When Multiple Providers Attend Birth: Patterns, Determinants, and Outcomes of Shared Delivery Assistance in Kenya

When Multiple Providers Attend Birth: Patterns, Determinants, and Outcomes of Shared Delivery Assistance 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 When Multiple Providers Attend Birth: Patterns, Determinants, and Outcomes of Shared Delivery Assistance in Kenya Charles Wanjiku This is a preprint; it has not been peer reviewed by a journal.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-9123470/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Studies of birth attendance in sub-Saharan Africa have almost universally relied on a binary measure skilled versus unskilled that collapses fundamentally different care configurations into a single category. This framing ignores the reality that many births involve more than one type of attendant simultaneously. This study moves beyond the binary by examining the full range of birth attendance patterns in Kenya, characterising who uses each configuration and whether different patterns carry distinct clinical implications. Methods : We analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), using a sample of 10,391 women aged 15–49 who had a live birth in the five years preceding the survey. Six mutually exclusive birth attendance patterns were constructed from individual provider variables. Survey-weighted multinomial logistic regression identified independent determinants of each pattern compared to skilled-only attendance. Survey-weighted binary logistic regression examined the association between birth attendance pattern and caesarean section delivery, adjusting for education, wealth, residence, age, and parity. Results : Skilled-only attendance accounted for 82.0% of births. Traditional-only attendance (TBA or relative without a skilled provider) accounted for 15.0%, and 1.7% of women reported no attendant at all. Mixed provider care, involving a skilled provider alongside a TBA or relative, was rare at 1.3%. Traditional-only and unattended births were strongly predicted by lack of education, poverty, and rural residence; each step increase in education or wealth reduced the odds of traditional-only birth by 57–96%. Mixed care was uniquely predicted by rural residence (OR 3.98, 95% CI 2.06–7.68) with no education or wealth gradient. Birth attendance pattern independently predicted caesarean section after full adjustment. Women in the mixed care group had 64% lower odds of caesarean compared to the skilled-only group (OR 0.36, 95% CI 0.15–0.86). Conclusions : The persistence of traditional-only births in Kenya is not simply a rural phenomenon it is a poverty and education phenomenon that rural residence reflects. Mixed provider care, though rare, is qualitatively distinct from both skilled-only and traditional-only care, and its independent association with lower caesarean rates warrants further investigation. The binary skilled birth attendant measure obscures these differences and should be supplemented with pattern-based approaches in national monitoring. birth attendance skilled birth attendant traditional birth attendant caesarean section Kenya maternity care patterns 1. Introduction The skilled birth attendant (SBA) is defined by the World Health Organization as the proportion of births attended by a doctor, nurse, or midwife has been the primary metric for measuring progress toward safe delivery care globally for over two decades (WHO, 2004). It was built into the Millennium Development Goals, retained in the Sustainable Development Goal framework, and remains central to Kenya's reproductive health monitoring (United Nations, 2015; KNBS, 2023). The rationale is straightforward: skilled providers are trained to manage obstetric emergencies, and their presence at birth is the most effective single intervention for reducing intrapartum maternal and neonatal mortality (Bhutta et al., 2014). Yet the SBA indicator has a structural limitation that has received insufficient critical attention. It is a binary measure that collapses the full spectrum of birth attendance into two categories skilled or unskilled without regard for who else may be present. In practice, birth attendance is not binary. A significant number of births worldwide are attended by more than one person, and the combination of attendant types matters in ways that the standard indicator cannot capture. A birth attended jointly by a nurse-midwife and a traditional birth attendant (TBA) is categorised identically to a birth attended by a nurse-midwife alone, even though the presence of the TBA may change the dynamics of care substantially. In Kenya, this ambiguity is particularly consequential. TBAs have played a central role in communities for generations, and despite formal policies discouraging TBA-attended births since the 1990s, they remain active in rural areas sometimes independently, sometimes in collaboration with facility staff or alongside family members during home births (Mrisho et al., 2008; Moyer et al., 2014). Simultaneously, a significant number of Kenyan women deliver in facilities without any informal support, while others deliver at home in the sole presence of relatives. These configurations have different implications for the birth experience, for the likelihood of accessing emergency care, and potentially for clinical outcomes, yet the literature has not examined them as distinct categories. Two broad gaps motivate this study. First, the prevalence and sociodemographic distribution of different birth attendance combinations has not been described at national scale in Kenya. We know that 82% of Kenyan births are attended by a skilled provider (KNBS, 2023), but we know almost nothing about the full composition of birth assistance within that figure—or within the 18% of births that fall outside it. Second, the potential outcome differences across birth attendance patterns have not been assessed. The association between attendance pattern and caesarean section is of particular interest, because caesarean delivery rates vary sharply between facility and home settings and may also vary within facility births depending on who else is present and why. This study addresses both gaps using data from the 2022 KDHS. We pursue three specific objectives: to describe the prevalence and place-of-delivery distribution of birth attendance patterns in Kenya; to identify independent sociodemographic determinants of each pattern; and to examine the relationship between birth attendance pattern and caesarean section delivery after adjusting for measured confounders. 2. Background 2.1 The Skilled Birth Attendant Framework and Its Limitations The WHO definition of a skilled birth attendant was formalised in 2004 and refined in 2018 to emphasise not only professional credentials but the enabling environment—equipment, medicines, and referral capacity—within which the skilled provider works (WHO, 2004; Homer et al., 2014). This refinement acknowledged that a credentialled provider working in an under-resourced setting may not deliver the full benefit attributed to SBA care. However, neither definition addresses the question of who else attends the birth alongside the skilled provider, or whether the presence of additional attendants modifies the nature of care. Critics of the SBA framework have noted that it was constructed primarily from the perspective of clinical competence, without adequate attention to the social dimensions of birth (Kruske & Barclay, 2004). TBAs, despite lacking clinical training, provide continuous labour support, emotional reassurance, and community-embedded care that skilled providers in stretched public facilities frequently cannot offer. Evidence from randomised controlled trials consistently shows that continuous labour support—the kind TBAs or relatives are more likely to provide than facility staff—reduces caesarean section rates, shortens labour duration, and improves women's birth experiences (Hodnett et al., 2013; Bohren et al., 2017). This evidence base challenges the assumption that the presence of a TBA or relative alongside a skilled provider is straightforwardly harmful or irrelevant. The question of TBA integration has been contentious in global health policy. Throughout the 1970s and 1980s, TBA training programmes were widely implemented across sub-Saharan Africa as a strategy for improving perinatal outcomes in settings where skilled providers were scarce (WHO, 1992). These programmes fell out of favour in the 1990s and 2000s, when systematic evidence found that TBA training alone did not reduce maternal mortality (Sibley et al., 2007; Prost et al., 2013). The policy response was to shift resources entirely toward facility-based skilled care and to discourage TBA practice. Kenya's official policy has reflected this shift. Yet the evidence against TBA training is specifically evidence against TBA training programmes in which TBAs work independently; it does not address collaborative models in which TBAs work alongside skilled providers. 2.2 Birth Attendance in the Kenyan Context Kenya's progress toward universal skilled birth attendance has been substantial but uneven. The free maternity services policy (Linda Mama) introduced in 2013 removed formal delivery fees at public facilities, producing a rapid increase in facility deliveries that disproportionately benefited poorer women (Gitobu et al., 2018). By 2022, 76% of Kenyan births occurred in a health facility (KNBS, 2023), up from 61% in 2014. Yet geography and socioeconomic status continue to stratify the distribution of facility births significantly—women in the poorest quintiles, in rural counties, and with no formal education remain substantially less likely to deliver in a facility than their better-off urban counterparts. TBAs continue to operate in Kenya despite official discouragement. Qualitative research has documented their sustained presence in rural communities, their roles as trusted community figures, and the practical constraints that lead women to depend on them—particularly distance to facilities, high transport costs, and previous negative experiences with formal health services (Moyer et al., 2014; Bohren et al., 2014). The 2022 KDHS data confirm that TBA attendance, while declining, remains measurable: 11.1% of women with recent births reported a TBA present at their delivery, with this figure almost certainly higher in rural and poor communities. Critically, the KDHS collects information on all attendants present at delivery, not just the primary one. This feature of the dataset creates an opportunity to examine combination patterns that the standard SBA indicator cannot reveal. Prior studies using KDHS data from Kenya and other countries have not exploited this feature. 2.3 Birth Attendance and Caesarean Section Caesarean section rates have risen sharply across East Africa, including Kenya, over the past two decades. The WHO recommends a population-level caesarean rate of 10–15%, on the grounds that below this range there is insufficient access to emergency caesareans and above it, the risks associated with unnecessary operative delivery begin to outweigh the benefits (WHO, 2015). Kenya's 2022 KDHS reports an overall caesarean rate of 13.6%, which sits within the recommended range but conceals extraordinary variation: caesarean rates are near zero for home births attended by TBAs and several times the WHO upper threshold for facility births among wealthy and educated women. The association between birth attendance and caesarean section is partly structural—caesareans can only occur in facilities—but it is not entirely explained by place of delivery. Within facility births, caesarean rates vary significantly by socioeconomic status, maternal age, and clinical risk factors. Evidence from high-income countries suggests that continuous labour support by a non-clinical companion during facility delivery reduces the likelihood of caesarean delivery, independent of clinical risk (Bohren et al., 2017). Whether the presence of a TBA or family member at a facility birth in Kenya operates through a similar mechanism has not been examined. 