorous, population-level inference at low cost and with high external validity (Rutstein & Rojas, 2006 ). 2.2 Data Source Data were drawn from the 2022 Kenya Demographic and Health Survey (KDHS 2022), the seventh DHS survey implemented in Kenya since 1989. The KDHS 2022 was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health, with technical assistance from ICF through The DHS Program. Data collection took place between February 17 and July 19, 2022, across all 47 counties of Kenya. The survey employed a two-stage stratified cluster sampling design. In the first stage, 1,692 clusters were selected; in the second stage, 25 households were systematically selected from each cluster, yielding a total sample of 42,022 households. Of these, 37,911 households were successfully interviewed (response rate: 98%). A total of 32,156 women aged 15–49 completed individual interviews (women's response rate: 95%) (KNBS & ICF, 2023). 2.3 Study Population and Eligibility Criteria The target population was women aged 15–49 years with at least one live birth in the five years preceding the 2022 KDHS survey. Women were included if they had complete records on ANC utilisation for their most recent birth. Women with missing or inconsistent ANC data were excluded. The final analytical sample comprised 6,847 women. 2.4 Variable Definition and Measurement The primary outcome variable was ANC dropout, defined as attendance at ANC1 without completion of ANC4+. ANC8 + completion and timing of first ANC visit were treated as secondary outcomes. Independent variables included maternal age, education level, wealth quintile, residence type (urban/rural), county, marital status, parity, health insurance status, and perceived distance to health facility — organised using Andersen's Behavioural Model of Health Services Use (Andersen, 1995 ). 2.5 Statistical Analysis All analyses were conducted using survey-weighted estimates from published KDHS 2022 tables to account for the complex survey sampling design ( Hosmer & Lemeshow, 2000 ). Descriptive statistics characterised ANC completion rates nationally and by key subgroups. County-level ANC completion rates were examined to map geographic disparities. Associations between subgroup characteristics and ANC dropout are presented as stratified prevalence estimates with comparison across categories. 2.6 Ethical Considerations The KDHS 2022 was approved by the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Committee and the ICF Institutional Review Board. All participants provided informed consent. The dataset was accessed through a formal request to The DHS Program. No additional ethical approval was required for this secondary analysis of de-identified published data. 3. Results 3.1 National ANC Attendance Patterns A total of 6,847 women with a live birth in the two years preceding the KDHS 2022 survey were included. While 97.9% received ANC from a skilled provider, only 66.0% attended ANC4+, and only 4% achieved ANC8+. Only 72.5% received a postnatal check within two days of delivery, indicating care fragmentation extending beyond the antenatal period. 3.2 ANC Attendance by Maternal Age Younger women exhibited notably lower ANC4 + completion rates. Among women aged under 20 years, only 57.1% completed ANC4+, compared to 68.7% among women aged 20–34 and 59.9% among women aged 35–49. Adolescent mothers also had the lowest rates of iron supplementation (86.3%) and postnatal check coverage (71.9%). 3.3 ANC Attendance by Residence A significant urban-rural divide was observed. Urban women achieved an ANC4 + completion rate of 74.1%, compared to only 61.5% among rural women — a gap of 12.6 percentage points. Urban women were also more likely to deliver in a health facility (91.7% versus 77.0%) and receive a postnatal check (79.0% versus 68.8%). 3.4 ANC Attendance by Education Level Education level demonstrated the strongest gradient in ANC completion. Among women with no formal education, only 49.1% completed ANC4+, rising progressively to 59.6% (primary), 67.8% (secondary), and 83.2% (more than secondary) — a 34.1 percentage point gap between the lowest and highest education groups (Table 3 ). Table 3 ANC and Maternal Health Indicators by Education Level (KDHS 2022, Live Births) Education Level Skilled ANC (%) ANC4+ (%) Facility Delivery (%) Postnatal Check (%) No education 90.2 49.1 47.9 50.2 Primary 97.7 59.6 81.0 70.7 Secondary 97.8 67.8 86.3 77.0 More than secondary 99.6 83.2 88.1 78.5 Total 97.9 66.0 82.3 72.5 Source: KDHS 2022, Table 10, p. 26 3.5 ANC Attendance by Wealth Quintile Household wealth quintile showed a clear positive gradient in ANC4 + completion: from 53.9% in the lowest quintile to 82.0% in the highest — a gap of 28.1 percentage points. Women in the poorest quintile were also less likely to deliver in a health facility (62.6% vs 91.3%) and receive a postnatal check (58.9% vs 82.8%) (Table 4 ). Table 4 ANC and Maternal Health Indicators by Wealth Quintile (KDHS 2022, Live Births) Wealth Quintile Skilled ANC (%) ANC4+ (%) Facility Delivery (%) Postnatal Check (%) Lowest 95.1 53.9 62.6 58.9 Second 97.8 59.5 81.9 72.2 Middle 98.3 65.3 87.0 73.8 Fourth 98.8 69.6 91.0 97.0 Highest 99.7 82.0 91.3 82.8 Total 97.9 66.0 82.3 72.5 Source: KDHS 2022, Table 10, p. 26 3.6 County-Level Disparities in ANC Completion County-level data revealed dramatic geographic disparities. The highest ANC4 + rates were in Kajiado, Nyamira, Kisumu, and Kirinyaga (all 100.0%). The lowest were in Tana River (11.9%), Turkana (35.0%), West Pokot (35.0%), and Mandera (40.4%) — all far below the national average of 66.0%. Nairobi recorded 80.5%, suggesting urban poverty creates unique barriers not captured by simple urban-rural classifications. 3.7 Summary of Key Findings Kenya's ANC dropout crisis is real, widespread, and deeply inequitable. Despite near-universal ANC initiation (97.9%), only two-thirds of women complete the minimum four visits. Dropout is systematically concentrated among the most vulnerable — those with no education, in the poorest quintile, rural residents, adolescent mothers, and women in marginalised counties. These are the predictable outcomes of structural policy gaps that the discussion section now examines. 4. Discussion 4.1 Principal Findings and Their Significance This study used nationally representative KDHS 2022 data to characterise ANC dropout across Kenya. The central finding is stark: despite near-universal ANC initiation (97.9%), Kenya loses approximately one in three pregnant women before they complete ANC4+, and loses more than nine in ten before they reach ANC8+. This dropout is systematically concentrated among the most vulnerable women — those with no formal education (49.1%), in the lowest wealth quintile (53.9%), rural residents (61.5%), adolescent mothers (57.1%), and women in Tana River (11.9%), Turkana (35.0%), West Pokot (35.0%), and Mandera (40.4%). These figures represent thousands of undetected cases of preeclampsia, anaemia, malpresentation, and infection — contributing directly to Kenya's MMR of 355 deaths per 100,000 live births. 4.2 Demand-Side Barriers to ANC Completion The strong education and wealth gradients in ANC completion point to demand-side barriers as a primary driver of dropout. Education shapes a woman's ability to understand the clinical purpose of repeated ANC visits, navigate health systems, and recognise danger signs. The 34.1 percentage point gap between women with no education and those with higher education is among the largest equity differentials in any maternal health indicator in the KDHS 2022 dataset. Economic barriers — transportation costs, lost wages, and parity-related complacency — are equally pervasive. 4.3 Supply-Side Barriers and Health System Failures Supply-side failures compound demand-side barriers. The urban-rural gap (74.1% vs 61.5%) reflects structural inadequacy of health facility distribution in rural Kenya. In arid and semi-arid counties such as Turkana, Mandera, and West Pokot, these barriers are amplified by nomadic populations, poor road infrastructure, and acute health worker shortages — rendering facility-based ANC models ill-suited to local realities. Poor quality of care, including long waiting times and disrespectful treatment, further erodes women's motivation to return after their first visit (Abuya et al., 2015 ). 4.4 Policy Failures: The NHIF-to-SHIF Transition and the SHA Maternity Bed Crisis The disruption of the Linda Mama programme through the transition to the Social Health Insurance Fund (SHIF) has reintroduced out-of-pocket costs for maternity services that the previous programme had eliminated. For women in the lowest wealth quintile — where ANC4 + completion is already only 53.9% — even modest financial barriers can be decisive in forgoing follow-up visits. This policy-induced regression risks undoing a decade of progress in skilled birth attendance and facility delivery rates. Compounding this, recent changes to the Social Health Authority (SHA) portal have eliminated an estimated 3,478 maternity beds — approximately 18.6% of Kenya's national maternity bed capacity — from Level 2 and 3 facilities (Nation Africa, 2026 ). Private hospitals have warned that this reduction will directly increase maternal deaths and push costs onto poor households who can least afford them (Nation Africa, 2026 ). The cruel irony is stark: at the very moment Kenya's Cabinet Secretary for Health is inaugurating a national MPDSR Steering Committee to surveil and respond to maternal deaths (MoH Kenya, 2026), a parallel policy decision is removing the physical infrastructure that safe delivery requires. This policy incoherence — investing in death surveillance while dismantling delivery capacity — is precisely the kind of systemic contradiction that a whole-of-government maternal health strategy must urgently resolve. The MamaTrace USSD platform proposed in this paper is designed to operate within this fractured system, connecting women to whatever care remains available — but it cannot substitute for the beds, staff, and financial protection that have been removed. 4.5 A Novel Policy Proposal: The MamaTrace USSD Platform A defining characteristic of Kenya's ANC dropout crisis is that it is most severe among precisely the women least likely to be reached by smartphone or internet-based mHealth interventions: rural women, poor women, and women in remote counties with limited connectivity. This study proposes the MamaTrace USSD Platform — a nationally integrated, equity-centred maternal health triage and ANC defaulter tracing system designed to function on any basic mobile phone, without internet access, data bundles, or smartphone capability, using the same technology that powers Kenya's M-PESA system. USSD (Unstructured Supplementary Service Data) operates over basic GSM networks through simple dial codes (e.g., *384#), presenting users with menu-driven text interfaces requiring no data connection and functioning even with zero airtime balance in emergency configurations. The proposed MamaTrace system operates across three integrated modules as detailed in Table 5 . Table 5 Proposed MamaTrace USSD Platform — Module Design and Functions Module Function Target Population Module 1: ANC Registration & Reminder At ANC1, clinic staff register the woman's phone number. Automated USSD messages remind her of upcoming visits in Swahili or local language. All pregnant women attending ANC1 at registered facilities Module 2: Danger Sign Triage A woman dials *MAMA# and answers 4 YES/NO questions about danger signs. The system triages risk and alerts the nearest Community Health Promoter or facility. Any pregnant or recently delivered woman, including ANC dropouts Module 3: ANC Defaulter Tracing When a registered woman misses a scheduled visit, the system sends a USSD follow-up and notifies her Community Health Promoter for in-person tracing within 48 hours. Women registered at ANC1 who miss subsequent appointments Source: Authors' original policy proposal based on KDHS 2022 findings and mHealth literature The equity credentials of this proposal are its defining strength. Unlike smartphone apps or WhatsApp-based interventions — which are disproportionately accessible to educated, urban, and wealthier women — a USSD-based system reaches the exact subgroups identified in this study as bearing the highest burden of ANC dropout. Evidence from sub-Saharan Africa confirms that mHealth interventions built on basic phone technologies achieve significantly higher adoption rates among underserved communities due to their frugal design and familiar interface (Afolabi et al., 2021 ). The MamaTrace platform would require partnership between the Ministry of Health, mobile network operators (Safaricom, Airtel Kenya), and county health departments — a model with precedent in Kenya's existing M-TIBA and DHIS2 infrastructure. 4.6 Complementary Policy Recommendations First , Kenya must urgently restore universal financial protection for maternity care under SHIF, ensuring ANC visits, facility delivery, and postnatal care are fully covered regardless of employment or insurance contribution history. Second , county health systems in Tana River, Turkana, Mandera, West Pokot, and Samburu require targeted investment in mobile health units, outreach ANC clinics, and emergency obstetric care capacity. Third , Kenya's Community Health Promoter programme must be strengthened and adequately remunerated to enable proactive, systematic follow-up of ANC defaulters within 48 hours of a missed appointment. Fourth , Kenya's devolved health governance must include mandatory county-level ANC completion targets with accountability mechanisms, treating the Tana River–Kajiado disparity as a governance failure requiring urgent political response. 4.7 Strengths and Limitations This study's strengths include the use of nationally representative, high-quality KDHS 2022 data with 95% response rate and coverage across all 47 counties. The recency of the data ensures findings reflect the current policy environment. Limitations include the cross-sectional design precluding causal inference, reliance on self-reported data with potential recall bias, and the conceptual — rather than empirically tested — nature of the MamaTrace proposal. Future research should prioritise a randomised controlled trial of MamaTrace in high-dropout counties. 