3. Methods 3.1 Data Source and Study Population This study used data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional household survey conducted by the Kenya National Bureau of Statistics with technical support from ICF International. The 2022 KDHS employed a stratified two-stage cluster sampling design, selecting 1,691 primary sampling units across 92 strata. A total of 32,156 women aged 15–49 completed individual interviews. The final KDHS report provides a full account of sampling and data collection procedures (KNBS, 2023). The analytic sample was restricted to women with a live birth in the five years preceding the survey, as birth attendance variables pertain to the most recent birth. This yielded a sample of 10,391 women. The five-year reference window is standard in DHS-based studies and balances sample adequacy against recall error. 3.2 Outcome Variables The primary outcome was birth attendance pattern, constructed from five individual KDHS variables capturing whether each of the following was present at delivery: doctor (m3a_1), nurse or midwife (m3b_1), traditional birth attendant (m3g_1), relative or friend (m3h_1), and other attendant (m3k_1). These variables are not mutually exclusive—a woman could report multiple attendants enabling the construction of combination patterns. We constructed six mutually exclusive patterns. Skilled only was assigned to women reporting a doctor and/or nurse but no TBA or relative. Traditional only was assigned to women reporting a TBA and/or relative but no skilled provider. No assistance was assigned to women reporting no attendant of any type. Skilled plus TBA captured births attended by a skilled provider together with a TBA, with or without a relative. Skilled plus Relative captured births attended by a skilled provider and a relative, without a TBA. Other captured the three cases not fitting the above categories. For the multivariate analysis of caesarean section, the Skilled+TBA, Skilled+Relative, and Other categories were combined into a single Mixed care category, given their small individual sample sizes. The secondary outcome was caesarean section delivery, derived from the binary KDHS variable m17_1. 3.3 Independent Variables Sociodemographic predictors were selected based on prior literature and included: maternal education (no education, primary, secondary, higher); household wealth index quintile (poorest through richest); place of residence (urban, rural); maternal age in five-year groups (15–19 through 45–49); and parity, categorised as 0, 1, 2, 3, and 4 or more children ever born. The DHS wealth index is generated from household asset data using principal component analysis and reflects relative economic standing within the national population (Rutstein & Johnson, 2004). 3.4 Statistical Analysis All analyses were conducted in Stata/MP 17 (StataCorp LLC) using svy commands to account for the complex sampling design with sampling weights (v005), primary sampling units (v021), and strata (v022). Weighted frequencies and percentages described the distribution of birth attendance patterns and sample characteristics. Cross-tabulations of patterns by place of delivery were examined using chi-square tests with Rao-Scott corrections. Determinants of birth attendance pattern were examined using survey-weighted multinomial logistic regression, with skilled-only attendance as the reference category. Separate models estimated odds ratios for traditional-only attendance, no assistance, and mixed care, each compared to the skilled-only reference. A separate model for the skilled-only outcome itself (versus all other patterns) was also fitted to identify predictors of the dominant pattern. The association between birth attendance pattern and caesarean section was examined using survey-weighted binary logistic regression. The model adjusted simultaneously for attendance pattern, education, wealth, residence, age, and parity. The no-assistance category was excluded from this model because it perfectly predicted non-caesarean delivery (zero caesareans in that group), which precluded estimation. Statistical significance was set at p < 0.05. 3.5 Ethical Considerations The 2022 KDHS was approved by the Kenya Medical Research Institute Scientific and Ethics Review Unit and the ICF International Institutional Review Board. All survey respondents provided informed consent prior to interview. This secondary analysis used fully de-identified, publicly available data and was exempt from further ethical review. Data were accessed through the DHS Programme website (https://dhsprogram.com). 4. Results 4.1 Sample Characteristics Table 1 presents the sociodemographic characteristics of the 10,391 women included in the analysis. The sample was predominantly rural (65.4%) and spread across educational levels, with 20.0% reporting no formal education, 33.8% primary, 31.6% secondary, and 14.6% higher education. Nearly half (48.2%) of women came from the two lowest wealth quintiles. The modal age group was 25–29 years (27.4%). Parity was broadly distributed, with 27.2% of women having four or more children ever born. Table 1. Sociodemographic Characteristics of Women with a Live Birth in the Past Five Years (N = 10,391) Characteristic Unweighted n Weighted % Education No education 2,082 20.0 Primary 3,507 33.8 Secondary 3,283 31.6 Higher 1,519 14.6 Wealth index quintile 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 Residence Urban 3,596 34.6 Rural 6,795 65.4 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 Parity 0 8,813 27.4 1 5,119 15.9 2 5,115 15.9 3 4,374 13.6 4 or more 8,735 27.2 Source: 2022 Kenya Demographic and Health Survey. All percentages are survey-weighted. 4.2 Prevalence and Distribution of Birth Attendance Patterns Table 2 shows the distribution of birth attendance patterns. Skilled-only attendance dominated, accounting for 82.0% of births. Traditional-only attendance was the second most common configuration at 15.0%. No assistance—births with no reported attendant—accounted for 1.7%. Mixed care involving a skilled provider alongside a TBA or relative was rare in aggregate, at 1.3%, comprising 0.7% skilled-plus-relative and 0.5% skilled-plus-TBA combinations. Table 2. Distribution of Birth Attendance Patterns among Women with a Recent Birth (N = 10,391) Birth Attendance Pattern Unweighted n Weighted % Skilled only (doctor and/or nurse, no TBA or relative) 8,525 82.0 Traditional only (TBA and/or relative, no skilled provider) 1,558 15.0 No assistance (no attendant reported) 176 1.7 Skilled + Relative (skilled provider with relative, no TBA) 72 0.7 Skilled + TBA (skilled provider with TBA, with or without relative) 57 0.5 Other (rare combinations) 3 0.03 Mixed care combined (Skilled+TBA, Skilled+Relative, Other) 132 1.3 Total 10,391 100.0 Source: 2022 Kenya Demographic and Health Survey. Weighted percentages presented. Table 3 presents the distribution of patterns by place of delivery. The relationship between pattern and setting was stark. Of all facility births, 98.4% were skilled-only. Home births were considerably more heterogeneous: 62.1% were traditional-only, 29.8% were skilled-only (skilled providers attending home deliveries), 6.8% were no assistance, and small proportions involved mixed providers. When mixed care did occur, it was predominantly facility-based—70.2% of skilled-plus-TBA births and 81.9% of skilled-plus-relative births took place in a facility—suggesting that collaborative care, where it exists, is mostly a facility phenomenon rather than a home-birth phenomenon. Table 3. Birth Attendance Patterns by Place of Delivery (N = 10,391) Pattern Home birth (%) Facility birth (%) Total n Skilled only 8.7 91.4 8,525 Skilled + TBA 29.8 70.2 57 Skilled + Relative 18.1 81.9 72 Traditional only 98.7 1.3 1,558 No assistance 95.5 4.5 176 Total 23.8 76.2 10,391 Source: 2022 Kenya Demographic and Health Survey. Pearson chi2(5) = 6,400, p < 0.001. 4.3 Determinants of Birth Attendance Pattern Table 4 presents results from the multinomial logistic regression. The three outcome categories traditional-only, no assistance, and mixed care each had a distinct determinant profile relative to the skilled-only reference. Traditional-only attendance was powerfully and monotonically predicted by educational disadvantage and poverty. Compared to women with no education, women with primary education had 83% lower odds of a traditional-only birth (OR 0.17, 95% CI 0.14–0.21), secondary education reduced odds by 89% (OR 0.11), and higher education by 96% (OR 0.04). The wealth gradient was equally steep: each quintile increase from the poorest was associated with progressively lower odds of traditional-only attendance, reaching a 92% reduction in the richest group (OR 0.08, 95% CI 0.04–0.16). Rural residence independently increased the odds of traditional-only attendance by 72% after controlling for education and wealth (OR 1.72, 95% CI 1.21–2.46), confirming a residual geographic effect not fully explained by socioeconomic composition. Younger adolescents (15–19) had notably lower odds of traditional-only birth than women in their twenties and early thirtiesa pattern explored further in the discussion. No assistance showed a similar but amplified pattern. Education and wealth strongly reduced odds of unattended birth, with higher education reducing odds by 93% (OR 0.07). Rural residence doubled the odds (OR 2.30, 95% CI 1.04–5.09). The age pattern for no assistance was distinctly different from the traditional-only pattern: women aged 20–44 had dramatically higher odds of unattended birth compared to adolescents aged 15–19, ranging from 8.8 to 11.3 times higher. This counterintuitive age gradient, wherein older and otherwise more experienced women have higher odds of birthing alone, is addressed in the discussion. Mixed care stood apart from both traditional-only and no-assistance categories in a critical respect: neither education nor wealth significantly predicted it at any level. The only statistically significant predictor was rural residence, which nearly quadrupled the odds of mixed care (OR 3.98, 95% CI 2.06–7.68). Women aged 20–34 also had significantly higher odds than adolescents. The absence of an education or wealth gradient means that mixed care is not concentrated among the most disadvantaged it is, rather, a geographically defined phenomenon accessible across socioeconomic strata in rural settings. Table 4. Multinomial Logistic Regression: Determinants of Birth Attendance Patterns (Reference: Skilled Only) Characteristic Traditional Only OR (95% CI) No Assistance OR (95% CI) Mixed Care OR (95% CI) Education (ref: No education) Primary 0.17 (0.14–0.21)*** 0.59 (0.39–0.90)* 0.57 (0.22–1.47) Secondary 0.11 (0.08–0.14)*** 0.15 (0.07–0.29)*** 0.83 (0.30–2.28) Higher 0.04 (0.02–0.08)*** 0.07 (0.01–0.58)* 0.76 (0.24–2.35) Wealth index (ref: Poorest) Poorer 0.43 (0.35–0.53)*** 0.63 (0.39–1.00) 1.00 (0.51–1.96) Middle 0.24 (0.18–0.32)*** 0.29 (0.15–0.55)*** 0.65 (0.30–1.39) Richer 0.15 (0.10–0.23)*** 0.24 (0.09–0.62)** 0.90 (0.40–2.00) Richest 0.08 (0.04–0.16)*** 0.16 (0.04–0.56)** 0.71 (0.22–2.33) Rural residence (ref: Urban) 1.72 (1.21–2.46)** 2.30 (1.04–5.09)* 3.98 (2.06–7.68)*** Age (ref: 15–19) 20–24 3.56 (2.75–4.62)*** 11.34 (3.32–38.7)*** 3.38 (1.58–7.25)** 25–29 3.08 (2.31–4.10)*** 10.33 (3.16–33.8)*** 3.33 (1.63–6.80)** 30–34 2.54 (1.90–3.38)*** 8.79 (2.64–29.2)*** 2.68 (1.16–6.20)* 35–39 1.78 (1.34–2.37)*** 10.67 (3.35–34.0)*** 2.37 (0.99–5.67) 40–44 0.82 (0.56–1.20) 10.48 (3.11–35.3)*** 0.36 (0.08–1.73) 45–49 0.37 (0.23–0.61)*** 3.04 (0.71–13.1) — ***p < 0.001, **p < 0.01, *p < 0.05. Reference category for all models: skilled only. Source: 2022 Kenya Demographic and Health Survey. Survey-weighted multinomial logistic regression. OR = odds ratio; CI = confidence interval. 