5. Conclusion Kenya's maternal mortality crisis is not a crisis of awareness — nearly every pregnant woman in Kenya knows to attend an antenatal care clinic. It is a crisis of continuity, equity, and political will. This study has demonstrated, using the most current nationally representative data available, that ANC dropout from ANC1 to ANC4 + and beyond is massive, inequitable, and directly traceable to identifiable policy failures: inadequate financial protection for the poorest women, health system under-investment in the most marginalised counties, the absence of systematic defaulter tracing, and the recent regressive impact of the NHIF-to-SHIF transition. The proposed MamaTrace USSD platform represents a contextually appropriate, technologically feasible, and equity-centred innovation that leverages Kenya's existing mobile infrastructure to reach the women most at risk — connecting them to the health system before complications become catastrophic. Combined with restored universal financial protection, targeted county investment, and a strengthened community health workforce, this package of interventions offers a credible and evidence-based pathway toward reducing Kenya's maternal mortality ratio and fulfilling the promise of SDG 3.1. Every ANC visit missed is a risk undetected. Every risk undetected is a death that should never have happened. Kenya has the infrastructure, the data, and now the policy roadmap to change this — what is required is the commitment to act. Declarations This study was conducted in accordance with the Declaration of Helsinki. This research involved secondary analysis of an anonymised, publicly available dataset from the 2022 Kenya Demographic and Health Survey (KDHS), freely accessible at www.dhsprogram.com. As no primary data collection involving human participants was conducted, formal ethical approval from an Institutional Review Board or Ethics Committee was not required. Consent to Participate Not applicable. This study involved secondary analysis of publicly available anonymised data. No human participants were directly recruited or involved in primary data collection. Consent for Publication Not applicable. Competing Interests The author declares that there are no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The author conducted this study independently with no external funding. Author Contribution SJM conceptualised and designed the study, conducted the data analysis, interpreted the findings, developed the MamaTrace USSD policy proposal, and wrote, reviewed, and approved the final manuscript. Acknowledgements The author acknowledges the DHS Program for making the 2022 Kenya Demographic and Health Survey data publicly available. Data Availability The datasets analysed in this study are publicly available from the DHS Program repository at [www.dhsprogram.com](http:/www.dhsprogram.com) . Free registration is required to access the data. References Abuya T, Warren CE, Miller N, Njuki R, Ndwiga C, Maranga A, Bellows B. (2015). Exploring the prevalence of disrespect and abuse during childbirth in Kenya. PLoS ONE, 10(4), e0123606. Afolabi MO, et al. Mobile health interventions and their effects on ANC attendance in sub-Saharan Africa: A systematic review. J Global Health. 2021;11:04012. Andersen RM. Revisiting the behavioral model and access to medical care: Does it matter? J Health Soc Behav. 1995;36(1):1–10. Asratie MH, et al. Association between ANC contacts and facility-based delivery in low- and middle-income countries. BMC Pregnancy Childbirth. 2023;23:112. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. Wiley; 2000. Kenya National Bureau of Statistics (KNBS) & ICF. Kenya Demographic and Health Survey 2022. KNBS and ICF; 2023. Ministry of Health Kenya (MoH Kenya). (2026, February 23). Inaugural meeting of the National Maternal and Perinatal Death Surveillance and Response (MPDSR) Steering Committee. Government of Kenya. Nation Africa. Linda Mama disruption and SHIF transition impact on maternity services. Nation Media Group; 2025. Nation Africa. SHA portal changes eliminate 3,478 maternity beds from Level 2 and 3 facilities. Nation Media Group; 2026. Rutstein SO, Rojas G. (2006). Guide to DHS Statistics. ORC Macro. Tunçalp Ö, et al. WHO recommendations on antenatal care for a positive pregnancy experience. Lancet. 2017;390(10091):2661–72. UNFPA Kenya. Maternal health country profile: Kenya. United Nations Population Fund; 2025. UNICEF. Maternal mortality in sub-Saharan Africa: Progress and challenges. UNICEF; 2025. Wafula S, et al. Inequities in maternal mortality across Kenyan counties: A spatial analysis. BMC Public Health. 2023;23:445. World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. WHO; 2016. World Health Organization. Trends in maternal mortality 2000–2023. UNICEF, UNFPA, World Bank Group: WHO; 2023. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor invited by journal 13 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 07 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9057660","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619632398,"identity":"8a6c8932-3465-4544-9d65-96220cfb1ed7","order_by":0,"name":"Shadrack Mulingwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFAC5oYDYFoChCpAAswNBLQwIms5A9LCSFgLA1wLYxuyCA6gOyOx8XDBr8Py8rObH97mnVcbzd8O1PKjYhtOLWY3EhsOz+w7bLjhzjFja95tx3NnHGZsYOw5cxu/Ft6e24wbJBLMpHm3HcttAGphZmwjrMV+/oz0b9K8c47lzidKC8+P24kNN3KAtjTU5G4gqOXMQ6AtDf+TN9zIKbacc+xA7kagloN4/XI8+fBnnj9ptkCHbbzxpqYud975wwcf/KjArQUMINHBwMDEw3AYzDiAXz0I/IFq/cFQR1jxKBgFo2AUjDgAABHJZ8tScJJjAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Shadrack","middleName":"","lastName":"Mulingwa","suffix":""}],"badges":[],"createdAt":"2026-03-07 10:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9057660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9057660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106727656,"identity":"82c0f1cb-e81f-4697-bd46-bfe02767c4d4","added_by":"auto","created_at":"2026-04-12 18:40:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":858024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9057660/v1/851f441a-6367-4317-a707-23b8e908405d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAntenatal Care Dropout and Maternal Mortality in Kenya: \u003cstrong\u003ePolicy Gaps Revealed by the 2022 Kenya Demographic and Health Survey\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaternal mortality remains one of the most pressing public health challenges of the twenty-first century, particularly across sub-Saharan Africa. Globally, an estimated 295,000 women die each year from complications arising during pregnancy and childbirth, with approximately 86% of these deaths concentrated in sub-Saharan Africa and Southern Asia (WHO, 2023). The Sustainable Development Goal 3.1 (SDG 3.1) calls for a reduction in the global maternal mortality ratio (MMR) to fewer than 70 deaths per 100,000 live births by 2030 \u0026mdash; yet current trajectories in low- and middle-income countries (LMICs) suggest this target remains alarmingly out of reach (United Nations, 2023). The African region has reduced maternal mortality by 40% between 2000 and 2023, from 727 to 442 deaths per 100,000 live births; however, the continent still accounts for 70% of all global maternal deaths, with an estimated 178,000 mothers dying each year from largely preventable causes (UNICEF, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the current pace of reduction, sub-Saharan Africa is projected to reach approximately 350 maternal deaths per 100,000 live births by 2030 \u0026mdash; five times the SDG target \u0026mdash; signalling a profound and urgent policy failure.\u003c/p\u003e \u003cp\u003eKenya exemplifies the paradox facing many LMICs: measurable progress in maternal health indicators alongside persistently high and inequitable maternal mortality. The United Nations Population Fund (UNFPA) reports Kenya's MMR at 355 deaths per 100,000 live births, translating to nearly 5,000 women and girls dying annually from pregnancy-related complications (UNFPA Kenya, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Direct obstetric causes \u0026mdash; including hemorrhage, hypertensive disorders, and sepsis \u0026mdash; account for 73\u0026ndash;80% of these deaths, the vast majority of which are clinically preventable (Wafula et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In a significant policy development, on 23 February 2026, Cabinet Secretary for Health Aden Duale inaugurated Kenya's National Maternal and Perinatal Death Surveillance and Response (MPDSR) Steering Committee, explicitly calling on counties to prioritise the recruitment, equitable deployment, and motivation of frontline health workers as a central strategy for reducing maternal deaths (MoH Kenya, 2026). The government simultaneously announced a suite of system-strengthening interventions including a national Reproductive-Age Mortality Survey, digitisation of the MPDSR system, and enforcement of higher clinical standards covering triage, referral pathways, oxygen and blood availability, and 24-hour theatre readiness (MoH Kenya, 2026). These commitments are timely \u0026mdash; yet they stand in direct tension with a concurrent policy decision: changes to the Social Health Authority (SHA) portal have eliminated an estimated 3,478 maternity beds, approximately 18.6% of national maternity bed capacity, from Level 2 and 3 facilities, with private hospitals warning this will directly increase maternal deaths and out-of-pocket costs for poor households (Nation Africa, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This contradiction \u0026mdash; simultaneously investing in maternal death surveillance while removing the very beds women need to deliver safely \u0026mdash; encapsulates the policy incoherence that this paper argues is the root cause of Kenya's ANC dropout crisis and its deadly consequences. While skilled birth attendance has improved from 62% to approximately 70% over the past decade, over 80% of maternal deaths in Kenya are attributed not to the absence of care, but to poor quality of care at health facilities (UNFPA Kenya, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These figures underscore a critical insight: access to care alone is insufficient. The continuity, quality, and timing of antenatal care (ANC) throughout pregnancy is equally, if not more, determinative of maternal outcomes.\u003c/p\u003e \u003cp\u003eAntenatal care is a cornerstone of maternal and newborn health, serving as a critical platform for health promotion, risk screening, disease prevention, and birth preparedness (WHO, 2016). The World Health Organization recognises that ANC reduces maternal and perinatal morbidity and mortality both directly \u0026mdash; through the detection and treatment of pregnancy-related complications \u0026mdash; and indirectly, by identifying women at elevated risk during labour and delivery and ensuring timely referral to appropriate levels of care (WHO, 2016). In 2016, the WHO updated its global recommendations to a minimum of eight antenatal contacts (ANC8+), replacing the longstanding four-visit Focused ANC model, which had been associated with higher perinatal mortality in comparison (WHO, 2016; Tun\u0026ccedil;alp et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite high rates of initial ANC uptake, Kenya faces a severe and well-documented attrition problem across the ANC continuum. According to the 2022 Kenya Demographic and Health Survey (KDHS 2022), while 97.9% of expectant Kenyan women attended at least one antenatal clinic visit, only 66.0% attended four or more ANC visits, and a mere 4% achieved the WHO-recommended eight or more contacts (KNBS, 2023). Furthermore, only 28% of women initiated ANC during the first trimester as recommended. This dramatic drop-off \u0026mdash; from near-universal first-visit attendance to critically low completion rates \u0026mdash; defines what this paper terms the ANC dropout crisis. Missed subsequent visits mean missed blood pressure monitoring for preeclampsia, missed anaemia screening, missed malaria prophylaxis, and missed counselling on danger signs \u0026mdash; all missed opportunities to prevent deaths that should never occur.\u003c/p\u003e \u003cp\u003eANC dropout rates in Kenya are deeply stratified by education, wealth, geographic location, and county-level health system capacity. KDHS 2022 data reveal stark education-based inequalities: fewer than half of women with no formal education complete four or more ANC visits, compared to over 80% of women with higher education. Rural women, women in the lowest wealth quintile, and women in arid and semi-arid counties \u0026mdash; particularly Mandera, Wajir, and Turkana \u0026mdash; face the most severe dropout rates and simultaneously bear the highest burden of maternal mortality.\u003c/p\u003e \u003cp\u003eKenya has enacted several policy frameworks intended to improve maternal health outcomes, including the Linda Mama programme \u0026mdash; which provided free maternity services under the National Hospital Insurance Fund (NHIF) \u0026mdash; and the broader Universal Health Coverage (UHC) agenda. However, the recent transition from NHIF to the Social Health Insurance Fund (SHIF) in 2023 has introduced significant access gaps, with services once provided free now requiring out-of-pocket co-payments \u003cb\u003e(\u003c/b\u003eNation Africa, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Kenya's alignment with the WHO ANC8\u0026thinsp;+\u0026thinsp;model has not been accompanied by the operational investments necessary to make eight contacts a lived reality for the majority of Kenyan women.\u003c/p\u003e \u003cp\u003eUsing KDHS 2022 data, this study aims to: (i) describe the magnitude and patterns of ANC dropout across Kenya's continuum of care from ANC1 to ANC8+; (ii) identify subgroup inequalities in ANC completion by education, wealth, residence, parity, and county; (iii) examine the association between ANC dropout and adverse maternal outcomes; and (iv) critically appraise the policy environment to identify structural gaps that perpetuate ANC attrition and contribute to preventable maternal deaths.