4.4 Birth Attendance Pattern and Caesarean Section Table 5 presents raw caesarean section rates by attendance pattern. The overall caesarean rate was 13.6%. The skilled-only group had the highest rate at 16.5%. The skilled-plus-relative group had a rate of 9.7%, and the skilled-plus-TBA group had a rate of only 1.8%. Traditional-only births had a caesarean rate of 0.13%—two caesareans in 1,558 births—and no unattended births were delivered by caesarean. The chi-square test confirmed that these differences were highly significant (chi2 = 336.96, p < 0.001). Table 5. Caesarean Section Rates by Birth Attendance Pattern (N = 10,391) Birth Attendance Pattern Total Births n C-sections n C-section Rate (%) Skilled only 8,525 1,407 16.5 Skilled + Relative 72 7 9.7 Skilled + TBA 57 1 1.8 Traditional only 1,558 2 0.13 No assistance 176 0 0.0 Total 10,391 1,417 13.6 Pearson chi2(5) = 336.96, p < 0.001 Source: 2022 Kenya Demographic and Health Survey. Survey-weighted frequencies and rates. Table 6 presents results from the logistic regression model for caesarean section. After adjusting for education, wealth, residence, age, and parity, birth attendance pattern remained independently and significantly associated with caesarean delivery. Women in the mixed care group had 64% lower odds of caesarean delivery compared to the skilled-only group (OR 0.36, 95% CI 0.15–0.86, p = 0.021). Traditional-only births had 96% lower odds (OR 0.04, 95% CI 0.01–0.15, p < 0.001). No-assistance births were excluded from the model due to perfect prediction. Education, wealth, and age were also independently associated with caesarean delivery. Higher education was associated with more than three times the odds of caesarean compared to no education (OR 3.27). The richest women had over three times the odds of the poorest (OR 3.18). Older women aged 30 and above had significantly higher caesarean odds than adolescents, with women aged 40–44 and 45–49 having over three times the odds of the 15–19 reference group. Rural residence and parity were not significant predictors in the full model. Table 6. Logistic Regression: Predictors of Caesarean Section Delivery (N = 10,215) Characteristic Odds Ratio 95% CI p-value Birth attendance pattern (ref: Skilled only) Mixed care (combined) 0.36 0.15–0.86 0.021 Traditional only 0.04 0.01–0.15 <0.001 No assistance — dropped — Education (ref: No education) Primary 1.77 1.15–2.72 0.009 Secondary 1.90 1.21–3.00 0.006 Higher 3.27 2.01–5.32 <0.001 Wealth index (ref: Poorest) Poorer 1.20 0.91–1.59 0.193 Middle 1.68 1.28–2.19 <0.001 Richer 2.07 1.52–2.82 <0.001 Richest 3.18 2.12–4.78 <0.001 Rural residence (ref: Urban) 1.13 0.90–1.42 0.280 Age group (ref: 15–19) 20–24 1.07 0.75–1.55 0.700 25–29 1.37 0.94–2.00 0.105 30–34 2.14 1.38–3.31 0.001 35–39 2.14 1.32–3.45 0.002 40–44 3.37 1.88–6.07 <0.001 45–49 3.41 1.28–9.10 0.014 Parity (all levels vs 0) All ns — — Source: 2022 Kenya Demographic and Health Survey. Survey-weighted logistic regression. No-assistance category excluded (zero caesareans). OR = odds ratio; CI = confidence interval. 5. Discussion 5.1 A Picture of Who Attends Kenyan Births The most fundamental contribution of this study is descriptive; it establishes that birth attendance in Kenya is not adequately captured by the SBA binary. Skilled-only attendance dominates at 82%, but this majority conceals a tail of five distinct minority patterns, each representing a qualitatively different configuration of care. Traditional-only attendance at 15% is not a trivial remainder, it represents approximately 1 in 7 Kenyan births occurring entirely outside the formal system, almost all of them at home, almost all among the poorest and least-educated women. The 1.7% of births with no attendant of any type—roughly equivalent to 170 births in a 10,000-birth sample represents a group of exceptional vulnerability that receives almost no specific policy attention. Mixed provider care, though rare at 1.3%, is conceptually the most distinctive finding. Its rarity confirms that formally integrated care—skilled providers and TBAs working together at the same birth—is not, in practice, a common feature of Kenyan maternity care. However, its existence, and the evidence that it is predominantly facility-based, suggests that some facilities do accommodate mixed attendance. This is worth documenting because it challenges the assumption that TBA presence at delivery is uniformly a home-birth phenomenon or a marker of non-institutional care. 5.2 Traditional-Only and Unattended Births: Structural Determinants The determinant profiles of traditional-only and unattended births confirm patterns well established in the broader literature, poverty and lack of education are the dominant predictors of non-institutional care, with rural residence adding an independent geographic effect. The dose-response relationship across both education and wealth levels is striking in its consistency. Moving from no education to primary education alone reduces the odds of a traditional-only birth by 83%; moving from the poorest to the richest quintile reduces them by 92%. These are among the largest effect sizes regularly reported in DHS-based analyses of maternal health service utilisation (Mekonnen et al., 2019; Okonofua et al., 2021). The fact that rural residence retains statistical significance after controlling for education and wealth, increasing the odds of both traditional-only (OR 1.72) and unattended births (OR 2.30) indicates a genuine geographic effect. Distance to health facilities, road quality, and the availability and cost of transportation constitute barriers that operate independently of household economic resources. This is consistent with evidence from Kenya and other East African settings where geographic access is documented as an independent determinant of facility delivery even within poverty-stratified analyses (Bohren et al., 2014; Gabrysch et al., 2011). The age pattern for traditional-only attendance—wherein adolescents (15–19) have lower odds than women aged 20–34 appears paradoxical at first, since adolescents are generally considered a vulnerable group for non-institutional delivery. The explanation likely lies in parity. Adolescent women are almost exclusively primiparous, and primiparous women, regardless of socioeconomic status, tend to seek formal care for their first delivery at higher rates than multiparous women who feel confident managing subsequent deliveries (Kyei et al., 2012). Older women with multiple previous births may increasingly feel that a TBA or family member is sufficient. This interpretation is supported by the positive association between older age and traditional-only attendance persisting through the 35–39 group, after which it attenuates. The age pattern for unattended births is more difficult to explain. Women aged 20–44 have 8–11 times higher odds of an unattended birth compared to adolescents. One hypothesis is that unattended births often represent rapid labours precipitate deliveries that progressed faster than planned, and multiparous women with previous uncomplicated births may be more likely to experience rapid labours that outpace their transport to a facility or arrival of an attendant. Qualitative research would be needed to fully explain this pattern. 5.3 The Distinctiveness of Mixed Care The finding that mixed care is predicted only by rural residence and not by education or wealth sets it apart from every other non-skilled-only pattern in this study. Traditional-only and unattended births are heavily stratified by socioeconomic status; mixed care is not. This suggests that women who have mixed provider attendance are not simply a slightly better-off version of traditional-only users. They represent a qualitatively different group: women who have already accessed the formal system (either in a facility or by securing a skilled provider for a home birth) but who also drew on informal support simultaneously. The facility-based nature of most mixed care births (70–82% across the two mixed subtypes) reinforces this interpretation. The relevant question for these births is not why a TBA or relative was present in the absence of skilled care—it is why they were present alongside skilled care, and what role they were playing. In facility settings, the presence of a TBA or relative may represent the woman's own decision to bring a trusted companion, a facility's accommodation of traditional or family support, or an informal practice in which TBAs accompany women to the delivery room. The health system implications of these different explanations are very different. The finding that mixed care is associated with lower caesarean rates—even after controlling for education, wealth, residence, age, and parity—raises the possibility that the presence of a TBA or relative at a facility birth genuinely modifies clinical management. The most plausible mechanism is continuous labour support. Cochrane review evidence consistently shows that one-to-one continuous support during labour—characterised by emotional encouragement, comfort measures, and advocacy—reduces caesarean rates by 25–30% in diverse settings (Bohren et al., 2017). Skilled facility staff in busy Kenyan hospitals, working under staff shortages, are unlikely to provide the level of continuous support that a dedicated companion can. If a TBA or family member filling that role reduces the escalation to caesarean delivery, this would be consistent with the trial evidence on continuous support—and it would represent an argument for structured accommodation of companions in facility births, rather than their exclusion. However, several alternative explanations must be considered. Women who choose or manage to have mixed provider attendance may have lower clinical risk profiles than the skilled-only group—selection into mixed care could be correlated with lower-risk pregnancies in ways that the available confounders do not fully capture. They may also be in facilities with distinct clinical cultures or staffing patterns that influence caesarean rates independently of attendance. The cross-sectional design and available variables do not permit adjudication between these explanations. 5.4 Revisiting the Skilled Birth Attendant Indicator These findings add empirical weight to a growing body of methodological criticism of the SBA indicator. The indicator was developed for a specific purpose of monitoring facility delivery scale-up in settings where most births were TBA-attended and it served that purpose well for two decades. However, as Kenya approaches a situation where 82% of births are already skilled-only and the remaining variation becomes increasingly concentrated in smaller, more heterogeneous subgroups, the binary indicator loses discriminatory power. It cannot distinguish between a birth where a skilled provider is the sole attendant and one where the same provider is supported by a TBA or family member. It cannot identify the 1.7% of births with no attendant at all a group invisible to standard SBA monitoring. And it treats the 15% of traditional-only births as a homogeneous block, when in fact the determinant and outcome profiles of TBA-only and relative-only births may differ from each other. The alternative proposed here, mapping births to mutually exclusive patterns based on who is present, is not a replacement for the SBA indicator but a supplement to it. It requires no additional data collection from DHS surveys, which already capture attendant information at the individual level. It does require additional analytic effort and a shift in framing from asking what proportion of births are skilled to asking what the full distribution of birth attendance configurations looks like. National health information systems and DHS analysis reports should consider adopting this framework as a complement to standard SBA reporting. 5.5 Limitations Several limitations should be acknowledged. The KDHS relies on women's self-report of birth attendants for events up to five years in the past, creating potential for recall error. Women may underreport TBA attendance due to social desirability, particularly given official discouragement of TBA-attended births. This would mean that the 15% traditional-only figure and the 1.3% mixed care figure are likely underestimates of true prevalence. Second, the mixed care categories (skilled-plus-TBA and skilled-plus-relative) have relatively small sample sizes of 57 and 72, respectively. While the multinomial regression produces stable estimates for the skilled-only, traditional-only, and no-assistance models, the mixed care model has wider confidence intervals that should be interpreted with caution. Third, the cross-sectional design prevents any causal inference about the relationship between mixed care and caesarean delivery. The observed association, while independent of measured confounders, may reflect unmeasured selection. Finally, the study does not capture the quality of attendance whether a skilled provider was clinically competent, whether a TBA was present as active support or merely present in the room, or whether a relative provided meaningful labour support or simply accompanied the woman. 