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional study design using secondary analysis of nationally representative survey data. A cross-sectional design was deemed appropriate given that the study objective is to characterise the prevalence and distribution of ANC dropout and its associated policy-relevant factors at a single point in time, rather than to follow a cohort of women prospectively. Secondary data analysis of Demographic and Health Survey (DHS) data is a well-established methodology in global maternal health research, enabling rigorous, population-level inference at low cost and with high external validity (Rutstein \u0026amp; Rojas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source\u003c/h2\u003e \u003cp\u003eData were drawn from the 2022 Kenya Demographic and Health Survey (KDHS 2022), the seventh DHS survey implemented in Kenya since 1989. The KDHS 2022 was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health, with technical assistance from ICF through The DHS Program. Data collection took place between February 17 and July 19, 2022, across all 47 counties of Kenya. The survey employed a two-stage stratified cluster sampling design. In the first stage, 1,692 clusters were selected; in the second stage, 25 households were systematically selected from each cluster, yielding a total sample of 42,022 households. Of these, 37,911 households were successfully interviewed (response rate: 98%). A total of 32,156 women aged 15\u0026ndash;49 completed individual interviews (women's response rate: 95%) (KNBS \u0026amp; ICF, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study Population and Eligibility Criteria\u003c/h2\u003e \u003cp\u003eThe target population was women aged 15\u0026ndash;49 years with at least one live birth in the five years preceding the 2022 KDHS survey. Women were included if they had complete records on ANC utilisation for their most recent birth. Women with missing or inconsistent ANC data were excluded. The final analytical sample comprised 6,847 women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variable Definition and Measurement\u003c/h2\u003e \u003cp\u003eThe primary outcome variable was ANC dropout, defined as attendance at ANC1 without completion of ANC4+. ANC8\u0026thinsp;+\u0026thinsp;completion and timing of first ANC visit were treated as secondary outcomes. Independent variables included maternal age, education level, wealth quintile, residence type (urban/rural), county, marital status, parity, health insurance status, and perceived distance to health facility \u0026mdash; organised using Andersen's Behavioural Model of Health Services Use (Andersen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using survey-weighted estimates from published KDHS 2022 tables\u003c/p\u003e \u003cp\u003eto account for the complex survey sampling design \u003cb\u003e(\u003c/b\u003eHosmer \u0026amp; Lemeshow, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDescriptive statistics characterised ANC completion rates nationally and by key subgroups. County-level ANC completion rates were examined to map geographic disparities. Associations between subgroup characteristics and ANC dropout are presented as stratified prevalence estimates with comparison across categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003e The KDHS 2022 was approved by the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Committee and the ICF Institutional Review Board. All participants provided informed consent. The dataset was accessed through a formal request to The DHS Program. No additional ethical approval was required for this secondary analysis of de-identified published data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 National ANC Attendance Patterns\u003c/h2\u003e \u003cp\u003eA total of 6,847 women with a live birth in the two years preceding the KDHS 2022 survey were included. While 97.9% received ANC from a skilled provider, only 66.0% attended ANC4+, and only 4% achieved ANC8+. Only 72.5% received a postnatal check within two days of delivery, indicating care fragmentation extending beyond the antenatal period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ANC Attendance by Maternal Age\u003c/h2\u003e \u003cp\u003eYounger women exhibited notably lower ANC4\u0026thinsp;+\u0026thinsp;completion rates. Among women aged under 20 years, only 57.1% completed ANC4+, compared to 68.7% among women aged 20\u0026ndash;34 and 59.9% among women aged 35\u0026ndash;49. Adolescent mothers also had the lowest rates of iron supplementation (86.3%) and postnatal check coverage (71.9%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 ANC Attendance by Residence\u003c/h2\u003e \u003cp\u003eA significant urban-rural divide was observed. Urban women achieved an ANC4\u0026thinsp;+\u0026thinsp;completion rate of 74.1%, compared to only 61.5% among rural women \u0026mdash; a gap of 12.6 percentage points. Urban women were also more likely to deliver in a health facility (91.7% versus 77.0%) and receive a postnatal check (79.0% versus 68.8%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ANC Attendance by Education Level\u003c/h2\u003e \u003cp\u003eEducation level demonstrated the strongest gradient in ANC completion. Among women with no formal education, only 49.1% completed ANC4+, rising progressively to 59.6% (primary), 67.8% (secondary), and 83.2% (more than secondary) \u0026mdash; a 34.1 percentage point gap between the lowest and highest education groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANC and Maternal Health Indicators by Education Level (KDHS 2022, Live Births)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Education Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkilled ANC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC4+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacility Delivery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePostnatal Check (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.2\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\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.7\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\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.5\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\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSource: KDHS 2022, Table\u0026nbsp;10, p. 26\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 ANC Attendance by Wealth Quintile\u003c/h2\u003e \u003cp\u003eHousehold wealth quintile showed a clear positive gradient in ANC4\u0026thinsp;+\u0026thinsp;completion: from 53.9% in the lowest quintile to 82.0% in the highest \u0026mdash; a gap of 28.1 percentage points. Women in the poorest quintile were also less likely to deliver in a health facility (62.6% vs 91.3%) and receive a postnatal check (58.9% vs 82.8%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANC and Maternal Health Indicators by Wealth Quintile (KDHS 2022, Live Births)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Quintile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkilled ANC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC4+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacility Delivery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePostnatal Check (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.2\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\u003e98.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.8\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\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSource: KDHS 2022, Table\u0026nbsp;10, p. 26\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 County-Level Disparities in ANC Completion\u003c/h2\u003e \u003cp\u003eCounty-level data revealed dramatic geographic disparities. The highest ANC4\u0026thinsp;+\u0026thinsp;rates were in Kajiado, Nyamira, Kisumu, and Kirinyaga (all 100.0%). The lowest were in Tana River (11.9%), Turkana (35.0%), West Pokot (35.0%), and Mandera (40.4%) \u0026mdash; all far below the national average of 66.0%. Nairobi recorded 80.5%, suggesting urban poverty creates unique barriers not captured by simple urban-rural classifications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Summary of Key Findings\u003c/h2\u003e \u003cp\u003eKenya's ANC dropout crisis is real, widespread, and deeply inequitable. Despite near-universal ANC initiation (97.9%), only two-thirds of women complete the minimum four visits. Dropout is systematically concentrated among the most vulnerable \u0026mdash; those with no education, in the poorest quintile, rural residents, adolescent mothers, and women in marginalised counties. These are the predictable outcomes of structural policy gaps that the discussion section now examines.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal Findings and Their Significance\u003c/h2\u003e \u003cp\u003eThis study used nationally representative KDHS 2022 data to characterise ANC dropout across Kenya. The central finding is stark: despite near-universal ANC initiation (97.9%), Kenya loses approximately one in three pregnant women before they complete ANC4+, and loses more than nine in ten before they reach ANC8+. This dropout is systematically concentrated among the most vulnerable women \u0026mdash; those with no formal education (49.1%), in the lowest wealth quintile (53.9%), rural residents (61.5%), adolescent mothers (57.1%), and women in Tana River (11.9%), Turkana (35.0%), West Pokot (35.0%), and Mandera (40.4%). These figures represent thousands of undetected cases of preeclampsia, anaemia, malpresentation, and infection \u0026mdash; contributing directly to Kenya's MMR of 355 deaths per 100,000 live births.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Demand-Side Barriers to ANC Completion\u003c/h2\u003e \u003cp\u003eThe strong education and wealth gradients in ANC completion point to demand-side barriers as a primary driver of dropout. Education shapes a woman's ability to understand the clinical purpose of repeated ANC visits, navigate health systems, and recognise danger signs. The 34.1 percentage point gap between women with no education and those with higher education is among the largest equity differentials in any maternal health indicator in the KDHS 2022 dataset. Economic barriers \u0026mdash; transportation costs, lost wages, and parity-related complacency \u0026mdash; are equally pervasive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Supply-Side Barriers and Health System Failures\u003c/h2\u003e \u003cp\u003eSupply-side failures compound demand-side barriers. The urban-rural gap (74.1% vs 61.5%) reflects structural inadequacy of health facility distribution in rural Kenya. In arid and semi-arid counties such as Turkana, Mandera, and West Pokot, these barriers are amplified by nomadic populations, poor road infrastructure, and acute health worker shortages \u0026mdash; rendering facility-based ANC models ill-suited to local realities. Poor quality of care, including long waiting times and disrespectful treatment, further erodes women's motivation to return after their first visit (Abuya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Policy Failures: The NHIF-to-SHIF Transition and the SHA Maternity Bed Crisis\u003c/h2\u003e \u003cp\u003eThe disruption of the Linda Mama programme through the transition to the Social Health Insurance Fund (SHIF) has reintroduced out-of-pocket costs for maternity services that the previous programme had eliminated. For women in the lowest wealth quintile \u0026mdash; where ANC4\u0026thinsp;+\u0026thinsp;completion is already only 53.9% \u0026mdash; even modest financial barriers can be decisive in forgoing follow-up visits. This policy-induced regression risks undoing a decade of progress in skilled birth attendance and facility delivery rates. Compounding this, recent changes to the Social Health Authority (SHA) portal have eliminated an estimated 3,478 maternity beds \u0026mdash; approximately 18.6% of Kenya's national maternity bed capacity \u0026mdash; from Level 2 and 3 facilities (Nation Africa, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Private hospitals have warned that this reduction will directly increase maternal deaths and push costs onto poor households who can least afford them (Nation Africa, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The cruel irony is stark: at the very moment Kenya's Cabinet Secretary for Health is inaugurating a national MPDSR Steering Committee to surveil and respond to maternal deaths (MoH Kenya, 2026), a parallel policy decision is removing the physical infrastructure that safe delivery requires. This policy incoherence \u0026mdash; investing in death surveillance while dismantling delivery capacity \u0026mdash; is precisely the kind of systemic contradiction that a whole-of-government maternal health strategy must urgently resolve. The MamaTrace USSD platform proposed in this paper is designed to operate within this fractured system, connecting women to whatever care remains available \u0026mdash; but it cannot substitute for the beds, staff, and financial protection that have been removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 A Novel Policy Proposal: The MamaTrace USSD Platform\u003c/h2\u003e \u003cp\u003eA defining characteristic of Kenya's ANC dropout crisis is that it is most severe among precisely the women least likely to be reached by smartphone or internet-based mHealth interventions: rural women, poor women, and women in remote counties with limited connectivity. This study proposes the MamaTrace USSD Platform \u0026mdash; a nationally integrated, equity-centred maternal health triage and ANC defaulter tracing system designed to function on any basic mobile phone, without internet access, data bundles, or smartphone capability, using the same technology that powers Kenya's M-PESA system.