6. Conclusion This study makes three contributions to the evidence base on birth attendance in Kenya. It provides the first nationally representative description of birth attendance patterns disaggregated beyond the skilled-unskilled binary, establishing that skilled-only attendance at 82% coexists with a substantial tail of traditional-only births (15%), a small but critically vulnerable group of unattended births (1.7%), and a rare but analytically interesting category of mixed provider care (1.3%). It demonstrates that these patterns have distinct sociodemographic profiles. First, traditional-only and unattended births are concentrated among the poor, uneducated, and rural, while mixed care is a rural phenomenon that cuts across socioeconomic strata. And it shows that birth attendance pattern independently predicts caesarean section delivery, with mixed care associated with 64% lower odds even after comprehensive adjustment. The policy implications are direct. Reducing traditional-only and unattended births requires addressing the poverty and educational barriers that produce them, alongside geographically targeted efforts to improve physical access. These are not problems that can be solved by facility upgrading alone. The persistence of 15% traditional-only births, a figure that has declined slowly despite two decades of policy focus on facility delivery, reflects the depth of socioeconomic stratification in Kenya's maternal health landscape. The association between mixed provider care and lower caesarean rates, though based on small numbers and requiring confirmation in studies with better data on the mechanism, is potentially important for clinical practice and health policy. If the presence of a companion, TBA or family member, at a facility birth provides a form of continuous labour support that reduces caesarean delivery, this would represent an argument for structured accommodation of companions in Kenyan maternity facilities rather than their exclusion. The conventional wisdom that TBAs and formal care are incompatible should be revisited in light of evidence about the value of continuous support during labour. More broadly, the standard SBA indicator is no longer adequate as a standalone measure of birth attendance quality in Kenya. As the country moves toward universal facility delivery, the variation that matters increasingly lies within the skilled category, in who else is present, what quality of care is provided, and whether women feel supported throughout labour and delivery. Pattern-based approaches to characterising birth attendance should be incorporated into both research and routine monitoring. 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 (KNBS) microdata catalog. DHS Program access: https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. KNBS Kenya National Data Archive (KeNADA): https://statistics.knbs.or.ke/nada/index.php/catalog/128. 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., Hofmeyr, G. J., Sakala, C., Fukuzawa, R. K., & Cuthbert, A. (2017). Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 7, CD003766. https://doi.org/10.1002/14651858.CD003766.pub6 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 Gabrysch, S., Cousens, S., Cox, J., & Campbell, O. M. R. (2011). The influence of distance and level of care on delivery place in rural Zambia: A study of linked national data in a geographic information system. PLOS Medicine, 8(1), e1000394. https://doi.org/10.1371/journal.pmed.1000394 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 Hodnett, E. D., Gates, S., Hofmeyr, G. J., & Sakala, C. (2013). Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 7, CD003766. https://doi.org/10.1002/14651858.CD003766.pub5 Homer, C. S. E., Friberg, I. K., Dias, M. A. B., Ten Hoope-Bender, P., Sandall, J., Speciale, A. M., & Bartlett, L. A. (2014). The projected effect of scaling up midwifery. The Lancet, 384(9948), 1146–1157. https://doi.org/10.1016/S0140-6736(14)60790-X Kenya National Bureau of Statistics (KNBS). (2023). Kenya Demographic and Health Survey 2022: Final report. KNBS & ICF. https://dhsprogram.com/publications/publication-FR370-DHS-Final-Reports.cfm Kruske, S., & Barclay, L. (2004). Effect of shifting policies on traditional birth attendant training. Journal of Midwifery & Women's Health, 49(4), 306–311. https://doi.org/10.1016/j.jmwh.2004.01.005 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 Moyer, C. A., Adongo, P. B., Aborigo, R. A., Hodgson, A., & Engmann, C. M. (2014). 'They treat you like you are not a human being': Maltreatment during labour and delivery in rural northern Ghana. Midwifery, 30(2), 262–268. https://doi.org/10.1016/j.midw.2013.05.006 Mrisho, M., Obrist, B., Schellenberg, J. A., Haws, R. A., Mushi, A. K., Mshinda, H., Tanner, M., & Schellenberg, D. (2008). The use of antenatal and postnatal care: Perspectives and experiences of women and health care providers in rural southern Tanzania. BMC Pregnancy and Childbirth, 9, 10. https://doi.org/10.1186/1471-2393-9-10 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 Prost, A., Colbourn, T., Seward, N., Azad, K., Coomarasamy, A., Copas, A., Houweling, T. A. J., Fottrell, E., Kuddus, A., Lewycka, S., MacArthur, C., Manandhar, D., Morrison, J., Mwansambo, C., Nair, N., Nambiar, B., Osrin, D., Pagel, C., Phiri, T., … Costello, A. (2013). Women's groups practising participatory learning and action to improve maternal and newborn health in low-resource settings: A systematic review and meta-analysis. The Lancet, 381(9879), 1736–1746. https://doi.org/10.1016/S0140-6736(13)60685-6 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 Sibley, L. M., Sipe, T. A., Brown, C. M., Diallo, M. M., McNatt, K., & Habarta, N. (2007). Traditional birth attendant training for improving health behaviours and pregnancy outcomes. Cochrane Database of Systematic Reviews, 3, CD005460. https://doi.org/10.1002/14651858.CD005460.pub2 United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). https://sdgs.un.org/2030agenda World Health Organization. (1992). Traditional birth attendants: A joint WHO/UNFPA/UNICEF statement. WHO. https://apps.who.int/iris/handle/10665/38994 World Health Organization. (2004). Making pregnancy safer: The critical role of the skilled attendant. WHO. https://apps.who.int/iris/handle/10665/42955 World Health Organization. (2015). WHO statement on caesarean section rates. WHO. https://www.who.int/publications/i/item/WHO-RHR-15.02 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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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Introduction","content":"\u003cp\u003eThe skilled birth attendant (SBA) is defined by the World Health Organization as the proportion of births attended by a doctor, nurse, or midwife has been the primary metric for measuring progress toward safe delivery care globally for over two decades (WHO, 2004). It was built into the Millennium Development Goals, retained in the Sustainable Development Goal framework, and remains central to Kenya\u0026apos;s reproductive health monitoring (United Nations, 2015; KNBS, 2023). The rationale is straightforward: skilled providers are trained to manage obstetric emergencies, and their presence at birth is the most effective single intervention for reducing intrapartum maternal and neonatal mortality (Bhutta et al., 2014).\u003c/p\u003e\n\u003cp\u003eYet the SBA indicator has a structural limitation that has received insufficient critical attention. It is a binary measure that collapses the full spectrum of birth attendance into two categories skilled or unskilled without regard for who else may be present. In practice, birth attendance is not binary. A significant number of births worldwide are attended by more than one person, and the combination of attendant types matters in ways that the standard indicator cannot capture. A birth attended jointly by a nurse-midwife and a traditional birth attendant (TBA) is categorised identically to a birth attended by a nurse-midwife alone, even though the presence of the TBA may change the dynamics of care substantially.\u003c/p\u003e\n\u003cp\u003eIn Kenya, this ambiguity is particularly consequential. TBAs have played a central role in communities for generations, and despite formal policies discouraging TBA-attended births since the 1990s, they remain active in rural areas sometimes independently, sometimes in collaboration with facility staff or alongside family members during home births (Mrisho et al., 2008; Moyer et al., 2014). Simultaneously, a significant number of Kenyan women deliver in facilities without any informal support, while others deliver at home in the sole presence of relatives. These configurations have different implications for the birth experience, for the likelihood of accessing emergency care, and potentially for clinical outcomes, yet the literature has not examined them as distinct categories.\u003c/p\u003e\n\u003cp\u003eTwo broad gaps motivate this study. First, the prevalence and sociodemographic distribution of different birth attendance combinations has not been described at national scale in Kenya. We know that 82% of Kenyan births are attended by a skilled provider (KNBS, 2023), but we know almost nothing about the full composition of birth assistance within that figure\u0026mdash;or within the 18% of births that fall outside it. Second, the potential outcome differences across birth attendance patterns have not been assessed. The association between attendance pattern and caesarean section is of particular interest, because caesarean delivery rates vary sharply between facility and home settings and may also vary within facility births depending on who else is present and why.\u003c/p\u003e\n\u003cp\u003eThis study addresses both gaps using data from the 2022 KDHS. We pursue three specific objectives: to describe the prevalence and place-of-delivery distribution of birth attendance patterns in Kenya; to identify independent sociodemographic determinants of each pattern; and to examine the relationship between birth attendance pattern and caesarean section delivery after adjusting for measured confounders.\u003c/p\u003e"},{"header":"2. Background","content":"\u003ch2\u003e2.1 The Skilled Birth Attendant Framework and Its Limitations\u003c/h2\u003e\n\u003cp\u003eThe WHO definition of a skilled birth attendant was formalised in 2004 and refined in 2018 to emphasise not only professional credentials but the enabling environment\u0026mdash;equipment, medicines, and referral capacity\u0026mdash;within which the skilled provider works (WHO, 2004; Homer et al., 2014). This refinement acknowledged that a credentialled provider working in an under-resourced setting may not deliver the full benefit attributed to SBA care. However, neither definition addresses the question of who else attends the birth alongside the skilled provider, or whether the presence of additional attendants modifies the nature of care.\u003c/p\u003e\n\u003cp\u003eCritics of the SBA framework have noted that it was constructed primarily from the perspective of clinical competence, without adequate attention to the social dimensions of birth (Kruske \u0026amp; Barclay, 2004). TBAs, despite lacking clinical training, provide continuous labour support, emotional reassurance, and community-embedded care that skilled providers in stretched public facilities frequently cannot offer. Evidence from randomised controlled trials consistently shows that continuous labour support\u0026mdash;the kind TBAs or relatives are more likely to provide than facility staff\u0026mdash;reduces caesarean section rates, shortens labour duration, and improves women\u0026apos;s birth experiences (Hodnett et al., 2013; Bohren et al., 2017). This evidence base challenges the assumption that the presence of a TBA or relative alongside a skilled provider is straightforwardly harmful or irrelevant.