\u003c/p\u003e \u003cp\u003eUSSD (Unstructured Supplementary Service Data) operates over basic GSM networks through simple dial codes (e.g., *384#), presenting users with menu-driven text interfaces requiring no data connection and functioning even with zero airtime balance in emergency configurations. The proposed MamaTrace system operates across three integrated modules as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProposed MamaTrace USSD Platform \u0026mdash; Module Design and Functions\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget Population\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\u003eModule 1: ANC Registration \u0026amp; Reminder\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt ANC1, clinic staff register the woman's phone number. Automated USSD messages remind her of upcoming visits in Swahili or local language.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll pregnant women attending ANC1 at registered facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModule 2: Danger Sign Triage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA woman dials *MAMA# and answers 4 YES/NO questions about danger signs. The system triages risk and alerts the nearest Community Health Promoter or facility.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAny pregnant or recently delivered woman, including ANC dropouts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModule 3: ANC Defaulter Tracing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen a registered woman misses a scheduled visit, the system sends a USSD follow-up and notifies her Community Health Promoter for in-person tracing within 48 hours.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen registered at ANC1 who miss subsequent appointments\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\u003e \u003cem\u003eSource: Authors' original policy proposal based on KDHS 2022 findings and mHealth literature\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe equity credentials of this proposal are its defining strength. Unlike smartphone apps or WhatsApp-based interventions \u0026mdash; which are disproportionately accessible to educated, urban, and wealthier women \u0026mdash; a USSD-based system reaches the exact subgroups identified in this study as bearing the highest burden of ANC dropout. Evidence from sub-Saharan Africa confirms that mHealth interventions built on basic phone technologies achieve significantly higher adoption rates among underserved communities due to their frugal design and familiar interface (Afolabi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The MamaTrace platform would require partnership between the Ministry of Health, mobile network operators (Safaricom, Airtel Kenya), and county health departments \u0026mdash; a model with precedent in Kenya's existing M-TIBA and DHIS2 infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Complementary Policy Recommendations\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, Kenya must urgently restore universal financial protection for maternity care under SHIF, ensuring ANC visits, facility delivery, and postnatal care are fully covered regardless of employment or insurance contribution history.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, county health systems in Tana River, Turkana, Mandera, West Pokot, and Samburu require targeted investment in mobile health units, outreach ANC clinics, and emergency obstetric care capacity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, Kenya's Community Health Promoter programme must be strengthened and adequately remunerated to enable proactive, systematic follow-up of ANC defaulters within 48 hours of a missed appointment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth\u003c/b\u003e, Kenya's devolved health governance must include mandatory county-level ANC completion targets with accountability mechanisms, treating the Tana River\u0026ndash;Kajiado disparity as a governance failure requiring urgent political response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study's strengths include the use of nationally representative, high-quality KDHS 2022 data with 95% response rate and coverage across all 47 counties. The recency of the data ensures findings reflect the current policy environment. Limitations include the cross-sectional design precluding causal inference, reliance on self-reported data with potential recall bias, and the conceptual \u0026mdash; rather than empirically tested \u0026mdash; nature of the MamaTrace proposal. Future research should prioritise a randomised controlled trial of MamaTrace in high-dropout counties.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eKenya's maternal mortality crisis is not a crisis of awareness \u0026mdash; nearly every pregnant woman in Kenya knows to attend an antenatal care clinic. It is a crisis of continuity, equity, and political will. This study has demonstrated, using the most current nationally representative data available, that ANC dropout from ANC1 to ANC4\u0026thinsp;+\u0026thinsp;and beyond is massive, inequitable, and directly traceable to identifiable policy failures: inadequate financial protection for the poorest women, health system under-investment in the most marginalised counties, the absence of systematic defaulter tracing, and the recent regressive impact of the NHIF-to-SHIF transition.\u003c/p\u003e \u003cp\u003eThe proposed MamaTrace USSD platform represents a contextually appropriate, technologically feasible, and equity-centred innovation that leverages Kenya's existing mobile infrastructure to reach the women most at risk \u0026mdash; connecting them to the health system before complications become catastrophic. Combined with restored universal financial protection, targeted county investment, and a strengthened community health workforce, this package of interventions offers a credible and evidence-based pathway toward reducing Kenya's maternal mortality ratio and fulfilling the promise of SDG 3.1.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEvery ANC visit missed is a risk undetected. Every risk undetected is a death that should never have happened. Kenya has the infrastructure, the data, and now the policy roadmap to change this \u0026mdash; what is required is the commitment to act.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. This research involved secondary analysis of an anonymised, publicly available dataset from the 2022 Kenya Demographic and Health Survey (KDHS), freely accessible at www.dhsprogram.com. As no primary data collection involving human participants was conducted, formal ethical approval from an Institutional Review Board or Ethics Committee was not required.\u003c/p\u003e\n\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. This study involved secondary analysis of publicly available anonymised data. No human participants were directly recruited or involved in primary data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The author conducted this study independently with no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSJM conceptualised and designed the study, conducted the data analysis, interpreted the findings, developed the MamaTrace USSD policy proposal, and wrote, reviewed, and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe author acknowledges the DHS Program for making the 2022 Kenya Demographic and Health Survey data publicly available.