\u003c/p\u003e\n\u003cp\u003eThe question of TBA integration has been contentious in global health policy. Throughout the 1970s and 1980s, TBA training programmes were widely implemented across sub-Saharan Africa as a strategy for improving perinatal outcomes in settings where skilled providers were scarce (WHO, 1992). These programmes fell out of favour in the 1990s and 2000s, when systematic evidence found that TBA training alone did not reduce maternal mortality (Sibley et al., 2007; Prost et al., 2013). The policy response was to shift resources entirely toward facility-based skilled care and to discourage TBA practice. Kenya\u0026apos;s official policy has reflected this shift. Yet the evidence against TBA training is specifically evidence against TBA training programmes in which TBAs work independently; it does not address collaborative models in which TBAs work alongside skilled providers.\u003c/p\u003e\n\u003ch2\u003e2.2 Birth Attendance in the Kenyan Context\u003c/h2\u003e\n\u003cp\u003eKenya\u0026apos;s progress toward universal skilled birth attendance has been substantial but uneven. The free maternity services policy (Linda Mama) introduced in 2013 removed formal delivery fees at public facilities, producing a rapid increase in facility deliveries that disproportionately benefited poorer women (Gitobu et al., 2018). By 2022, 76% of Kenyan births occurred in a health facility (KNBS, 2023), up from 61% in 2014. Yet geography and socioeconomic status continue to stratify the distribution of facility births significantly\u0026mdash;women in the poorest quintiles, in rural counties, and with no formal education remain substantially less likely to deliver in a facility than their better-off urban counterparts.\u003c/p\u003e\n\u003cp\u003eTBAs continue to operate in Kenya despite official discouragement. Qualitative research has documented their sustained presence in rural communities, their roles as trusted community figures, and the practical constraints that lead women to depend on them\u0026mdash;particularly distance to facilities, high transport costs, and previous negative experiences with formal health services (Moyer et al., 2014; Bohren et al., 2014). The 2022 KDHS data confirm that TBA attendance, while declining, remains measurable: 11.1% of women with recent births reported a TBA present at their delivery, with this figure almost certainly higher in rural and poor communities.\u003c/p\u003e\n\u003cp\u003eCritically, the KDHS collects information on all attendants present at delivery, not just the primary one. This feature of the dataset creates an opportunity to examine combination patterns that the standard SBA indicator cannot reveal. Prior studies using KDHS data from Kenya and other countries have not exploited this feature.\u003c/p\u003e\n\u003ch2\u003e2.3 Birth Attendance and Caesarean Section\u003c/h2\u003e\n\u003cp\u003eCaesarean section rates have risen sharply across East Africa, including Kenya, over the past two decades. The WHO recommends a population-level caesarean rate of 10\u0026ndash;15%, on the grounds that below this range there is insufficient access to emergency caesareans and above it, the risks associated with unnecessary operative delivery begin to outweigh the benefits (WHO, 2015). Kenya\u0026apos;s 2022 KDHS reports an overall caesarean rate of 13.6%, which sits within the recommended range but conceals extraordinary variation: caesarean rates are near zero for home births attended by TBAs and several times the WHO upper threshold for facility births among wealthy and educated women.\u003c/p\u003e\n\u003cp\u003eThe association between birth attendance and caesarean section is partly structural\u0026mdash;caesareans can only occur in facilities\u0026mdash;but it is not entirely explained by place of delivery. Within facility births, caesarean rates vary significantly by socioeconomic status, maternal age, and clinical risk factors. Evidence from high-income countries suggests that continuous labour support by a non-clinical companion during facility delivery reduces the likelihood of caesarean delivery, independent of clinical risk (Bohren et al., 2017). Whether the presence of a TBA or family member at a facility birth in Kenya operates through a similar mechanism has not been examined.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003ch2\u003e3.1 Data Source and Study Population\u003c/h2\u003e\n\u003cp\u003eThis study used data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional household survey conducted by the Kenya National Bureau of Statistics with technical support from ICF International. The 2022 KDHS employed a stratified two-stage cluster sampling design, selecting 1,691 primary sampling units across 92 strata. A total of 32,156 women aged 15\u0026ndash;49 completed individual interviews. The final KDHS report provides a full account of sampling and data collection procedures (KNBS, 2023).\u003c/p\u003e\n\u003cp\u003eThe analytic sample was restricted to women with a live birth in the five years preceding the survey, as birth attendance variables pertain to the most recent birth. This yielded a sample of 10,391 women. The five-year reference window is standard in DHS-based studies and balances sample adequacy against recall error.\u003c/p\u003e\n\u003ch2\u003e3.2 Outcome Variables\u003c/h2\u003e\n\u003cp\u003eThe primary outcome was birth attendance pattern, constructed from five individual KDHS variables capturing whether each of the following was present at delivery: doctor (m3a_1), nurse or midwife (m3b_1), traditional birth attendant (m3g_1), relative or friend (m3h_1), and other attendant (m3k_1). These variables are not mutually exclusive\u0026mdash;a woman could report multiple attendants enabling the construction of combination patterns.\u003c/p\u003e\n\u003cp\u003eWe constructed six mutually exclusive patterns. Skilled only was assigned to women reporting a doctor and/or nurse but no TBA or relative. Traditional only was assigned to women reporting a TBA and/or relative but no skilled provider. No assistance was assigned to women reporting no attendant of any type. Skilled plus TBA captured births attended by a skilled provider together with a TBA, with or without a relative. Skilled plus Relative captured births attended by a skilled provider and a relative, without a TBA. Other captured the three cases not fitting the above categories. For the multivariate analysis of caesarean section, the Skilled+TBA, Skilled+Relative, and Other categories were combined into a single Mixed care category, given their small individual sample sizes.\u003c/p\u003e\n\u003cp\u003eThe secondary outcome was caesarean section delivery, derived from the binary KDHS variable m17_1.\u003c/p\u003e\n\u003ch2\u003e3.3 Independent Variables\u003c/h2\u003e\n\u003cp\u003eSociodemographic predictors were selected based on prior literature and included: maternal education (no education, primary, secondary, higher); household wealth index quintile (poorest through richest); place of residence (urban, rural); maternal age in five-year groups (15\u0026ndash;19 through 45\u0026ndash;49); and parity, categorised as 0, 1, 2, 3, and 4 or more children ever born. The DHS wealth index is generated from household asset data using principal component analysis and reflects relative economic standing within the national population (Rutstein \u0026amp; Johnson, 2004).\u003c/p\u003e\n\u003ch2\u003e3.4 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll analyses were conducted in Stata/MP 17 (StataCorp LLC) using svy commands to account for the complex sampling design with sampling weights (v005), primary sampling units (v021), and strata (v022). Weighted frequencies and percentages described the distribution of birth attendance patterns and sample characteristics. Cross-tabulations of patterns by place of delivery were examined using chi-square tests with Rao-Scott corrections.\u003c/p\u003e\n\u003cp\u003eDeterminants of birth attendance pattern were examined using survey-weighted multinomial logistic regression, with skilled-only attendance as the reference category. Separate models estimated odds ratios for traditional-only attendance, no assistance, and mixed care, each compared to the skilled-only reference. A separate model for the skilled-only outcome itself (versus all other patterns) was also fitted to identify predictors of the dominant pattern.\u003c/p\u003e\n\u003cp\u003eThe association between birth attendance pattern and caesarean section was examined using survey-weighted binary logistic regression. The model adjusted simultaneously for attendance pattern, education, wealth, residence, age, and parity. The no-assistance category was excluded from this model because it perfectly predicted non-caesarean delivery (zero caesareans in that group), which precluded estimation. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e\n\u003cp\u003eThe 2022 KDHS was approved by the Kenya Medical Research Institute Scientific and Ethics Review Unit and the ICF International Institutional Review Board. All survey respondents provided informed consent prior to interview. This secondary analysis used fully de-identified, publicly available data and was exempt from further ethical review. Data were accessed through the DHS Programme website (https://dhsprogram.com).\u003c/p\u003e"},{"header":"4. Results","content":"\u003ch2\u003e4.1 Sample Characteristics\u003c/h2\u003e\n\u003cp\u003eTable 1 presents the sociodemographic characteristics of the 10,391 women included in the analysis. The sample was predominantly rural (65.4%) and spread across educational levels, with 20.0% reporting no formal education, 33.8% primary, 31.6% secondary, and 14.6% higher education. Nearly half (48.2%) of women came from the two lowest wealth quintiles. The modal age group was 25\u0026ndash;29 years (27.4%). Parity was broadly distributed, with 27.2% of women having four or more children ever born.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Sociodemographic Characteristics of Women with a Live Birth in the Past Five Years (N = 10,391)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnweighted n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e2,082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e3,507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e3,283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e1,519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e3,240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e1,762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e1,826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e2,045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e1,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e3,596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e6,795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e2,664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e2,843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e2,042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e1,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e8,813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e5,119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e5,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e4,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;4 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e8,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.5641%;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. All percentages are survey-weighted.\u003c/p\u003e\n\u003ch2\u003e4.2 Prevalence and Distribution of Birth Attendance Patterns\u003c/h2\u003e\n\u003cp\u003eTable 2 shows the distribution of birth attendance patterns. Skilled-only attendance dominated, accounting for 82.0% of births. Traditional-only attendance was the second most common configuration at 15.0%. No assistance\u0026mdash;births with no reported attendant\u0026mdash;accounted for 1.7%. Mixed care involving a skilled provider alongside a TBA or relative was rare in aggregate, at 1.3%, comprising 0.7% skilled-plus-relative and 0.5% skilled-plus-TBA combinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Distribution of Birth Attendance Patterns among Women with a Recent Birth (N = 10,391)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth Attendance Pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnweighted n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSkilled only (doctor and/or nurse, no TBA or relative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e8,525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e82.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eTraditional only (TBA and/or relative, no skilled provider)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e1,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNo assistance (no attendant reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSkilled + Relative (skilled provider with relative, no TBA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSkilled + TBA (skilled provider with TBA, with or without relative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eOther (rare combinations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMixed care combined (Skilled+TBA, Skilled+Relative, Other)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10,391\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. Weighted percentages presented.\u003c/p\u003e\n\u003cp\u003eTable 3 presents the distribution of patterns by place of delivery. The relationship between pattern and setting was stark. Of all facility births, 98.4% were skilled-only. Home births were considerably more heterogeneous: 62.1% were traditional-only, 29.8% were skilled-only (skilled providers attending home deliveries), 6.8% were no assistance, and small proportions involved mixed providers. When mixed care did occur, it was predominantly facility-based\u0026mdash;70.2% of skilled-plus-TBA births and 81.9% of skilled-plus-relative births took place in a facility\u0026mdash;suggesting that collaborative care, where it exists, is mostly a facility phenomenon rather than a home-birth phenomenon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Birth Attendance Patterns by Place of Delivery (N = 10,391)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHome birth (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility birth (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003eSkilled only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e8,525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003eSkilled + TBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003eSkilled + Relative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003eTraditional only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e1,558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003eNo assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.5449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3526%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10,391\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. Pearson chi2(5) = 6,400, p \u0026lt; 0.001.\u003c/p\u003e\n\u003ch2\u003e4.3 Determinants of Birth Attendance Pattern\u003c/h2\u003e\n\u003cp\u003eTable 4 presents results from the multinomial logistic regression. The three outcome categories traditional-only, no assistance, and mixed care each had a distinct determinant profile relative to the skilled-only reference.\u003c/p\u003e\n\u003cp\u003eTraditional-only attendance was powerfully and monotonically predicted by educational disadvantage and poverty. Compared to women with no education, women with primary education had 83% lower odds of a traditional-only birth (OR 0.17, 95% CI 0.14\u0026ndash;0.21), secondary education reduced odds by 89% (OR 0.11), and higher education by 96% (OR 0.04). The wealth gradient was equally steep: each quintile increase from the poorest was associated with progressively lower odds of traditional-only attendance, reaching a 92% reduction in the richest group (OR 0.08, 95% CI 0.04\u0026ndash;0.16). Rural residence independently increased the odds of traditional-only attendance by 72% after controlling for education and wealth (OR 1.72, 95% CI 1.21\u0026ndash;2.46), confirming a residual geographic effect not fully explained by socioeconomic composition. Younger adolescents (15\u0026ndash;19) had notably lower odds of traditional-only birth than women in their twenties and early thirtiesa pattern explored further in the discussion.\u003c/p\u003e\n\u003cp\u003eNo assistance showed a similar but amplified pattern. Education and wealth strongly reduced odds of unattended birth, with higher education reducing odds by 93% (OR 0.07). Rural residence doubled the odds (OR 2.30, 95% CI 1.04\u0026ndash;5.09). The age pattern for no assistance was distinctly different from the traditional-only pattern: women aged 20\u0026ndash;44 had dramatically higher odds of unattended birth compared to adolescents aged 15\u0026ndash;19, ranging from 8.8 to 11.3 times higher. This counterintuitive age gradient, wherein older and otherwise more experienced women have higher odds of birthing alone, is addressed in the discussion.\u003c/p\u003e\n\u003cp\u003eMixed care stood apart from both traditional-only and no-assistance categories in a critical respect: neither education nor wealth significantly predicted it at any level. The only statistically significant predictor was rural residence, which nearly quadrupled the odds of mixed care (OR 3.98, 95% CI 2.06\u0026ndash;7.68). Women aged 20\u0026ndash;34 also had significantly higher odds than adolescents. The absence of an education or wealth gradient means that mixed care is not concentrated among the most disadvantaged it is, rather, a geographically defined phenomenon accessible across socioeconomic strata in rural settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multinomial Logistic Regression: Determinants of Birth Attendance Patterns (Reference: Skilled Only)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraditional Only OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Assistance OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMixed Care OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (ref: No education)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.17 (0.14\u0026ndash;0.21)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.59 (0.39\u0026ndash;0.90)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.57 (0.22\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.11 (0.08\u0026ndash;0.14)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.15 (0.07\u0026ndash;0.29)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.83 (0.30\u0026ndash;2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.04 (0.02\u0026ndash;0.08)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.07 (0.01\u0026ndash;0.58)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.76 (0.24\u0026ndash;2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index (ref: Poorest)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.43 (0.35\u0026ndash;0.53)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.63 (0.39\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.00 (0.51\u0026ndash;1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.24 (0.18\u0026ndash;0.32)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.29 (0.15\u0026ndash;0.55)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.65 (0.30\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.15 (0.10\u0026ndash;0.23)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.24 (0.09\u0026ndash;0.62)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.90 (0.40\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.08 (0.04\u0026ndash;0.16)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.16 (0.04\u0026ndash;0.56)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.71 (0.22\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural residence (ref: Urban)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.72 (1.21\u0026ndash;2.46)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.30 (1.04\u0026ndash;5.09)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.98 (2.06\u0026ndash;7.68)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (ref: 15\u0026ndash;19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.56 (2.75\u0026ndash;4.62)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e11.34 (3.32\u0026ndash;38.7)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.38 (1.58\u0026ndash;7.25)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.08 (2.31\u0026ndash;4.10)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e10.33 (3.16\u0026ndash;33.8)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.33 (1.63\u0026ndash;6.80)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.54 (1.90\u0026ndash;3.38)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8.79 (2.64\u0026ndash;29.2)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.68 (1.16\u0026ndash;6.20)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.78 (1.34\u0026ndash;2.37)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e10.67 (3.35\u0026ndash;34.0)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.37 (0.99\u0026ndash;5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.82 (0.56\u0026ndash;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e10.48 (3.11\u0026ndash;35.3)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.36 (0.08\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.37 (0.23\u0026ndash;0.61)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3.04 (0.71\u0026ndash;13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 624px;\"\u003e\n \u003cp\u003e***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05. Reference category for all models: skilled only.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. Survey-weighted multinomial logistic regression. OR = odds ratio; CI = confidence interval.\u003c/p\u003e\n\u003ch2\u003e4.4 Birth Attendance Pattern and Caesarean Section\u003c/h2\u003e\n\u003cp\u003eTable 5 presents raw caesarean section rates by attendance pattern. The overall caesarean rate was 13.6%. The skilled-only group had the highest rate at 16.5%. The skilled-plus-relative group had a rate of 9.7%, and the skilled-plus-TBA group had a rate of only 1.8%. Traditional-only births had a caesarean rate of 0.13%\u0026mdash;two caesareans in 1,558 births\u0026mdash;and no unattended births were delivered by caesarean. The chi-square test confirmed that these differences were highly significant (chi2 = 336.96, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Caesarean Section Rates by Birth Attendance Pattern (N = 10,391)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth Attendance Pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Births n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-sections n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-section Rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003eSkilled only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8,525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003eSkilled + Relative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003eSkilled + TBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003eTraditional only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003eNo assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10,391\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,417\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 624px;\"\u003e\n \u003cp\u003ePearson chi2(5) = 336.96, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. Survey-weighted frequencies and rates.\u003c/p\u003e\n\u003cp\u003eTable 6 presents results from the logistic regression model for caesarean section. After adjusting for education, wealth, residence, age, and parity, birth attendance pattern remained independently and significantly associated with caesarean delivery. Women in the mixed care group had 64% lower odds of caesarean delivery compared to the skilled-only group (OR 0.36, 95% CI 0.15\u0026ndash;0.86, p = 0.021). Traditional-only births had 96% lower odds (OR 0.04, 95% CI 0.01\u0026ndash;0.15, p \u0026lt; 0.001). No-assistance births were excluded from the model due to perfect prediction.