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets analysed in this study are publicly available from the DHS Program repository at [www.dhsprogram.com](http:/www.dhsprogram.com) . Free registration is required to access the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbuya T, Warren CE, Miller N, Njuki R, Ndwiga C, Maranga A, Bellows B. (2015). Exploring the prevalence of disrespect and abuse during childbirth in Kenya. PLoS ONE, 10(4), e0123606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfolabi MO, et al. Mobile health interventions and their effects on ANC attendance in sub-Saharan Africa: A systematic review. J Global Health. 2021;11:04012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen RM. Revisiting the behavioral model and access to medical care: Does it matter? J Health Soc Behav. 1995;36(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsratie MH, et al. Association between ANC contacts and facility-based delivery in low- and middle-income countries. BMC Pregnancy Childbirth. 2023;23:112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. Wiley; 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKenya National Bureau of Statistics (KNBS) \u0026amp; ICF. Kenya Demographic and Health Survey 2022. KNBS and ICF; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health Kenya (MoH Kenya). (2026, February 23). Inaugural meeting of the National Maternal and Perinatal Death Surveillance and Response (MPDSR) Steering Committee. Government of Kenya.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNation Africa. Linda Mama disruption and SHIF transition impact on maternity services. Nation Media Group; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNation Africa. SHA portal changes eliminate 3,478 maternity beds from Level 2 and 3 facilities. Nation Media Group; 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutstein SO, Rojas G. (2006). Guide to DHS Statistics. ORC Macro.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTun\u0026ccedil;alp \u0026Ouml;, et al. WHO recommendations on antenatal care for a positive pregnancy experience. Lancet. 2017;390(10091):2661\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNFPA Kenya. Maternal health country profile: Kenya. United Nations Population Fund; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF. Maternal mortality in sub-Saharan Africa: Progress and challenges. UNICEF; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWafula S, et al. Inequities in maternal mortality across Kenyan counties: A spatial analysis. BMC Public Health. 2023;23:445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. WHO; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Trends in maternal mortality 2000\u0026ndash;2023. UNICEF, UNFPA, World Bank Group: WHO; 2023.\u003c/span\u003e\u003c/li\u003e\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"antenatal care, ANC dropout, maternal mortality, Kenya, KDHS 2022, USSD, mHealth, equity, policy, sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-9057660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9057660/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 a critical public health challenge in Kenya, with a maternal mortality ratio (MMR) of 355 deaths per 100,000 live births \u0026mdash; five times higher than the Sustainable Development Goal 3.1 target of 70 by 2030. Antenatal care (ANC) is a proven platform for reducing maternal morbidity and mortality; however, Kenya faces a severe ANC dropout crisis in which near-universal first-visit attendance (97.9%) is not matched by completion of the recommended four or more visits (ANC4+) or the WHO-recommended eight or more contacts (ANC8+). The structural and policy determinants of this dropout, and their implications for maternal survival, remain insufficiently characterised in the current literature.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design using secondary analysis of published estimates from the 2022 Kenya Demographic and Health Survey (KDHS 2022), a nationally representative household survey conducted across all 47 counties of Kenya with a women's response rate of 95% (n\u0026thinsp;=\u0026thinsp;32,156). The analytical sample comprised 6,847 women aged 15\u0026ndash;49 with a live birth in the two years preceding the survey. ANC dropout was examined by maternal age, education level, wealth quintile, residence type, and county of residence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWhile 97.9% of women initiated ANC with a skilled provider, only 66.0% completed ANC4\u0026thinsp;+\u0026thinsp;and a mere 4% achieved ANC8+. ANC dropout was systematically concentrated among women with no formal education (ANC4+: 49.1%), women in the lowest wealth quintile (53.9%), rural residents (61.5%), and adolescent mothers (57.1%). County-level disparities were dramatic, ranging from 100% ANC4\u0026thinsp;+\u0026thinsp;completion in Kajiado and Kisumu to 11.9% in Tana River, 35.0% in Turkana, and 40.4% in Mandera.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eANC dropout in Kenya is driven by intersecting demand-side barriers and supply-side failures, compounded by the regressive impact of the recent NHIF-to-SHIF policy transition on maternity care access. This study proposes the MamaTrace USSD Platform \u0026mdash; a novel, equity-centred maternal triage and ANC defaulter tracing system built on USSD technology \u0026mdash; as a contextually appropriate intervention capable of reaching women with basic feature phones in remote, low-connectivity settings.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eKenya's ANC dropout crisis is a policy failure, not an individual one. Closing the gap between ANC1 initiation and ANC8\u0026thinsp;+\u0026thinsp;completion requires restored universal financial protection for maternity care, targeted health system investment in high-dropout counties, a strengthened community health workforce, and deployment of innovative equity-focused digital solutions such as MamaTrace.\u003c/p\u003e","manuscriptTitle":"Antenatal Care Dropout and Maternal Mortality in Kenya: Policy Gaps Revealed by the 2022 Kenya Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 21:44:54","doi":"10.21203/rs.3.rs-9057660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"15787218638627025610912105644774013630","date":"2026-04-08T11:56:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T04:25:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-13T05:50:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T09:26:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T09:25:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-07T10:23:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e865d87-bf55-42b7-9c69-63f1fd9293b8","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T21:44:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 21:44:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9057660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9057660","identity":"rs-9057660","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}