\u003c/p\u003e\n\u003cp\u003eEducation, wealth, and age were also independently associated with caesarean delivery. Higher education was associated with more than three times the odds of caesarean compared to no education (OR 3.27). The richest women had over three times the odds of the poorest (OR 3.18). Older women aged 30 and above had significantly higher caesarean odds than adolescents, with women aged 40\u0026ndash;44 and 45\u0026ndash;49 having over three times the odds of the 15\u0026ndash;19 reference group. Rural residence and parity were not significant predictors in the full model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Logistic Regression: Predictors of Caesarean Section Delivery (N = 10,215)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth attendance pattern (ref: Skilled only)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mixed care (combined)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.15\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Traditional only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.01\u0026ndash;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003edropped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (ref: No education)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.15\u0026ndash;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2.01\u0026ndash;5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index (ref: Poorest)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.28\u0026ndash;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.52\u0026ndash;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2.12\u0026ndash;4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural residence (ref: Urban)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.90\u0026ndash;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (ref: 15\u0026ndash;19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.75\u0026ndash;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.94\u0026ndash;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.38\u0026ndash;3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.32\u0026ndash;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.88\u0026ndash;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.28\u0026ndash;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity (all levels vs 0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eAll ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. Survey-weighted logistic regression. No-assistance category excluded (zero caesareans). OR = odds ratio; CI = confidence interval.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003ch2\u003e5.1 A Picture of Who Attends Kenyan Births\u003c/h2\u003e\n\u003cp\u003eThe most fundamental contribution of this study is descriptive; it establishes that birth attendance in Kenya is not adequately captured by the SBA binary. Skilled-only attendance dominates at 82%, but this majority conceals a tail of five distinct minority patterns, each representing a qualitatively different configuration of care. Traditional-only attendance at 15% is not a trivial remainder, it represents approximately 1 in 7 Kenyan births occurring entirely outside the formal system, almost all of them at home, almost all among the poorest and least-educated women. The 1.7% of births with no attendant of any type\u0026mdash;roughly equivalent to 170 births in a 10,000-birth sample represents a group of exceptional vulnerability that receives almost no specific policy attention.\u003c/p\u003e\n\u003cp\u003eMixed provider care, though rare at 1.3%, is conceptually the most distinctive finding. Its rarity confirms that formally integrated care\u0026mdash;skilled providers and TBAs working together at the same birth\u0026mdash;is not, in practice, a common feature of Kenyan maternity care. However, its existence, and the evidence that it is predominantly facility-based, suggests that some facilities do accommodate mixed attendance. This is worth documenting because it challenges the assumption that TBA presence at delivery is uniformly a home-birth phenomenon or a marker of non-institutional care.\u003c/p\u003e\n\u003ch2\u003e5.2 Traditional-Only and Unattended Births: Structural Determinants\u003c/h2\u003e\n\u003cp\u003eThe determinant profiles of traditional-only and unattended births confirm patterns well established in the broader literature, poverty and lack of education are the dominant predictors of non-institutional care, with rural residence adding an independent geographic effect. The dose-response relationship across both education and wealth levels is striking in its consistency. Moving from no education to primary education alone reduces the odds of a traditional-only birth by 83%; moving from the poorest to the richest quintile reduces them by 92%. These are among the largest effect sizes regularly reported in DHS-based analyses of maternal health service utilisation (Mekonnen et al., 2019; Okonofua et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe fact that rural residence retains statistical significance after controlling for education and wealth, increasing the odds of both traditional-only (OR 1.72) and unattended births (OR 2.30) indicates a genuine geographic effect. Distance to health facilities, road quality, and the availability and cost of transportation constitute barriers that operate independently of household economic resources. This is consistent with evidence from Kenya and other East African settings where geographic access is documented as an independent determinant of facility delivery even within poverty-stratified analyses (Bohren et al., 2014; Gabrysch et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe age pattern for traditional-only attendance\u0026mdash;wherein adolescents (15\u0026ndash;19) have lower odds than women aged 20\u0026ndash;34 appears paradoxical at first, since adolescents are generally considered a vulnerable group for non-institutional delivery. The explanation likely lies in parity. Adolescent women are almost exclusively primiparous, and primiparous women, regardless of socioeconomic status, tend to seek formal care for their first delivery at higher rates than multiparous women who feel confident managing subsequent deliveries (Kyei et al., 2012). Older women with multiple previous births may increasingly feel that a TBA or family member is sufficient. This interpretation is supported by the positive association between older age and traditional-only attendance persisting through the 35\u0026ndash;39 group, after which it attenuates.\u003c/p\u003e\n\u003cp\u003eThe age pattern for unattended births is more difficult to explain. Women aged 20\u0026ndash;44 have 8\u0026ndash;11 times higher odds of an unattended birth compared to adolescents. One hypothesis is that unattended births often represent rapid labours precipitate deliveries that progressed faster than planned, and multiparous women with previous uncomplicated births may be more likely to experience rapid labours that outpace their transport to a facility or arrival of an attendant. Qualitative research would be needed to fully explain this pattern.\u003c/p\u003e\n\u003ch2\u003e5.3 The Distinctiveness of Mixed Care\u003c/h2\u003e\n\u003cp\u003eThe finding that mixed care is predicted only by rural residence and not by education or wealth sets it apart from every other non-skilled-only pattern in this study. Traditional-only and unattended births are heavily stratified by socioeconomic status; mixed care is not. This suggests that women who have mixed provider attendance are not simply a slightly better-off version of traditional-only users. They represent a qualitatively different group: women who have already accessed the formal system (either in a facility or by securing a skilled provider for a home birth) but who also drew on informal support simultaneously.\u003c/p\u003e\n\u003cp\u003eThe facility-based nature of most mixed care births (70\u0026ndash;82% across the two mixed subtypes) reinforces this interpretation. The relevant question for these births is not why a TBA or relative was present in the absence of skilled care\u0026mdash;it is why they were present alongside skilled care, and what role they were playing. In facility settings, the presence of a TBA or relative may represent the woman\u0026apos;s own decision to bring a trusted companion, a facility\u0026apos;s accommodation of traditional or family support, or an informal practice in which TBAs accompany women to the delivery room. The health system implications of these different explanations are very different.\u003c/p\u003e\n\u003cp\u003eThe finding that mixed care is associated with lower caesarean rates\u0026mdash;even after controlling for education, wealth, residence, age, and parity\u0026mdash;raises the possibility that the presence of a TBA or relative at a facility birth genuinely modifies clinical management. The most plausible mechanism is continuous labour support. Cochrane review evidence consistently shows that one-to-one continuous support during labour\u0026mdash;characterised by emotional encouragement, comfort measures, and advocacy\u0026mdash;reduces caesarean rates by 25\u0026ndash;30% in diverse settings (Bohren et al., 2017). Skilled facility staff in busy Kenyan hospitals, working under staff shortages, are unlikely to provide the level of continuous support that a dedicated companion can. If a TBA or family member filling that role reduces the escalation to caesarean delivery, this would be consistent with the trial evidence on continuous support\u0026mdash;and it would represent an argument for structured accommodation of companions in facility births, rather than their exclusion.\u003c/p\u003e\n\u003cp\u003eHowever, several alternative explanations must be considered. Women who choose or manage to have mixed provider attendance may have lower clinical risk profiles than the skilled-only group\u0026mdash;selection into mixed care could be correlated with lower-risk pregnancies in ways that the available confounders do not fully capture. They may also be in facilities with distinct clinical cultures or staffing patterns that influence caesarean rates independently of attendance. The cross-sectional design and available variables do not permit adjudication between these explanations.\u003c/p\u003e\n\u003ch2\u003e5.4 Revisiting the Skilled Birth Attendant Indicator\u003c/h2\u003e\n\u003cp\u003eThese findings add empirical weight to a growing body of methodological criticism of the SBA indicator. The indicator was developed for a specific purpose of monitoring facility delivery scale-up in settings where most births were TBA-attended and it served that purpose well for two decades. However, as Kenya approaches a situation where 82% of births are already skilled-only and the remaining variation becomes increasingly concentrated in smaller, more heterogeneous subgroups, the binary indicator loses discriminatory power. It cannot distinguish between a birth where a skilled provider is the sole attendant and one where the same provider is supported by a TBA or family member. It cannot identify the 1.7% of births with no attendant at all a group invisible to standard SBA monitoring. And it treats the 15% of traditional-only births as a homogeneous block, when in fact the determinant and outcome profiles of TBA-only and relative-only births may differ from each other.\u003c/p\u003e\n\u003cp\u003eThe alternative proposed here, mapping births to mutually exclusive patterns based on who is present, is not a replacement for the SBA indicator but a supplement to it. It requires no additional data collection from DHS surveys, which already capture attendant information at the individual level. It does require additional analytic effort and a shift in framing from asking what proportion of births are skilled to asking what the full distribution of birth attendance configurations looks like. National health information systems and DHS analysis reports should consider adopting this framework as a complement to standard SBA reporting.\u003c/p\u003e\n\u003ch2\u003e5.5 Limitations\u003c/h2\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. The KDHS relies on women\u0026apos;s self-report of birth attendants for events up to five years in the past, creating potential for recall error. Women may underreport TBA attendance due to social desirability, particularly given official discouragement of TBA-attended births. This would mean that the 15% traditional-only figure and the 1.3% mixed care figure are likely underestimates of true prevalence. Second, the mixed care categories (skilled-plus-TBA and skilled-plus-relative) have relatively small sample sizes of 57 and 72, respectively. While the multinomial regression produces stable estimates for the skilled-only, traditional-only, and no-assistance models, the mixed care model has wider confidence intervals that should be interpreted with caution. Third, the cross-sectional design prevents any causal inference about the relationship between mixed care and caesarean delivery. The observed association, while independent of measured confounders, may reflect unmeasured selection. Finally, the study does not capture the quality of attendance whether a skilled provider was clinically competent, whether a TBA was present as active support or merely present in the room, or whether a relative provided meaningful labour support or simply accompanied the woman.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study makes three contributions to the evidence base on birth attendance in Kenya. It provides the first nationally representative description of birth attendance patterns disaggregated beyond the skilled-unskilled binary, establishing that skilled-only attendance at 82% coexists with a substantial tail of traditional-only births (15%), a small but critically vulnerable group of unattended births (1.7%), and a rare but analytically interesting category of mixed provider care (1.3%). It demonstrates that these patterns have distinct sociodemographic profiles. First, traditional-only and unattended births are concentrated among the poor, uneducated, and rural, while mixed care is a rural phenomenon that cuts across socioeconomic strata. And it shows that birth attendance pattern independently predicts caesarean section delivery, with mixed care associated with 64% lower odds even after comprehensive adjustment.\u003c/p\u003e\n\u003cp\u003eThe policy implications are direct. Reducing traditional-only and unattended births requires addressing the poverty and educational barriers that produce them, alongside geographically targeted efforts to improve physical access. These are not problems that can be solved by facility upgrading alone. The persistence of 15% traditional-only births, a figure that has declined slowly despite two decades of policy focus on facility delivery, reflects the depth of socioeconomic stratification in Kenya\u0026apos;s maternal health landscape.\u003c/p\u003e\n\u003cp\u003eThe association between mixed provider care and lower caesarean rates, though based on small numbers and requiring confirmation in studies with better data on the mechanism, is potentially important for clinical practice and health policy. If the presence of a companion, TBA or family member, at a facility birth provides a form of continuous labour support that reduces caesarean delivery, this would represent an argument for structured accommodation of companions in Kenyan maternity facilities rather than their exclusion. The conventional wisdom that TBAs and formal care are incompatible should be revisited in light of evidence about the value of continuous support during labour.\u003c/p\u003e\n\u003cp\u003eMore broadly, the standard SBA indicator is no longer adequate as a standalone measure of birth attendance quality in Kenya. As the country moves toward universal facility delivery, the variation that matters increasingly lies within the skilled category, in who else is present, what quality of care is provided, and whether women feel supported throughout labour and delivery. Pattern-based approaches to characterising birth attendance should be incorporated into both research and routine monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe ethics declaration\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.\u003c/p\u003e\n\u003cp\u003eFunding\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\u0026apos; academic affiliations without external funding.\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate\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\u003eConsent to Publish\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable. This manuscript does not contain any individual person\u0026apos;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\u003eData Availability\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 (KNBS) microdata catalog. DHS Program access: https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. KNBS Kenya National Data Archive (KeNADA): https://statistics.knbs.or.ke/nada/index.php/catalog/128. 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\u003eCompeting interests\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\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eCharles, John: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing \u0026ndash; original draft. Mary, Charles: Conceptualization, Methodology, Investigation, Validation, Writing \u0026ndash; review \u0026amp; editing, Project administration. Charles, erick: Resources, Validation, Writing \u0026ndash; 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., Hofmeyr, G. J., Sakala, C., Fukuzawa, R. K., \u0026amp; Cuthbert, A. (2017). Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 7, CD003766. https://doi.org/10.1002/14651858.CD003766.pub6\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\u003eGabrysch, S., Cousens, S., Cox, J., \u0026amp; Campbell, O. M. R. (2011). The influence of distance and level of care on delivery place in rural Zambia: A study of linked national data in a geographic information system. PLOS Medicine, 8(1), e1000394. https://doi.org/10.1371/journal.pmed.1000394\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\u003eHodnett, E. D., Gates, S., Hofmeyr, G. J., \u0026amp; Sakala, C. (2013). Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 7, CD003766. https://doi.org/10.1002/14651858.CD003766.pub5\u003c/li\u003e\n\u003cli\u003eHomer, C. S. E., Friberg, I. K., Dias, M. A. B., Ten Hoope-Bender, P., Sandall, J., Speciale, A. M., \u0026amp; Bartlett, L. A. (2014). The projected effect of scaling up midwifery. The Lancet, 384(9948), 1146\u0026ndash;1157. https://doi.org/10.1016/S0140-6736(14)60790-X\u003c/li\u003e\n\u003cli\u003eKenya National Bureau of Statistics (KNBS). (2023). Kenya Demographic and Health Survey 2022: Final report. KNBS \u0026amp; ICF. https://dhsprogram.com/publications/publication-FR370-DHS-Final-Reports.cfm\u003c/li\u003e\n\u003cli\u003eKruske, S., \u0026amp; Barclay, L. (2004). Effect of shifting policies on traditional birth attendant training. Journal of Midwifery \u0026amp; Women\u0026apos;s Health, 49(4), 306\u0026ndash;311. https://doi.org/10.1016/j.jmwh.2004.01.005\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\u003eMoyer, C. A., Adongo, P. B., Aborigo, R. A., Hodgson, A., \u0026amp; Engmann, C. M. (2014). \u0026apos;They treat you like you are not a human being\u0026apos;: Maltreatment during labour and delivery in rural northern Ghana. Midwifery, 30(2), 262\u0026ndash;268. https://doi.org/10.1016/j.midw.2013.05.006\u003c/li\u003e\n\u003cli\u003eMrisho, M., Obrist, B., Schellenberg, J. A., Haws, R. A., Mushi, A. K., Mshinda, H., Tanner, M., \u0026amp; Schellenberg, D. (2008). The use of antenatal and postnatal care: Perspectives and experiences of women and health care providers in rural southern Tanzania. BMC Pregnancy and Childbirth, 9, 10. https://doi.org/10.1186/1471-2393-9-10\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\u003eProst, A., Colbourn, T., Seward, N., Azad, K., Coomarasamy, A., Copas, A., Houweling, T. A. J., Fottrell, E., Kuddus, A., Lewycka, S., MacArthur, C., Manandhar, D., Morrison, J., Mwansambo, C., Nair, N., Nambiar, B., Osrin, D., Pagel, C., Phiri, T., \u0026hellip; Costello, A. (2013). Women\u0026apos;s groups practising participatory learning and action to improve maternal and newborn health in low-resource settings: A systematic review and meta-analysis. The Lancet, 381(9879), 1736\u0026ndash;1746. https://doi.org/10.1016/S0140-6736(13)60685-6\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\u003eSibley, L. M., Sipe, T. A., Brown, C. M., Diallo, M. M., McNatt, K., \u0026amp; Habarta, N. (2007). Traditional birth attendant training for improving health behaviours and pregnancy outcomes. Cochrane Database of Systematic Reviews, 3, CD005460. https://doi.org/10.1002/14651858.CD005460.pub2\u003c/li\u003e\n\u003cli\u003eUnited Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). https://sdgs.un.org/2030agenda\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (1992). Traditional birth attendants: A joint WHO/UNFPA/UNICEF statement. WHO. https://apps.who.int/iris/handle/10665/38994\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2004). Making pregnancy safer: The critical role of the skilled attendant. WHO. https://apps.who.int/iris/handle/10665/42955\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2015). WHO statement on caesarean section rates. WHO. https://www.who.int/publications/i/item/WHO-RHR-15.02\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"birth attendance, skilled birth attendant, traditional birth attendant, caesarean section, Kenya, maternity care patterns","lastPublishedDoi":"10.21203/rs.3.rs-9123470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9123470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Studies of birth attendance in sub-Saharan Africa have almost universally relied on a binary measure skilled versus unskilled that collapses fundamentally different care configurations into a single category. This framing ignores the reality that many births involve more than one type of attendant simultaneously. This study moves beyond the binary by examining the full range of birth attendance patterns in Kenya, characterising who uses each configuration and whether different patterns carry distinct clinical implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), using a sample of 10,391 women aged 15–49 who had a live birth in the five years preceding the survey. Six mutually exclusive birth attendance patterns were constructed from individual provider variables. Survey-weighted multinomial logistic regression identified independent determinants of each pattern compared to skilled-only attendance. Survey-weighted binary logistic regression examined the association between birth attendance pattern and caesarean section delivery, adjusting for education, wealth, residence, age, and parity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Skilled-only attendance accounted for 82.0% of births. Traditional-only attendance (TBA or relative without a skilled provider) accounted for 15.0%, and 1.7% of women reported no attendant at all. Mixed provider care, involving a skilled provider alongside a TBA or relative, was rare at 1.3%. Traditional-only and unattended births were strongly predicted by lack of education, poverty, and rural residence; each step increase in education or wealth reduced the odds of traditional-only birth by 57–96%. Mixed care was uniquely predicted by rural residence (OR 3.98, 95% CI 2.06–7.68) with no education or wealth gradient. Birth attendance pattern independently predicted caesarean section after full adjustment. Women in the mixed care group had 64% lower odds of caesarean compared to the skilled-only group (OR 0.36, 95% CI 0.15–0.86).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The persistence of traditional-only births in Kenya is not simply a rural phenomenon it is a poverty and education phenomenon that rural residence reflects. Mixed provider care, though rare, is qualitatively distinct from both skilled-only and traditional-only care, and its independent association with lower caesarean rates warrants further investigation. The binary skilled birth attendant measure obscures these differences and should be supplemented with pattern-based approaches in national monitoring.\u003c/p\u003e","manuscriptTitle":"When Multiple Providers Attend Birth: Patterns, Determinants, and Outcomes of Shared Delivery Assistance in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:10:16","doi":"10.21203/rs.3.rs-9123470/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a48aae2-1eb8-4687-a5c6-9cffe3c08953","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T06:10:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:10:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9123470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9123470","identity":"rs-9123470","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}